Household, Income And Labour Dynamics In Australia Survey Research Articles

The association between unpaid labour and mental health in working-age adults in Australia from 2002 to 2020: a longitudinal population-based cohort study.

Unpaid labour is a daily part of most people's lives, none more so than for women. Yet, in comparison to paid work, the effect of unpaid labour on mental health is an under-researched area. This study aims to address key gaps in the extant literature, examining how unpaid labour is associated with mental health in working-age men and women, and whether gender differences exist. In this longitudinal population-based cohort study, 19 waves of the Household, Income, and Labour Dynamics in Australia (HILDA) survey were used to employ a fixed effects regression analysis to examine the associations between unpaid labour and mental health in working-age (aged 25-64 years) Australian adults. Mental health was assessed using the MHI-5 scale. Both the individual and the combined effects of four different domains of unpaid labour (household work, childcare, care for adults, and outdoor tasks) were interrogated, as were the gender differences. Of the 37 352 participants (297 036 observations) in waves 2002-20 of the HILDA Survey, 22 832 people (190 207 observations) were aged 25-64 years, and after excluding participants with missing data, 21 014 participants (150 163 observations) were included in the analysis. Increasing time in household work was negatively associated with mental health in both men (β coefficient=-0·026 [95% CI -0·04 to -0·01]) and women (β coefficient=-0·009 [-0·02 to 0·001]), as was care for adults (disabled or older people) in women (β coefficient=-0·027 [-0·04 to -0·01]). Conversely, increasing time in childcare for women (β coefficient=0·016 [0·01 to 0·02]) and outdoor tasks for men, was positively associated with mental health (β coefficient=0·067 [0·04 to 0·09]). A null finding for the overall cumulative total unpaid labour exposure for both men and women was probably attributable to the opposing direction of effects between the individual domains that constituted the total load. This study reveals considerable variance and nuance in how different domains of unpaid labour affect mental health, as well as continued inequity in the division of unpaid labour in households, with women doing considerably more unpaid labour than men. This study also exposes important challenges associated with measuring and understanding total (combined) unpaid labour as a determinant of health. University of Melbourne Research Training Scholarship, Australian Research Council Discovery Early Career Award, Australian Research Council Linkage Project.

Using machine learning to analyze longitudinal data: A tutorial guide and best‐practice recommendations for social science researchers

AbstractThis article introduces the research community to the power of machine learning over traditional approaches when analyzing longitudinal data. Although traditional approaches work well with small to medium datasets, machine learning models are more appropriate as the available data becomes larger and more complex. Additionally, machine learning methods are ideal for analyzing longitudinal data because they do not make any assumptions about the distribution of the dependent and independent variables or the homogeneity of the underlying population. They can also analyze cases with partial information. In this article, we use the Household, Income, and Labour Dynamics in Australia (HILDA) survey to illustrate the benefits of machine learning. Using a machine learning algorithm, we analyze the relationship between job‐related variables and neuroticism across 13 years of the HILDA survey. We suggest that the results produced by machine learning can be used to generate generalizable rules from the data to augment our theoretical understanding of the domain. With a technical guide, this article offers critical information and best‐practice recommendations that can assist social science researchers in conducting machine learning analysis with longitudinal data.

Estimating the effects of physical violence and serious injury on health-related quality of life: Evidence from 19 waves of the Household, Income and Labour Dynamics in Australia Survey

ObjectiveThis study aims to investigate the effect of physical violence and serious injury on health-related quality of life in the Australian adult population.MethodsThis study utilised panel data from the Household, Income and Labour Dynamics in Australia (HILDA) Survey. HRQoL was measured through the physical component summary (PCS), mental component summary (MCS), and short-form six-dimension utility index (SF-6D) of the 36-item Short-Form Health Survey (SF-36). Longitudinal fixed-effect regression models were fitted using 19 waves of the HILDA Survey spanning from 2002 to 2020.ResultsThis study found a negative effect of physical violence and serious injury on health-related quality of life. More specifically, Australian adults exposed to physical violence and serious injury exhibited lower levels of health-related quality of life. Who experienced physical violence only had lower MCS (β = −2.786, 95% CI: −3.091, −2.481) and SF-6D (β = −0.0214, 95% CI: −0.0248, −0.0181) scores if switches from not experiencing physical violence and serious injury. Exposed to serious injury had lower PCS (β = −5.103, 95% CI: −5.203, −5.004), MCS (β = −2.363, 95% CI: −2.480, −2.247), and SF-6D (β = −0.0585, 95% CI: −0.0598, −0.0572) score if the adults not experiencing physical violence and serious injury. Further, individuals exposed to both violence and injury had substantially lower PCS (β = -3.60, 95% CI: -4.086, -3.114), MCS (β = −6.027, 95% CI: −6.596, −5.459), and SF-6D (β = −0.0716, 95% CI: −0.0779, −0.0652) scores relative to when the individuals exposed to none.ConclusionOur findings indicate that interventions to improve Australian adults’ quality of life should pay particular attention to those who have experienced physical violence and serious injury. Our findings suggest unmet mental health needs for victims of physical violence and serious injuries, which calls for proactive policy interventions that provide psychological and emotional therapy.

