Projecting small area statistics with Australian spatial microsimulation model (SpatialMSM).
Vidyattama, Yogi ; Tanton, Robert
1. INTRODUCTION
In the past decade, the need to analyse local or regional economies
has brought dynamic spatial microsimulation into the forefront of
microsimulation research. There is an increasing recognition of the
importance of regional economies in terms of sub-national economies and
the way they evolve over time (Neary, 2001). This has made small area
statistics as well as projections for small areas increasingly crucial.
The increasing need of many governments in the world to plan their
economy at a regional level has also increased the need for small area
statistics and their projections.
The strong demand for small area information by planning agencies,
especially State and Territory governments in the case of Australia, has
mainly focused on the characteristics of individuals and households and
the small area impact of possible policy changes. There are several
reasons for this. First, such information is required, for example, by
those government agencies with responsibility for allocating scarce
resources to where they are most needed--ranging from the most effective
placement of child care or aged care services to disability programs and
services targeted towards youth-at-risk. Second, governments often need
accurate information about the degree to which deprivation or
disadvantage is concentrated in particular places, to inform social
policy formation more generally. Third, an ability to estimate the
spatial impact of a policy before the policy change is introduced helps
to prevent the emergence of unintended small area consequences.
Despite this great need for small area statistics for planning
purposes, the data can be very hard to obtain. National censuses are
typically conducted relatively infrequently and their extensive
geographic detail comes at the price of containing only a limited range
of information about households. On the other hand, surveys obtain much
richer information, but are designed for national, or at most, state
level estimation. They are therefore unsuitable for directly estimating
statistics for small areas due to small sample sizes in small areas
(Heady et al., 2003). Therefore, various techniques have been developed
to achieve small area estimates from sample surveys. Spatial
microsimulation techniques are among the techniques used to estimate
small area statistics. (see Rahman, 2008 for a review of the
literature). The spatial microsimulation technique essentially reweights
survey data to match new small area benchmarks from the Census
Dynamic microsimulation allows the user to predict regional
economic and demographic conditions in the future as well as predict the
impact of policy at a regional level. Although the development of the
dynamic microsimulation model was started a half century ago by Guy
Orcutt in 1957, the development of dynamic microsimulation for small
areas is fairly recent. This is mainly because the use of
microsimulation in a spatial context is somewhat rare (Birkin et al.,
1996). Birkin and Clarke (1988, 1989) and Williamson (1992) are among
the first microsimulation applications that involve spatial estimation.
SVERIGE, which was built in 1996 in Sweden, is considered to be the
first dynamic spatial microsimulation model that covers the entire
nation (Vencatasawmy et al., 1999; Holm et al 2001). The model was
developed based on CORSIM, a dynamic microsimulation model to estimate
new indicators of wealth in the USA (Caldwell 1990). At the time SVERIGE
was built, there was another dynamic spatial microsimulation that was
being built in the Netherlands (Hooimeijer, 1996). Other dynamic spatial
microsimulation models built in recent years include SimBritain (Ballas
et al., 2005a), SMILE (Ballas et al., 2005b) and an agent based spatial
microsimulation model (Wu et al., 2008).
In general, there are many methodologies that have been developed
to make a microsimulation model (including a spatial microsimulation
model) dynamic. These methodologies have also been used to produce more
sophisticated and more accurate projections from the model. These
methodologies can be categorised into fully dynamic and semi or pseudo
dynamic microsimulation models. While fully dynamic models simulate the
dynamic behaviour of the unit record in the survey data (or microdata),
a semi or pseudo dynamic model projects the dynamic constraints from the
census data (Caldwell, 1990). Other researchers have named the
pseudo-dynamic approach static ageing (O'Donoghue, 2001). The
static ageing method is traditionally used if the microsimulation model
is a static model. Static ageing adjusts the benchmark table to account
for changes in the population structure, price structure (inflation),
the distribution of income and to some extent changes in policy rules
(O'Donoghue , 2001). In many cases these adjustments are based on
national macro economics forecast (Eason, 1996; Gupta and Kapur, 1996).
This paper describes an effort to project small area statistics in
Australia by employing an existing spatial microsimulation model for
Australia (SpatialMSM). In particular, this paper shows how we have
modified the Australian static spatial microsimulation model SpatialMSM
to make it a pseudo dynamic microsimulation model.
Section two briefly discusses spatial microsimulation and overseas
efforts to derive projections from spatial microsimulation models.
Section three will introduce SpatialMSM, the Australian spatial
microsimulation model, as a static spatial microsimulation model for
Australia, while section four describes the projection methodology we
have developed and its reliability. Section five contains conclusions.
2. PROJECTIONS USING SPATIAL MICROSIMULATION
Spatial microsimulation involves creating synthetic spatial
microdata. (1) Some of the early research in this field was undertaken
by geographers and concentrated upon whether it was possible to create
small area specific microdata from the UK Census one per cent sample
(Williamson et al, 1998; Voas and Williamson, 2000; Williamson, 2001).
While various approaches to reconstructing spatially detailed microdata
have been trialled, including data fusion and synthetic reconstruction
(Voas and Wiliamson, 2000, p. 349), the more successful endeavours
essentially involve methods of reweighting the original sample survey
data to match small area population targets from a relevant Census.
Ballas et al (2006a, p. 65) explain these techniques 'involve the
merging of census and survey data to simulate a population of
individuals within households (for different geographic units), whose
characteristics are as close to the real population as it is possible to
estimate'.
Once synthetic household microdata have been created for each small
area, then it becomes feasible to use this microdata for microsimulation
modelling. Microsimulation models were initially developed within the
discipline of economics (Orcutt et al, 1986) and have today become very
widely used by governments across the developed world for analysis of
the fine-grained distributional impact of possible changes in government
programs (Harding, 1996; Gupta and Kapur, 2000; Mitton et al, 2000;
Harding and Gupta, 2007b). However, importantly, the overwhelming
majority of these microsimulation models have been national models,
constructed on top of national sample survey microdata and predicting
the distributional impact of policy change for an entire country, rather
than for a small region within a country.
