Labour Force Survey products guide

Guide to labour statistics

Learn about all the products and outputs we produce from the monthly Labour Force Survey and how to use them

Released
18/03/2025

Overview

We produce a range of outputs that use data from the Labour Force Survey (LFS). It can be challenging to choose which data product to use. This guide will help you understand which product is right for you and where to find it. The guide also includes:

The LFS provides information about the work activities of Australia's resident civilian population aged 15 years and older. It provides key figures on employment, unemployment, underemployment, participation, and hours worked for Australia, as well as the states and territories.

The Labour Force Survey methodology page has more information on how we produce the data.

Labour Force Survey products

There are a variety of ways to access LFS data depending on your requirements. LFS data are available in:

Labour Force, Australia is the first release. This product includes final Australia, state and territory level statistics on employment, unemployment, underemployment, underutilisation, participation, monthly and quarterly hours worked and gross flows. Information is presented in a summary publication and pre-prepared spreadsheets.

The second release of data occurs one week later in Labour Force, Australia, Detailed. This publication includes spreadsheets with additional detail relating to the monthly estimates published in Labour Force, Australia. Additional information is collected in the LFS each February, May, August, and November. This information is published with the second release of data for these months.

LFS data are a key source of regional labour market information, to learn more see the Regional labour market data guide.

You can find more information about the different ABS data platforms on the Compare data services page.

Labour Force, Australia
  • Primary source of Labour Force data
  • Includes headline estimates of employment, unemployment, underemployment, participation and hours worked for Australia
  • State and Territory level headline estimates
  • Original, Seasonally Adjusted and Trend data series
Labour Force, Australia, Detailed
  • Source of a wider range of labour market concepts and compositional breakdowns
  • Regional labour market data (SA4), specifically the modelled estimates
Labour Force Data Explorer
  • Online tool that presents data in a searchable, flexible and dynamic way
  • Automatically generate API calls to pull data into your own system
  • Includes small number of headline estimates and compositional breakdowns
  • Original, Seasonally Adjusted and Trend data series
Longitudinal Labour Force, Australia
  • Detailed longitudinal LFS microdata available in DataLab
  • Enables analysis of how the labour force status and other characteristics of respondents changes for each of the 8 months they are in the LFS
  • Contains data from each monthly LFS, along with data collected from labour supplementary surveys and multipurpose household surveys
  • Available from October 1982 onwards
Labour Force TableBuilder
  • Customise and save your own tables using LFS data
  • Wide range of labour market concepts and compositional breakdowns available
  • Contains data from August 2006
  • Includes International Standard Industry Classification (ISIC) and International Standard Classification of Occupations (ISCO)
Labour Force PLIDA Modular Product
  • Detailed LFS microdata available in DataLab
  • Enables in-depth analysis of LFS data integrated with administrative datasets including, health, education, government payments and taxation
  • Monthly datasets from June 2023 to present

LFS data are also a key input to the quarterly Labour Account, which combines multiple sources of data to create a consistent set of aggregate labour market statistics. The Labour Account is the best source of industry employment and jobs over time (see our Industry employment guide).

LFS products at a glance...

Product characteristics

Dataset types

Labour Force Survey data is available in 2 dataset types, summary and unit record level.

Summary or aggregated data is the primary format for statistics available in Labour Force, Australia, Labour Force, Australia, Detailed, and Data Explorer. Unit record level data is available as microdata from the Longitudinal Labour Force product and the LFS PLIDA module, and is used for customised analysis. The LFS TableBuilder product draws on unit record level data to enable you to produce tables customised to your specifications.

Key LFS data is available as original, seasonally adjusted and trend series data in Labour Force, Australia, Labour Force, Australia, Detailed, and Data Explorer. Seasonal adjustment is a statistical technique that attempts to measure and remove the effects of calendar related patterns (i.e. which happen at the same time every year). This allows other influences on the series to be more clearly recognised. Trend series data is smoothed seasonally adjusted data and reduces the impact of non-seasonal influences.

