PDG Occasional Paper Number 2
Understanding consumer profile
Kim Walsh1
November 2011
Contents
1
Introduction ...............................................................................................................................................................2
2
Population ...................................................................................................................................................................2
3
4
2.1
Current
2.1.1
2.1.2
2.1.3
population................................................................................................... 2
Factors influencing population growth
3
Trends in national population growth
5
Estimating the current population in a municipality
6
2.2
Projecting population into the future...................................................................... 8
Households ................................................................................................................................................................8
3.1
The difference between households and consumer units ..................................... 8
3.2
Why data on numbers of households is still important ....................................... 10
3.3
Current
3.3.1
3.3.2
3.3.3
3.4
Projecting the number of households into the future.......................................... 12
number of households ............................................................................. 10
Factors influencing household growth
10
Trends in national household growth
11
Estimating the current number of households in a municipality
12
Consumer units .................................................................................................................................................... 12
4.1
Types of consumer units ....................................................................................... 12
4.1.1 Residential consumer units
13
4.1.2 Non-residential consumer units
13
4.1.3 The typical consumer profile in different municipal sub-categories
14
4.2
Current
4.2.1
4.2.2
4.2.3
4.2.4
4.3
Projecting consumer units into the future ............................................................ 16
4.3.1 Growth in number of residential consumer units
16
4.3.2 Changes in the income distribution
16
4.3.3 Growth in number of non-residential consumer units
16
consumer units ......................................................................................... 14
Number of residential consumer units
14
Income profile of residential consumer units
15
Size of residential consumer units
15
Number of non-residential consumer units
15
References ................................................................................................................................................................................. 18
1
Kim Walsh is a Director at PDG.
PDG Occasional Paper Number 2: Understanding the consumer profile
1
Introduction
A central element to the sustainable management of service provision is accurate
information on the current demand for services, as well as informed projections
regarding demand into the future.
The demographics of an area and its economic structure are the basic determinants of
both current and future demand for services. A service provider must have a good
understanding of the current demographic and economic situation, and be able to
make informed projections of future trends.
This paper looks at methods for estimating important demographic data, namely the
current population, number of households and number of consumer units in a
municipality. An understanding of the trends in these numbers will allow projections
into the future to be made.
2
Population
Many services provided by a municipality are provided per plot or ‘consumer unit’.
However, some services (most notably roads and ‘public services’ such as community
services) are provided to individuals. The population of a municipality is a key driver of
the demand for such services.
2.1
Current population
The national census conducted by Statistics South Africa (Stats SA) is the only source
of official population statistics in South Africa. A new census has just been conducted,
but final results will only be available in March 20132. The last census was conducted in
2001 and is thus very out of date. However, in February 2007 Stats SA conducted a
large-scale community survey, referred to as Community Survey 2007, intended to
provide demographic and socio-economic data at municipal level. The data has had
some flaws, and cautions have been made regarding the reliability of the data for
individual municipalities (see, for example, SASC, 2007); however, this is the best and
most recent source of population data currently available.
The table below shows the population in the various municipal sub-categories in 2007.
Table 1: Population in municipal sub-categories in South Africa according to Community
Survey 2007
A
B1
B2
B3
B4
DMAs
Total
Population
16,974,430
8,233,208
4,086,147
5,874,455
13,238,190
70,577
48,447,007
% of total
35%
17%
8%
12%
27%
0%
Data source: Community Survey 2007 Statistical Release P0301.1
Until updated official data on demographics in South Africa becomes available from
