219 research outputs found
Pushing It To The Edge: Extending Generalised Regression As A Spatial Microsimulation Method
This paper extends a spatial microsimulation model to test how the model behaves after adding different constraints, and how results using univariate constraint tables rather than multivariate constraint tables compare. This paper also tests how well non-Capital city households from a survey can estimate areas within capital cities. Using all households available in Australian survey means that the spatial microsimulation method has more households to choose from to represent the constraints in the area being estimated. In theory, this should improve the fit of the model. However, a household from another area may not be representative of households in the area being estimated. We found that, in the case that the estimated statistics is already closely related to the benchmarks used, adding a number of benchmarks had little effect on the number of areas where estimates couldn’t be made, and had little effect on the accuracy of our estimates in areas where estimates could be made. However, the advantage of using more benchmarks was that the weights can be used to estimate a wider variety of outcome variables. We also found that more complex bi-variate benchmarks gave better results compared to simpler univariate benchmarks; and that using a specific sub-sample of observations from a survey gave better results in smaller capital cities in Australia (Adelaide and Perth).
Methodological Issues in Spatial Microsimulation Modelling for Small Area Estimation
In this paper, some vital methodological issues of spatial microsimulation modelling for small area estimation have been addressed, with a particular emphasis given to the reweighting techniques. Most of the review articles in small area estimation have highlighted methodologies based on various statistical models and theories. However, spatial microsimulation modelling is emerging as a very useful alternative means of small area estimation. Our findings demonstrate that spatial microsimulation models are robust and have advantages over other type of models used for small area estimation. The technique uses different methodologies typically based on geographic models and various economic theories. In contrast to statistical model-based approaches, the spatial microsimulation model-based approaches can operate through reweighting techniques such as GREGWT and combinatorial optimization. A comparison between reweighting techniques reveals that they are using quite different iterative algorithms and that their properties also vary. The study also points out a new method for spatial microsimulation modellingBayesian prediction approach; combinatorial optimisation; GREGWT; microdata; small area estimation; spatial microsimulation
Issues in spatial microsimulation estimation: a case study of child poverty
Spatial microsimulation techniques have become an increasingly popular way to fulfil the need for generating small area data estimates. Nevertheless, this technique poses numerous methodological challenges, including those that relate to fundamental differences between the multiple data sources which spatial microsimulation techniques seek to combine. Using two different databases simultaneously to produce estimates of population characteristics may come up against problems related to different distributions of key variables within the two databases. Such differences can make it difficult to adequately validate small area estimates, as it can be hard to assess whether differences between synthetic and original data are due to failures or inaccuracies within the estimation procedure, or simply to the differences within the underlying data. This study presents a case study of this problem using a very important small area estimate – child poverty rates. We compare how income distributions for children are different in two Australian databases being combined within a spatial microsimulation model. We then assess the extent to which this affects our estimates of child poverty, and gauge its impact on the apparent validity of these synthetic small area poverty rates.Microsimulation, Spatial, Inequality
Simulating electric vehicle policy in the Australian capital territory
Increased use of electric vehicles (EVs) has the potential to reduce carbon emissions. Therefore, predicting the impact of governments' EV incentive policies on the future uptake of EVs is important. This study estimates the impact of incentive policies introduced in the Australian Capital Territory. This estimation is conducted through constructing a microsimulation model and using it to assess the impact of the incentive policies on the purchase of EVs for households in different income quintiles. In the model, the decision about purchasing an EV is based largely on the total cost of ownership of an EV compared to the vehicle already owned and the additional utility of having a new vehicle. The application of the model shows that, regardless of incentives, a drop in the price of EVs will play the most important role in uptake. Incentives will help lower to middle income households, although EV demand is dominated by those in the highest income quintile. Importantly, however, incentives can increase uptake in locations with previously low uptake. Future work needs to focus on the reliability of data on EV's, and how to incorporate the rapid change in the market (eg, rapid uptake of EV's in the ACT) seen in the last few years
Old, single and poor