The ABS considers that trend series data provides a more reliable guide to the underlying direction of the data, and are more suitable for supporting most business decisions and policy advice.

For more information see Seasonal Adjustment and Changing Seasonality and Time Series Analysis FAQ

Customisation

There are a large range of pre-prepared tables available for download in the main LFS publications and Data Explorer. However, if the exact combination of information you are interested in is not available, the TableBuilder product offers more flexibility and customisation. 

The TableBuilder product provides a much larger range of information, and is the only source of data using international classifications, the International Standard Industry Classification (ISIC) and International Standard Classification of Occupations (ISCO).

As the level of customisation increases, you need higher levels of statistical skill and understanding to use it. So for advanced users seeking detailed customised analysis of LFS data the Longitudinal Labour Force microdata LFS PLIDA modular products are available.

Cost and access

There are a variety of ways to access LFS data depending on your requirements. You can access the main LFS publications and pre-prepared tables for free on the ABS website. The LFS Data Explorer tables are available through API and on the Data Explorer website

The Longitudinal Labour Force and PLIDA modular products are only available in the DataLab environment. Specific project approval and online training need to be completed before access is granted. For more information see the DataLab access page.

Labour Force TableBuilder access is by organisation and is free but requires registration.

Inclusion of modelled unit records

Improvements to LFS estimation introduced in early 2024, have resulted in a mixture of direct and modelled unit records in LFS products. Inclusion of modelled unit records start from August 2016.

Direct unit records are household responses that are collected during the LFS enumeration period. The methodology page has more information on this process.

Modelled unit records use secondary data sources combined with specific benchmarks to model 2 per cent of the LFS sample. This results in more representative estimates of the Australian civilian population. More information is available in this article: Improvements to Labour Force estimation method.

The main LFS publications include all direct and all modelled unit records. To maintain usability, both the Longitudinal and TableBuilder products include a random sample of the modelled unit records along with all directly collected unit records. The LFS PLIDA module contains all directly collected records and no modelled unit records.

Modelled regional labour market estimates

The LFS has traditionally been the most used source of regional labour market data. This is because the data is available more frequently than other data sources. However, the usefulness of the LFS regional estimates is limited by the smaller sample counts that contribute to each region.

The modelled regional labour force estimates overcome this by using the combined power of administrative data and statistical modelling with the direct unit records. The administrative data sources used include de-identified Single Touch Payroll data from the Australian Taxation Office and Youth Allowance and JobSeeker data from the Department of Social Services.

The ABS recommends using these over the direct survey estimates whenever possible. See the 'Modelled v direct estimates' section of the Regional labour market guide for more information.

Which product is right for me?

There are many different ways to interact with and discover data from the Labour Force Survey. A good place to start is by defining the question you are trying to answer with LFS data. Below is a table that provides some examples to help you get started and find which product is right for you.

ProductExample uses / questions
Labour Force, Australia
  • What is the national unemployment rate?
  • How has the monthly hours worked by females changed over the last 5 years?
Labour Force, Australia, Detailed
  • What is the participation rate of people residing in Toowoomba (SA4 region)?
  • How many people born overseas are currently employed?
Labour Force Data Explorer
  • Best for setting up regular repetitive exports of headline estimates
Longitudinal Labour Force, Australia
  • Best for analysis of data over long periods of time, including labour force status transitions
Labour Force TableBuilder
  • What are the education attainment estimates of underemployed people?
  • Using the international standard classification of occupations, how many clerical support workers were there in December 2023?
LFS PLIDA Module
  • How does the LFS unemployed population overlap with the benefit recipient population?
Labour Account
  • What is the proportion of multiple job holders in the Construction industry?

Differences in estimates

In 2024, improvements were made to LFS estimation methods for some relatively small population groups within the sample that are traditionally hard to enumerate, by modelling records using auxiliary administrative data. 