New generations of respondents: assessing the representativity of the HILDA Survey's child sample.

An important aspect of an indefinite life household panel study is to provide a sample of children who become new generations of respondents over time. The representativity of children and young adults in the Household, Income and Labour Dynamics in Australia (HILDA) Survey is assessed after 16 waves. Estimates from the HILDA Survey are compared to official data sources of the Australian Bureau of Statistics (ABS) and include demographic, education, employment, income and residential mobility variables. Both cross-section and longitudinal estimates are assessed. Overall, the HILDA Survey estimates are relatively close to the ABS estimates with the exception of the year of arrival of recent immigrants, having foreign-born parents, having a certificate level qualification, type of relationship in household, having zero income, the main source of income, and residential mobility. Most of these exceptions can be explained by differences in questionnaire design, respondent recall error, linkage error, and differences in the amount of missing data. The estimate of particular concern is the proportion of immigrants arriving in the last five years, which is underestimated in the HILDA Survey due to undercoverage of recent immigrants. This could be addressed by regular refreshment samples of recent immigrants.

Separation From the Life Partner and Exit From Self-Employment.

The survival of businesses in the market often hinges on contributions of the business owner’s household members. Partners of the self-employed as well as their children may, for example, provide emotional support but also cheap and flexible labor. Although the household composition of self-employed individuals has been analyzed in many earlier studies, little is known about what happens to the self-employed individual and his or her business when one separates from a life partner. We argue that separation from a life partner has profound financial and social consequences for the business owner. Specifically, we propose that a decrease in household income and social functioning (which is the degree of interference with social activities due to mental and/or physical problems) after separation from the life partner may lead to an exit from self-employment. Our empirical analysis draws on data from the longitudinal HILDA (Household, Income and Labour Dynamics in Australia) survey, for the period 2002–2017. Based on information from 4,044 self-employed individuals aged 18–64 years (18,053 individual-year observations), we find that separating from the life partner in the past year significantly increases the probability of exit from self-employment in the next year. Furthermore, we find that the positive association between separation from the life partner and exit from self-employment can be explained for 29.7% by a reduction in social functioning and for 10.7% by a reduction in household income. We study five exit routes out of self-employment and find that separation from the life partner mainly increases the probabilities of becoming a wage worker and of re-entering self-employment after experiencing an exit. For exit to unemployment or to a position outside the labor force (voluntarily inactive/retirement or any other non-labor force position), we find insignificant relationships with separation from the life partner. Furthermore, for all exit routes except retirement, we find significant indirect effects implying that decreased household income and levels of social functioning are important mechanisms through which separation from the life partner is related to exit from self-employment.

Measuring geographic mobility: Comparison of estimates from longitudinal and cross-sectional data

Attrition of sample members from a longitudinal survey can undermine the quality of the data and its research potential, especially when the sample members who drop out are different from those who do not. People who move house are more likely to drop out of the survey as they are harder to locate, and once located, may be harder to interview in the remaining fieldwork time available. Moving coincides with many other life events (such as changes in marital status, the birth of a child, buying a home, changes in employment, or retirement) and if movers are not adequately interviewed, this may result in the study under-representing these changes and the events that occur after a move. This paper examines the weighted estimates of the rate of moving by age in a long running household panel study, the Household, Income and Labour Dynamics in Australia (HILDA) Survey, compared to official cross-sectional data sources and probabilistically linked Census data. Geographic mobility is examined over one-, five- and 10-year periods. Some of the differences that occur in the mobility estimates is a result of item non-response or recall error in the cross-sectional sources but little evidence is found of the differential impact of attrition in the HILDA Survey. There is, however, some indication that the longitudinal survey data may underrepresent long distance moves. Other differences between the data sources are investigated by fitting logistic regression models of mobility to estimate the effect of housing tenure and education levels over the life course. These models show similar overall trends, but there is some evidence of differential effects for renters with lower education levels which may be due, at least in part, to the differential role recall error plays in these measures. Overall, these findings reassure longitudinal data users of the quality of geographic mobility estimates from the HILDA Survey and encourage similar comparisons to be made for other longitudinal data sources.