Census 2011, the size of the current population in a municipality must be determined
based on assumptions about how population has grown since 2007.
2
According to http://www.statssa.gov.za/census2011/faq.asp, accessed on 7 November 2011.
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PDG Occasional Paper Number 2: Understanding the consumer profile
2.1.1 Factors influencing population growth
Population growth is a function of the fertility and mortality rates in an area, modified
by migration into and out of the area.
Fertility rates
South Africa as a whole is going through a demographic transition from higher to lower
fertility. This transition is occurring more rapidly in higher income groups. As a result,
fertility rates remain comparatively high among the African racial grouping and in rural
areas. However, even among these groups fertility is declining, and Swartz (2002)
concludes that fertility will reach replacement level by 2020 or 2025.
Mortality rates and the impact of HIV/AIDS
Typically, the demographic transition from higher to lower fertility is accompanied by a
transition from higher to lower mortality. In South Africa (and indeed much of SubSaharan Africa), this mortality transition has been distorted by HIV/AIDS. While
mortality rates in South Africa fell for many decades until the mid-1980s, this decline
levelled off thereafter, and mortality rates have subsequently risen in some population
and age groups (Dorrington, Moultrie and Timeaus, 2004).
The precise magnitude of the impact of HIV/AIDS on mortality rates and thus overall
population growth is unknown, but almost all of the research agrees that it will be
significant.
Note that the prevalence of HIV varies between provinces, as well as by settlement
type. As a result, the impact of HIV/AIDS on population growth in these areas is likely
to differ.
35
25.8
HIV prevalence (%)
30
23.1
25
18.5
17.7
15.2
20
15.2
13.7
15
9.0
10
5.3
5
0
KZN
MP
FS
NW
GP
EC
LP
NC
WC
Figure 1: HIV prevalence among adults aged 15-49 years by province, South Africa 20083
3
Data source: Shisana, O et al (eds) (2009) South African national HIV prevalence, HIV incidence,
behaviour and communication survey, 2005 , Nelson Mandela Foundation, p. 35
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PDG Occasional Paper Number 2: Understanding the consumer profile
20
17.6
18
HIV prevalence (%)
16
14
11.6
12
9.9
9.1
10
8
6
4
2
0
Urban formal
Urban informal
Rural formal
Rural informal
Figure 2: HIV prevalence in population aged two years and above by settlement type, South
Africa, 20054
Note also that there is some disagreement regarding the overall prevalence of HIV in
South Africa, as shown in the figure below.
14
12.0
HIV prevalence (%)
12
11.0
10.8
11.5
9.6
10
8
6
4
2
0
Stats SA
ASSA
UNAIDS
HSRC
DoH
Figure 3: HIV prevalence rate in total population in 2005 according to various sources5
Internal migration
Internal migration refers to movements of people between and within provinces and
municipalities. According to the Forced Migration Studies Programme at WITS
University, internal migration is the most significant movement of people in South
Africa and poses the biggest challenge to planning (Polzer, 2010).
Urbanisation (the process by which rural populations move to cities and towns) is
occurring at a rapid rate in South Africa. This is within the context of rapid urbanisation
4
5
Data source: Shisana, O et al (eds) (2005) South African national HIV prevalence, HIV incidence,
behaviour and communication survey, 2005 , Nelson Mandela Foundation
Figure drawn from Dorrington et al (2006).
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PDG Occasional Paper Number 2: Understanding the consumer profile
in Sub-Saharan Africa as a whole (Kessides, 2006). However, the process by which
urbanisation is occurring is not straightforward.
The urban population is growing due to migration, but not all of this growth is
permanent. Strong links remain between many city dwellers and rural areas, and
movement occurs both from rural to urban areas and from urban to rural. According to
Collinson et al (2006), small towns are emerging as key migration nodes, and people
moving to small towns typically do not return to rural villages.
Note that due to a lack of necessary data, the relative size of the contribution of
urbanisation to population growth in urban areas (compared to ‘natural increase’ due
to births and deaths) is not known (Kok and Collinson, 2006).
Immigration
Immigration (which refers to people moving into South Africa from other countries) is
politically sensitive in South Africa, but the data suggests that it is far less numerically
significant than many South African citizens and policy makers believe (Polzer, 2010).
Recent estimates place this number at between 1.6 and 2.0 million or 3-4% of the
national population (ibid).
International migrants are heavily concentrated in metros, Johannesburg in particular,
with moderate numbers (2 000 to 10 000 people per district) in Limpopo and
Mpumalanga provinces (Forced Migration Studies Programme, 2010).
Projecting net immigration in the future, particularly from neighbouring countries, is
obviously difficult, with much depending in political and economic circumstances in