While this has resulted in a small improvement in the quality of LFS estimates (published in Labour Force, Australia and Labour Force, Australia, Detailed), it does mean that there may be differences in LFS summary figures derived from each of the Longitudinal Labour Force dataset, LFS TableBuilder and the LFS PLIDA modular product. It is important to understand these differences when choosing how to access LFS data.

The Longitudinal Labour Force microdata product contains a random subset of the modelled sample records resulting in a slight difference in aggregated estimates compared to the main publications. 

The TableBuilder product is based on the Longitudinal Labour Force dataset therefore, the same differences to the main LFS publications are present in both products. In addition, the TableBuilder product is subject to confidentiality restrictions specific to the TableBuilder format resulting in further small but random differences in aggregated totals. This has a negligible impact on the underlying patterns of the data but will be more obvious for more complex customised tables.

The LFS PLIDA module does not contain any modelled records. There will be differences between summary figures derived from this dataset compared to the main estimates. The ABS recommends using this module only for integrated data analysis. The use of this dataset requires linkage to the PLIDA spine which introduces additional limitations on the data that must be accounted for as part of any analysis conducted. For more information on linkage and the LFS PLIDA module see Explanatory notes for the Labour Force Survey PMP modules.

Labour Force Survey concepts and data item guide

Definitions for the concepts and data items published in Labour Force, Australia and Labour Force, Australia, Detailed.

Released
18/03/2025

Overview

This guide provides information on:

  • Labour Force Survey concepts and data items A-Z, including descriptions and definitions and where each data item can be sourced within Labour Force Survey standard products
  • Detailed output information, including the Labour Force Survey standard outputs and data items contained within each spreadsheet

More detailed information is available in Labour Statistics: Concepts Sources and Methods, describing the history of changes to the Labour Force Survey and the alignment of concepts and data items to conceptual frameworks and international definitions.

Concepts and data items A-Z

Concepts and data items A

Concepts and data items B - E

Concepts and data items F - K

Concepts and data items L - N

Concepts and data items O - S

Concepts and data items T - Z

Detailed output information

The detailed output, including the data items contained within each pre-prepared spreadsheet available for download, is shown below.

Labour Force, Australia outputs

Labour Force, Australia, Detailed outputs

Data files

Previous catalogue number

This release previously used catalogue number 6103.0.

Labour Force Survey-PLIDA linked microdata product

Explanatory notes providing information about the Labour Force Survey-PLIDA linked microdata product, and how to use it.

Released
18/03/2025

Introduction

Official employment and unemployment estimates are available from Labour Force, Australia. To complement these official estimates of employment and unemployment, ABS also produces a microdata product (available in ABS DataLab to approved users) which links the Labour Force Survey (LFS) to the Person Level Integrated Data Asset (PLIDA). This linked LFS-PLIDA product, also known as the LFS PLIDA Modular Product (PMP), enables in-depth analysis of LFS data integrated with administrative datasets including health, education, government payments and taxation data. The LFS module in PLIDA contains monthly data from June 2023 onwards.

Given the nature of this linked dataset, it is not possible to replicate official employment and unemployment estimates, so data from the PMP should not be used to produce nor replicate official estimates. Any analysis or conclusions drawn from the linked LFS data should acknowledge the potential and, where possible, actual impact of bias on the interpretation of results. The data available in PLIDA are not market sensitive. 

The monthly Labour Force Survey (LFS) provides information about the labour market activity of Australia's resident civilian population aged 15 years and over. 

The LFS sample is designed and selected primarily to provide accurate estimates of employment and unemployment for the whole of Australia and, secondarily, for each state and territory. The ABS has been conducting the Labour Force Survey since 1960, initially as a quarterly survey. In February 1978, the frequency of the survey was changed from quarterly to monthly. 

Households within selected dwellings are interviewed each month for eight months, with one-eighth of the sample being replaced each month. Information is obtained either by trained interviewers or through self-completion online. More detailed information about the LFS is available on the Labour Force, Australia methodology page of the ABS website.

DataLab researchers may request access to the LFS module commencing January 2026.