Trajectories of Social and Economic Outcomes and Problem Gambling Risk in Australia

While there are many cross-sectional studies that provide data on gambling behaviour and the characteristics of those who gamble, there are few large-scale population based longitudinal studies of gambling. This is a serious limitation. It is increasingly being recognised that longitudinal data is required to properly understand gambling behaviour, trajectories, risks and consequences. This paper makes use of a large-scale longitudinal survey that includes questions on gambling behaviour in Australia: the Household, Income and Labour Dynamics in Australia (HILDA) survey. Although the HILDA survey currently provides data on gambling at a single point in time in 2015, there are data on the individuals, in the majority of cases, back to 2001. This paper analyses the trajectories of a range of social, health and economic outcomes according to self-reported risk of experiencing gambling related problems. The analysis clearly shows that problem gamblers experience significantly worse outcomes than those without gambling problems, and poor outcomes go back a number of years. In a number of cases, outcomes are becoming progressively poorer, which may suggest either increasingly risky gambling behaviour or the cumulative effects of a sustained period of gambling problems. The long-term nature of the poor health and social outcomes experienced by problem gamblers strongly suggests that programs designed to address gambling problems will have to be cognisant of, and address these underlying issues. Effective treatment is thus likely to be long-term and challenging. It also suggests that restricting access to gambling via mechanisms such as pre-commitment or self-exclusion will be effective.

Declining realisation of reproductive intentions with age.

What is the likelihood of having a child within 4years for men and women with strong short-term reproductive intentions, and how is it affected by age? For women, the likelihood of realising reproductive intentions decreased steeply from age 35: the effect of age was weak and not significant for men. Men and women are postponing childbearing until later ages. For women, this trend is associated with a higher risk that childbearing plans will not be realised due to increased levels of infertility and pregnancy complications. This study analyses two waves of the nationally representative Household, Income and Labour Dynamics in Australia (HILDA) survey. The analytical sample interviewed in 2011 included 447 men aged 18-45 and 528 women aged 18-41. These respondents expressed a strong intention to have a child in the next 3years. We followed them up in 2015 to track whether their reproductive intention was achieved or revised. Multinomial logistic regression is used to account for the three possible outcomes: (i) having a child, (ii) not having a child but still intending to have one in the future and (iii) not having a child and no longer intending to have one. We analyse how age, parity, partnership status, education, perceived ability to conceive, self-rated health, BMI and smoking status are related to realising or changing reproductive intentions. Almost two-thirds of men and women realised their strong short-term fertility plans within 4years. There was a steep age-related decline in realising reproductive intentions for women in their mid- and late-30s, whereas men maintained a relatively high probability of having the child they intended until age 45. Women aged 38-41 who planned to have a child were the most likely to change their plan within 4years. The probability of realising reproductive intention was highest for married and highly educated men and women and for those with one child. Our study cannot separate biological, social and cultural reasons for not realising reproductive intentions. Men and women adjust their intentions in response to their actual circumstances, but also in line with their perceived ability to have a child or under the influence of broader social norms on reproductive age. Our results give a new perspective on the ability of men and women to realise their reproductive plans in the context of childbearing postponement. They confirm the inequality in the individual consequences of delayed reproduction between men and women. They inform medical practitioners and counsellors about the complex biological, social and normative barriers to reproduction among women at higher childbearing ages. This research was partly supported by a Research School of Social Sciences Visiting Fellowship at the Australian National University and an Australian Research Council Discovery Project (DP150104248). Éva Beaujouan's work was partly funded by the Austrian Science Fund (FWF) project 'Later Fertility in Europe' (Grant agreement no. P31171-G29). This paper uses unit record data from the HILDA Survey. The HILDA Project was initiated and is funded by the Australian Government Department of Social Services (DSS) and is managed by the Melbourne Institute of Applied Economic and Social Research (Melbourne Institute). The findings and views reported in this paper, however, are those of the authors and should not be attributed to either DSS or the Melbourne Institute. The authors have no conflicts of interest.

