Lack of data on the spatial distribution of the social conditions of Australia's Indigenous peopl... more Lack of data on the spatial distribution of the social conditions of Australia's Indigenous peoples has created difficulties in the allocation of government and community programs. Small-area estimation methods can overcome this lack of data, but typically require access to a unit record file. However, strict confidentiality rules applied to these unit record files may hinder the development of these models. In Australia, unit record data for the Indigenous population is analysable only using Australian Bureau of Statistics servers remotely. This study looks specifically at this issue and offers a solution to the problem of confidentiality restrictions by using a synthetic database. The results show that reasonable small-area estimates of social conditions for Indigenous Australians can be derived from a small-area estimation (spatial microsimulation) model using a synthetic database. While this application is for Australia, the method developed can be used for any small-area model requiring unit record data that are not available due to confidentiality restrictions.
Changing patterns of longevity, fertility and migration in Australia have driven substantial chan... more Changing patterns of longevity, fertility and migration in Australia have driven substantial changes in population age structure and household size and composition. Of the various dimensions of population change, population ageing is expected to present major challenges to the financing and sustainability of welfare state programs in industrialized countries. One key issue for many of these countries will be assessing where particular services will be required in the future. This paper outlines the application of new forecasting techniques that age a spatial microdataset to 2027. Two illustrative examples are provided to highlight the potential capacities of the new modelling approach for government service delivery planners. For many older people, ageing in place is important, but is more difficult when the person is single: and so the first illustrative application focuses on where aged single people will be living in 2027. The second application examines where future childcare places will be required given the projected growth in the number of children aged 3–4 years living in families where all parents are working. This information will be important for Government planners in deciding the best location for childcare places. The creation of synthetic small-area household microdata for future years offers great potential for a number of purposes, such as analysis of the likely future sociodemographic characteristics of individuals and families at the local level and assessment of the future geographic effect of alternative scenarios such as changes in labour force participation or fertility rates.
In recent months in Australia there has been extended debate about whether the age pension, parti... more In recent months in Australia there has been extended debate about whether the age pension, particularly with regard to single pensioners, is sufficiently high to allow older Australians to attain an acceptable standard of living. This is an important policy consideration given Australia’s rapidly ageing population. By using microdata and microsimulation models, this paper examines the national and spatial impacts on the distribution of poverty among older single people of an increase in the single age-pension rate. This paper shows that the cost of increasing the single age-pension to 66 per cent of the couple-age pension rate would be about $A1.3 billion and would benefit about 824,000 single age-pensioners. Further, it is estimated that such an increase would reduce poverty rates for lone older persons from 46.5 per cent to 36.5 per cent, a 10-percentage point reduction. Looking at the spatial distribution of such benefits, the effect of the policy change seems to be generally stronger in capital cities, and in bands of rural areas in New South Wales and Victoria.
ABSTRACT Housing stress is usually measured on the basis of income and direct housing costs such ... more ABSTRACT 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 transport costs make. ...
The effective tax rates and possible work disincentives created by Australia’s tax and welfare sy... more 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.
Lack of data on the spatial distribution of the social conditions of Australia's Indigenous peopl... more Lack of data on the spatial distribution of the social conditions of Australia's Indigenous peoples has created difficulties in the allocation of government and community programs. Small-area estimation methods can overcome this lack of data, but typically require access to a unit record file. However, strict confidentiality rules applied to these unit record files may hinder the development of these models. In Australia, unit record data for the Indigenous population is analysable only using Australian Bureau of Statistics servers remotely. This study looks specifically at this issue and offers a solution to the problem of confidentiality restrictions by using a synthetic database. The results show that reasonable small-area estimates of social conditions for Indigenous Australians can be derived from a small-area estimation (spatial microsimulation) model using a synthetic database. While this application is for Australia, the method developed can be used for any small-area model requiring unit record data that are not available due to confidentiality restrictions.
... Ann Harding Director, NATSEM, University of Canberra, ACT 2601. Mandy Yap Centre for Aborigin... more ... Ann Harding Director, NATSEM, University of Canberra, ACT 2601. Mandy Yap Centre for Aboriginal Economic Policy Research, Australian National University, Canberra ACT 2601. ...Mandy Yap was at NATSEM at the time the paper was written. Page 2. ...