Scope and coverage

The LFS surveys approximately 24,000 households each month which is equivalent to around 50,000 individuals. 

The scope of the LFS is the resident civilian population of Australia aged 15 years and over, it excludes:

  • Members of the permanent Australian defence forces
  • Certain diplomatic personnel of overseas governments
  • Overseas residents in Australia
  • Members of non-Australian defence forces (and their dependents) stationed in Australia

The LFS uses administrative data in place of directly-collected responses for certain difficult to enumerate segments of the Australian population, consequently LFS data present in the PLIDA Modular Product also excludes populations living in:

  • very remote areas
  • non-private dwellings (e.g. prisons and care homes).

The LFS applies coverage rules to ensure that each person is associated with only one dwelling, and hence has only one chance of selection. The chance of a person being enumerated at two separate dwellings in the one survey is considered to be negligible. 

The Labour Force Survey (LFS) is designed to survey the same household for eight consecutive months. However, individuals can move in and out of the LFS sample for several reasons, for example if they:

  • do not complete the survey in a given month,
  • are visiting the selected dwelling,
  • move house permanently.

Data items

The LFS module consists of data obtained from the monthly survey. Survey month acts as a structural element. The monthly datasets can be joined to each other, and to the Spine, using linkage keys (see Usage Tips and Duplicate Links).

Individual respondents to the LFS can be observed for up to eight months, making the data suitable for use in analysis of cross sections, pooled cross sections and short panels. Each month, approximately 6,000 individuals are added to the survey’s sample as new rotation groups are enumerated.

On average, each cross section has approximately 48,000 observations. Each cross section is used separately to produce the headline LFS statistics. The unit record weights provided with the LFS module are those used to produce the published LFS statistics. 

The weights in the LFS module do not aggregate to the population, and will not produce results comparable to published estimates due to the exclusion of out-of-coverage populations (i.e. residents of very remote areas and non-private dwellings).

This is further impacted, when interpreting estimates produced from linked records, due to the limitations inherent in linking data. No adjustments are made to the included weights to adjust for sample that cannot be linked. More information on the limitations and consequences of the linkage process is included in Usage Tips and Duplicate Links, Linkage and Bias, and Limitations for Longitudinal Linkage.

For more information about weighting and population benchmarking for the purposes of producing headline estimates, refer to the Labour Force, Australia methodology page.

Estimates from the Labour Force Survey are predominantly based on individual records. However, hierarchical characteristics, such as items related to households and families, are attributed to each person record.

For variable and value descriptions, refer to the LFS PMP Data Item List.

LFS PMP Data Item List

Scheduled updates

When new LFS data becomes available, the LFS module will undergo an incremental update. This occurs monthly, with a new month of data available in PLIDA around one week after the release of Labour Force, Australia, Detailed. Unit weights for records in previous months’ data will be revised each quarter consistent with the population benchmarking process for the LFS.

Safe data treatments

More information about safe data treatments is available within DataLab in the PLIDA Modular Product User Guide. However, the following steps were taken to treat the LFS data.

  • The LFS person identifier is replaced with an anonymised PLIDA person ID SYNTHETIC_AEUID.
  • The LFS household and dwelling identifiers HHID and DWELLID are anonymised.
  • Detailed hours worked data items have been top coded to 99+ hours, this affects:
    • hours actually worked (HRAWMJ2)
    • hours usually worked (HRUWAJ99)
    • hours usually worked in main job (MUSLHRFX99)
    • hours actually worked in all jobs last week (WKDHOUR2)
    • number of hours preferred (underemployed) (PREFHOUR99)

Usage tips and duplicate links

General advice on working with PLIDA modules is available within DataLab in the PLIDA Modular Product User Guide.

ID Variables

The following table lists all ID variables present in the LFS module. These identifiers can be used to join information across monthly tables in the LFS module, and to identify relationships between LFS respondents. SYNTHETIC_AEUID can be used as the linkage key to link LFS to the PLIDA Spine via linkage files available in DataLab.

