Labour Force, Australia, Detailed

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A monthly range of excel spreadsheets and excel pivot tables covering all the major items of the Labour Force Survey in time series format

Reference period

February 2020

Released

26/03/2020

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Main features

Data from the monthly Labour Force Survey are released in two stages. The Labour Force, Australia, Detailed - Electronic Delivery (cat. no. 6291.0.55.001) and Labour Force, Australia, Detailed, Quarterly (cat. no. 6291.0.55.003) are part of the second release, and include detailed data not contained in the Labour Force, Australia (cat. no. 6202.0) product set, which is released one week earlier.

The Labour Force, Australia, Detailed - Electronic Delivery (cat. no. 6291.0.55.001) is released monthly. Labour Force, Australia, Detailed, Quarterly (cat. no. 6291.0.55.003) includes data only collected in February, May, August and November (including industry and occupation).

Since these products are based on the same data as the Labour Force, Australia (cat. no. 6202.0) publication, the Labour Force, Australia - Explanatory Notes (cat. no. 6202.0) are relevant to both releases.

For advice on reporting data from our regional labour force products (Pivot tables RM1, RM3 and Tables 16, 16B, 16C), please refer to: Advice on reporting regional labour force data.

Managing the impact of Coronavirus (COVID-19) on Labour Force statistics

The ABS is currently monitoring for potential impacts from the Coronavirus (COVID-19) on Labour Force statistics and will continue to do so for the period of the pandemic.

Quality assurance of data for February did not identify any notable impact to headline statistics. It is important to note that the reference weeks for February fell in the first half of the month, at a point where there was only a relatively low number of confirmed COVID-19 cases within Australia and before it was declared a global pandemic.

As with other major disruption to the economy, early impacts are usually most evident in the Monthly hours worked in all jobs series (Spreadsheet 19) in Labour Force, Australia (cat. no. 6202.0). Additional information on hours worked data, including some analysis and advice in using these data, can be found in this month's spotlight article. This is part of a suite of additional information that was announced on 16 March 2020 to help measure the economic impact of COVID-19.

In addition to providing further analysis of Labour Force statistics, the ABS will be actively managing the potential risks from COVID-19 to its survey operations. The ABS suspended face-to-face interviews in the final week of the March survey, with all remaining interviews being conducted by telephone. Each month, only around 10% of households respond through face-to-face interviews, with around 90% responding through telephone interviews or online.

Respondents to the Labour Force Survey may note further changes in how the ABS contacts them over the coming months. The ABS acknowledges the tremendous support that the Australian community is continuing to provide in supporting high quality and critically important Labour Force statistics.

On 18 March, the ABS also released some interactive employment maps based on the most recent data from Jobs in Australia (cat. no 6160.0), which is based on summary administrative data, to support a regional assessment of potential impacts of COVID-19 on employment.

Managing the impact of bushfires on Labour Force statistics

Bushfires resulted in disruption to ABS data collection operations in some regions in New South Wales and Victoria during January 2020. A few areas in New South Wales, particularly within the ‘Capital Region’, were also unable to be surveyed in February 2020. These areas amounted to around half the sample which was unable to be surveyed in January 2020 in the state.

As with January data, the ABS confirmed that the disruption from the bushfires did not have a notable impact on key headline statistics and that no additional statistical interventions were required.

Region level data for the New South Wales regions of the ‘Capital Region’, ‘Southern Highlands and Shoalhaven’ and ‘Mid North Coast’ for January 2020 will not be published in the February 2020 Labour Force, Australia, Detailed - Electronic Delivery (cat. no. 6291.0.55.001) on 26 March. February 2020 estimates for the ‘Capital Region’ will also not be published.

The ABS expects to resume publishing these estimates with the release of March 2020 data (in April). January 2020 data for 'Latrobe - Gippsland', in Victoria, will be available with the February release.

Managing the impact of the Townsville floods in February 2019

Flooding in Townsville in February 2019 resulted in a major disruption to the operation of the Labour Force Survey. Given the severity of these disruptions, which affected almost the entire region, and to ensure that this loss of sample did not affect data for Australia and Queensland, the ABS imputed sample for Townsville for February 2019. The imputation drew upon previous information that had recently been collected from people in Townsville.

The ABS re-assessed this imputation and has not revised the imputed data. However, users of the Townsville data, and totals for ‘Rest of Queensland’ data, should still exercise some extra caution when looking at yearly and monthly movements in February 2020.

Changes in future issues - new ABS website and improvements to detailed labour force data in 2020

The upcoming move to new ABS website provides an opportunity to simplify the way Labour Force information is released - so that it is more integrated and easier to find and use. The approach to releasing detailed Labour Force data will be changing, with all the detailed monthly and quarterly spreadsheets and datacubes that are currently released in two separate products (6291.0.55.001 and 6291.0.55.003) planned to be included in a single release (within the monthly 6291.0.55.001 product). The way the spreadsheets and datacubes are presented within this combined release will also be improved on the new website, with data grouped together thematically so that it is easier to find the data you're looking for.

This expanded monthly detailed release will always include the latest monthly and quarterly data, noting that for the 'non-quarter' months, the quarterly spreadsheets and datacubes will have a different reference period. For example, the May issue will contain May monthly and May quarterly data, while the June issue will contain June monthly and May quarterly data.

This change will occur with the release of detailed March 2020 data on 23 April, in Labour Force, Australia, Detailed - Electronic Delivery (cat. no. 6291.0.55.001). This release will be expanded to also include the detailed quarterly time series spreadsheets and datacubes currently published in Labour Force, Australia, Detailed, Quarterly (cat. no. 6291.0.55.003) (for the February 2020 reference period). The quarterly publication Labour Force, Australia, Detailed, Quarterly (cat. no. 6291.0.55.003) will still be published in respect of May 2020, but is likely to be ceased after that, in conjunction with the move to the new website. While the location of the quarterly time series spreadsheets and datacubes will change, the file names, table names and time series IDs will all remain the same.

Additionally, after a period of feedback from stakeholders, the ABS will be ceasing publishing data relating to Sector of main job (public / private) due to data quality concerns. This affects Tables 26a, 26b, 27 and 28, currently published in Labour Force, Australia, detailed, Quarterly (cat. no. 6291.0.55.003). This change is expected to also coincide with the move to the new ABS website in 2020. High quality sector information will continue to be available quarterly within the Labour Accounts (cat. no. 6150.0.55.003) and annually from Employment and Earnings, Public Sector, Australia (cat. no. 6248.0.55.002).

If you have any questions or feedback on these changes, please email us at labour.statistics@abs.gov.au.