Lack of data on the spatial distribution of the social conditions of Australia's Indigenous peopl... more Lack of data on the spatial distribution of the social conditions of Australia's Indigenous peoples has created difficulties in the allocation of government and community programs. Small-area estimation methods can overcome this lack of data, but typically require access to a unit record file. However, strict confidentiality rules applied to these unit record files may hinder the development of these models. In Australia, unit record data for the Indigenous population is analysable only using Australian Bureau of Statistics servers remotely. This study looks specifically at this issue and offers a solution to the problem of confidentiality restrictions by using a synthetic database. The results show that reasonable small-area estimates of social conditions for Indigenous Australians can be derived from a small-area estimation (spatial microsimulation) model using a synthetic database. While this application is for Australia, the method developed can be used for any small-area model requiring unit record data that are not available due to confidentiality restrictions.
Changing patterns of longevity, fertility and migration in Australia have driven substantial chan... more Changing patterns of longevity, fertility and migration in Australia have driven substantial changes in population age structure and household size and composition. Of the various dimensions of population change, population ageing is expected to present major challenges to the financing and sustainability of welfare state programs in industrialized countries. One key issue for many of these countries will be assessing where particular services will be required in the future. This paper outlines the application of new forecasting techniques that age a spatial microdataset to 2027. Two illustrative examples are provided to highlight the potential capacities of the new modelling approach for government service delivery planners. For many older people, ageing in place is important, but is more difficult when the person is single: and so the first illustrative application focuses on where aged single people will be living in 2027. The second application examines where future childcare places will be required given the projected growth in the number of children aged 3–4 years living in families where all parents are working. This information will be important for Government planners in deciding the best location for childcare places. The creation of synthetic small-area household microdata for future years offers great potential for a number of purposes, such as analysis of the likely future sociodemographic characteristics of individuals and families at the local level and assessment of the future geographic effect of alternative scenarios such as changes in labour force participation or fertility rates.
In recent months in Australia there has been extended debate about whether the age pension, parti... more In recent months in Australia there has been extended debate about whether the age pension, particularly with regard to single pensioners, is sufficiently high to allow older Australians to attain an acceptable standard of living. This is an important policy consideration given Australia’s rapidly ageing population. By using microdata and microsimulation models, this paper examines the national and spatial impacts on the distribution of poverty among older single people of an increase in the single age-pension rate. This paper shows that the cost of increasing the single age-pension to 66 per cent of the couple-age pension rate would be about $A1.3 billion and would benefit about 824,000 single age-pensioners. Further, it is estimated that such an increase would reduce poverty rates for lone older persons from 46.5 per cent to 36.5 per cent, a 10-percentage point reduction. Looking at the spatial distribution of such benefits, the effect of the policy change seems to be generally stronger in capital cities, and in bands of rural areas in New South Wales and Victoria.
ABSTRACT Housing stress is usually measured on the basis of income and direct housing costs such ... more ABSTRACT 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 transport costs make. ...
The effective tax rates and possible work disincentives created by Australia’s tax and welfare sy... more 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.
Lack of data on the spatial distribution of the social conditions of Australia's Indigenous peopl... more Lack of data on the spatial distribution of the social conditions of Australia's Indigenous peoples has created difficulties in the allocation of government and community programs. Small-area estimation methods can overcome this lack of data, but typically require access to a unit record file. However, strict confidentiality rules applied to these unit record files may hinder the development of these models. In Australia, unit record data for the Indigenous population is analysable only using Australian Bureau of Statistics servers remotely. This study looks specifically at this issue and offers a solution to the problem of confidentiality restrictions by using a synthetic database. The results show that reasonable small-area estimates of social conditions for Indigenous Australians can be derived from a small-area estimation (spatial microsimulation) model using a synthetic database. While this application is for Australia, the method developed can be used for any small-area model requiring unit record data that are not available due to confidentiality restrictions.
... Ann Harding Director, NATSEM, University of Canberra, ACT 2601. Mandy Yap Centre for Aborigin... more ... Ann Harding Director, NATSEM, University of Canberra, ACT 2601. Mandy Yap Centre for Aboriginal Economic Policy Research, Australian National University, Canberra ACT 2601. ...Mandy Yap was at NATSEM at the time the paper was written. Page 2. ...
Intergenerational disadvantage has been defined as “disadvantage induced by the attitudes, social... more Intergenerational disadvantage has been defined as “disadvantage induced by the attitudes, social circumstances or economic limitations of a person’s parents’ (Vinson, 2009, P. 1). This disadvantage could be in terms of poverty, labour force, or lack of access to opportunities that other children may have.