Financial hardship, mastery and social support: Explaining poor mental health amongst the inadequately employed using data from the HILDA survey

ObjectiveThis study analysed data from the Household Income and Labour Dynamics in Australia (HILDA) Survey to examine the relationship between employment status and mental health, and the mediating effects of financial hardship, mastery and social support. In addition, the study sought to explore the effects of duration of unemployment on mental health. MethodsThe primary analysis used three waves of data from the HILDA Survey with 4965 young adult respondents. Longitudinal population-averaged logistic regression models assessed the association of employment status and mental health, including the contribution of mastery, financial hardship and social support in explaining this association between employment groups (unemployed vs. employed; under employed vs. employed). Sensitivity analyses utilised a fixed-effects approach and also considered the full-range of working-age respondents. Regression analysis was used to explore the effect of duration of unemployment on mental health. ResultsRespondents’ who identified as unemployed or underemployed were at higher risk of poor mental health outcomes when compared to their employed counterparts. This association was ameliorated when accounting for mastery, financial hardship and social support for the unemployed, and was fully mediated for the underemployed. The fixed-effects models showed the transition to unemployment was associated with a decline in mental health and that mastery in particular contributed to that change. The same results were found with a broader age range of respondents. Finally, the relationship between duration of unemployment and mental health was not linear, with mental health showing marked decline across the first 9 weeks of unemployment. Conclusions and implicationsMastery, social support and financial hardship are important factors in understanding the association of poor mental health with both unemployment and underemployment. Furthermore, the results suggest that the most deleterious effects on mental health may occur in the first two months of unemployment before plateauing. In order to prevent deterioration in mental health, these findings suggest intervention should commence immediately following job loss.

The Economics of Cognitive Impairment: Volunteering and Cognitive Function in the HILDA Survey

Background: The economic impact of older-age cognitive impairment has been estimated primarily by the direct and indirect costs associated with dementia care. Other potential costs associated with milder cognitive impairment in the community have received little attention. Objective: To quantify the cost of nonclinical cognitive impairment in a large population-based sample in order to more fully inform cost-effectiveness evaluations of interventions to maintain cognitive health. Methods: Volunteering by seniors has economic value but those with lower cognitive function may contribute fewer hours. Relations between hours volunteering and cognitive impairment were assessed using the Household, Income and Labour Dynamics in Australia (HILDA) survey data. These findings were extrapolated to the Australian population to estimate one potential cost attributable to nonclinical cognitive impairment. Results: In those aged ≥60 years in HILDA (n = 3,127), conservatively defined cognitive impairment was present in 3.8% of the sample. Impairment was defined by performance ≥1 standard deviation below the age- and education-adjusted mean on both the Symbol Digit Modalities Test and Backwards Digit Span test. In fully adjusted binomial regression models, impairment was associated with the probability of undertaking 1 h 9 min less volunteering a week compared to being nonimpaired (β = -1.15, 95% confidence interval -1.82 to -0.47, p = 0.001). In the population, 3.8% impairment equated to probable loss of AUD 302,307,969 per annum estimated by hours of volunteering valued by replacement cost. Conclusion: Nonclinical cognitive impairment in older age impacts upon on the nonmonetary economy via probable loss of volunteering contribution. Valuing loss of contribution provides additional information for cost-effectiveness evaluations of research and action directed toward maintaining older-age cognitive functioning.

Greenspace, physical activity and well-being in Australian capital cities: how does population size moderate the relationship?