Quarterly population benchmarking

The ABS has revised the original Labour Force series for the previous 19 months to reflect the latest available preliminary and final estimates of Estimated Resident Population. This quarterly process ensures that the Labour Force series promptly reflects any change in population trends and minimises the size of revisions that can occur when the series are rebenchmarked following each Census of Population and Housing. It is expected that, on most occasions, the quarterly revisions will not be significant.

For further information about rebenchmarking, refer to the Rebenchmarking Labour Force Estimates article published in the February 2015 issue of Labour Force, Australia (cat. no. 6202.0).

Data downloads - time series spreadsheets

Table 01. Labour force status by age, social marital status and sex

Data files

Download xls [72.54 MB]

I-Note

Table 02. Labour force status by state, territory, greater capital city and rest of state (ASGS) and sex

Data files

Data files

Download xls [40.85 MB]

I-Note

Queensland Flood 2011

Due to the flooding in Queensland in January 2011, the Relative Standard Errors (RSE) for January 2011 vary across labour market regions and are higher than normal in some. The RSEs for the Darling Downs-South West and Ipswich City labour market regions are approximately 50% higher, while the RSEs for the Brisbane City Inner Ring labour market region increased by approximately 25%. The Brisbane City Outer Ring, West Moreton and Mackay-Fitzroy-Central West labour market regions have RSEs approximately 10% higher. All other labour market regions have minimal differences. From February 2011, the data returns to normal. Refer to the article Impact of the floods on the Labour Force Survey in the January 2011 issue of Labour Force, Australia, (cat. no. 6202.0) for more information.

Note, the labour market regions above are referenced under the old Australian Standard Geographical Classification (ASGC). The data in this data cube is published under the Australian Statistical Geography Standard (ASGS).

Labour force sample post 2011 Census

The labour force sample selected after the 2011 Census was phased-in over four months from May to August 2013. See the article titled "New Labour Force Sample Design" in the May 2013 issue of Labour Force, Australia (cat. no. 6202.0) for more information. During phase in of the new sample, standard errors associated with key labour force data are expected to increase by approximately 10% at a national level, however increased standard errors and variability in the estimates may be more evident in detailed regional data during this time

Townsville Flood 2019 (Spreadsheets Table 02, Table 16, Table 16b, Table 16c)

During 2018, the ABS estimated that employed people in Townsville accounted for around 1 per cent of all employed people in Australia, around 4 per cent of employed people in Queensland, and around 9 per cent of employed people in the regions in Queensland outside of Brisbane.

Data files

Data files

Download xlsx [1.14 MB]

All monthly Pivot Tables

Data files

Download zip [252.61 MB]

Spotlight: Insights into detailed Labour Force hours worked data

This spotlight highlights detailed Labour Force data that will be useful in analysing the impact of the coronavirus (COVID-19) on the Australia labour market in the coming months.

Assessing February 2020 for an early impact from COVID-19

Analysis of February 2020 Labour Force data did not identify any notable impact from COVID-19. It is important to note that the reference weeks for the monthly Labour Force Survey fall in the first half of each month. During the first two weeks of February 2020, there was only a relatively low number of confirmed COVID-19 cases within Australia, and it was yet to be declared a global pandemic.

Changes in hours worked often lead changes in employment

As with other major disruption to the economy, early labour market impacts are usually most evident in the monthly hours worked in all jobs series, which can be found in Table 19 of Labour Force, Australia (cat. no. 6202.0). Additional information, such as the reason people worked fewer hours than usual, and the number of people working in different categories of hours, are available within the release of detailed Labour Force data, a week later.

Hours worked can change more quickly than employment, given the variability in individual circumstances for people and businesses from one week to the next. During an economic downturn, reducing hours is often an early response taken by businesses, often with the view to avoiding people losing their jobs.

Given the variety of ways in which people work and the conditions they are employed under, the ABS uses a longstanding, comprehensive and international best practice framework for determining whether someone is employed. This framework also effectively covers employment arrangements that are more common in an economic downturn. Based on this framework some of the common examples of how people could be categorised during the COVID-19 period include:

If a person takes any kind of paid leave while not working, they will be classified as employed.
If a person is away from a job, business or farm for four weeks or less without pay for any reason, and believes they still have a job to go back to, they will be considered to be employed.
A person away from a job, business or farm for one month or more will only be considered employed if they were paid for some part of the previous 4 weeks.
If a person believes they no longer have a job, business or farm to be absent from, they will be asked additional questions to determine whether they are unemployed or not in the labour force. That is, the person must be actively looking for work, and available to start work. Those people that do not meet this criteria will be classified as Not in the Labour Force.

It is for this reason that hours worked analysis can provide an early indication of aggregate labour market impacts from any major disruption to the economy, ahead of changes in other Labour Force indicators.

Trend and seasonally adjusted hours worked data: an aggregate view

Measuring changes in the number of hours worked is critical to an effective understanding of how the labour market is changing over time. However, hours worked data reflect a high degree of systematic seasonal factors and effects, many of which are closely related to public holidays, school holidays and other major events in the year (eg. Christmas).

For this reason, the ABS produces trend and seasonally adjusted series to provide insights into how aggregate hours worked within the labour market are changing over time. These series adjust for the seasonality in the underlying original data, and also convert the weekly hours measures into hours representing the entire month.

This enables analysis of total hours worked in the month alongside employment. For example, the three charts below show annual growth rates in trend employment and trend monthly hours worked for all people, males and females.

These charts show that hours worked growth rate for females has remained relatively strong over the past three years, usually around or above the growth rate for employment. In contrast, the growth rate for hours worked for males has been slowing more quickly than employment for the past two years, with trend hours worked now declining.

Chart 1 - Year on year trend growth in total employed and hours worked
	Monthly hours worked in all jobs ('000 Hours) (%)	Employed total ('000) (%)
Feb-17	1.1	1.2
Mar-17	1.3	1.4
Apr-17	1.6	1.7
May-17	2.0	1.9
Jun-17	2.3	2.2
Jul-17	2.6	2.5
Aug-17	2.8	2.7
Sep-17	2.9	2.9
Oct-17	3.1	3.1
Nov-17	3.1	3.3
Dec-17	3.1	3.4
Jan-18	3.0	3.4
Feb-18	2.9	3.3
Mar-18	2.8	3.1
Apr-18	2.6	2.9
May-18	2.5	2.7
Jun-18	2.4	2.5
Jul-18	2.3	2.5
Aug-18	2.2	2.4
Sep-18	2.1	2.4
Oct-18	2.0	2.4
Nov-18	2.0	2.3
Dec-18	2.1	2.2
Jan-19	2.3	2.3
Feb-19	2.5	2.3
Mar-19	2.5	2.4
Apr-19	2.4	2.5
May-19	2.3	2.6
Jun-19	2.1	2.6
Jul-19	1.9	2.5
Aug-19	1.8	2.4
Sep-19	1.8	2.3
Oct-19	1.7	2.2
Nov-19	1.5	2.1
Dec-19	1.3	2.0
Jan-20	1.0	1.9
Feb-20	0.8	1.9