One of the limitations of this concept is that it only takes into account direct family, so it is only how a person’s parents affect their disadvantage. However, we know that the local community also affects disadvantage, and that disadvantage tends to cluster. The obvious question that this paper tries to answer is do areas with high levels of disadvantage have high levels of disadvantage for all age groups in the area? Or are there areas where a high proportion of disadvantaged elderly people and a low proportion of disadvantage children live? And where are these areas (eg, rural/regional areas, capital cities, inner urban areas).
Answering this question will give some idea of which areas have entrenched disadvantage, so disadvantage that covers a number of generations. It is these areas where broad policies to reduce disadvantage for everyone are important, rather than targeted policies to reduce disadvantage for children or the elderly.
This paper finds that remote areas suffer the greatest proportion of people in areas where there are four generations disadvantaged, and urban areas have the most people in areas where no generations are in disadvantage. This does suggest that the Government should be concentrating on efforts to reduce multiple generation disadvantage in remote areas.
In 2010, the Secretary to the Treasury of the Government of Australia (Ken Henry) chaired a revie... more In 2010, the Secretary to the Treasury of the Government of Australia (Ken Henry) chaired a review of Australia’s Tax system (called the Henry Review). This review outlined a number of significant changes to Australia’s tax system, which could be implemented over a number of years, including reducing the corporate tax rate; and the introduction of a new mining tax.
This review provides a long term strategy for Australia’s tax system, but also shows that Australia’s current static Tax/Transfer model (STINMOD) will need to be re-configured to be able to respond to future demands for policy modelling. As a start to this, NATSEM and the Commonwealth Government have instituted a full review of the STINMOD model, from a full code review through to fundamental questions about how the model will be able to respond to future policy requirements.
This paper will highlight how the model was reviewed, what was considered in the review, and what the conclusions of the review were.
This paper presents initial results from work being done on the reasons that people
experience h... more This paper presents initial results from work being done on the reasons that people
experience homeostatic defeat in subjective wellbeing. Subjective wellbeing shows signs of
homeostasis, meaning it always gravitates to one number (on average 75 on a scale of 1 to
100). The range around this average is also very small, suggesting that homeostasis is
acting as a protective factor for wellbeing.
Homeostatic defeat is when homeostasis stops operating as a protective factor in subjective
wellbeing. Homeostatic defeat occurs after challenges to subjective wellbeing become too
much for the homeostatic system to deal with.
This paper derives a point of homeostatic failure using data from the HILDA survey, and
then identifies the group of people who have experienced homeostatic failure from one
wave to the next of HILDA. Changes in social capital and life events experienced by these
people over these two waves are calculated. A logistic regression model is then used to
identify which of these changes have a significant effect on homeostatic failure.
We find that, after controlling for changes in social capital and health, only two major life
events (birth of a child and separation) have an effect on homeostatic failure. The birth of a
child is associated with a lower probability of homeostatic failure; and separation is
associated with a higher probability. Worsening of health and a reduction in leisure time
are also associated with a higher probability of homeostatic failure. Income was
significantly associated with a lower probability of homeostatic failure, so it is a protective
factor.
Spatial microsimulation techniques have become an increasingly popular way to fulfil the
need fo... more 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.
This paper shows how a microsimulation model has been used in Australia to identify how
differen... more This paper shows how a microsimulation model has been used in Australia to identify how
different clients are affected by regulatory and/or concessional pricing. The regulatory and
concessional pricing is in the areas of Electricity, Gas and Water utilities provision.
The model is based on survey data from the client, and allows the client to model how
changing regulatory and/or concessional pricing will affect different families (so a Winners
and Losers analysis), how it will affect the revenue of the utilities companies, and how it
will affect the Government’s expenditure (as they fund the concessions).
The latest version of the Utility Concessions Model was created for the Australian state of
New South Wales, and uses an Excel front end Interface with the model being written in
SAS and run from Excel. This paper describes how this was done, and outlines the
advantages of this approach when there are a number of parameters.
Much research about child poverty and disadvantage provides national estimates of
child wellbein... more Much research about child poverty and disadvantage provides national estimates of
child wellbeing, due to the ready availability of microdata at the national level.