ObjectivesThe purpose of this study is to investigate the synergy between greenspace and physical activity and its implications for well-being. In particular, how this synergy may depend on population size in the neighborhood. Study designCross-sectional analysis of resident-level responses from the Household, Income and Labour Dynamics in Australia (HILDA) survey for 2013 subset to Australia's major capital cities and linked to Geographic Information Systems (GIS) data. MethodsGIS data on greenspace and Australian Bureau of Statistics data on population size for the neighborhood are matched to the residents in the HILDA survey on the basis of the Census Collection District in which they reside. A cluster-specific fixed effects model is estimated for the outcomes of mental health and psychological distress. A battery of sociodemographic and location characteristics were also adjusted for. Interaction terms are used to discern the extent to which population size may moderate any synergistic well-being benefits associated with physical activity and greenspace. This question is ultimately operationalized as a three-way interaction effect (greenspace × physical activity × population size). ResultsThe results indicate that physical activity is most strongly and positively associated with mental health (statistically significant at the 1% level), with an estimated coefficient of 0.6307. The results also reveal that physical activity is negatively associated with psychological distress (statistically significant at the 10% level), with an estimated coefficient of −0.2447. Unexpectedly, for both mental health and psychological distress the greenspace and population variables are not found to have separate statistically significant effects.Furthermore, while the results fail to find, on average, the hypothesized synergy between greenspace and physical activity, a closer inspection reveals that this link may depend on the population size of a neighborhood. The interaction term for greenspace, physical activity and population bears a coefficient estimate of 0.0033, statistically significant at the 5% level in the mental health regression and a coefficient of −0.0032, statistically significant at the 1% level in the psychological distress regression. ConclusionThe results indicate that physical activity is linked differently to mental health and psychological distress. The results initially provide no evidence of the hypothesized greenspace–physical activity synergy. The results provide evidence that this synergy is greater in more populated neighborhoods.

Pathways to Retirement in Australia: Evidence From the HILDA Survey

Using data from the first 8 waves of the Household Income and Labour Dynamics in Australia (HILDA) Survey, this article adds to existing knowledge of how pathways to retirement are decided. Two complementary estimation strategies are used to model the labor force transitions of mature age men and women. First, a standard multinomial logit model is used to determine the characteristics associated with specific patterns of labor force participation. Second, a dynamic mixed multinomial logit model is used to estimate labor market states in each year. Both approaches provide new evidence about coordinated retirement among mature age couples, not only in the timing of retirement but how the transition to retirement is made. The results also provide new evidence about the different effects of specific components of household wealth on how the transition to retirement is made. Controlling for unobserved heterogeneity in the dynamic multinomial model confirms the existence of true state dependence in the labor force states of mature age men and women. This implies that policies aimed at encouraging older workers to delay retirement will be more effective in boosting mature age participation than policies aimed at encouraging older workers back into the workforce after a period of nonparticipation.

One-year reciprocal relationship between community participation and mental wellbeing in Australia: A panel analysis

The links between social capital and mental wellbeing are established but the direction of the social capital-wellbeing relationship is rarely systematically examined. This omission undermines the validity of social capital as a basis for health interventions. The aim of this paper was to explore the short-term (one-year) reciprocal relationship between community participation – an important component of social capital – and mental wellbeing. We used nationally representative Household, Income and Labour Dynamics in Australia (HILDA) Survey data, 2005–11. The HILDA Survey is an annual cohort study from which was extracted a sub-sample of panel data (the same people participating across multiple waves) enabling us to use fixed effects regression methods to model the longitudinal association of mental health and participation controlling for individual heterogeneity. The results showed that better mental wellbeing in one year was generally related to more community participation the next year, while greater past community participation was linked to better mental wellbeing the next year independent of (i) initial mental wellbeing, (ii) multiple potentially confounding factors and (iii) unobserved and time-constant heterogeneity. Political participation was marginally related to worse mental health in both directions. The results also showed that the association between community participation and mental wellbeing the next year is weaker for those with poor initial wellbeing than for initially healthier respondents. Our findings may inform the trial and scientific evaluation of programs aimed at increasing informal social connectedness and civic engagement to promote mental wellbeing.

The HILDA Survey: a case study in the design and development of a successful Household Panel Survey

The Household, Income and Labour Dynamics in Australia (HILDA) Survey is one of only a small number of well established, large nationally representative household panel studies conducted in the world. With annual data collection commencing in 2001 there are now over 10 years of unit record data available to researchers, with the promise of many more to come. While the design of the HILDA Survey owes much to other older household panel studies conducted elsewhere, it has a number of features which make it relatively unique. This paper provides a brief history of the HILDA Survey’s progress to date, focusing first on its design, content and data collection processes, before reviewing its achievements with respect to survey response and usage.

