Robert Tanton

Measuring Housing Stress: How Much do Definitions Matter?

There has been extensive debate in Australia in recent years about the most appropriate measure of housing stress. This article presents estimates of housing stress in Australia and examines the comparative effects of various definitions on the proportion of households in housing stress. Six measures of housing stress are tested, all with different types of income and criteria. Up-to-date estimates of housing stress at the national and state/territory level are produced from the 2005–2006 Survey of Income and Housing from the Australian Bureau of Statistics. These estimates are then compared to identify the least sensitive indicator of housing stress across household tenure type.

Improving Work Incentives and Incomes for Parents: The National and Geographic Impact of Liberalising the Family Tax Benefit Income Test

The effective tax rates and possible work disincentives created by Australia’s tax and welfare systems have been receiving extensive policy attention in recent years. Family Tax Benefit-Part A (FTB-A) is one of the key causes of high effective marginal tax rates for many families. This study uses national and spatial microsimulation models to evaluate the national and local impacts of a possible FTB-A reform option, which involves reducing the income test withdrawal rate associated with the FTB-A income test. The modelling suggests that the option would be an effective way to reduce high effective marginal tax rates for around 415,000 parents of FTB-A children, would benefit around 850,000 families, and would deliver additional assistance to middle income families living on the outskirts of our cities.

Simulating the characteristics of populations at the small area level

These days spatial microsimulation modelling plays a vital role in policy analysis for small areas. Most developed countries are using these tools in ways to make knowledgeable decisions on major policy issues at local levels. However, building an appropriate model is very difficult for many reasons. For example, the creation of reliable spatial microdata is still challenging. In addition there has not been much research on testing statistical significance of the model outputs yet, and deriving estimates of how reliable these outputs may be. This paper deals with the spatial microsimulation model building procedure for simulating synthetic spatial microdata, and then estimating small area housing stress in Australia. Geographic maps for small area housing stress estimates are illustrated. The research also demonstrates a new system to test the statistical significance of the model estimates.

Lost productive life years caused by chronic conditions in Australians aged 45-64 years, 2010-2030.

Objectives: To estimate (1) productive life years (PLYs) lost because of chronic conditions in Australians aged 45–64 years from 2010 to 2030, and (2) the impact of this loss on gross domestic product (GDP) over the same period.

The Effect of Transport Costs on Housing-related Financial Stress in Australia

Housing-related financial stress or housing stress is usually measured on the basis of income and direct housing costs such as mortgage repayments and rents. One cost that is not included in calculations of housing stress, but which may be important, is the difference made by transport costs. This paper shows how a measure of housing stress that incorporates transport costs can give a different picture of housing stress from a measure that ignores transport costs. The result shows that, in capital cities, bringing transport costs into a housing stress measure particularly affects households with children. It is also found that the impact of transport costs is even greater outside capital cities and that access to public transport may play a role in determining the spatial pattern of housing stress and hence the impact of transport cost on housing stress.

Validation of Spatial Microsimulation Models

Spatial microsimulation models, both static and dynamic, are a useful means to estimate area-level data, whatever these data are regarding, be it health, socio-economic status or income/finance. However, in order for planners and government to be able to use and rely on these data, it is essential that the modellers can show that the estimates are an accurate presentation of the real world and are reliable. Generally, to verify the integrity of any model, it is necessary to validate the model outputs, using both internal and external validation methods. However, for spatial microsimulation models, validation is a massive challenge. This is because, generally, these models are used to estimate data that does not otherwise exist, perhaps due to confidentiality reasons (e.g. income or medical data for individuals) and/or because it would be expensive and time consuming to try to collect a large sample of data for the population in question (particularly as, in many countries, national sample datasets already exist, thus it would also be a duplication of both time and money).

The Impact of Diabetes on the Labour Force Participation, Savings and Retirement Income of Workers Aged 45-64 Years in Australia

Background 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. Methods 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. Results 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

The impact of diabetes on the labour force participation and income poverty of workers aged 45-64 years in Australia.

35,000. This is far lower than the median value of total savings for those who remained in the labour force full time with no chronic condition, projected to have

The impact of chronic conditions of care recipients on the labour force participation of informal carers in Australia: Which conditions are associated with higher rates of non-participation in the labour force?

638,000 at age 65. Conclusions 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 Challenges of Combining Two Databases in Small-Area Estimation: An Example Using Spatial Microsimulation of Child Poverty

Objective To quantify the poverty status and level of disadvantage experienced by Australians aged 45–64 years who have left the labour force due to diabetes in 2010. Research Design and Methods A purpose-built microsimulation model, Health&WealthMOD2030, was used to estimate the poverty status and level of disadvantage of those aged 45–64 years who prematurely retire from the workforce due to diabetes. A multiple regression model was used to identify significant differences in rates of income poverty and the degree of disadvantage between those out of the labour force due to diabetes and those employed full- or part-time with no diabetes. Results 63.9% of people aged 45–64 years who were out of the labour force due to diabetes were in poverty in 2010. The odds of being in poverty for those with no diabetes and employed full-time (OR of being in poverty 0.02 95%CI: 0.01–0.04) or part-time (OR of being in poverty 0.10 95%CI: 0.05–0.23) are significantly lower than those for persons not in the labour force due to diabetes. Amongst those with diabetes, those who were able to stay in either full- or part-time employment were as much as 97% less likely to be in poverty than those who had to retire early because of the condition. Sensitivity analysis was used to assess impacts of different poverty line thresholds and key socioeconomic predictors of poverty. Conclusions This study has shown that having diabetes and not being in the labour force because of this condition significantly increases the chances of living in poverty. Intervening to prevent or delay the onset of diabetes is likely to improve their living standards.