KeyEntity TypeOther PMP modules that use this key
SYNTHETIC_AEUIDLFS respondent identifierN/A
HHIDHousehold identifierN/A
DWELLIDDwelling identifierN/A

Records not linked to the Spine

When using the LFS module, you may encounter records which do not correspond to a Spine element (SPINE_ID = NULL). This can occur for the following reasons:

  • Incomplete or inaccurate personal information was provided for the LFS respondent. For example, a respondent may decline to provide their first and/or last name, or their date of birth.
  • Inconsistent information between the LFS and the Spine. For example, the respondent may have recently changed residential address.
  • Absence from the Spine. The LFS respondent may not appear in the datasets used to form the Spine.

Duplicate links to the Spine

No statistical linkage process is perfect. In some rare instances, multiple SYNTHETIC_AEUIDs may be linked to a single SPINE_ID. Theoretically, this represents the linkage of multiple different LFS respondents to the same person represented on the Spine. However, in many of these instances a single LFS respondent is erroneously represented by multiple SYNTHETIC_AEUIDs.

In some (but not all) instances, apparent duplicate records are appropriate for use and should not necessarily be excluded from analysis. However, where apparent duplicate records cause inexplicable results, we advise removing all applicable records or using only the earliest instance of a record impacted by duplication (i.e. from the first survey month in which the record appears).

Broadly, the duplicate links can be categorised into two groups based on whether the root cause is on the survey side, or due to the PLIDA linkage method and/or spine creation. 

The majority of the apparent duplicate links are a result of limitations with the LFS survey design, processing or responses. NB: These limitations affect only the quality of the linkage and not the published LFS estimates.

LFS-based duplicates

Duplicates may be observed when analysing a single month’s LFS dataset for the following reasons:

1. Changes in household composition over time.

  • Where an individual is not continuously in sample (i.e. they move into and out of a dwelling, or they change between being a visitor to and resident of a dwelling) they may be assigned a different person number within a household at different points in time. Where that occurs, they will be assigned multiple identifiers (which appear as SYNTHETIC_AEUIDs in PLIDA) over their time in sample.
  • Additionally, a new resident or visitor in the dwelling may be assigned a vacant, but previously assigned, person number. This can result in the re-use of an LFS identifier over a dwelling’s time-in-sample. After the first month-in, the LFS-PLIDA linkage method matches on the LFS identifier (i.e. the method assumes the persistent use of LFS identifiers). Subsequently, this can result in multiple different people being linked to the same Spine record within a given month.

2. Very rarely, errors in the way individual respondents are enumerated in the survey.

  • Where a respondent completes the survey incorrectly it is possible for an individual to be enumerated more than once within the same month. This will appear as multiple SYNTHETIC_AEUIDs being linked to a single Spine entity).

Duplicate links may be observed when analysing multiple months of LFS datasets:

  1. For the same reasons as for a single month. In particular, changes in household composition are more prevalent when analysing across multiple months than for any single month in isolation.
  2. Additionally, apparent duplicate links may occur where the same person has continued to complete the survey after changing address, or an individual has been re-selected in the survey at a later date. 

Where duplicate records are observed in the data for more than 8 months, and for discussion of issues specific to longitudinal analysis, please refer to Limitations for Longitudinal Linkage.

PLIDA-based duplicates

Instances of duplicate links as a result of PLIDA processes are very rare with only a handful of examples across the 90,000+ unique records linked between LFS and the PLIDA Spine (as of December 2025).

Within a given month, they may occur: 

  • Where the linkage method identifies that two individual respondents are the same person. There are legitimate instances where this occurs but there is also error associated with this process, for example, twins living at the same address and who have very similar names may be erroneously linked to the same Spine entity.
  • Due to Spine dataset imperfections, for example, where information from multiple unique people is intertwined in the formation of a single Spine entity resulting in links being found to multiple LFS respondents.