The prevalence and correlates of psychological distress in Australian tertiary students compared to their community peers

To examine differences between university students, vocational education and training (VET) students, tertiary students combined and non-students in the prevalence of psychological distress and the socio-demographic and economic characteristics associated with psychological distress. The Kessler Psychological Distress Scale was used to estimate the prevalence of moderate (16-21) and high (22-50) distress with data from three national surveys: the 2007 Household, Income and Labour Dynamics in Australia (HILDA) survey, the 2007-08 National Health Survey (NHS), and the 2007 National Survey of Mental Health and Wellbeing (NSMHWB). Multinomial logistic regression models were also estimated using the HILDA survey to examine any differences in the characteristics associated with moderate and high distress between the groups. There was evidence of a higher prevalence of moderate distress in tertiary students than non-students in the HILDA survey (27.1% vs. 21.2%, p < 0.05) and the NSMHWB (27.4% vs. 19.5%, p < 0.05), but not the NHS (26.1% vs. 22.5%, p > 0.05). However, standardized rates for age and gender attenuated the difference in moderate distress in the HILDA survey and the NSMHWB. The prevalence of high distress was similar between the groups in all three surveys. The multinomial regression analyses using the HILDA survey showed the following subgroups of students to be at a greater risk of high distress relative to those with low distress: younger university students, and university and VET students with financial problems. Compared to VET students and non-students, younger university students and those who worked 1-39 hours per week in paid employment were at a greater risk of high distress. There is evidence that tertiary students have a greater prevalence of moderate, but not high distress than non-students. Financial factors increase the risk of high distress and are likely to take on more importance as the participation rate of socio-economically disadvantaged students increases.

Psychosocial job adversity and health in Australia: analysis of data from the HILDA Survey

This study examines measures of psychosocial job quality developed from the Household Income and Labour Dynamics in Australia (HILDA) Survey, and reports on associations with physical and mental health. The study used seven waves of data from the HILDA Survey with 5,548 employed respondents. Longitudinal random-intercept regression models assessed the association of time-varying and between-person measures of psychosocial job quality job adversity with physical and mental health. Respondents' specific experience of psychosocial job adversity, except marketability, was associated with increased risk of mental health problems, whereas the association between psychosocial job adversity and physical health was largely driven by differences between people. Moving into jobs with different psychosocial quality is associated with changes in mental health. In contrast, individuals with poor physical health show an increased propensity to work in poor-quality jobs but it seems that changes in physical health are not as strongly tied to changes in job quality. Differences in the relationship between physical and mental health and psychosocial job quality have implications for the design of employment, health and social policy. The HILDA Survey is an important resource for policy development in Australia, and the availability of valid measures of psychosocial of job quality will enhance its use to better understand this important determinant and correlate of health.

Structure and Distribution of Household Wealth in Australia: Cohort Differences and Retirement Issues

Structure and Distribution of Household Wealth in Australia: Cohort Differences and Retirement Issues

Paid Annual Leave and Working Hours: Evidence from the HILDA Survey

Using data from wave 5 of the Household, Income and Labour Dynamics in Australia (HILDA) Survey, this study examines: (1) the extent to which Australian employees use their annual leave entitlements; and (2) the association between annual leave taking and weekly hours of work. After restricting attention to employees likely to have entitlement to at least 4 weeks of paid annual leave, it is found that the mean number of days of leave taken per year is around 16 and that the majority of employees (63%) take less than 20. The incidence of annual leave taking is found to vary positively with the number of usual weekly hours of work, but the size of this effect is small and weak.

The HILDA Survey and its Contribution to Economic and Social Research (So Far)*

The Household, Income and Labour Dynamics in Australia (HILDA) Survey is Australia's first nationally representative household panel survey. This article reviews the achievements of the HILDA Survey since its inception in 2001. It briefly describes the design of the survey and the data collection process, provides summary statistics about response and respondent characteristics, and reviews key research findings that have been published using the first four waves of data.

The dynamics of income poverty in Australia: Evidence from the first three waves of the HILDA Survey

This paper reports an analysis of income poverty dynamics in Australia using longitudinal data from the first three waves of the Household, Income and Labour Dynamics in Australia (HILDA) Survey. As in other developed countries, far fewer people are found to be living in persistent poverty than are poor on an annual basis. With a poverty threshold set at 50 per cent of median equivalised income, just over four per cent of Australians were measured as being in income poverty in all three waves. Among those who were poor during 2000‐01, about half subsequently had incomes above the 50 per cent threshold. However, the longer people remained in poverty, the less likely they were to exit, the greater was their risk of re‐entering poverty, and the lower were their incomes if they temporarily escaped poverty.