Chart 1 - Year on year trend growth in total employed and hours worked

["","Monthly hours worked in all jobs ('000 Hours)","Employed total ('000)"]

[["Feb-17","Mar-17","Apr-17","May-17","Jun-17","Jul-17","Aug-17","Sep-17","Oct-17","Nov-17","Dec-17","Jan-18","Feb-18","Mar-18","Apr-18","May-18","Jun-18","Jul-18","Aug-18","Sep-18","Oct-18","Nov-18","Dec-18","Jan-19","Feb-19","Mar-19","Apr-19","May-19","Jun-19","Jul-19","Aug-19","Sep-19","Oct-19","Nov-19","Dec-19","Jan-20","Feb-20"],[[1.0535149869564],[1.3133239696400001],[1.6290445090382],[1.9690527298983],[2.2931450707316001],[2.5704582333647998],[2.7782319677288001],[2.9391709134838999],[3.0504246158265],[3.1067786155907999],[3.1071655983564002],[3.0418070814766001],[2.9178842421164002],[2.7755866660070998],[2.6343506885371002],[2.5114172711074998],[2.4068882655850001],[2.2853235804228],[2.1665338271132999],[2.0575675591553],[1.9909404274300999],[2.0104012444267001],[2.1343260657023002],[2.3199700449277998],[2.4728395787688],[2.5127185630592002],[2.4357318629660001],[2.2684520356619999],[2.0632077787659999],[1.9115330393579],[1.8316686007819001],[1.7847758659434001],[1.6997522074546001],[1.5212466471159001],[1.2710974184892001],[1.0050977999696999],[0.7752971995322]],[[1.1845803595341],[1.4090361942380001],[1.6751035658755999],[1.9475129782603999],[2.2104398544506001],[2.4532963196835],[2.6773831659359],[2.9004378478244002],[3.1140874558452998],[3.2966234068642],[3.4142783289555001],[3.4222402669346002],[3.3159745787617001],[3.1280069998234001],[2.8982376972724002],[2.6907396246232],[2.5492707904102998],[2.470246897585],[2.431897041939],[2.3991615603177001],[2.3515669614424999],[2.2895108245546001],[2.2471362573129001],[2.2596915152799002],[2.3332734811725002],[2.4392431576467999],[2.5466378634444],[2.6166367608195999],[2.6148688313631001],[2.5457903219389002],[2.4271988810591001],[2.2862107757700998],[2.1584175744251999],[2.065620558594],[1.9991329593538001],[1.9415768955813],[1.8898367880246001]]]

[]

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Chart 2 - Year on year trend growth in male employed and hours worked
	Monthly hours worked in all jobs ('000 Hours) (%)	Employed total ('000) (%)
Feb-17	1.0	1.0
Mar-17	1.1	1.2
Apr-17	1.4	1.5
May-17	1.6	1.7
Jun-17	1.9	1.9
Jul-17	2.2	2.1
Aug-17	2.4	2.3
Sep-17	2.5	2.4
Oct-17	2.6	2.5
Nov-17	2.5	2.7
Dec-17	2.5	2.7
Jan-18	2.4	2.7
Feb-18	2.2	2.6
Mar-18	2.1	2.4
Apr-18	2.0	2.2
May-18	1.9	2.1
Jun-18	1.7	2.0
Jul-18	1.6	2.1
Aug-18	1.5	2.2
Sep-18	1.3	2.2
Oct-18	1.2	2.3
Nov-18	1.2	2.2
Dec-18	1.4	2.2
Jan-19	1.6	2.1
Feb-19	1.8	2.2
Mar-19	1.8	2.3
Apr-19	1.7	2.5
May-19	1.5	2.5
Jun-19	1.2	2.4
Jul-19	1.0	2.2
Aug-19	0.9	1.9
Sep-19	0.8	1.7
Oct-19	0.8	1.4
Nov-19	0.6	1.3
Dec-19	0.3	1.3
Jan-20	0.0	1.2
Feb-20	-0.2	1.2

Chart 2 - Year on year trend growth in male employed and hours worked

["","Monthly hours worked in all jobs ('000 Hours)","Employed total ('000)"]

[["Feb-17","Mar-17","Apr-17","May-17","Jun-17","Jul-17","Aug-17","Sep-17","Oct-17","Nov-17","Dec-17","Jan-18","Feb-18","Mar-18","Apr-18","May-18","Jun-18","Jul-18","Aug-18","Sep-18","Oct-18","Nov-18","Dec-18","Jan-19","Feb-19","Mar-19","Apr-19","May-19","Jun-19","Jul-19","Aug-19","Sep-19","Oct-19","Nov-19","Dec-19","Jan-20","Feb-20"],[[0.96125425667700004],[1.1356881050256999],[1.3565073407596],[1.6338381934660999],[1.9415913899999999],[2.2234914607016001],[2.4290591692216998],[2.5377671998039002],[2.5625127563543999],[2.5338035027786998],[2.4679004076590001],[2.3549480237878999],[2.2079916593217002],[2.0770381097986998],[1.9686209263698999],[1.8591345820653999],[1.7428362846417],[1.5976238856708],[1.4591801051233999],[1.3273380446954],[1.2401147660933001],[1.2436323776541001],[1.3762794312385001],[1.5958623831437],[1.7782617070529001],[1.814992723799],[1.7041769553206001],[1.4948170960039],[1.2398536135788001],[1.0253879344899],[0.89166380344090002],[0.82965814315060005],[0.7577691989159],[0.58545230955670002],[0.31983776396169999],[0.0224972186561],[-0.2341772794775]],[[0.98021967261830001],[1.2164238533075],[1.4867631100326999],[1.7327513677677],[1.9444833256591001],[2.1134472542871001],[2.2543641236903],[2.3974943298429001],[2.5416495623856998],[2.6712897634338],[2.7457307168442999],[2.7196640257903],[2.5835709705283998],[2.3856380591025999],[2.1908097499618],[2.0684129285372999],[2.0440566469883001],[2.0913340551675001],[2.1771987061507998],[2.2403644375919001],[2.2528304937604],[2.2064476394116999],[2.1506744468047998],[2.1484372580194],[2.2253197697104001],[2.3485641791315999],[2.4541783329392],[2.4803120042794],[2.3873793337039002],[2.1948776217165],[1.9355161921157],[1.6657111578382],[1.4454840211210001],[1.3142541166752],[1.2559205784262],[1.2112025399170001],[1.1536782528077001]]]