A new development during the past decade has been the construction
of spatial microsimulation models, constructed using the synthetic
spatial microdata bases described earlier. This rapidly growing field
now includes simulation of the small area impact of changes in income
taxes and cash transfers (Chin et al, 2005; Harding et al. 2009b);
development of small area measures of poverty and housing stress (Tanton
et al, 2009; McNamara et al, 2007); small area modelling of Activities
of Daily Living Status and need for different types of care (Lymer et
al, 2006, 2008a, 2008b); development of the SimObesity model to examine
small area obesity among children (Procter, 2007); small area
health-related conditions (Ballas et al, 2006a); the socio-economic
impacts of major job gain or loss at the local level (Ballas et al,
2006b) and a range of other applications (Ballas et al, 2005a, 2005b;
Clarke 1996).
A further development has been the attempt to 'age' the
spatial microsimulation databases forward through time, so as to provide
projections. As noted in Harding and Gupta (2007a), a conceptual
distinction can be drawn here between models that undertake 'static
ageing' (such as reweighting the small area dataset to future
population projections) and those that attempt 'dynamic
ageing', which involves updating the characteristics of the
micro-units through time.
As outlined in the introduction, there are a number of dynamic
microsimulation models already in existence (SVERIGE, CORSIM, SMILE).
There are also examples of pseudo-dynamic models in the UK, which are
not fully dynamic in that they do not model individual life experiences
like mortality, fertility and migration (as SVERIGE and SMILE do); but
reweight to projections of Census tables, so use static ageing. Examples
of these models include SimBritain (Ballas et al., 2005a).
SVERIGE uses the pattern of emigration, immigration, employment and
earnings, education, leaving home, divorce, cohabitation and marriage,
as well as mortality and fertility as the dynamic individual behaviours
in the model. The Monte Carlo simulation picks individuals in the
Microdata to experience any of the above behaviours based on simple
probabilities and hence updates the individual characteristics in the
microdata. So central to creating projections in this model are accurate
probabilities of each behaviour. In SVERIGE, these probabilities are
obtained using either probabilities from past experience or estimated
logistic regression equations.
SMILE is built as both a static and dynamic spatial microsimulation
model (Ballas et al., 2005b). It is constructed to estimate and project
small area statistics in Ireland. The model starts as a static model
using an iterative proportional fitting (IPF) method to spatially
disaggregate the aggregate microdata. Once this has been done, the
demographic processes of mortality, fertility and migration are
simulated. The mortality process is simulated by using the probability
of death based on age, gender and location while the probability of
birth is simulated based on age, marital status and location. The
simulation of the migration process uses random sampling from calculated
migration probabilities derived from the 1991 and 1996 Census of
Population. These data provide migration probabilities from one area to
another by age, gender and location.
SimBritain (Ballas et al., 2005a) is a spatial microsimulation
model for Britain's small areas. Unlike SVERIGE and SMILE,
SimBritain is constructed as a pseudo dynamic microsimulation model. The
model projects benchmark tables from 2001 to 2011 and 2021 using the
long term trend of each small area based on data from the UK 1971, 1981,
and 1991 census.' The benchmark projections are calculated using a
logistic model of the changing population proportion in each category of
each benchmark table. After all the 6 benchmark tables in SimBritain are
projected, the microdata are reweighted to the projections, and new
weights are calculated for each household or person on the microdata.
3. PROJECTING SMALL AREAS STATISTICS IN AUSTRALIA
SpatialMSM is a spatial microsimulation model that has been
developed to estimate small area statistics in Australia. The model has
been under development for several years, initially reweighting a
household expenditure survey to 2001 Census small area benchmarks (see
Chin et al. 2005; 2006; Chin and Harding, 2006, 2007 and, for
documentation of the earliest efforts, see Melhuish et al 2002). Later
versions of the modelling have reweighted ABS income surveys to 2001
Census benchmarks (Tanton et al, 2009), while the version described in
this paper utilises the latest 2006 Census benchmarks.
Besides estimating small area statistics, this model has also been
linked to a static microsimulation model in Australia called STINMOD to
estimate small area impacts of policy change. The model is also used by
various service delivery agencies to derive small area estimates of
groups that will require services from the service providers. The
general method has also been used to develop a small area spatial
microsimulation model for projecting customer service needs, CUSP
(Phillips, 2007); develop HOUSEMOD for examining the impact of changes
in housing assistance (McNamara et al, 2007); and to develop CAREMOD for
assessing small area care needs (Lymer et al., 2006).
The SpatialMSM model employs an Australian Bureau of
Statistics' reweighting program called GREGWT (Tanton et al, 2009).
The GREGWT algorithm uses a regression technique to create initial
weights for the Microdata and then because the optimisation process is
constrained to having no weights less than 0, it iterates until the new
weights produce an overall characteristic that is close to the
constraints or benchmarks for a small area. The general method is
outlined in more detail in Lymer et al. (2008b) and Chin et al. (2006).
3.1 SPATIALMSM/08C
The version of the spatial microsimulation model used for this
paper is called SpatialMSM/08C. This version of the modelling has been
designed to derive results for Statistical Local Areas (SLA) across
Australia, using the 2006 Australian Standard Geographic Classification.
This is done by reweighting households and individuals from the 2002-03
and 2003-04 Surveys of Income and Housing to Statistical Local Area
benchmarks from the 2006 Australian Census of Population and Housing
(with all of the above being produced by the Australian Bureau of
Statistics (ABS)).
The first step in producing the small area estimates involves
combining information from two surveys--the 2002-03 and 2003-04 ABS
Survey of Income and Housing (SIH) Confidentialised Unit Record Files
(CURFs)--and the 2006 Australian Census of Population and Housing. This
process uses GREGWT to reweight the national sample survey microdata
files to the 2006 SLA Census tables, based on the 11 Census benchmarks
shown in Table 1 below.
Given that the two national sample surveys and the census were
conducted at different points in time, there are some adjustments needed
before the reweighting process can start. The gross incomes from the
surveys are uprated to 2006 dollar values, using changes in average
weekly earnings to make the income values in both SIH years comparable
to gross income values from the Census. The weekly household rent and
mortgage on the surveys are also uprated using the changes in the
housing component of the ABS Consumer Price Index (ABS 2008a).
The Statistical Local Area (SLA) is the spatial unit used in this
paper. The SLA is one of the standard spatial units described in the
Australian Standard Geographic Classification 2006 (ABS 2007). There
were two main reasons why the SLA was chosen as the unit of analysis in
this study. First, the SLA is the smallest unit in the ASGC where there
are not substantial issues with confidentiality, as occur with Census
Collection Districts. (The ABS applies a confidentialising process to
table cells with a small cell size.) Second, SLAs cover the whole of
Australia (as opposed to Local Government Areas which do not cover areas
with no local government) and also cover contiguous areas (unlike some
postcodes).