these countries.
2.1.2 Trends in national population growth
Several organisations produce demographic estimates using models based on
assumptions about population growth. These are typically produced at national and
provincial level only, rather than municipal level. However, the findings of these models
regarding population growth rates can be adapted (using local knowledge) to estimate
the growth rates at municipal level.
As may be seen in the table below, different models draw different conclusions about
population growth. This is because the models make different assumptions about the
various factors that influence this growth.
Table 2: Estimated annual population growth rates, 2001 to 2006, from various sources
Stats SA*
Actuarial Society of
South Africa**
Bureau of Market
Research***
2001 to 2002
1.40%
1.2%
1.18%
2002 to 2003
1.30%
1.1%
0.97%
2003 to 2004
1.21%
0.9%
0.82%
2004 to 2005
1.16%
0.8%
0.68%
2005 to 2006
1.13%
0.8%
0.56%
2006 to 2007
1.11%
0.7%
0.46%
2007 to 2008
1.13%
0.7%
0.42%
2008 to 2009
1.12%
0.6%
0.38%
2009 to 2010
1.06%
0.6%
0.39%
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PDG Occasional Paper Number 2: Understanding the consumer profile
Data sources: * Statistics South Africa (2010) ** Dorrington et al (2006) *** Van Aardt (2006)
Note that, while the different models draw different conclusions regarding the
magnitude of population growth between 2001 and 2006, they all agree that the
population growth rate is declining.
2.1.3 Estimating the current population in a municipality
A number of different approaches may be taken to estimating the current population
size in a municipality.
Estimates based on assumptions regarding population growth rates
With this method, the starting point is the population in the municipality in 2007,
according to Community Survey6. The population in the current year is then estimated
based on an assumption regarding the population growth rate between 2007 and the
present.
There are three ways of estimating the population growth rate in a municipality.
Extrapolation
This method assumes that past trends in the growth rate will continue. So data on past
population growth in the municipality is simply projected into the future. This method
can be fairly accurate in the short term. However, over the longer term, it does not
take into account the fact that the factors that influence population growth (fertility,
mortality and migration) are dynamic. If trends in these factors change (for example, if
Anti-Retroviral roll-out accelerates and mortality rates due to HIV/AIDS thus decline),
then past population growth rates may no longer be a good predictor of future growth.
Using national or provincial estimates
As discussed in the previous sub-section, several organisations estimate national and
provincial population growth into the future, using fairly complex demographic models.
These estimates may simply be applied to the relevant municipality. This method does
not take local conditions that may result in growth rates higher or lower than the
national or provincial average into account.
Construct a local growth rate
The final approach is to construct a growth rate that takes local circumstances into
consideration.
First, the population should be divided into groups with similar socio-economic
characteristics and the birth and death rates for each of these groups should be
estimated. This could be done based on appropriate national or provincial rates, or
based on locally available data. The rate of population growth before migration can
then be calculated for the municipality.
Next, the extent of in- and out- migration should be estimated. This is likely to be more
difficult, and requires consideration of factors that may attract people to the area, and
factors that may encourage people to leave. The economic growth rate of the town in
relation to the growth rate in surrounding areas is likely to be of importance here, with
people likely to be attracted to areas of greater perceived economic opportunity. ‘Push’
6
Population in 2001 according to Census 2001 can be used as an alternative starting point, if this is
preferred. Note that some municipal boundaries have changed since 2001, but Stats SA does provide
key Census 2001 indicators, including population, for the 2005 municipal boundaries.
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PDG Occasional Paper Number 2: Understanding the consumer profile
factors in operation in surrounding areas also need to be considered, such as structural
changes in agriculture.
Estimates based on dwelling counts
It is possible to estimate the size of the current population based on dwelling counts
and number of people per dwelling in all settlements in the municipal area. An
estimate of the population in each settlement or area is then determined as follows:
Population = number of dwellings x average number of people per dwelling
The population of the area as a whole can then be calculated by adding the
populations of each settlement or area.
The easiest way to obtain accurate dwelling counts is to use aerial photography. Many
municipalities now maintain reasonably up-to-date aerial photographs of the areas for