Across different time periods, duplicate links may exist:

  • Where different LFS respondents have similar characteristics and consequently are each linked to the same single Spine entity.

Linkage and bias

Linkage

The LFS data from June 2023 to November 2023 are linked to the Spine using deterministic linkage methods. LFS data from December 2023 onwards are linked to the Spine using probabilistic linkage methods, see Person Linkage Spine. Monthly linkage reports are available within DataLab and provide information on linkage rates and missingness rates for key linkage variables.

Any analysis or conclusions drawn from the linked LFS data should acknowledge the potential and, where possible, actual impact of bias on the interpretation of results.

Bias in linked data

When looking at aggregate statistics derived from linked observations it is important to be aware of bias and to attempt to control for it where possible. Separating bias from standard error is difficult, particularly for estimates from small samples where the variability inherent in the estimate can be large relative to the size of the estimate.

The Labour Household Surveys section of the ABS conducted comparative analysis of the labour market characteristics of the LFS sample pre and post linkage for data collected between June 2023 and June 2025. This analysis may be helpful in understanding some of the limitations of and biases present in the linked data.

For the unemployment rate, there is bias downwards for the linked group. This bias is broadly consistent across the characteristics chosen for analysis.

For the employment to population ratio, in aggregate there is no clear bias for the linked group. However, this is largely due to offsetting differences. For example, most age groups have a higher rate of employment in the linked group than the unlinked group, with a notable exception being the group aged 65 and over. There are also notable differences in the bias by sex, state of usual residence, relationship in the household and country of birth.

For the share of hours worked by people employed full-time, there is some similarity with the employment to population ratio. In aggregate there is no clear bias between the two groups, but this is largely due to offsetting differences present in many of the demographic subgroups, including: sex, country of birth, state of usual residence and age group.

Researchers should exercise caution when interpreting results from the linked LFS data and should not assume the linked data is representative of the full LFS sample. Re-weighting the linked LFS data may increase its representativeness, however, the ABS is not currently offering advice on re-weighting approaches for LFS data.

Unemployment rate

The LFS sample that has been successfully linked to the PLIDA Spine (referred to here as the linked group) is less unemployed than the LFS sample where no link to the PLIDA Spine has been found (referred to as the unlinked group), and is consequently less unemployed than the full LFS sample.

This means that any conclusions drawn from linked data will understate unemployment. In 24 of the 25 months, the unemployment rate from the linked group was lower than the unlinked group, with the difference being statistically significant for 16 of those 24 months.

This difference can be understood, in part, by demographic characteristics such as the age profile of linked respondents, for example: 

The linked group has a lower share of 15-24 year old people than the unlinked group, and young people tend to have a higher rate of unemployment than other ages. Combined, this explains part of the observed difference. The lower linkage rates for young people may be a result of:

  • greater mobility, resulting in geographical missingness/timing inconsistencies
  • generational attitudes to sharing personal information, resulting in missing or inaccurate names, sex or ages and dates of birth
  • delayed enrolment in administrative programs that form the PLIDA Spine.

Other demographic characteristics were also analysed to help understand the lower unemployment rate in the linked group:

  • Sex: The difference in unemployment for women was more prevalent than for men: unlinked women were more unemployed than men relative to their respective linked populations, i.e. there’s a more frequent bias in the unemployment rate for women than there is for men.
  • State/territory: Significant differences between the linked and unlinked group were observed across states and territories. The more populous states showed the greatest number of significant differences, with all significant differences showing the linked group with lower unemployment than the unlinked group.
  • Relationship in household: Household relationships did not tend to exhibit significant differences in the unemployment rate between the linked and unlinked groups, except in the case of more populous relationship types. In particular, those who were a spouse/partner or a child of someone else in the household were less unemployed in the linked group. This was most prevalent for spouses/partners. 
    Conversely, the linked group was more unemployed than the unlinked group for visitors, unrelated individuals and male same-sex partners, but only when the groups were combined across the entire time period (individual periods had sample sizes too small to draw accurate conclusions).
  • The following variables were considered but did not result in significant differences that were consistent across time periods:
    • year of arrival
    • country of birth.
Employment to population ratio

The rate of employment relative to the population, varies between the linked and unlinked group. Of the 25 periods analysed, the linked group had a significantly higher rate of employment than the unlinked group for 9 months and significantly lower for 7 months, with the remaining 9 months not exhibiting significant differences and being split evenly between the two groups. Note, having a lower unemployment rate in a population group does not necessarily result in a higher level of employment. The population also includes people not in the labour force.