[]

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Chart 3 - Year on year trend growth in female employed and hours worked
	Monthly hours worked in all jobs ('000 Hours) (%)	Employed total ('000) (%)
Feb-17	1.2	1.4
Mar-17	1.6	1.6
Apr-17	2.0	1.9
May-17	2.5	2.2
Jun-17	2.8	2.5
Jul-17	3.1	2.8
Aug-17	3.3	3.2
Sep-17	3.5	3.5
Oct-17	3.8	3.8
Nov-17	4.0	4.0
Dec-17	4.1	4.2
Jan-18	4.1	4.2
Feb-18	4.0	4.2
Mar-18	3.8	4.0
Apr-18	3.6	3.7
May-18	3.5	3.4
Jun-18	3.4	3.1
Jul-18	3.3	2.9
Aug-18	3.2	2.7
Sep-18	3.1	2.6
Oct-18	3.1	2.5
Nov-18	3.1	2.4
Dec-18	3.3	2.4
Jan-19	3.4	2.4
Feb-19	3.5	2.5
Mar-19	3.5	2.5
Apr-19	3.5	2.7
May-19	3.4	2.8
Jun-19	3.3	2.9
Jul-19	3.2	2.9
Aug-19	3.2	3.0
Sep-19	3.2	3.0
Oct-19	3.1	3.0
Nov-19	2.9	2.9
Dec-19	2.7	2.8
Jan-20	2.4	2.8
Feb-20	2.2	2.7

Chart 3 - Year on year trend growth in female employed and hours worked

["","Monthly hours worked in all jobs ('000 Hours)","Employed total ('000)"]

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[]

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Source: 6202.0 Tables 1 and 19

Original hours worked data: a compositional view

Released in the week following the headline Labour Force aggregate measures, the detailed monthly and quarterly Labour Force releases (6291.0.55.001 and 6291.0.55.003) contain additional hours worked data, in original terms. Care should be taken in interpreting month-to-month changes in the detailed original data, given the systematic seasonality in hours worked data. Detailed data for specific sub-populations will also be inherently more volatile than higher level aggregates, and the ABS generally recommends using smoothing techniques when using very detailed data.

Much of the analysis presented in the remainder of this spotlight focuses on February data, and how February 2020 compares with February data from the 2015-2019 period. The year-on-year comparison approach helps to control for the effects of seasonality in the original data.

Every month the ABS releases information on the number of people working in different groups of hours worked, by various characteristics. Table 1 below shows the distribution of employed males and females across the hours worked categories, over the past 5 years. The data shows that, over the past 5 years:

the proportion of males working very long hours (60 hours or more) consistently fell, while the proportion working part time hours (under 35 hours) rose.
the proportion of females working 35-44 hours per week increased, while those working longer full time hours (45 hours or more) fell.

It is important to note that this underlying pattern largely continued from February 2019 to February 2020, with the weakness in male hours worked also evident in the aggregate measures.

Table 1 - Proportion of hours worked by male and females

Year	Males, hours worked in all jobs, Original					Females, hours worked in all jobs, Original
Year	1-19 hours	20-34 hours	35-44 hours	45-59 hours	60+ hours	1-19 hours	20-34 hours	35-44 hours	45-59 hours	60+ hours
Feb 2015	10.0%	13.5%	43.0%	22.6%	11.0%	21.6%	29.2%	35.4%	10.5%	3.3%
Feb 2016	9.2%	14.1%	43.5%	22.2%	10.9%	21.1%	29.1%	36.3%	10.1%	3.3%
Feb 2017	9.6%	14.5%	44.1%	21.7%	10.1%	21.7%	29.7%	34.9%	10.4%	3.3%
Feb 2018	9.4%	14.8%	43.7%	21.6%	10.5%	20.7%	29.7%	36.3%	10.3%	2.9%
Feb 2019	9.7%	14.4%	44.5%	21.9%	9.6%	19.8%	30.5%	37.0%	9.8%	2.9%
Feb 2020	10.3%	15.8%	44.3%	20.8%	8.8%	20.5%	29.7%	37.0%	9.9%	2.9%

Information is also available on the number of people working less hours than usual, which may provide an early indication that hours declined due to COVID-19. Chart 4 presents this information as an original data time series for the past 5 years, and shows that there was not a notable change in February 2020.

Chart 4 - In original terms, total employed who worked less than usual hours
	Total employed who worked less than usual hours ('000)
Feb-2015	2,004.9
Mar-2015	2,441.4
Apr-2015	5,568.0
May-2015	1,970.6
Jun-2015	3,189.7
Jul-2015	2,687.4
Aug-2015	2,092.0
Sep-2015	2,018.8
Oct-2015	3,008.6
Nov-2015	2,507.2
Dec-2015	2,070.1
Jan-2016	4,161.8
Feb-2016	1,944.7
Mar-2016	2,157.3
Apr-2016	2,349.3
May-2016	2,255.1
Jun-2016	2,275.4
Jul-2016	2,706.3
Aug-2016	2,128.7
Sep-2016	2,084.9
Oct-2016	3,029.8
Nov-2016	2,403.4
Dec-2016	1,985.1
Jan-2017	5,704.3
Feb-2017	2,114.2
Mar-2017	2,128.4
Apr-2017	4,077.1
May-2017	2,226.9
Jun-2017	2,941.9
Jul-2017	2,862.1
Aug-2017	2,192.9
Sep-2017	2,189.1
Oct-2017	3,400.5
Nov-2017	2,401.8
Dec-2017	2,077.1
Jan-2018	6,325.9
Feb-2018	2,117.5
Mar-2018	2,645.1
Apr-2018	4,371.4
May-2018	2,308.6
Jun-2018	3,123.5
Jul-2018	2,749.4
Aug-2018	2,160.9
Sep-2018	2,208.5
Oct-2018	3,628.0
Nov-2018	2,517.7
Dec-2018	2,103.4
Jan-2019	3,700.2
Feb-2019	2,105.4
Mar-2019	2,613.3
Apr-2019	2,514.4
May-2019	2,429.1
Jun-2019	3,228.5
Jul-2019	2,911.4
Aug-2019	2,381.5
Sep-2019	2,292.7
Oct-2019	3,695.3
Nov-2019	2,621.0
Dec-2019	2,150.9
Jan-2020	4,081.0
Feb-2020	2,339.2

Chart 4 - In original terms, total employed who worked less than usual hours

["","Total employed who worked less than usual hours"]

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In the Labour Force Survey, when someone responds that they worked less than their usual hours, they are asked for the reason they worked fewer hours. Table 2 below shows that the most common reason people worked fewer hours in February 2020 was because they were taking annual leave, holidays, flextime or long service leave. 15% of people were working fewer hours than usual due to illness, injury or sick leave, while 17% of people cited standard work arrangements (including shift work) as the reason they were working fewer hours. This is similar to proportions in February 2019.