The reweighting process in SpatialMSM uses an iterative constrained
optimisation technique to calculate weights to produce the SLA level
data that are closest to the Census Benchmarks. The procedure applies a
generalised regression procedure outlined in Bell (2000) in a SAS macro
developed within the ABS called GREGWT. The SpatialMSM model uses this
process to create a synthetic household microdata file for each
Statistical Local Area (SLA) in Australia, containing a set of synthetic
household weights which replicate, as closely as possible, the
characteristics of the real households living within each small area in
Australia.
Because the reweighting process is an iterative process, there are
areas where the procedure cannot find a solution (called
non-convergence). The original GREGWT criteria for non-convergence is
whether the maximum number of iterations (as specified by the user) was
reached and a solution was not found. For SpatialMSM, the number of
iterations was set to 30. After some experimenting, the original
criteria from GREGWT was found to be too strict, since for some areas,
the population estimates using the weights were still reasonable when
GREGWT showed that the procedure had not converged. Therefore, another
measure has been used in determining the reliability of the weights.
This measure is the total absolute error (TAE) from all the benchmarks.
This measure was developed by Paul Williamson for a combinatorial
optimisation reweighting method (see Williamson et al, 1998). The TAE
will be 0 if we can match the benchmarks perfectly, and will increase as
the estimation process fails to meet the benchmarks. This will be
related to the population of the area being estimated; so for an area
with a population of 100 people, a TAE of 50 is bad; but for an area
with a population of 10,000 people a TAE of 50 is good. So the criteria
used in this paper is that if the TAE divided by the population of the
area is greater than 1 then the area is dropped from any future
analysis.
The model SpatialMSM/08C has been used to produce weights for 1214
SLAs and failed to produce reliable weights (so the TAE was greater than
one) for 138 SLAs. Most of the areas where the TAE was greater than one
were industrial areas, office areas or military bases with very low
population counts. As a result, the proportion of people living in these
SLAs is very small (Table 2). Only 0.7 percent of the total Australian
population in 2006 were lost in the reweighting process.
While the results look acceptable for most states and territories,
it must be noted that estimates for one quarter of the population in the
Northern Territory had a high TAE--and thus small area estimates for the
Northern Territory from SpatialMSM/08C should be treated very
cautiously. (The Northern Territory contains many SLAs where a high
proportion of the population are indigenous
Australians. Such households are not well represented in the
sampling frame for the national ABS sample surveys that were reweighted,
so the reweighting process may struggle to find an acceptable solution.)
4. USING SPATIALMSM FOR PROJECTIONS OF SMALL AREA STATISTICS
In a prototype version of the modelling, a simple static ageing
procedure was adopted, which essentially involves reweighting the data
for each small area to population projections for each small area. This
is similar conceptually to the approach followed in SimBritain (Ballas
et al. 2005a) and in earlier work on projecting consumer characteristics
out to 2020 in Australia (Harding and Gupta. 2007b). However, in this
simple method, the 11 benchmarks are not projected using long-term
trends, as in SimBritain. The main reason why, for example, the
long-term trend away from home ownership and towards private rental for
younger generations has not been simulated (Tanton et al. 2008, p. 26)
is that such a technique requires data about such long-term trends at
the small area level. This is difficult to achieve, especially because
the changing boundaries of small areas makes the establishment of
long-term trends by SLA a challenging task. (This is will illustrated,
for example, in Vu et al. 2008, where they describe some of the
challenges faced when trying to make the 2001 and 2006 Census SLA
results comparable).
Another, more complex, approach is to project each one of the
benchmarks, and then reweight to these new projections. This is the
approach used by SimBritain, and also outlined in this paper for
SpatialMSM. The approach used to project the benchmark tables leverages
directly off the customised projections prepared for the Australian
Government Department of Health and Ageing (DOHA) by the Australian
Bureau of Statistics (ABS)
(http://www.health.gov.au/internet/main/publishing.nsf/Content/ageing-stats lapp.htm). These population projections contain age by sex
projections for each SLA in Australia until 2027 using the base
assumption that has been described in the explanatory notes for the
data, available from the DOHA website, and further discussed in ABS
(2008b).
Note that the population projections from the DOHA have been
produced using the cohort component method with the following
assumption. The national fertility rate will decline gradually to 1.8
babies per woman in 2021, the life expectancy will increase to 85-88 in
2055, while migration is based on the historical and trend data. The
population projections exclude 7 SLAs, being offshore and migratory
areas where no population projections are supplied. Therefore these SLAs
will not be in our projections.
4.1 Projection Process
As described above, one of the first steps in the creation of
SpatialMSM/08C essentially involves reweighting the two income survey
sample files to benchmark tables from the 2006 Census. Creating the out
years versions of the database again involves reweighting--but this time
to newly created estimated benchmark tables for future given years.
One of the advantages of reweighting to benchmark tables in future
years is that the projected benchmark tables can use a very rough
estimate in the first stage, and then the method for projecting each
benchmark table can be refined in the future, and the weights easily
recalculated using the more refined benchmark tables. The method used in
this paper to get the initial projections of the benchmark tables uses a
logistic regression model based on age by sex by labour force status
projections, but in the future any of the benchmark tables could be
refined and new weights calculated.
The first constraint or benchmark table that is projected is the
Labour Force by Age by Sex benchmark table, which has been projected up
to 2027. To project this database, the SLA level population projections
from DOHA are combined with projections of labour force status used in
the Australian Commonwealth Treasury's 2007 Inter Generational
Report (IGR) (Treasury 2007). The long run historical trend was also
used in the report to project the participation rates for men and women
of different ages. This incorporates the changing composition of the
labour force in Australia, especially with more women participating in
the labour force.
Our initial problem with the DOHA SLA level population projections
is that they are only available by age and sex, and not by labour force
status. The projection of age by sex by labour force status is
undertaken in two steps. The first is to take the DOHA age by sex by SLA
projections for 2007 (so the year after our benchmark table) and use the
labour force by age/sex by SLA splits from the 2006 Census data to
apportion labour force status onto the 2007 age/sex population
projections. The second step is to use the percentage point change in
the national projections of labour force status by age by sex from the
Commonwealth Treasury's IGR 2007 report to adjust the proportion of
persons in each labour force category for every SLA. It should be noted
here that the national growth trend has been applied to each SLA, in the
absence of any SLA specific labour force projections.
