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This document was added or updated on 28/05/2021.
Table 1: Summary of monthly cross sections(a)
For more information about the file structure, see the Data and file structure page.
For more information about the weights, see the Weighting and Benchmarks section of the Data Item List, available in the Downloads tab.
Every record of the file is uniquely identified by the item ABSRID. This identifier is a combination of the household identifier ABSHID and the person identifier ABSPID. All of these identifiers are used consistently across months to create longitudinal links.
Non-private dwellings and dwellings selected in Aboriginal and Torres Strait Islander communities are excluded from longitudinal linking. These include hotels, motels, hostels, hospitals, religious institutions providing accommodation, educational institutions providing accommodation, prisons, boarding houses, and short-stay caravan parks. These are given a new household identifier each month in this dataset. People in non-private dwellings are more likely to be older and not in the labour force than those in private dwellings.
Family units are identified by the item FAMNUM. This identifier is not used consistently across months due to the dynamic nature of family relationships. Its purpose is to identify family units within multiple family households for the particular circumstances of each month. The identification of family units in one month may not necessarily correspond to how family units are identified in subsequent or preceding months.
For more information about the identifiers, see the Record Identifiers section of the Data Item List, available in the Downloads tab.
The Labour Force Survey (LFS) is designed to survey the same household for eight consecutive months. However, this means that individuals can move in and out of the LLFS for a number of reasons:
While 5.6 million individuals are observed in total, just over 1.9 million are observed for the full eight months (Table 2).
Table 2: Counts of individuals
Some individuals are more likely to leave the LFS than others.
When linking individuals, this variability in the types of people who leave and stay in the LFS results in attrition bias overtime. It is important when looking at aggregate statistics that use linked observations to be aware of this bias and to attempt to control for it if possible. This can be done by adjusting the weights appropriately - increasing the weights for those who are more likely to leave the LFS.
It is also important to consider how the collection of the LFS has changed over time. The method of data collection has changed to allow a greater choice in how people respond, from face-to-face interviews to phone interviews to online self-completion.
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6602.0 - Microdata: Longitudinal Labour Force, Australia
Latest ISSUE Released at 11:30 AM (CANBERRA TIME) 26/06/2020