However, an increasing body of evidence suggests that there can be major
differences in well-being between children living in different geographic areas. In
addition, much recent debate has focussed on moving beyond income poverty to
broader measures of social exclusion. This paper describes results from an innovative
use of the 2001 Australian census microdata, in which tables were commissioned
from the Australian Bureau of Statistics which focussed on well-being and
disadvantage from the child’s perspective. Using this data, a composite index of
child social exclusion risk at a small area level was constructed and then regional
differences in risk analysed. The paper also analyses the specific indicators of social
exclusion which form the index. Substantial differences in child social exclusion, and
in specific characteristics related to social exclusion, are found across local areas. The
extent of correlation between a more traditional income-based measure of economic
wellbeing and the composite index is also examined.
ABSTRACT Housing stress is usually measured on the basis of income and direct housing costs such ... more ABSTRACT 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 transport costs make. ...
In recent years, the National Centre for Social and Economic Modelling at the University of Canbe... more In recent years, the National Centre for Social and Economic Modelling at the University of Canberra has been developing a method of calculating estimates for small areas using survey data. This methodology has now been linked to our microsimulation model of the Tax/Transfer system STINMOD, allowing the regional impact of policy changes to be estimated. The SPATIALMSM model reweights the
This study has examined the factors that contribute to homelessness in Australia; identified some... more This study has examined the factors that contribute to homelessness in Australia; identified some indicators that are associated with these risk factors; and then used these indicators to identify a ‘risk of homelessness’ index. This index can be used to identify areas where there is a high risk of homelessness, so policies can be better targeted to these areas. Once an area has been identified using the index, the indicators can pinpoint what risk factors exist in that area which could then create better targeted policies
The literature review identified a number of pathways into homelessness, including housing crisis; family breakdown; mental health issues; substance abuse; labour market difficulties; social network influences; economic and social factors; previous homelessness; and being a youth. Indicators were identified for a number of these pathways and grouped into domains.
The domains were then combined into an index, using statistical techniques, depending on how correlated the indicators were to each other.
The final indexes were mapped, allowing users to ascertain areas with a high risk of homelessness. Two indices were created, due to data limitations in a number of States and Territories. In the literature review, domestic violence was found to be a significant risk factor for homelessness, so where data on domestic violence was available (NSW, ACT and Qld), a separate index was constructed incorporating a domestic violence variable. Because these data were not available in all States and the Northern Territory, the main index uses the proportion of sole parent families as a proxy, which was available for all States and Territories.
Overall, we found that the NT and Tasmania had the highest proportion of people living in areas with the greatest risk of homelessness. There was also a greater proportion of people outside of capital cities living in areas with the highest risk of homelessness.
Version 2 of the RHI incorporates the following changes over Version 1:
1) Extreme housing stress is now calculated for everyone, rather than only those aged 55 years and over. This reflects a change in thinking that extreme housing stress places people of any age at risk of homelessness.
2) A change to the Public Housing variable from the Census from the proportion of households in public housing, to the proportion of people in public housing, reflecting a person based risk of homelessness, rather than the V1 household based indicator;
3) Public housing now includes renting from Housing co-operatives, community and church groups. In V1, it only included those renting from State/Territory Governments.
4) Housing stress has been changed from households in housing stress to people living in households experiencing housing stress. This reflects the individual nature of the RHI in which everyone in the household is at risk of homelessness.
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Papers by Robert Tanton
One of the limitations of this concept is that it only takes into account direct family, so it is only how a person’s parents affect their disadvantage. However, we know that the local community also affects disadvantage, and that disadvantage tends to cluster. The obvious question that this paper tries to answer is do areas with high levels of disadvantage have high levels of disadvantage for all age groups in the area? Or are there areas where a high proportion of disadvantaged elderly people and a low proportion of disadvantage children live? And where are these areas (eg, rural/regional areas, capital cities, inner urban areas).
Answering this question will give some idea of which areas have entrenched disadvantage, so disadvantage that covers a number of generations. It is these areas where broad policies to reduce disadvantage for everyone are important, rather than targeted policies to reduce disadvantage for children or the elderly.
This paper finds that remote areas suffer the greatest proportion of people in areas where there are four generations disadvantaged, and urban areas have the most people in areas where no generations are in disadvantage. This does suggest that the Government should be concentrating on efforts to reduce multiple generation disadvantage in remote areas.
This review provides a long term strategy for Australia’s tax system, but also shows that Australia’s current static Tax/Transfer model (STINMOD) will need to be re-configured to be able to respond to future demands for policy modelling. As a start to this, NATSEM and the Commonwealth Government have instituted a full review of the STINMOD model, from a full code review through to fundamental questions about how the model will be able to respond to future policy requirements.