Table 2 - Percent of employed who worked fewer hours than usual, February 2020

Reason worked fewer hours than usual	Percentage of Total
Annual leave, holidays, flextime, or long service leave	28%
Standard work arrangements or shift work	17%
Own illness or injury or sick leave	15%
No work, not enough work available, or stood down	14%
Personal reasons, study, caring for sick or injured family	10%
Worked fewer hours than usual for other reasons	6%
Bad weather or plant breakdown	5%
Maternity, paternity or parental leave	4%
Began, left or lost a job during the week	2%

People affected by the impacts of COVID-19 may fall into the following categories in the coming months:

Annual leave/holidays/flextime/long service leave (eg. if a person was self-isolating and taken paid leave);
Own illness or injury/sick leave (eg. if a person had cold/flu/COVID-19 symptoms or a confirmed diagnosis and had taken sick leave);
Personal reasons/study/caring for sick/injured family (eg. if a person was caring for a family member with COVID-19, or had self-isolated without accessing paid leave);
No work/not enough work available, or stood down (eg. if a person had been told not to attend work and they were not receiving any paid leave); and
Began, left or lost a job during the week (eg, if a person had changed jobs but had less hours, no longer had a job with an employer, or their business closed down)
Worked fewer hours than usual for other reasons (that is, a person may have felt that none of the other categories described their circumstances, and chose to select 'other').

When analysing these categories for potential COVID-19 impacts in February 2020, no discernible impact was detected. Table 3 below shows the relative monthly movements between January and February for the past six years, for three of these categories. The relative movements in February 2020 were comparable to those seen in February 2019.

Table 3 - Reasons people worked less than usual hours - monthly movements

Year	No work, not enough work available, or stood down (%)	Own illness or injury or sick leave (%)	Personal reasons, study, caring for sick or injured family (%)
Feb 2015	-6.2	47.1	48.9
Feb 2016	-10.9	45.0	69.1
Feb 2017	9.7	65.8	96.0
Feb 2018	10.8	95.8	108.7
Feb 2019	-11.8	34.2	54.9
Feb 2020	-14.0	31.9	50.3

Looking at the time series in original terms can provide additional insights into patterns in the labour market over time. For example, Chart 5 shows distinct seasonal patterns related to the cold and flu seasons in Australia in the winter months. If people begin to take sick leave due to COVID-19, then there may be an increase in the number of people in this category and a more pronounced seasonal pattern.

Chart 5 - Employed who worked less than usual hours - own illness or injury or sick leave
	Own illness or injury or sick leave ('000)
Feb-2015	333.1
Mar-2015	331.0
Apr-2015	235.6
May-2015	376.2
Jun-2015	375.1
Jul-2015	371.6
Aug-2015	450.0
Sep-2015	430.4
Oct-2015	287.9
Nov-2015	341.2
Dec-2015	301.1
Jan-2016	230.8
Feb-2016	322.3
Mar-2016	320.8
Apr-2016	347.9
May-2016	373.2
Jun-2016	408.1
Jul-2016	358.3
Aug-2016	426.1
Sep-2016	444.7
Oct-2016	312.6
Nov-2016	329.8
Dec-2016	333.4
Jan-2017	197.9
Feb-2017	308.3
Mar-2017	342.1
Apr-2017	273.6
May-2017	345.8
Jun-2017	371.3
Jul-2017	385.9
Aug-2017	501.4
Sep-2017	487.7
Oct-2017	342.4
Nov-2017	354.8
Dec-2017	346.7
Jan-2018	192.3
Feb-2018	361.0
Mar-2018	370.2
Apr-2018	290.7
May-2018	379.9
Jun-2018	391.0
Jul-2018	379.6
Aug-2018	449.3
Sep-2018	433.9
Oct-2018	308.8
Nov-2018	349.2
Dec-2018	353.8
Jan-2019	268.3
Feb-2019	359.0
Mar-2019	358.4
Apr-2019	382.0
May-2019	390.2
Jun-2019	448.9
Jul-2019	449.2
Aug-2019	471.6
Sep-2019	446.9
Oct-2019	330.3
Nov-2019	375.6
Dec-2019	385.1
Jan-2020	284.3
Feb-2020	380.9

Chart 5 - Employed who worked less than usual hours - own illness or injury or sick leave

["","Own illness or injury or sick leave"]

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Quarterly hours worked information

The quarterly detailed labour Force release (cat. no. 6291.0.55.003) contains hours worked information by industry, occupation, and range of other characteristics.

Analysis of hours worked data by industry did not identify any unusual movements in February 2020 that would suggest an early indication of COVID-19 impacts.

Chart 6 below shows the hours worked for three industries which may potentially be impacted early by COVID-19. For each of these three industries, the hours worked series largely reflects existing patterns. This was also true for all industries in February 2020.

