Provides longitudinal data on the lives of Australian residents.
Introduction: Commenced in 2001, the Household, Income and Labour Dynamics in Australia (HILDA) Survey is a nationally representative panel study of Australian households. The study is funded by the Australian Government Department of Social Services (DSS) and is managed by the Melbourne Institute of Applied Economic and Social Research at the University of Melbourne. Roy Morgan Research has conducted the fieldwork since Wave 9 (2009), prior to which The Nielsen Company was the fieldwork provider. This is the ninth volume of the Annual Statistical Report of the HILDA Survey, examining data from the first 11 waves of the study, which were conducted between 2001 and 2011.
The HILDA Survey seeks to provide longitudinal data on the lives of Australian residents. It annually collects information on a wide range of aspects of life in Australia, including household and family relationships, employment, education, income, expenditure, health and wellbeing, attitudes and values on a variety of subjects, and various life events and experiences. Information is also collected at less frequent intervals on various topics, including household wealth, fertility-related behaviour and plans, relationships with non-resident family members and non-resident partners, health care utilisation, eating habits and retirement.
The important distinguishing feature of the HILDA Survey is that the same households and individuals are interviewed every year, allowing us to see how their lives are changing over time. By design, the study can be infinitely lived, following not only the initial sample members for the remainder of their lives, but also the lives of their children and grandchildren, and indeed all subsequent descendants. The HILDA Survey is therefore quite different to the cross-sectional household surveys regularly conducted by the Australian Bureau of Statistics (ABS). Cross-sectional data are of course very important, providing snapshots of the community at a given point in time—for example, the percentage of people married, in employment, or with a disability. But such data also have important limitations for understanding economic and social behaviour and outcomes.
Household longitudinal data, known as panel data, provide a much more complete picture because they document the life-course a person takes. Panel data tell us about dynamics—family, income and labour dynamics—rather than statics. They tell us about persistence and recurrence, for example about how long people remain poor, unemployed, or on welfare, and how often people enter and reenter these states. Perhaps most importantly, panel data can tell us about the causes and consequences of life outcomes, such as poverty, unemployment, marital breakdown and poor health, because we can see the paths that individuals’ lives took to those outcomes and the paths they take subsequently. Indeed, one of the valuable attributes of the HILDA panel is the wealth of information on a variety of life domains that it brings together in one dataset. This allows us to understand the many linkages
between these life domains; to give but one example, we can examine the implications of health for risk of poor economic outcomes.
While in principle a cross-sectional survey can ask respondents to recall their life histories, in practice this is not viable. Health, subjective wellbeing, perceptions, attitudes, income, wealth, labour market activity—indeed most things of interest to researchers and policy-makers—are very difficult for respondents to recall from previous periods in their life. Respondents even have trouble recalling seemingly unforgettable life events such as marital separations. The only way to reliably obtain information over the life-course is to obtain it as people actually take that course.
For these reasons, panel data are vital for government and public policy analysis. Understanding the persistence and recurrence of life outcomes and their consequences is critical to appropriate targeting of policy, and of course understanding the causes of outcomes is critical to the form those policies take. For example, it is important to distinguish between short-term, medium-term and long-term poverty because it is likely that for each issue there are different implications for policy: the nature of the policy, the priority it is accorded, and the target group of the policy.
Panel data are also important because they permit causal inferences in many cases that are more credible than other types of data permit. In particular, statistical methods known as ‘fixed-effects’ regression models can be employed to examine the effects of various factors on life outcomes such as earnings, unemployment, income and life satisfaction. These models can control for the effects of stable characteristics of individuals that are typically not observed, such as innate ability and motivation, that confound estimates of causal effects
in cross-sectional settings. For example, a crosssectional model of the determination of earnings may find that undertaking additional post-school education has a large positive impact on earnings of older workers, but this may not be the case if it is simply that more able individuals, who earn more irrespective of additional education, are more likely to undertake additional education. In principle, a fixed-effects model can ‘net out’ the effects of innate ability and thereby identify the true effect of additional post-school education for these workers.