which they are responsible.
Note that the number of people per dwelling is likely to differ for different dwelling
types, so the dwelling count should differentiate between dwellings of different types.
Household size could be used as an estimate of number of people per dwelling, but
note that the two are not necessarily the same, since in South Africa a household is
typically defined as an economic unit, or people who share resources, rather than
people who share a dwelling. The most accurate way of determining how many people
share a dwelling is to conduct a survey of a sample of dwellings. This survey should
cover all settlement types appearing in the municipality (i.e. urban formal, urban
informal, peri-urban etc) and, as mentioned above, should differentiate between
different dwelling types.
This method has recently been applied in eThekwini Metropolitan Municipality. The
table below shows population estimates for the municipality, based on dwelling counts
and assumed occupancy rates.
Table 3: Population estimates for eThekwini Metropolitan Municipality based on dwelling
counts
Dwellings
Occupancy rate
Population
Formal house
377,960
4.36
1,647,783
Formal flat
67,864
3.35
227,265
Informal shack
306,068
3.60
1,101,841
Informal backyard
37,422
3.90
145,946
Rural villages
166,714
1.72
286,643
Hostels
Total
111,445
956,028
3,520,922
Data sources: data provided by eThekwini Metropolitan Municipality. Dwelling numbers based
on 2007 aerial photography. People per dwelling based on occupancies established in 2001.
The dwelling counts used in the table above are fairly out-dated, but they demonstrate
the fact that the number of people sharing a dwelling differs with dwelling type, as well
as with settlement type. A household survey to establish occupancy rates, conducted in
1996, found that the number of people sharing a dwelling was significantly lower than
expected for almost all types of dwelling (Breetzke and Wright, 1996). This highlights
the importance of conducting at least some form of survey rather than relying on
conventional wisdom.
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PDG Occasional Paper Number 2: Understanding the consumer profile
Estimates based on local area studies
In some areas local universities, government departments, management consultants or
other bodies may have made recent population estimates. If more than one estimate is
available they should be compared. Where estimates differ greatly (which is often the
case) they should be treated with caution. Informed judgements are required to
evaluate the relative accuracy of the different estimates.
2.2
Projecting population into the future
As discussed in Section 2.1.3, three methods are available to estimate population
growth rates into the future.
Extrapolation
This method simply assumes that past trends in the growth rate will continue. These
estimates may be fairly accurate in the short term, but are less reliable in the longer
term.
Using national or provincial projections
Projections of national or provincial population growth produced by organisations such
as the Bureau for Market Research (BMR)7 may simply be applied to the relevant
municipality. These estimates do not take local circumstances into account.
Constructing a projected growth rate specific to the municipality
A growth rate that takes local circumstances into consideration can be constructed.
This would require assumptions to be made regarding how fertility, mortality and
migration trends are likely to change in the future.
3
Households
As already mentioned, many services are not provided to individual people, but rather
to a group of people who share a plot or a service connection. The unit provided with
services is referred to as the ‘consumer unit’. This is the unit most relevant to the
service provider. Consumer units are not necessarily exactly aligned to households, but
since households are the demographic unit most often measured by demographic
surveys, an understanding of household dynamics remains important in order to
understand consumer units.
3.1
The difference between households and consumer units
Simplistically, a household consists of one or more breadwinners and a number of
dependants who share the income8. A household thus functions as an economic unit.
In an uncomplicated world, each household would live in a separate dwelling unit on a
separate plot, and receive and pay for services as an individual unit. The real situation
is far more complex. In many formal townships more than one family unit resides on a
plot, either sharing the house or in backyard shacks. In informal areas it is possible
that a single family unit is spread over a number of dwelling units and would share a
7
See for example Van Aardt (2006).
8
Note that the definition used by Stats SA in Census 2001 is ‘a group of persons who live together and
provide themselves jointly with food and/or other essentials for living, or a single person who lives
alone.’
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PDG Occasional Paper Number 2: Understanding the consumer profile
single house if one were available. As a result, not all households interact directly with
the service provider:

Backyard shack dwellers make use of the services delivered to the plot and, from
the point of view of the service provider, all households residing on the plot form a
single unit.

The same applies where more than one household resides in a single dwelling unit.

Tenants in multiple dwelling units (such as blocks of flats and townhouse
complexes) are often not individually metered and billed by the service provider.
The body owning the complex is billed and pays the provider, and must then make
its own arrangements to recover payment from the tenants.

In rural areas, one tap, toilet or electricity supply point may be shared by a group
of dwellings and may thus serve multiple but related households.
A ‘consumer unit’ is the term used to refer to the unit provided with services by the
service provider. For the purposes of the service provider, this is the more relevant
unit.
Box 1: Consumer units and households in the City of Johannesburg
A survey of 386 plots in low income areas in the City of Johannesburg, conducted in 2006,
found that, on average, there was more than one dwelling on 50% of the plots surveyed.
50%
50%
40%
30%
16%
20%
1%
0%
0%
0%
0%
0%
10
11
12
4%
9
7%
8
11% 11%
10%
7
Frequency distribution of plots
60%
6
5
4
3
2
1
0%
Num ber of dw ellings per plot
Two thirds of secondary dwellings on multi-dwelling plots were occupied by tenants of the
main household, while one third were occupied by family members of the main household.
The consumer unit size differed significantly for single dwelling plots and multi-dwelling
plots. On average, four people lived on single dwelling plots, while 10 people lived on multidwelling plots.
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PDG Occasional Paper Number 2: Understanding the consumer profile
Frequency distribution of plots
25%
Single dw elling plots
20%
Multiple dw elling plots
15%
10%
5%
21
20
19
18
17
16
15
14
13
12
11
10
9
8
7
6
5
4
3
2
1
0%
Num ber of people per plot
Source: PDG (2007)
3.2
Why data on numbers of households is still important
There are a number of reasons why household data remains relevant to municipalities.
The first is that the household is the basic building block for socio-economic analysis.
As a result, much data, including data in the National Census, is collected for
households, and not for consumer units. Municipalities must understand the
relationship between the number of households and number of consumer units in their
jurisdiction in order to be able to convert data regarding the number of households
into data regarding the number of consumer units.
Secondly, in many cases consumer units comprise more than one household (or
households are spread over more than one consumer unit) by default rather than by
design.

Some consumer units comprising more than one household would separate,
given the opportunity; and

Some households in informal areas currently occupy more than one dwelling
unit but would move onto a formal plot as a single unit should plots be made
available.
Understanding these dynamics between households within consumer units is important
in projecting future growth in the number of consumer units.
3.3
Current number of households
As is the case with population, Community Survey 2007 is the most recent source of
official statistics on the number of households in municipalities in South Africa, despite
its shortcomings. The current number of households can be determined either through
a local survey or by estimating the rate of household growth since 2007.
3.3.1 Factors influencing household growth
Population growth is the primary factor that determines household growth. If the
average size of the household remains unchanged, then the household growth rate will
be the same as the population growth rate. However, a reduction (increase) in average
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PDG Occasional Paper Number 2: Understanding the consumer profile
household size will result in a household growth rate above (below) that of the
population.
There are a number of factors that influence average household size (Bongaarts,
2001). The number of children per household is primarily determined by fertility levels.
The number of adults per household may be influenced by:

Age at marriage;

Adult mortality rates;

The propensity of adult sons or daughters to remain in the parental household;

The risk of marital disruption and likelihood of remarriage;