Demographic characteristics were analysed to help understand the differences between the linked and unlinked groups. Some characteristics displayed differences between the two groups that were consistent across most time periods, while others varied across time.

Consistent differences:

  • Age: The differences were significant for most age groups for every time period. Instances where there was no statistically significant difference included for the groups:
    • aged 65 or over, which displayed no significant difference between the linked and unlinked groups
    • aged 15-24 and 25-34 which were significant in most, but not all, months.
  • Year of arrival: Overseas born populations in the linked group tended to have a higher rate of employment than those in the unlinked group. This was particularly true for more recent arrivals.

Variable differences:

  • Sex: females in the linked group tended to have a higher rate of employment than females in the unlinked group, whereas males in the linked group tended to have a lower rate of employment.
  • Relationship in household: People without children tended to have a lower rate of employment in the linked group, whereas people with children (and their children or dependents) tended to have a higher rate of employment in the linked group, relative to their respective unlinked cohorts. 
    This result was true for both males and females, but the results were accentuated when cross-classified by sex. In other words, there were a greater number of significant differences for females when the linked group had a higher rate of employment for a particular family relationship, and a greater number of significant differences for males when the linked group had a lower rate of employment.
  • State/territory:
    • Victoria, Queensland and the Northern Territory had significantly higher rates of employment in the linked group than the unlinked group
    • South Australia, Western Australia, Tasmania, and (for the most part) New South Wales, had significantly lower rates of employment in the linked group
  • Country of birth:
    • The cohorts born in Australia and North-West Europe had significantly lower rates of employment in the linked group than the unlinked group
    • The cohorts born in Africa and Asia had significantly higher rates of employment in the linked group than the unlinked group
    • The cohorts born in Southern and Eastern Europe, and the Americas had varying or few significant differences in the rates of employment between the linked and unlinked groups

Share of hours worked by full-time employees

The share of hours worked by full-time employees was very similar in the linked and unlinked groups when considered in aggregate. Only two periods had a significant difference between the two groups (both slightly higher in the linked group). However, examining the differences by various demographics exhibited some stark differences between the linked and unlinked groups. 

These results illustrate that care should be taken when analysing estimates related to hours worked. They also illustrate that results drawn from an aggregate population cannot necessarily be generalised across its component subpopulations.

  • Sex: females in the linked group had a smaller share of hours worked by full-time workers than females in the unlinked group, whereas males in the linked group had a larger share of hours worked by full-time workers.
  • Country of birth: the most consistent differences were evident for the cohort born in Australia offsetting that born in Southern and Central Asia. The Australian-born cohort had a smaller share of hours worked by full-time workers in the linked group than in the unlinked group, whereas the cohort born in Southern and Central Asia had the opposite: a larger share of hours worked by full-time workers in the linked group compared to the unlinked. The cohorts born in South-East Asia and the Americas had similar results to that born in Southern and Central, but the results were not as consistently significant across time.
  • Year of arrival: the results were very similar to those observed for country of birth. The cohort of migrants arriving 1 to 2 years ago had a larger share of hours worked by full-time workers in the linked group compared to the unlinked. This was offset by the opposite relationship between the linked and unlinked group being observed for the Australian-born population, as noted above.
  • State/territory: there were only a small number of statistically significant results when looking at state or territory. For New South Wales, there were 6 months with significant differences between the linked and unlinked groups, with each exhibiting a larger share of hours worked by full-time workers in the linked group. Tasmania exhibited the opposite, with a smaller share of hours worked by full-time workers in the linked group, with significant differences in 5 months.
  • Age: there were significant differences observed by age group, predominantly for the cohorts aged:
    • between 25 and 34 years (larger share of hours worked by full-time workers in the linked group compared to the unlinked group)
    • 65 years or older (smaller share of hours worked by full-time workers in the linked group compared to the unlinked group).
  • Relationship in household: the differences were rarely significant. Where there were significant differences, they were consistent within a cohort. They predominantly related to those who were:
    • living in group households (larger share of hours worked by full-time workers in the linked group compared to the unlinked group)
    • dependent students or those living alone (smaller share of hours worked by full-time workers in the linked group compared to the unlinked group).
Summary Table
Demographic CharacteristicUnemployment RateEmployment to Population RatioShare of hours worked by people employed full-time
OverallLinked group is less unemployed than linked group.Direction of difference between linked and unlinked groups employment is inconsistent across time.No significant differences in aggregate: offsetting differences by characteristic (see below)
SexLinked women less unemployed than unlinked women to a greater extent than linked men are less unemployed than unlinked men.