In this first attempt at projecting the benchmarks, the labour
force by age by sex table plays an important role in the projections of
all the other benchmarks since it is the exogenous variable used to
project the other benchmark tables. The projections for all the other
benchmark tables are calculated using the relationship between the
benchmark table and the labour force by age by sex table in the base
year (2006). The coefficients used to project all the other benchmark
tables are estimated using a log linear model:
Ln (PopBC) = f ([i=5j=6k=2.summation over (i=0j=1k=1)]
[[beta].sub.ijk]Ln(Pop[LF.sub.i][Age.sub.j][Sx.sub.k])) (1)
Where PopBC is the number of population in each benchmark table
category while Pop[LF.sub.i][Age.sub.j][Sx.sub.k] is the population in
labour force status i, age j, and sex k. The estimation is done using a
cross section regression with every SLA in Australia as an observation.
Given that the estimate of [[beta].sub.ijk] in equation (1) is the
growth elasticity of the population in the benchmark table to the
population in labour force status i, age j, and sex k, the population
growth in each benchmark table can be projected as:
[DELTA]Pop[BC.sub.2006-T] / Pop[BC.sub.2006] =
([i=5j=6k=2.summation over (i=0j=1k=1)] [beta].sub.ijk]
[DELTA]Pop[LF.sub.i][Age.sub.j][Sx.sub.k2006-T] /
Pop[LF.sub.i][Age.sub.j][Sx.sub.k2006] (2)
The estimation in equation (2) will give us the estimated growth
and hence the estimated number of every category's population in
the benchmark tables for any year into the future. Note that all the
financial data has been kept in 2006 prices, so we haven't inflated
rents, mortgages, incomes, etc. What we are projecting is the number of
people in each income category; or the number of people in each rent
category. So the categories stay the same each year; only the number of
people in each category changes.
To derive reasonable estimates from Equation 2, the total number of
people or households in each benchmark table must be the same. In many
cases (due to the ABS' randomisation rule), these totals are not
the same. Therefore, the number of people or households in each table is
adjusted so the totals are the same across all benchmark tables. This
adjustment process takes one table as having the correct number, and
then adjusts all the other tables so they match this first table. In
this paper, the priority is the same as it is in Table 1; so there is an
assumption that benchmark table 1 has the correct total for number of
people; and benchmark table 2 has the correct number for total number of
households. All other tables are then adjusted to match the totals in
these tables.
As in the base year (2006), the reweighting process uses an
iterative constrained optimisation technique to calculate the weights
for every household in the microdata for every projected year. One of
the problems with using this technique is the loss of estimates for some
SLAs because the iterative process failed to find the optimal solution
given the constraints from the 11 benchmark tables.
The results from the reweighting process for the projected
benchmarks shows that the further the model is projecting out, the more
SLAs fail to converge. In the base year of SpatialMSM/08C, there are 138
out of 1422 SLAs that did not converge. The number of non converging
SLAs increases to 157 out of 1415 SLAs in the 2010 projection, and
increases further to 208 SLAs and 236 SLAs in the 2020 and 2027
projections, respectively. Table 3 shows that besides the Australian
Capital Territory and Northern Territory, most of the additional SLAs
that fail to fulfil the TAE criteria are non capital city SLAs.
Losing 236 of the 1415 SLAs in the 2027 projection is still
considered as acceptable for the purposes of this study, since these
SLAs only contain 2.8 per cent of the whole population (Table 4). It
should be noted, however, that around one-quarter to one-third of the
Australian Capital Territory and Northern Territory populations live in
SLAs which fail our TAE test in 2027, so projections for the two
territories must be treated with caution. A special note, however, needs
to be given to Queensland that has substantially more SLAs than New
South Wales and Victoria. It was notable that around 18 per cent of the
SLAs outside Brisbane failed the TAE test in the projections. This
requires further investigation and may be related to the relative high
(5.1 percent) Census undercount outside Brisbane in 2006.
4.2 Reliability of the Projections
After the weights for future years are produced, the next step is
to check the reliability of the estimation using this set of future
weights. The validation process is the step that is commonly used to
check the reliability of the spatial microsimulation modelling.
There are two sources of model error in our projections. One comes
from the projections of each benchmark table; so it is to do with the
reliability of the coefficient [[beta].sub.ijk] in Equation 1. The
second source of error is in the generalised regression routine that
will reweight the survey data to the projected benchmarks.
In terms of the first source of model error, if the Age by Sex by
Labour Force projections are not very good at estimating our other
benchmarks, then the estimated weights for the projections will not be
accurate and the projections will be unreliable.
The estimate of the size of the errors in the forecasting of the
benchmarks can be looked at using the coefficient of determination
([R.sup.2]) of the regression process that produces the elasticity
coefficients (Equation 1). This figure will show how much variation in
the benchmark table in the base year can be explained by the age by sex
by labour force structure. As the regression was done separately, each
category in each benchmark table has it's own [R.sup.2]. However,
to simplify the analysis the means of the [R.sup.2] in the benchmark
tables will be presented. The range of [R.sup.2] values will also be
given to give a better idea as to the reliability.
Looking at Table 5, the [R.sup.2] indicate that most of the
variation in the original tables can be explained by the Age by Sex by
Labour force status table. This means that projections of these
benchmark tables using a coefficient calculated in the base year, while
not perfect, would be reasonable as a first attempt at projecting the
base microdata. Further work could enhance these projections, and one
option may be to introduce some historical time series where the
projections are particularly bad (as has been done for SimBritain--see
Ballas et al, 2005a), but for most of the benchmarks, the age by sex by
labour force status table explained on average more than 70 percent of
the variation in the other tables. However, there are 3 tables where the
average [R.sup.2] was below 70 percent, which are tenure by weekly
household rent, monthly household mortgage by weekly household income,
and weekly household rent by weekly household income. These would be the
first tables that further work could be conducted on getting better
projections.
In conclusion, on the basis of the [R.sup.2] for the model in
Equation 1, it is considered that the projected benchmarks were reliable
enough to use in the reweighting process.
The second set of validation tests check the accuracy of the
estimated projections against a projected variable that is not
benchmarked, but is available from the small area projections we have.
In our case, the number of children aged 3 and 4 years is not
benchmarked (we benchmark the number of children aged 0-17 years), can
be estimated from our model, and is available from the age/sex
projections.