Chart 6 - Number of hours actually worked in all jobs, original
	Retail Trade (hours)	Accommodation and Food Services (hours)	Transport, Postal and Warehousing (hours)
Feb-1991	27,316.0	14,721.3	16,290.6
May-1991	27,083.9	14,772.5	16,478.0
Aug-1991	26,526.4	14,532.5	16,108.6
Nov-1991	26,798.2	15,298.8	15,977.7
Feb-1992	27,167.7	15,143.8	15,381.1
May-1992	27,434.2	15,319.8	15,659.5
Aug-1992	26,460.6	14,803.2	15,093.4
Nov-1992	27,070.2	14,461.9	14,777.6
Feb-1993	26,866.1	14,342.7	15,217.3
May-1993	28,070.2	15,112.1	15,279.2
Aug-1993	26,381.1	14,283.9	15,018.4
Nov-1993	27,753.0	15,071.7	15,722.5
Feb-1994	27,245.7	15,585.3	15,847.0
May-1994	28,009.7	15,625.2	16,075.6
Aug-1994	27,774.5	16,118.8	15,602.8
Nov-1994	28,795.8	16,737.2	16,102.8
Feb-1995	28,684.2	17,302.0	16,524.6
May-1995	28,222.8	17,705.8	16,342.4
Aug-1995	27,740.6	17,305.7	15,944.3
Nov-1995	28,086.8	17,281.8	16,464.9
Feb-1996	29,509.1	16,602.1	17,070.0
May-1996	29,325.6	17,148.2	17,155.8
Aug-1996	28,196.6	17,264.8	16,709.0
Nov-1996	28,972.1	17,613.0	16,728.8
Feb-1997	28,633.5	17,858.6	16,886.6
May-1997	27,839.4	17,618.2	16,671.2
Aug-1997	26,972.4	17,482.2	17,062.1
Nov-1997	29,399.8	17,448.5	16,997.9
Feb-1998	29,496.9	17,365.9	16,911.8
May-1998	28,379.3	17,920.7	16,961.2
Aug-1998	28,509.8	17,623.4	16,474.4
Nov-1998	29,662.9	17,892.5	17,633.7
Feb-1999	30,250.0	17,908.1	17,887.5
May-1999	29,519.3	17,746.8	16,926.8
Aug-1999	30,464.7	17,627.1	17,044.7
Nov-1999	30,614.8	19,167.9	17,231.8
Feb-2000	31,324.4	18,838.1	17,160.8
May-2000	29,257.8	19,019.9	18,100.2
Aug-2000	29,633.6	19,208.3	18,416.2
Nov-2000	30,665.2	20,188.0	18,410.9
Feb-2001	30,435.9	19,533.2	18,333.9
May-2001	30,409.0	19,051.5	18,290.6
Aug-2001	30,062.1	18,609.1	17,590.4
Nov-2001	31,240.3	18,730.4	17,867.7
Feb-2002	31,295.4	19,587.7	17,341.3
May-2002	31,067.0	19,734.0	17,637.7
Aug-2002	31,255.2	18,221.5	17,222.5
Nov-2002	31,955.0	19,493.0	17,615.2
Feb-2003	33,602.4	18,814.6	17,623.0
May-2003	33,283.5	18,959.0	17,901.6
Aug-2003	31,842.0	18,896.5	18,010.0
Nov-2003	32,367.5	20,131.4	18,884.4
Feb-2004	32,699.4	19,861.6	18,338.8
May-2004	32,177.2	19,006.3	18,411.7
Aug-2004	32,477.2	18,710.8	18,906.7
Nov-2004	34,119.3	20,325.0	18,974.8
Feb-2005	34,767.8	20,253.5	18,981.7
May-2005	35,338.2	20,735.8	19,283.2
Aug-2005	34,673.9	20,052.8	19,392.4
Nov-2005	34,788.1	20,226.3	18,817.2
Feb-2006	34,505.3	19,377.9	18,590.5
May-2006	34,130.8	19,201.4	19,598.9
Aug-2006	35,222.6	18,575.9	19,143.3
Nov-2006	35,232.7	18,866.5	19,086.4
Feb-2007	36,120.7	20,364.3	19,561.2
May-2007	34,947.9	19,878.6	20,352.8
Aug-2007	35,720.3	20,688.7	21,097.9
Nov-2007	37,324.3	19,831.4	21,141.0
Feb-2008	37,190.4	20,416.4	20,403.9
May-2008	36,356.2	19,970.0	21,539.7
Aug-2008	35,278.6	20,396.6	21,948.9
Nov-2008	35,468.2	19,809.6	22,896.4
Feb-2009	35,624.7	19,178.2	21,517.0
May-2009	35,249.7	19,977.4	21,861.5
Aug-2009	33,559.3	20,230.7	21,005.7
Nov-2009	34,495.5	20,369.6	21,637.2
Feb-2010	33,849.9	20,330.3	20,858.2
May-2010	34,169.7	20,970.6	22,224.0
Aug-2010	33,806.9	19,880.8	21,206.2
Nov-2010	34,834.1	21,000.4	21,526.7
Feb-2011	34,831.9	21,749.9	22,174.1
May-2011	34,775.2	21,361.9	21,842.7
Aug-2011	35,075.8	21,223.7	22,294.0
Nov-2011	35,102.5	20,825.0	21,547.6
Feb-2012	34,812.0	20,368.2	20,524.2
May-2012	33,575.5	21,579.7	20,694.2
Aug-2012	34,435.0	21,175.1	20,540.0
Nov-2012	34,782.4	21,696.3	22,316.5
Feb-2013	34,547.9	21,241.9	22,439.9
May-2013	35,710.6	21,672.7	21,660.2
Aug-2013	33,871.1	21,632.4	21,680.6
Nov-2013	34,962.4	20,902.8	22,387.4
Feb-2014	34,908.7	20,053.5	21,883.6
May-2014	35,082.8	21,103.4	21,667.6
Aug-2014	35,208.9	21,055.2	21,862.9
Nov-2014	36,188.4	22,189.0	22,679.2
Feb-2015	35,601.0	22,775.9	23,024.2
May-2015	34,810.1	22,586.0	22,962.4
Aug-2015	35,244.3	21,586.1	22,296.7
Nov-2015	37,077.4	22,615.7	23,017.9
Feb-2016	37,444.3	22,674.2	23,967.2
May-2016	35,299.6	22,192.1	22,921.1
Aug-2016	33,630.3	22,906.8	22,386.5
Nov-2016	35,760.3	22,686.5	22,691.4
Feb-2017	34,609.4	22,692.2	22,001.0
May-2017	37,087.7	24,195.7	23,796.2
Aug-2017	35,833.6	23,304.5	24,320.4
Nov-2017	37,742.2	23,647.8	24,677.5
Feb-2018	38,158.9	22,789.0	24,485.1
May-2018	35,587.4	23,864.4	22,404.6
Aug-2018	34,856.8	22,898.1	23,466.6
Nov-2018	35,954.1	24,054.8	24,178.5
Feb-2019	37,000.0	24,338.5	25,027.7
May-2019	36,079.2	23,924.9	22,955.6
Aug-2019	34,300.8	23,420.7	24,538.3
Nov-2019	36,551.9	25,073.9	23,946.7
Feb-2020	35,688.5	24,057.2	23,727.0

Chart 6 - Number of hours actually worked in all jobs, original

["","Retail Trade","Accommodation and Food Services","Transport, Postal and Warehousing "]

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[]

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Source: 6291.0.55.003 Table 11

In future quarters, the ABS will also undertake analysis of reasons people worked less than usual hours by industry or groups of industries (following a similar approach to that taken inTable 3).

The ABS will also be undertaking analysis of hours worked by industry in the quarterly Labour Account (cat. no. 6150.0.55.003), given it is the best source of industry information. March quarter Labour Account estimates will be released in June 2020.

Additional data

Beyond hours worked information, other detailed Labour Force products will also be important in monitoring and understanding impacts to labour force status. This data includes information on:

redundancies (quarterly, in Table 29a and 29b);
the extent to which people expect to be with an employer for the next 12 months (quarterly, in Table 17);
duration of job search (monthly, in Table 14a, 14b, 14c, 14d, 14e); and
the gross flows product.

The ABS will include analysis from some of these in future months.