The tendency and ability of the elderly to live alone; and

The presence of other relatives and non-related individuals (such as lodgers).
Internationally, there is a trend away from more traditional, complex household
structures and towards nuclear household structures (Bongaarts, 2001).
3.3.2 Trends in national household growth
Evidence suggests that the trend in South Africa is towards smaller households.
According to Census 1996, the average household size was 4.48. Community Survey
2007 suggests that average household size was 3.85.
The table below shows projected household growth rates between 2001 and 2010,
compared to projected population growth rates for the same period, according to the
Bureau of Market Research.
Table 4: Estimated annual household and population growth rates, 2001 to 2010
Household growth rate
Population growth rate
2001 to 2002
2.58%
1.18%
2002 to 2003
2.38%
0.97%
2003 to 2004
2.23%
0.82%
2004 to 2005
2.09%
0.68%
2005 to 2006
1.97%
0.56%
2006 to 2007
1.87%
0.46%
2007 to 2008
1.83%
0.42%
2008 to 2009
1.80%
0.38%
2009 to 2010
1.81%
0.39%
Data source: Van Aardt (2006)
Household growth is estimated to be significantly higher than population growth. This
is an important conclusion for planning by municipalities. From the point of view of
meeting targets for the elimination of infrastructure backlogs, household growth in
excess of population growth poses a significant challenge to municipalities.
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PDG Occasional Paper Number 2: Understanding the consumer profile
3.3.3 Estimating the current number of households in a municipality
Three methods are available for estimating the current number of households in a
municipality.
Estimates based on assumptions regarding household sizes
An estimate of the number of households in a municipality can be obtained by dividing
the current population of the municipality by an assumed average household size.
An estimate of average household size could be obtained by:

Extrapolation from household sizes as determined by Census 2001 or
Community Survey 2007;

Using national or provincial estimates of household size; or

Conducting a simple household survey.
Note that household sizes tend to differ between income groups, and so the accuracy
of the estimate can be improved by using different average household sizes for
different income groups.
Estimates based on dwelling counts
A dwelling count would provide a reasonable first estimate of the number of
households in a municipality. However, there are situations in which a household
occupies more than one dwelling (for example, where family members occupy
backyard shacks) or where several households share a single dwelling. Data regarding
the extent to which such situations occur in a municipality can be obtained by a fairly
simple local household survey.
Estimates based on local area studies
As was the case for population estimates, local universities, government departments,
management consultants or other bodies may have made recent household estimates.
3.4
Projecting the number of households into the future
The simplest method of projecting the number of households into the future is to start
from population projections, and use an average household size to calculate the
number of households.
The comments made in Section 3.3 regarding current trends in household growth
should be taken into account when deciding what average household size to use.
4
Consumer units
A ‘consumer unit’ is the term used here to refer to the unit provided with services by
the service provider. A good understanding of the number and nature of the consumer
units served by the municipality is vital in order to plan for the demand for services.
4.1
Types of consumer units
Consumer units may be either residential or non-residential.
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PDG Occasional Paper Number 2: Understanding the consumer profile
4.1.1 Residential consumer units
The group of all residential consumer units in an area served by a municipality should
be disaggregated by income. This is because income impacts heavily on the amount of
services likely to be used as well as affordability and willingness to pay.
Because of the link between capital subsidies and income, it would be advisable for
service providers to use the income categories applicable to the allocation of housing
and other subsidies. A number of different income categories are used to allocate
subsidies in South Africa:

R800 per month: used for calculating the Basic Services component of the

R1 100 per month: used to calculate the amount of MIG allocated to municipalities;

R1 686 per month: used to determine eligibility for a state pension; and

R3 500 per month: used to determine eligibility for housing subsidies.
Equitable Share;
Note that subsidies are allocated based on household, not consumer unit, income.
Unfortunately, the most recent accurate data on income distributions in individual
municipalities is available from Census 2001. Stats SA use a limited number of income
categories, which do not necessarily coincide with those used for the allocation of
subsidies.
Suggested income categories to be used in classifying residential households are
presented in the table below.
Table 5: Suggested income categories to be used to classify residential households
Monthly income per household
Category
R800 or less
Indigent
R1 100 to R1 600
Low income
R1 600 to R3 200
Low income
R3 200 to R6 400
Middle income
More than R6 400
High income
Note: the income categories used in this table coincide with income categories used
by Stats SA in Census 2001.
The categories above will have to be adjusted to incomes per consumer unit, based on
the numbers of households per consumer unit in the municipality.
4.1.2 Non-residential consumer units
Four types of non-residential consumer unit may be identified:

Institutional consumer units. Schools, clinics, hospitals, churches and day care
centers, etc

Commercial consumer units. Shops, offices etc

Industrial consumer units. For the purposes of understanding water
consumption, industrial consumer should be further classified as ‘dry’ industrial
or ‘wet’ industrial. Industries that do not use water as part of their ‘core’
processes would be classified as ‘dry’, while those that do would be classified as
‘wet’.
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PDG Occasional Paper Number 2: Understanding the consumer profile
These consumer unit types use services in different ways, and so dealing with these
different types of non-residential consumer unit separately will result in improved
predictions of current and future demand for services.
Note that when classifying non-residential consumers there is likely to be a number of
ambiguous cases. For example, should a sports club be classified as an institutional or
a commercial consumer unit? These decisions need to be made at the local level, with
due regard for service consumption patterns. A consistent approach should be
adopted.
4.1.3 The typical consumer profile in different municipal subcategories
The table below shows the typical consumer profile for the various municipal subcategories.
Table 6: Typical consumer profile in municipal sub-categories
A
B1
B2
B3
B4
6%
5%
4%
4%
1%
R0 to R800
pm
19%
26%
26%
31%
40%
R801 to
R1600 pm
24%
30%
31%
35%
39%
R1601 to
R3500 pm
16%
16%
18%
15%
11%
More than
R3500 pm
40%
28%
25%
19%
10%
% of all CUs that are nonresidential
% of
residential
CUs falling
into
household
income
brackets in
2010
Notes: The residential income distributions shown here are calculated from Census 2001 data
with inflation taken into account. The percentage of CUs that are non-residential is based on
case studies.
The more urban municipalities (A and B1 municipalities) tend to have more economic
activity and thus higher proportions of CUs in these municipalities are non-residential.
These municipalities also tend to have better income distributions, with large
proportions of residential CUs falling into higher income brackets.
4.2
Current consumer units
4.2.1 Number of residential consumer units
As mentioned in Section 3.2, the household remains the basic unit for socio-economic
research. As a result, little data is usually available on the number of consumer units in
a municipality. The best way of estimating the current number of residential consumer
units is thus to start from the number of households, and convert this to number of
consumer units using the average number of households per consumer unit.
The number of households per consumer unit is most accurately determined through a
local survey. This number is likely to vary with settlement type and income group and
so such a survey should include all settlement types and income groups.
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PDG Occasional Paper Number 2: Understanding the consumer profile
Alternatively, it could be assumed that each household occupies a single dwelling9. The
number of dwellings per plot can then be assumed to equate roughly to the number of
households per consumer unit. The number of dwellings per plot in different areas of
the municipality can be determined using dwelling counts based on aerial photography.
Note that the number of consumer units currently receiving services for which they are
billed may be determined from the municipal billing database, with each residential
billed unit classified as a residential consumer unit. Billing data will, of course, exclude
any consumer units not currently provided with services, or currently provided with
services for which they are not billed. The number of consumer units not captured in
the billing database will have to be estimated using the methods outlined above.
4.2.2 Income profile of residential consumer units
Reliable information on income distribution is currently likely to be even more difficult
to find than estimates of population size or number of households. Income is recorded
in the national census, but the results of Census 2001 are now too old to be of much
value other than a rough first estimate of income. Income distribution was determined
in Community Survey 2007, but the results are regarded as too flawed for use. In
addition, both Census and Community Survey refer to household income, not
consumer unit income. Converting between the two is not a simple task.
The best source of information would be a well constructed and carefully executed
local survey. However, this is unlikely to be practical for many municipalities.
A rough approximation of the income profile may be arrived at by using property
values. The argument for this is that the developer selling the property assesses the
income of the household purchasing it (or receiving it if it is fully subsidised) and the
value of the property is closely related to household income. In addition, the property
held by a person gains value largely due to investments made by the household which
are strongly linked to the income of the household. This link between household
income and property value is, however, not always perfect, and estimates of household