One of indicators of accuracy that has been developed for the
validation process uses called the measure of accuracy (Miranti et al,
2008). This is essentially the dispersion of the estimated SLAs around
the more reliable number from ABS publication or administrative data
where the definition used has exactly the same definition. So measure of
accuracy or MA is calculated as:
MA = 1 - [summation][([y.sub.est] - [y.sub.ABS]).sup.2] /
[summation] [([y.sub.ABS] - [[bar.y].sub.ABS]).sup.2] (3)
Where
MA = measure of accuracy
[y.sub.est] = estimated number from spatial microsimulation
[y.sub.ABS] = estimated number from the ABS
[[bar.y].sub.ABS] = mean estimates of the ABS number
The formula of this measurement is similar to the formula for the
coefficient of determinant or [R.sup.2] in a regression model, which
also calculates the dispersion of the estimated value from the
regression to the actual data.
The measure of accuracy for the base year (2006) is 99.0 per cent
for the number of children aged 3-4, so we get an excellent result for
the base year. The measure of accuracy for the projection in 2027 is
95.1 per cent. This shows that our modelled projected data match very
well to the DOHA population projections.
5. APPLYING THE PROJECTION AND SCENARIO BUILDING
As mentioned in the introduction, these spatial microsimulation
projections are built to assist planning agencies such as government by
providing information about the characteristics of individuals and
households in certain small areas in the future. This information can
then be used to anticipate the need for resource allocation for each
small area in the future. Nevertheless, the information provided by
these projections is based on strict assumptions about the long term
projections of the benchmarks and maintaining the socio-economic
structure and relationship that exists in 2006. These assumptions may
not prevail and a good projection model should be ready to supply
alternative future scenarios.
Building a new scenario for a projection is undertaken by altering
the assumptions that are used in the base projection. The scenario built
will adjust the future socio-economic conditions based on different
assumptions about long term expectations or the socio-economic
conditions in 2006.
5.1 Projections of the base scenario
Without changing any assumptions or data in the model, it can
provide useful information for policy makers on projections of
populations who may demand certain types of services in the future. For
example, we would expect families where there are young children (below
school age) and where all parents are working to require childcare
services. So an estimate of the number of children aged 3-4 where both
parents are working may give policy makers in a State some idea on where
to locate child care centres.
A researcher may assume that the number of children aged 3-4 is a
reasonable proxy for the number of children aged 3-4 with all parents
working. What this section shows is the danger of using these simple
proxies.
An estimate of the number of children age 3 and 4 years who have
all their parents working in 2027 is produced by applying the record
unit data from the 2002-03 and 2003-04 SIH-CURFs to the projected small
area weights from the reweighting process. The variable representing the
number of children aged 3 and 4 in a household from the survey is
combined with person level data on the employment status of all people
in the household. This allows us to calculate the number of children
aged 3 and 4 where all parents are employed. Given that the spatial
microsimulation process calculates weights at a household level, the
number of children aged 3-4 in a household where all parents are working
is multiplied by the small area weight for each household.
The question that we are trying to answer for the service providers
is the demand for child care services in each area. What we have from
the DOHA population projections is projections of the number of children
aged 3-4 in small areas, but not all families will require childcare
services. The demand for child care services will also depend on who is
working in the family.
When we estimate the number of children with all parents working,
we find a correlation with the number of children aged 3-4 of 0.51 (see
Figure 1). This does suggest that our spatial microsimulation results,
which add the criteria of all parents working, make a significant
difference--so the number of children aged 3-4 is not a very good proxy
for the demand for childcare places in an area. Other variables such as
labour force status and family structure play an important part in
determining the number of children aged 3-4 with all parents working,
and only the spatial microsimulation model can add these criteria to the
projections.
Analysing the spatial pattern of children aged 3-4 with all parents
working is another way of examining whether this new variable adds any
further information. Figure 2 presents 4 maps. Map A and B show the
growth in the projected number of children aged 3-4 years from the DOHA
projections and the growth in the projection of children aged 3-4 years
with all parents working from SpatialMSM. The classes in the map are
distributed using natural breaks and the darkest colour shows the
highest estimate of growth.
These maps show that there are several SLAs on the western
outskirts of Sydney where the growth in the number of children aged 3-4
with all parents working is particularly high compared to the growth in
the 3-4 year old population. Liverpool-West, Blacktown-South-West, and
Fairfield-West are among those SLAs. These areas may have found a
significant lack of childcare places in 2027 if the estimates of
children aged 3-4 were used to show where future childcare places should
be allocated. A further investigation of this issue is discussed in
Harding et al (2009).
[FIGURE 1 OMITTED]
5.2 Building a Scenario
The second type of analysis we can do with this microsimulation
model is to change some of the assumptions, and build scenarios that
then affect the final weights, and the projections. The base model will
give an indication of what the future will be like given certain.
assumptions. However, no one really knows what will happen in the
future, and whether the conditions that become the basis for the base
projections will prevail. Therefore, the ability to build a scenario to
anticipate different assumptions allows planning agencies such as the
government to formulate an alternative plan given different assumptions
about the future.
Given that the projection methodology outlined in this paper is
mainly built on the projection of benchmark tables, any new scenarios
also have to be built by altering the benchmark tables. Looking back at
Section 4.1 of this paper, it can be seen that there are two steps in
projecting the benchmark tables. The first step is the logistic
regression using age, sex and labour force status that projects the
benchmark tables forward; and the second step is reweighting the survey
data to the new Census benchmarks.
[FIGURE 2 OMITTED]
As a consequence, any scenario has to be implemented in either of
these two steps. There is a major difference between implementing a
scenario in the first step and implementing one in the second step. The
introduction of a new scenario in the first step means changing the
labour force structure, age structure, sex structure or a combination of
those variables. These changes will also affect all other benchmark
tables since the projections for these tables are made using projections
of Age, Sex and Labour Force Status (Figure 3). The changes made to the
Age/Sex/Labour force status projections will flow through to each
benchmark table through the logistic regression model shown in Section
4.1.
Introducing a change into the second step of the projection process
involves identifying every table that could be affected by the proposed
change, and then making changes to those tables. The example shown in
this paper is a change to housing tenure, so modelling a trend out of
purchasing houses and into private rental, possibly because house prices
have increased or there is a societal shift away from purchasing houses
in Australia and towards renting houses, due to labour mobility. This is
only one scenario that could be modelled--in theory, any scenario can be
modelled, but different scenarios will affect different tables, so some
thought has to be put into which tables are affected, and how they are
[FIGURE 3 OMITTED]
In this case, Figure 4 shows how this scenario can be built in the
second step of the projection process. Looking at Figure 4, the proposed
scenario means that the proportion of private renters in the
"Tenure by Household Type" table, the "Tenure Type by
Weekly Household income" and the "Tenure by Weekly Household
Rent" tables should be increased. Because we don't want to
change the total population, and we are modelling people moving from
purchasing to renting, a change to the number of renters will increase
the number of people paying rent in the "Weekly Household Rent by
Weekly Household Income" table and decrease the number of
purchasers in the "Monthly Household Mortgage by Weekly Household
Income" table.