Other products beyond the core Labour Force products may also provide important information over the coming months, or assist in understanding the number of people who may be more affected by the labour market impacts of COVID-19. For example Characteristics of Employment (cat. no. 6333.0) contains information regarding employee earnings and working arrangements.

Insights from the original data

Sample composition

The Labour Force Survey sample can be thought of as comprising eight sub-samples (or rotation groups), with each sub-sample remaining in the survey for eight months, and one rotation group "rotating out" each month and being replaced by a new group "rotating in". This sample rotation is important in ensuring that seven-eighths of the sample are common from one month to the next, to ensure that changes in the estimates reflect real changes in the labour market, rather than the sample. In addition, the replacement sample is generally selected from the same geographic areas as the outgoing one, as part of a representative sampling approach.

When considering movements in the original estimates, it is possible to decompose the sample into three components:

the matched common sample (survey respondents who responded in both January and February);
the unmatched common sample (survey respondents who responded in February but who did not respond in January, or vice versa); and
the incoming rotation group (survey respondents who replaced respondents who rotated out in January).

The detailed decomposition of each of these movements is included in the data cube 'Insights From the Original Data'.

In considering the three components of the sample, it is important to remember that the matched common sample describes the change observed for the same respondents in January and February, while the other two components reflect differences between the aggregate labour force status of different groups of people.

While the rotation groups are designed to be representative of the population, the outgoing and incoming rotation groups will almost always have somewhat different characteristics, as a result of the groups representing a sample of different households and people. The design of the survey, including the weighting and estimation processes, ensures that these differences are generally relatively minor and seeks to ensure that differences in characteristics of rotation groups do not affect the representativeness of the survey and its estimates. Monthly estimates are always designed to be representative of their respective months, regardless of the relative contribution of the three components of the sample.

Incoming rotation group

In original terms, the incoming rotation group in February 2020 had a higher employment to population ratio than the group it replaced (62.7% in February 2020, compared to 61.8% in January 2020), however it was lower than the sample as a whole (62.8%). The incoming rotation group had a higher full-time employment to population ratio than the group it replaced (43.6% in February 2020, compared to 43.4% in January 2020), and was higher than the sample as a whole (43.2%).

The incoming rotation group had a higher unemployment rate than the group it replaced (5.9% in February 2020, compared to 5.3% in January 2020), and was higher than the sample as a whole (5.5%). The incoming rotation group had a higher participation rate than the group it replaced (66.6% in February 2020, compared to 65.2% in January 2020), and was higher than the sample as a whole (66.5%).

Outgoing rotation group

In looking ahead to the March 2020 estimates, in original terms, the outgoing rotation group in February 2020, that will be replaced by a new incoming rotation group in March 2020, had an employment to population ratio in February 2020 of (62.6%), lower than the sample as a whole (62.8%). The outgoing rotation group in February 2020 had a lower full-time employment to population ratio (42.6%) than the sample as a whole (43.2%).

The outgoing rotation group in February 2020 had a lower unemployment rate (5.1%) compared to the sample as a whole (5.5%). The outgoing rotation group in February 2020 had a lower participation rate (66.0%) compared to the sample as a whole (66.5%).

The importance of trend data

As the gross flows and rotation group data are presented in original terms they are not directly comparable to the seasonally adjusted and trend data discussed elsewhere in the commentary, and are included to provide additional information for the original data. Since the original data are unadjusted, they have a considerable level of inherent sampling variability, which is specifically adjusted for in the trend series. The trend data provides the best measure of the underlying behaviour of the labour market and is the focus of the commentary in this publication.

Rotation group analysis for states and territories

In addition to analysis across the entire sample, the ABS also undertakes similar analysis for the responding sample in each state and territory each month, and highlights where there is a notable change for users to be aware of. For example, in February 2020, the incoming rotation group in Victoria was more employed and less likely to be not in the labour force than the group it replaced, and was generally more employed and less likely to be not in the labour force than the matched sample. As with any notable month-to-month movement of this nature in state and territory estimates, the ABS recommends exercising a degree of caution in interpreting short-term changes.

As for its reporting for the entire sample, where the ABS has not highlighted a notable incoming rotation group effect, any larger changes should therefore be considered to reflect a broader change across the sample.

Article archive

This section provides an archive of articles and analysis published in Labour Force, Australia (cat. no. 6202.0) and Labour Force, Australia, Detailed - Electronic Delivery (cat. no. 6291.0.55.001) and Labour Force, Australia, Detailed, Quarterly (cat. no. 6291.0.55.003), promoting the effective use of labour force statistics. Articles are sorted by publication month.

Articles on labour related topics are also available in Australian Labour Market Statistics (cat. no. 6105.0) and Australian Social Trends (cat. no. 4102.0).

Labour Force Survey archive

2019

2018

2017

2016

2015

December

What's New in the Labour Force (cat. no. 6202.0, cat. no. 6291.0.55.001)

November

What's New in the Labour Force (cat. no. 6202.0, cat. no. 6291.0.55.001, cat. no. 6291.0.55.003)
Update on Recommendation 7 from the Independent Technical Review (cat. no. 6202.0, cat. no. 6291.0.55.001, cat. no. 6291.0.55.003)
Measures of Underemployment and Underutilisation (cat. no. 6202.0, cat. no. 6291.0.55.001, cat. no. 6291.0.55.003)
Measures of full-time, part-time job search (cat. no. 6291.0.55.001)
Measures of leave entitlements (cat. no. 6291.0.55.003)
Measures of current duration of employment (cat. no. 6291.0.55.003)
Volume measures of underutilisation (cat. no. 6291.0.55.003)
Measures of retrenchment (cat. no. 6291.0.55.003)
Measures of sector of main job (cat. no. 6291.0.55.003)

October

What's New in the Labour Force (cat. no. 6202.0, cat. no. 6291.0.55.001)

September

What's New in the Labour Force (cat. no. 6202.0, cat. no. 6291.0.55.001)

August

Online Collection in the Labour Force Survey (cat. no. 6202.0, cat. no. 6291.0.55.001, cat. no. 6291.0.55.003)

July

What's New in the Labour Force (cat. no. 6202.0, cat. no. 6291.0.55.001)
Progress with recommendations from the Independent Technical Review (cat. no. 6202.0, cat. no. 6291.0.55.001)
Change to Status in Employment Output (cat. no. 6202.0, cat. no. 6291.0.55.001)

June

What's New in the Labour Force (cat. no. 6202.0, cat. no. 6291.0.55.001)
Assessing Volatility in the Labour Force Series (cat. no. 6291.0.55.001)
Update on Recommendations 10 and 11 from the Independent Technical Review (cat. no. 6202.0, cat. no. 6291.0.55.001)