This paper uses microdata and NATSEM\u27s microsimulation models to examine the spatial distribution of poverty among older single people and to test the likely impact upon national and small area poverty rates of an increase in the single age pension rate. In recent months in Australia there has been extended debate about whether the age pension is sufficiently high to allow older Australians to attain an acceptable standard of living. This paper uses microdata and NATSEM\u27s microsimulation models to examine the spatial distribution of poverty among older single people and to test the likely impact upon national and small area poverty rates of an increase in the single age pension rate. The paper provides an illustration of the usefulness of microsimulation models to policy makers. Changes in a country\u27s tax and transfer systems can have a large effect on incomes, and can be targeted towards increasing incomes for the poor, thus reducing poverty rates. However, governments need an estimate of the extent to which a proposed policy change is likely to affect poverty rates, in order to be able to compare different proposals. Microsimulation models allow this comparison of proposed policies and can provide governments with an appreciation of how much a new policy is likely to cost; how many and what types of low income people will benefit; and the extent of any consequent reduction in the poverty rate. Until recently, microsimulation models have been able to estimate the effects of such changes only at a national or very broad regional level. NATSEM has now linked its tax/transfer microsimulation model (STINMOD) to spatially disaggregated census data, producing a spatial microsimulation model which can be used to identify the neighbourhood effects of policy changes for small areas
The indirect costs of ischemic heart disease through lost productive life years for Australia from 2015 to 2030:Results from a microsimulation model
The Impact of Diabetes on the Labour Force Participation, Savings and Retirement Income of Workers Aged 45-64 Years in Australia
Diabetes is a debilitating and costly condition. The costs of reduced labour force participation due to diabetes can have severe economic impacts on individuals by reducing their living standards during working and retirement years.A purpose-built microsimulation model of Australians aged 45-64 years in 2010, Health&WealthMOD2030, was used to estimate the lost savings at age 65 due to premature exit from the labour force because of diabetes. Regression models were used to examine the differences between the projected savings and retirement incomes of people at age 65 for those currently working full or part time with no chronic health condition, full or part time with diabetes, and people not in the labour force due to diabetes.All Australians aged 45-65 years who are employed full time in 2010 will have accumulated some savings at age 65; whereas only 90.5% of those who are out of the labour force due to diabetes will have done so. By the time they reach age 65, those who retire from the labour force early due to diabetes have a median projected savings of less than 638,000 at age 65.Not only does premature retirement due to diabetes limit the immediate income available to individuals with this condition, but it also reduces their long-term financial capacity by reducing their accumulated savings and the income these savings could generate in retirement. Policies designed to support the labour force participation of those with diabetes, or interventions to prevent the onset of the disease itself, should be a priority to preserve living standards comparable with others who do not suffer from this condition
The economic impact of diabetes through lost labour force participation on individuals and government: evidence from a microsimulation model
Background\ud
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Diabetes is a costly and debilitating disease. The aim of the study is to quantify the individual and national costs of diabetes resulting from people retiring early because of this disease, including lost income; lost income taxation, increased government welfare payments; and reductions in GDP.\ud
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Methods\ud
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A purpose-built microsimulation model, Health&WealthMOD2030, was used to estimate the economic costs of early retirement due to diabetes. The study included all Australians aged 45–64 years in 2010 based on Australian Bureau of Statistics' Surveys of Disability, Ageing and Carers. A multiple regression model was used to identify significant differences in income, government welfare payments and taxation liabilities between people out of the labour force because of their diabetes and those employed full time with no chronic health condition.\ud
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Results\ud
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The median annual income of people who retired early because of their diabetes was significantly lower (AU384 million in individual earnings by those with diabetes, an extra AU56 million in taxation revenue, and a loss of AU$1 324 million in GDP in 2010: all attributable to diabetes through its impact on labour force participation. Sensitivity analysis was used to assess the impact of different diabetes prevalence rates on estimates of lost income, lost income taxation, increased government welfare payments, and reduced GDP.\ud
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Conclusions\ud
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Individuals bear the cost of lost income in addition to the burden of the disease. The Government endures the impacts of lost productivity and income taxation revenue, as well as spending more in welfare payments. These national costs are in addition to the Government's direct healthcare costs
The Impact of Diabetes on the Labour Force Participation and Income Poverty of Workers Aged 45-64 Years in Australia
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