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 socio-economic 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 1971, 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., 2007). However, such dynamic population microsimulation models involve a very high degree of complexity and cost (Harding, 2007). 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 (LP775396), 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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