income based on property value should be treated with some caution.
4.2.3 Size of residential consumer units
The size of residential consumer units has implications for the ‘consumption’ of
services. The average size of a consumer unit can be determined either through a local
survey or estimated based on the average household size, as shown in the following
example. If the average household size of backyard shacks is 2.5, that of formal
dwellings in a township is 4, and the average number of shacks per plot is 1, then the
combined average household size of formal plots with backyard shacks is 6.5. If 60
percent of the plots in a particular township have backyard shacks, the weighted
average consumer unit size for the township would be:
(60% x 6.5) + (40% x 4) = 5.5
Dwelling counts based on aerial photography can be used to determine the number of
dwelling units per plot in different areas, as well as the percentage of plots that have
more than one dwelling unit.
4.2.4 Number of non-residential consumer units
Probably the best source of information on non-residential consumers is municipal bills,
with each billed unit classified as a consumer unit. Billing data is likely to exclude some
9
Bear in mind that this is not strictly true. As mentioned previously in this chapter, a single household may
occupy several dwellings. Alternatively, several households may share a dwelling in some instances.
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PDG Occasional Paper Number 2: Understanding the consumer profile
informal non-residential consumer units, such as churches, day-care centres and retail
outlets in informal areas. These should be included even though their current
consumption may be small, because they form part of the backlog of service provision.
Information on the numbers and characteristics of these establishments is unlikely to
be readily available, and will probably need to be estimated on the basis of local
knowledge.
4.3
Projecting consumer units into the future
4.3.1 Growth in number of residential consumer units
The rate of growth of residential consumer units is likely to be similar to household
growth in most areas.
However, in areas where high concentrations of backyard shacks exist due to housing
shortages, it is possible that shack numbers will decline over the years as more
housing becomes available. For each backyard shack that is replaced by an
independent site a new consumer unit is formed. This source of consumer unit growth
can be fairly substantial in towns with high proportions of backyard shacks, and must
be taken into account.
Another potential source of discrepancy between household and consumer unit growth
lies in the transition from informal to formal settlements. For example, two or more
units currently identified as separate consumer units in an informal settlement may
jointly occupy a single formal site. Some may become backyard shack dwellers while
others join to form an extended family unit. It will probably be fairly difficult to predict
this type of effect, and in the absence of information to the contrary it is probably
safest to assume that each dwelling in an informal settlement will form a separate
consumer unit when formal sites are provided to replace informal settlements.
4.3.2 Changes in the income distribution
The income distribution in the future is determined by the relative rates of economic
and population growth, combined with the distributive nature of that growth. Other
redistributive measures taken locally or nationally (such as state pensions, child care
grants, or employment in public works programmes) also need to be taken into
account. Predicting future income distribution is thus a complicated and imprecise
process. However, projections of demand involve assumptions about future income
distribution, and these should be made explicit.
The simplest approach is to assume that income distribution will remain unchanged
over the forecast period. This will be the case if predicted economic and residential
consumer unit growth rates are similar, and may be a reasonably realistic assumption.
Rapid economic growth attracts people to an area, and so it is unlikely that economic
growth will exceed consumer unit growth significantly for a prolonged period unless
this is a national phenomenon. On the other hand, very poor performance may see an
exodus of households from an area.
Without well substantiated reason to believe the contrary, an assumption of unchanged
income distribution is probably the safest.
4.3.3 Growth in number of non-residential consumer units
The rate of growth in non-residential consumer units is likely to be closely related to
the economic growth rate. Higher economic growth in an area will attract more
businesses and industries to locate there, while poor economic growth is likely to have
the opposite effect.
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PDG Occasional Paper Number 2: Understanding the consumer profile
The safest assumption for the rate of growth in non-residential consumer units is thus
to assume it equal to the forecast economic growth rate for the municipality.
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PDG Occasional Paper Number 2: Understanding the consumer profile
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