Note that we could also assume that 90 percent of the new renters
were previously purchasers; and 10 per cent were previously some other
tenure (like public housing or employer provided housing). So we
don't have to assume that all the new renters were previously
purchasers--we can make this scenario as complicated as we need to.
It can be seen that the effect of changing one variable can be
quite complicated, and the changes need to be made explicitly to each of
the benchmark tables, requiring some thinking about the secondary
effects of any scenario. However, because we are making changes to each
benchmark table, the scenarios can be as simple or as complicated as we
need.
Because the reweighting algorithm is re-run, there will also be a
different number of areas dropped due to not meeting the TAE criteria.
The first group of scenario changes modelled implemented a change to the
unemployment rate, implemented in the first step of the projection
process. Three different scenarios for unemployment were used to test
the stability of the model. One of these is the base scenario, where the
change to unemployment is the national change as projected in the
Inter-Generational Report. The second scenario introduces a two
percentage point increase in unemployment for every SLA, while the third
scenario uses the unemployment rates from 2006 (so the unemployment rate
remains unchanged over the projection years).
[FIGURE 4 OMITTED]
A change in unemployment is chosen for two reasons. First, the
unemployment rate is a good indicator of whether the economy growing or
shrinking, so changing the unemployment rate can allow us to simulate a
better or worse economy out to the future. Second, changing unemployment
impacts a number of benchmark tables, as shown in Figure 3, so any
instability in the model should be clearly shown.
Table 6 shows that the model is more stable in the capital cities
when a change is made to the unemployment rate. As can be seen,
increasing the unemployment rate by two percentage points for all SLAs
has caused more SLAs to fail the TAE test in NSW-Balance of State,
Victoria-Balance of State and Queensland-Balance of State than in the
capital cities, where a maximum of three additional SLAs failed the
test. This may also confirm the earlier analysis that the projection
model itself is not as stable in non capital city SLAs, as shown in
Table 3 of Section 4.1. Furthermore, the scenario using the 2006
unemployment rate came up with slightly fewer SLAs that failed the TAE,
which shows that the closer the scenario is to the 2006 data, the fewer
the number of SLAs that will fail the TAE test. However, the difference
between this scenario and using the IGR projected unemployment rates is
small, so it may also be due to the fact that the IGR does not predict a
major change in unemployment.
6. STRENGTHS, WEAKNESSES AND THE WAY FORWARD
Sections 2 to 5 of this paper have revealed how statistics for
small areas in Australia can be estimated and projected using the
SpatialMSM model. In this section, we sum up the strengths and
weaknesses of this projection model. We will also look at the way
forward for this projection model.
6.1 Strengths and Weaknesses
The main strength of this projection model is the ability to
provide a picture of the household composition and future conditions
according to assumptions given by other models, such as population
projections from the Australian Bureau of Statistics and labour force
projections from the Inter-Generational Report. Demographic and labour
force projections are the main determinants of the projections from
SpatialMSM and there are models that can produce these projections using
various assumptions about fertility, mortality, and migration for the
population projections, and different economic projections for the
unemployment rate. So it is easy to bring in new scenarios for
population growth and a change in the labour force status. Nevertheless,
it is also important to note that the interpretation and the performance
of this model is highly dependent on the assumptions underlying the
projections.
Another strength of this projection model is the possibility of
altering any of the variables in the benchmark tables. Although
initially projected by the growth in the population by age, sex and
labour force status based on the elasticities in 2006, it is possible to
alter any of the benchmark tables, with some care given to which tables
are affected by the new scenario. The example used in this paper is a
change to the housing tenure driven by people moving from home ownership
to renting. The effect of the scenario on the benchmark tables can be as
complicated as required.
The next strength of this model is the independence of each SLA in
the model. This means that each SLA can have a scenario change applied
separately and as long as the SLA does not fail the TAE criteria, then
the model can provide projections for just that SLA. However, this
feature is also one of the weaknesses of this projection method, as SLAs
may interact through population movement, especially if unemployment
rates are changed in one SLA; and this population movement is not
modelled (although it could be in the future through a dynamic model).
The main weakness of the model is the fact that the projection
relies on the relationship between the labour force by age by sex
composition and the composition of the other benchmark tables based on
the 2006 population census. This is a reasonable assumption if the model
projects into the near future, but may be unreasonable for a long term
projection. Any change in personal preferences could make this
assumption invalid. For example, one change modelled in this paper is
people preferring to rent instead of buying their own house in the
future, due to labour mobility. As a result, even if there are no
changes in the structure of the labour force by age by sex, the number
of people who live in rental dwellings may still be increasing.
Not only are the benchmarks based on 2006 Census data projected
forward, the survey data used is from 2002/03 and 2003/04. There is a
strong possibility that these data do not represent individual
households in the long term. Cassells and Harding (2001) show that the
generation born between 1916 and 1991 (generation Y) has different
characteristic to the previous generation in terms of working and having
families, which are two variables we benchmark to.
Because this is a static microsimulation model, we are not ageing
the population at all; we are just benchmarking this 2002/03 and 2003/04
data to future projections. So in 2002, this generation (Gen Y) is aged
between 11 and 26. The characteristics in the benchmarks for this age
group are projected into the future, and then people aged 11 to 26 in
2021 will be benchmarked to these tables. So we are applying the GenY
characteristics to people aged 11 to 26 in 2021. But people aged 11 to
26 in 2021 may be very different from the GenY group in 2002. This also
works the other way. So the GenY group from 2002 will be aged between 36
and 51 in 2021, and their characteristics may be very different from
people aged between 36 and 51 in 2002.
Again, the flexibility of this model means we could assume some
other preferences for this group in 2021, and adjust the benchmark
tables using some behavioural model; but we really have no information
on what preferences these people will have in 2021. So using the
preferences from 2006 may be the best information we have.