Linked women more employed than unlinked women*.

Linked men less employed than unlinked men*.

Linked women: lesser share of hours worked by full-time workers.

Linked men: greater share of hours worked by full-time workers.

AgeLinked group is less unemployed: fewer young people in linked group.Linked group is more likely to be employed than the unlinked group for every age except 65+ years old.

Linked cohort aged 65+: lesser share of hours worked by full-time workers.

Linked cohort aged 25-34: greater share of hours worked by full-time workers.

State and territoryLinked group was less unemployed than unlinked irrespective of geography.

Linked Vic, QLD and NT groups were more employed than unlinked groups*.

Linked NSW, SA, WA and Tas groups were less employed than the unlinked group*.

Linked Tasmania cohort: lesser share of hours worked by full-time workers.

Linked NSW cohort: greater share of hours worked by full-time workers.

Country of birthNo significant differencesLinked overseas-born people more likely to be employed than unlinked overseas-born group.

Linked cohort born in Australia: lesser share of hours worked by full-time workers.

Linked cohort born in Southern and Central Asia: larger share of hours worked by full-time workers.

Relationship in householdLinked people with partner/spouse in household less unemployed than unlinked group.

Linked people with children tend to be more employed than unlinked group*.

Linked people with children tend to be less employed than unlinked group*.

Some significant differences for small cohorts: people living group households, dependent students, and people living alone.

* Strength of bias (significance of difference) is variable across time.

Further action

It is clear, from the results outlined above, that there are many statistically significant differences between the linked and unlinked LFS sample. These differences will result in bias when producing estimates from the linked sample. 

Researchers may wish to consider re-weighting the linked records to account for bias. However, the ABS is not currently offering advice on re-weighting approaches for LFS data.

As noted previously, any analysis or conclusions drawn from the linked LFS data should acknowledge the potential and, where possible, actual impact of bias on the interpretation of results.

Limitations for longitudinal linkage

For general information about the longitudinal aspect of Labour Force Survey data, and some of its limitations, please refer to Microdata: Longitudinal Labour Force, Australia.

Along with the duplicates and bias elements, there are additional considerations which apply when analysing the linked LFS data longitudinally. In the linked LFS data there are some instances where individual people appear to be linked to the Spine for more than 8 survey months. In most instances this is a result of people with similar linkage characteristics (name, age, sex), who have been selected in the LFS sample within a similar geographical area as those in previously selected dwellings, being linked to a single Spine entity. These are different people being linked to the same Spine entity, at different points in time, due to the quality of their linkage information combined with the linking method. 

There may also be rare instances of the same person completing the LFS over more than 8 months, as a result of respondents changing residence and their new dwelling being selected in a new or existing rotation group. 

Contact us

Please get in contact with us via labour.statistics@abs.gov.au if you have any queries or feedback relating to this guide.