May

April

What's New in the Labour Force (cat. no. 6202.0, cat. no. 6291.0.55.001)

March

What's New in the Labour Force (cat. no. 6202.0, cat. no. 6291.0.55.001)
Annual Seasonal Reanalysis (cat. no. 6202.0, cat. no. 6291.0.55.001)
Update on Recommendations from the Independent Technical Review (cat. no. 6202.0, cat. no. 6291.0.55.001)

February

What's New in the Labour Force (cat. no. 6202.0, cat. no. 6291.0.55.001, cat. no. 6291.0.55.003)
Online Collection in the Labour Force Survey (cat. no. 6202.0, cat. no. 6291.0.55.001, cat. no. 6291.0.55.003)
Rebenchmarking Labour Force Estimates (cat. no. 6202.0, cat. no. 6291.0.55.001, cat. no. 6291.0.55.003)

January

What's New in the Labour force (cat. no. 6202.0, cat. no. 6291.0.55.001)

2014

2013

2012

2011

Previous catalogue number

This release previously used catalogue number 6291.0.55.001.

Methodology

Labour Force, Australia, Detailed methodology, February 2020

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APA

Labour Force, Australia, Detailed

APA

Main features

Managing the impact of Coronavirus (COVID-19) on Labour Force statistics

Managing the impact of bushfires on Labour Force statistics

Managing the impact of the Townsville floods in February 2019

Changes in future issues - new ABS website and improvements to detailed labour force data in 2020

Quarterly population benchmarking

Data downloads - time series spreadsheets

Table 01. Labour force status by age, social marital status and sex

I-Note

Table 02. Labour force status by state, territory, greater capital city and rest of state (ASGS) and sex

I-Note

Table 03. Labour force status for 15-24 year olds by age, educational attendance (full-time) and sex and by state, territory and educational attendance (full-time)

Table 08. Employed persons by status in employment of main job and sex

Table 09. Employed persons by hours actually worked in all jobs and sex

Table 10. Employed persons by hours usually worked in all jobs and sex

Table 14a. Unemployed persons by duration of job search and sex

Table 14b. Unemployed persons by duration of job search and sex - trend, seasonally adjusted and original

Table 14c. Median duration of job search by state, territory and duration of job search

Table 14d. Median duration of job search by age and duration of job search

Table 14e. Median duration of job search by relationship in household and duration of job search

Table 16. Labour force status by labour market region (ASGS) and sex

I-Note

Table 16b. Labour force status by labour market region (ASGS) and sex, annual averages of the previous 12 months

I-Note

Table 16c. Median duration of job search by labour market region (ASGS) and duration of job search

I-Note

Table 20a - unemployed persons who looked for full-time and part-time work by age

Table 20b - unemployed persons who looked for full-time and part-time work by sex

Table 20c - unemployed persons who looked for full-time and part-time work by state and territory

All time series spreadsheets

Data downloads - data cubes

LM1 - labour force status by age, greater capital city and rest of state (ASGS), marital status and sex, February 1978 onwards (pivot table)

LM3 - labour force status for 15-24 year olds by age, educational attendance (full-time), sex and year left school, April 1986 onwards (pivot table)

LM3a - labour force status for 15-24 year olds by age, educational attendance (full-time), highest year of school completed (ASCED) and sex, August 2015 onwards (pivot table)

LM4 - labour force status by elapsed years since arrival, major country group (subcontinent) of birth (SACC), sex, state and territory, January 1991 onwards (pivot table)

LM5 - labour force status by age, major country group (subcontinent) of birth (SACC), and sex, January 1991 onwards (pivot table)

LM7 - labour force status by elapsed years since arrival, main English-speaking countries, sex, state and territory, January 1991 onwards (pivot table)

LM9 - labour force status by age (detailed), greater capital city and rest of state (ASGS) and sex, January 1991 onwards (pivot table)

EM1a - employed persons by age, hours actually worked in all jobs and sex, January 1991 onwards (pivot table)

EM1b - employed persons by hours actually worked in all jobs, state and territory, January 1991 onwards (pivot table)

EM2a - employed persons who worked fewer hours than usual by hours actually worked in all jobs and sex, January 1991 onwards (pivot table)

EM2b - employed persons who worked fewer hours than usual by hours actually worked in all jobs, state and territory, January 1991 onwards (pivot table)

EM3a - employed persons by age, hours usually worked in all jobs and sex, April 2001 onwards (pivot table)

EM3b - employed persons by hours usually worked in all jobs, state and territory, April 2001 onwards (pivot table)

EM4a - employed persons by age, hours actually worked in main job and sex, April 2001 onwards (pivot table)

EM4b - employed persons by hours actually worked in main job, state and territory, April 2001 onwards (pivot table)

EM5a - employed persons by age, hours usually worked in main job and sex, July 2014 onwards (pivot table)

EM5b - employed persons by hours usually worked in main job, state and territory, July 2014 onwards (pivot table)

EM6 - employed persons by hours actually worked in all jobs, sex and status in employment of main job, January 1991 onwards (pivot table)

EM6a - employed persons by hours actually worked in all jobs, sex, status in employment of main job, and state, January 1991 (pivot table)

UM2 - unemployed persons by duration of job search, state and territory, January 1991 onwards (pivot table)

UM3 - unemployed persons by age and duration of job search, January 1991 onwards (pivot table)

RM1 - labour force status by age, labour market region (ASGS) and sex, October 1998 onwards (pivot table)

I-Note

RM3 - unemployed persons by duration of job search and labour market region (ASGS), July 1991 onwards (pivot table)

I-Note

FM1 - labour force status by relationship in household, sex, state and territory, January 1991 onwards (pivot table)

I-Note

FM2 - labour force status by age and relationship in household, January 1991 onwards (pivot table)

I-Note

FM3 - employed persons by hours actually worked in all jobs and relationship in household, January 1991 onwards (pivot table)

I-Note

FM4 - unemployed persons by duration of job search and relationship in household, January 1991 onwards (pivot table)

I-Note

NM1 - persons not in the labour force (NILF) by age, reason not in the labour force and sex, January 1991 onwards (pivot table)

NM2 - persons not in the labour force (NILF) by reason not in the labour force, state and territory, January 1991 onwards (pivot table)

All monthly Pivot Tables

Spotlight: Insights into detailed Labour Force hours worked data

Assessing February 2020 for an early impact from COVID-19

Changes in hours worked often lead changes in employment

Trend and seasonally adjusted hours worked data: an aggregate view

Original hours worked data: a compositional view

Table 1 - Proportion of hours worked by male and females

Table 2 - Percent of employed who worked fewer hours than usual, February 2020

Table 3 - Reasons people worked less than usual hours - monthly movements

Quarterly hours worked information

Additional data