6.2 The Way Forward
One of the limitations of this model is that it is a static model,
so there is no dynamic ageing process. Making the model more dynamic is
a clear way forward. The simple static ageing procedure employed in this
model utilises the correlation between the labour force by age by sex
status and other socioeconomic variables in 2006 to create projected
benchmark tables for the model. The model then uses the unit record data
from the ABS SIH 2002/03 and 2003/04 to populate the small area given
these new projected benchmarks. By doing this, the model may fail to
capture any trend that changes the relationship between labour force by
age by sex and other socio-economic variables in the future.
Furthermore, using unit record data with 2002-2004 characteristics may
also give false projections if there is a generational trend that alters
the characteristics of households in the future. In theory, this affects
any projection model--nobody really knows how these generational changes
will develop in the future.
There are two steps that we think may improve this model. The first
may be to capture and induce a long term trend in the benchmark table
projection process. While this would give a more accurate picture of any
change over time (for instance, a long term move from purchasing to
renting that could be carried on in the projections), there are problems
with it. One is that it would be done for every SLA, so it may just be
picking up a local short term trend that may not continue into the
future. This could be ameliorated by looking at national trends and
applying these trends to the small areas; however, we are then ignoring
local effects. So there is a balance between these two that would need
to be considered before implementing a time trend into the projected
benchmarks. The other problem is that the benchmark tables are from the
Australian Census, which is conducted every five years; and the small
areas and the data definitions change every Census. So creating a
comparable time series of the benchmark tables using Census data is
going to be difficult.
The alternative is to use a simple shift share for the growth
projections such as used in SimBritain (Ballas et al, 2005a). This uses
linear exponential proportional smoothing of 1911, 1981, and 1991 data
to project the constraint for 2001, 2011 and 2021. Again, this would be
difficult to apply for each SLA in Australia because in every Census,
the SLA boundaries and some data definitions can change, but it may be
possible to project State aggregates and then redistribute the projected
proportions back to SLAs.
The second way to improve this model is to update the unit record
data so they become more representative of future conditions. This could
be implemented by making the model a dynamic microsimulation model, so
individually updating the characteristics of each individual and family
contained within the model for each time period. In Australia, the
Australian Population and Policy Simulation model (APPSIM) has been
developed to update unit record characteristics (Cassells et al., 2001).
However, such dynamic population microsimulation models involve a very
high degree of complexity and cost (Harding, 2001). In addition, there
would also be problems getting appropriate longitudinal data to estimate
the relevant transition probabilities at a small area level.
So there are significant barriers to either of these modifications,
although not insurmountable. Further, these two modifications do not
have to be applied simultaneously. In the model as it currently stands,
it is possible to apply the first change without having the unit record
data updated, and continue to use the model with the limitation that the
underlying survey datasets are not updated.
Converting the model to a dynamic model is a much larger step, as
the dynamic process needs to be modelled for every SLA. However, using
this process, projections could be derived without using the reweighting
process to age the population, as the population is dynamically aged.
The reweighting process could still be used for aligning the dynamically
aged survey data to external benchmarks from the Census, but the change
made would be minimal. So the totals would match the Census benchmarks
due to the alignment; but the relationships between variables may have
changed because of the dynamic ageing process.
7. CONCLUSIONS
This paper has given an overview of a model that can address not
only the need for small area information for the present, but also for
the future. In the past decade, this need has become more and more
apparent as planning agencies in Australia (such as its local and
federal governments) need to focus on service delivery for local areas
given the characteristics of individuals and households in those areas.
The paper started with the current spatial microsimulation model in
Australia named SpatialMSM/08C and then described the first attempt to
develop projections from this model.
A static ageing process is the approach taken in developing the
projection model given the very high degree of complexity, cost and data
requirements in building a fully dynamic microsimulation model. The
static ageing model is undertaken by employing the currently available
population and labour force projections to estimate the various
constraint tables used in SpatialMSM/08C. The model then uses the
reweighting process in SpatialMSM/08C to reweight the microdata or unit
record data according to the projected constraints.
As this paper has shown, the model has been able to produce
information for small area planning into the future with a reasonable
degree of reliability. The model is also able to take some simple
scenarios to model some changes in the future, and seems to be most
reliable for capital cities. Nevertheless, the static ageing approach
that the model uses means that it is difficult to model any behavioural
change, without identifying the effect of the behavioural change and
implementing this in the benchmark tables. Further, while we have not
tested this, we expect that any large changes in the characteristics of
the society in the future will be difficult to estimate, as the large
changes in the benchmarks will mean the reweighting process will fail to
find reasonable weights for a high proportion of areas.
This has led to some potential improvements to the model that we
have considered, and two steps have been identified. The first is to
explicitly acknowledge the long term trend of socio-economic changes in
society while the second step is to use a dynamic microsimulation method
to update the unit record data into the future. Both these steps have
problems that would need to be resolved, but the problems are not
insurmountable and could be the subject of future research.
ACKNOWLEDGMENTS
This paper has been funded by a Linkage Grant from the Australian
Research Council (LP115396), with our research partners on this grant
being the NSW Department of Community Services; the Australian Bureau of
Statistics; the ACT Chief Minister's Department; the Queensland
Department of Premier and Cabinet; Queensland Treasury; the Victorian
Departments of Education and Early Childhood and Planning and Community
Development; and Paul Williamson, University of Liverpool, UK. We would
like to gratefully acknowledge the support provided by these agencies,
individuals, and the participants of Pacific Regional Science Conference
Organisation, Gold Coast, 2009 for their valuable input in the
conference.
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(1) Unit record data (alternatively termed 'microdata')
usually consist of thousands of individual records of persons, families
or households in a computer readable format. Such microdata are the
essential building block for microsimulation models, which in the past
two decades have revolutionised the quality of information available to
policy makers about the likely distributional impact of policy reforms
that they are contemplating (Harding and Gupta, 2007a).
Yogi Vidyattama
Research Fellow, National Centre for Social and Economic Modelling
(NATSEM), University of Canberra.
Robert Tanton
A/g Research Director of the Social Inclusion and Small Area
Modelling Team, National Centre for Social and Economic Modelling
(NATSEM), University of Canberra.