This paper will highlight how the model was reviewed, what was considered in the review, and what the conclusions of the review were.
experience homeostatic defeat in subjective wellbeing. Subjective wellbeing shows signs of
homeostasis, meaning it always gravitates to one number (on average 75 on a scale of 1 to
100). The range around this average is also very small, suggesting that homeostasis is
acting as a protective factor for wellbeing.
Homeostatic defeat is when homeostasis stops operating as a protective factor in subjective
wellbeing. Homeostatic defeat occurs after challenges to subjective wellbeing become too
much for the homeostatic system to deal with.
This paper derives a point of homeostatic failure using data from the HILDA survey, and
then identifies the group of people who have experienced homeostatic failure from one
wave to the next of HILDA. Changes in social capital and life events experienced by these
people over these two waves are calculated. A logistic regression model is then used to
identify which of these changes have a significant effect on homeostatic failure.
We find that, after controlling for changes in social capital and health, only two major life
events (birth of a child and separation) have an effect on homeostatic failure. The birth of a
child is associated with a lower probability of homeostatic failure; and separation is
associated with a higher probability. Worsening of health and a reduction in leisure time
are also associated with a higher probability of homeostatic failure. Income was
significantly associated with a lower probability of homeostatic failure, so it is a protective
factor.
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.
different clients are affected by regulatory and/or concessional pricing. The regulatory and
concessional pricing is in the areas of Electricity, Gas and Water utilities provision.
The model is based on survey data from the client, and allows the client to model how
changing regulatory and/or concessional pricing will affect different families (so a Winners
and Losers analysis), how it will affect the revenue of the utilities companies, and how it
will affect the Government’s expenditure (as they fund the concessions).
The latest version of the Utility Concessions Model was created for the Australian state of
New South Wales, and uses an Excel front end Interface with the model being written in
SAS and run from Excel. This paper describes how this was done, and outlines the
advantages of this approach when there are a number of parameters.
child wellbeing, due to the ready availability of microdata at the national level.
However, an increasing body of evidence suggests that there can be major
differences in well-being between children living in different geographic areas. In
addition, much recent debate has focussed on moving beyond income poverty to
broader measures of social exclusion. This paper describes results from an innovative
use of the 2001 Australian census microdata, in which tables were commissioned
from the Australian Bureau of Statistics which focussed on well-being and
disadvantage from the child’s perspective. Using this data, a composite index of
child social exclusion risk at a small area level was constructed and then regional
differences in risk analysed. The paper also analyses the specific indicators of social
exclusion which form the index. Substantial differences in child social exclusion, and
in specific characteristics related to social exclusion, are found across local areas. The
extent of correlation between a more traditional income-based measure of economic
wellbeing and the composite index is also examined.
The literature review identified a number of pathways into homelessness, including housing crisis; family breakdown; mental health issues; substance abuse; labour market difficulties; social network influences; economic and social factors; previous homelessness; and being a youth. Indicators were identified for a number of these pathways and grouped into domains.
The domains were then combined into an index, using statistical techniques, depending on how correlated the indicators were to each other.
The final indexes were mapped, allowing users to ascertain areas with a high risk of homelessness. Two indices were created, due to data limitations in a number of States and Territories. In the literature review, domestic violence was found to be a significant risk factor for homelessness, so where data on domestic violence was available (NSW, ACT and Qld), a separate index was constructed incorporating a domestic violence variable. Because these data were not available in all States and the Northern Territory, the main index uses the proportion of sole parent families as a proxy, which was available for all States and Territories.
Overall, we found that the NT and Tasmania had the highest proportion of people living in areas with the greatest risk of homelessness. There was also a greater proportion of people outside of capital cities living in areas with the highest risk of homelessness.
Version 2 of the RHI incorporates the following changes over Version 1:
1) Extreme housing stress is now calculated for everyone, rather than only those aged 55 years and over. This reflects a change in thinking that extreme housing stress places people of any age at risk of homelessness.
2) A change to the Public Housing variable from the Census from the proportion of households in public housing, to the proportion of people in public housing, reflecting a person based risk of homelessness, rather than the V1 household based indicator;
3) Public housing now includes renting from Housing co-operatives, community and church groups. In V1, it only included those renting from State/Territory Governments.
4) Housing stress has been changed from households in housing stress to people living in households experiencing housing stress. This reflects the individual nature of the RHI in which everyone in the household is at risk of homelessness.