Table 1. Benchmark tables used in the reweighting algorithm
Number Benchmark Table Level
1 Age by sex by labour force status Total Person
number of households by dwelling type
(Occupied private
2 dwelling/Non private dwelling) Household
3 Tenure by weekly household rent Household
4 Tenure by household type Household
5 Dwelling structure by household family Household
composition
6 Number of adults usually resident in Household
household
7 Number of children usually resident in Household
household
8 Monthly household mortgage by weekly Household
household income
9 Persons in non-private dwelling Person
10 Tenure type by weekly household income Household
11 Weekly household rent by weekly household Household
income
Note: Most Benchmark Tables contain the total number of persons or
households in occupied private dwellings (OPD) except for Table 2
and Table 9. These tables include people in non-private dwellings.
People in non-private dwellings include people in prisons,
hospitals, aged care facilities, etc.
Source: ABS Census Population and Housing 2006.
Table 2. Number of SLAs dropped due to failed accuracy criteria in
SpatialMSM/08C
State/ SLAs with Total Per cent of SLAs
Territory failed TAE SLAs with failed TAE (%)
NSW 2 200 1.0
VIC 4 210 1.9
QLD 43 479 9.0
SA 7 128 5.5
WA 17 156 10.9
TAS 1 44 2.3
NT 48 96 50.0
ACT 16 109 14.7
Australia 138 1422 9.7
Per cent of all persons
State/ living in SLAs with
Territory failed TAE (%)
NSW 0.4
VIC 0.0
QLD 0.8
SA 0.4
WA 0.9
TAS 0.1
NT 25.2
ACT 1.0
Australia 0.7
Source: SpatialMSM/08C applied to 2002/03 and 2003/04 SIH CURF.
Table 3. Number of SLAs dropped due to failed TAE in the projections
Major Statistical SLAs with Total SLAs SLAs with
Region (MSR) failed TAE in Projected failed TAE in
SpatialMSM/ 2010
08c projection
Sydney 1 64 0
NSW-Balance of 1 135 2
State
Melbourne 0 79 2
VIC-Balance of 4 130 7
State
Brisbane 3 215 7
QLD-Balance of 40 263 40
State
Adelaide 0 55 0
SA-Balance of 7 72 10
State
Perth 2 37 2
WA-Balance of 15 118 17
State
Hobart 0 8 1
TAS-Balance of 1 35 2
State
Darwin 6 41 6
NT-Balance of 42 54 43
State
Canberra 15 108 17
ACT-Balance of 1 1 1
State
Australia 138 1415 157
Major Statistical SLAs with SLAs with
Region (MSR) failed TAE failed TAE
in 2020 2027
projection projection
Sydney 0 0
NSW-Balance of 10 15
State
Melbourne 2 2
VIC-Balance of 14 25
State
Brisbane 6 8
QLD-Balance of 48 46
State
Adelaide 0 0
SA-Balance of 18 20
State
Perth 1 2
WA-Balance of 24 27
State
Hobart 1 1
TAS-Balance of 3 3
State
Darwin 10 12
NT-Balance of 44 43
State
Canberra 26 31
ACT-Balance of 1 1
State
Australia 208 236
Source: SpatialMSM/08C projections
Table 4. Number of SLAs dropped due to failed TAE criteria
in the 2027 projection
State/ SLAs with Total Per cent of SLAs with
Territory failed TAE SLAs failed TAE (%)
NSW 15 199 7.5
VIC 27 209 12.9
QLD 54 478 11.3
SA 20 127 15.7
WA 29 155 18.7
TAS 4 43 9.3
NT 55 95 57.9
ACT 32 109 29.4
Australia 236 1415 16.7
Per cent of all persons
State/ living in SLAs with
Territory failed TAE (%)
NSW 1.6
VIC 2.6
QLD 2.3
SA 3.4
WA 1.6
TAS 2.5
NT 32.5
ACT 24.7
Australia 2.8
Source: SpatialMSM/08C projections
Table 5. [R.sup.2] for benchmarks used in the reweighting algorithm
Table No. Benchmark Table Lowest [R.sup.2]
2 Total number of 0.542
households by dwelling
type (Occupied private
dwelling/Non private
dwelling)
3 Tenure by weekly 0.424
household rent
4 Tenure by household type 0.516
5 Dwelling structure by 0.386
household family
composition
6 Number of adults usually 0.952
resident in household
7 Number of kids usually 0.957
resident in household
8 Monthly household 0.176
mortgage by weekly
household income
9 Persons in non-private 0.295
dwelling
10 Tenure type by weekly 0.428
household income
11 Weekly household rent by 0.136
weekly household income
Table No. Benchmark Table Highest [R.sup.2] Mean
2 Total number of 0.993 0.767
households by dwelling
type (Occupied private
dwelling/Non private
dwelling)
3 Tenure by weekly 0.862 0.635
household rent
4 Tenure by household type 0.984 0.826
5 Dwelling structure by 0.975 0.706
household family
composition
6 Number of adults usually 0.995 0.971
resident in household
7 Number of kids usually 0.997 0.977
resident in household
8 Monthly household 0.928 0.643
mortgage by weekly
household income
9 Persons in non-private 0.719 0.420
dwelling
10 Tenure type by weekly 0.977 0.760
household income
11 Weekly household rent by 0.825 0.598
weekly household income
Source: authors' calculations
Table 6. Number of SLAs dropped due to failed accuracy
criteria in 2021 projection
SLAs with failed
TAE in 2027 with
Major a 2 pct. point
Statistical Total SLAs with failed unemployment
Region (MSR) SLAs TAE in 2027 increase from the
Projected (with IGR) base
Sydney 64 0 1
NSW-Balance 135 15 35
of State
Melbourne 79 2 3
VIC-Balance 130 25 38
of State
Brisbane 215 8 11
QLD-Balance 263 46 62
of State
Adelaide 55 0 0
SA-Balance of 72 20 25
State
Perth 37 2 2
WA-Balance 118 27 33
of State
Hobart 8 1 1
TAS-Balance 35 3 7
of State
Darwin 41 12 12
NT-Balance of 54 43 44
State
Canberra 108 31 30
ACT-Balance 1 1 1
of State
Australia 1415 236 305
SLAs with failed
Major TAE in 2027 if
Statistical the 2006
Region (MSR) unemployment
rate is used
Sydney 1
NSW-Balance 12
of State
Melbourne 4
VIC-Balance 20
of State
Brisbane 8
QLD-Balance 47
of State
Adelaide 0
SA-Balance of 18
State
Perth 2
WA-Balance 24
of State
Hobart 1
TAS-Balance 4
of State
Darwin 11
NT-Balance of 42
State
Canberra 33
ACT-Balance 1
of State
Australia 228
Source: SpatialMSM/08C projections
