Period-specific information

Response Rates

Survey	Reference period	Response rate	Sample rate
HLFS	Each week during the quarter (1 October 2022 – 31 December 2022)	Target: 90 percent Achieved: 78.0 percent	Target: 76 percent Achieved: 70.1 percent
QES	The pay week ending on, or before, 20 November 2022	Target: See note Achieved: 88.3 percent	N/A
LCI	Pay rates at 15 November 2022	Target: 94 percent Achieved: 86.3 percent	N/A

Note: due to changes in how response rates for QES are being reported, there is not a comparable target. The response rate for December 2022 is lower than previous quarters, but investigation has determined that the data is still fit for purpose. More response rates for the QES are presented in a table towards the end of this page.

See Household Labour Force Survey sources and methods: 2016 for more information on the sample rate and response rates.

HLFS

Coverage rates

Usually, the undercoverage rate in the HLFS is around 20 percent. The overall undercoverage rate for the HLFS in the December 2022 quarter was 20.0 percent. This compares with 19.5 in the September 2022 quarter and 18.6 percent in the December 2021 quarter.

HLFS Infoshare series updates

We have published four new persons employed series to our Infoshare tables with splits by work location, perceived chance of losing main job, relationship between actual and usual hours worked, and reason working fewer hours (main job) or absent (all jobs).

• HLFQ.SMC% – Persons employed by relationship between actual and usual hours worked
• HLFQ.SME% – Persons employed by work location
• HLFQ.SVE% – Persons employed by reason working fewer hours (main job) or absent (all jobs)
• HLFQ.SVF% – Persons employed by perceived chance of losing main job

Data quality

Annual comparisons between the December 2021 and December 2022 quarters may be impacted by COVID-19 restrictions during the December 2021 quarter. We remind users to exercise caution when making annual comparisons, particularly for disproportionately impacted groups.

We continue to investigate possible ways of improving the resilience of our data following HLFS data collection challenges.

We will let our customers know about adjustments we make to the data as a result of any improvements.

In the meantime, we continue to recommend looking at longer term trends and taking sample errors into account when looking at changes in estimates for the smaller population groups.

See Labour Market Statistics: December 2021 quarter, Period-specific information for more information about the affected subgroups and possible adjustments being tested.

Outliers

During the seasonal adjustment process, X-13-ARIMA-SEATS can give less weight to the irregular component. Specifically, if the estimated irregular component at a point in time is sufficiently large compared with the standard deviation of the irregular component as a whole, then the irregular component at that point can be downweighted or removed completely and re-estimated. We refer to such observations as partial- and zero-outliers, respectively. In practice, the downweighting of outliers does little to seasonally adjusted data, but the impact of the outliers on the trend series will generally be reduced. However, if an outlier ceases to be an outlier as more data becomes available, then significant revisions to the trend series become possible.

Outliers
Quarters	Male employed	Female employed	Male unemployed	Female unemployed	Male not in the labour force	Female not in the labour force
Mar 2022	..	..	..	..	..	..
Jun 2022	P	..	..	..	Z	..
Sep 2022	P	P	P	..	..	Z
Dec 2022	..	..	..	..	..	..

Key: .. – no adjustment P – partial weight Z – zero weight

Revisions to HLFS

Each quarter, we apply the seasonal adjustment process to the latest quarter and all previous quarters. Every estimate is subject to revision each quarter as new data is added, which means that seasonally adjusted estimates for previous quarters may change slightly. In practice, estimates more than two years from the end-point will change little.

This table lists the changes in estimates between the current and previous quarters for the seasonally adjusted data.

Percent revision from last estimate, seasonally adjusted
Quarter	Male employed	Female employed	Male unemployed	Female unemployed	Male not in labour force	Female not in labour force
Dec 2021	0.00	0.02	-1.07	0.94	0.21	0.09
Mar 2022	0.01	0.03	-0.21	0.17	0.03	-0.02
Jun 2022	0.01	-0.02	0.62	-0.45	-0.08	-0.01
Sep 2022	-0.01	-0.04	0.85	-0.87	-0.18	-0.07

This table presents revisions for the trend estimates. Trend revisions are generally larger than those of the seasonally adjusted data.

Percent revision from last estimate, trend
Quarter	Male employed	Female employed	Male unemployed	Female unemployed	Male not in labour force	Female not in labour force
Dec 2021	0.01	0.03	-0.86	0.46	0.14	-0.24
Mar 2022	0.01	0.04	-0.65	0.34	0.11	-0.12
Jun 2022	-0.03	-0.03	0.93	0.03	-0.10	0.11
Sep 2022	-0.17	-0.39	7.39	-2.24	-0.80	0.35

The table below shows the average of all such absolute revisions, expressed relatively, and indicates to what extent the current estimates might be revised when the revised data for the next quarter becomes available.

Mean absolute percent revisions
	Seasonally adjusted		Trend
	1-step	4-step	1-step	4-step
Male employed	0.05	0.09	0.20	0.21
Female employed	0.06	0.10	0.26	0.28
Male unemployed	0.54	0.91	2.22	2.29
Female unemployed	0.54	0.98	2.13	2.33
Male not in labour force	0.10	0.17	0.41	0.41
Female not in labour force	0.10	0.16	0.39	0.42

QES

Response rate

The total response rate for the Quarterly Employment Survey in the December 2022 quarter was 88.3 percent. The reference period was the pay week ending on, or before, 20 November 2022.

Introducing additional context around response rates

The above response rate is calculated as a percentage of firms in the sample that we received usable data for. While this was the lowest response rate achieved for the current design of the QES, we monitored coverage across industry and sector throughout the quarter and ensured the continued quality of our imputation process. We have therefore concluded the data is fit for purpose.

This response rate includes full coverage data for approximately 2,400 Ministry of Education schools which are gathered through an administrative source. Excluding these units, the response rate would be 81.4 percent.

Previously we had reported the QES response rate as percent of non-key firm’s weighted RME (rolling mean employment). Rolling mean employment is an estimate of a firm’s average employee count, which is then weighted up to represent a portion of similar sized firms in the economy. Key firms (the largest, most economically significant firms) were excluded as the aim is to collect all the key firms in the quarter. With this method we would have reported an 84.1 percent response rate in the December 2022 quarter. This method provides an estimate of the achieved coverage of the survey but does not provide strictly the number of firms that responded to the survey. This method excludes key-firms and any administrative data we sourced.

Below is a table of achieved QES response rates using the different measurements.

Quarter	Total response rate	Response rate excluding administrative school data	Non-key firm weighted RME response rate
December 2022	88.3%	81.4%	84.1%
September 2022	90.9%	85.6%	88.3%
June 2022	92.2%	87.8%	89.1%
March 2022	92.5%	88.1%	90.1%
December 2021	89.4%	83.1%	89.5%
September 2021	92.5%	88.1%	90.5%
June 2021	92.3%	87.7%	92.0%
March 2021	90.9%	85.4%	87.3%

For consistency with our other releases and to give a better measure of data quality going forward we will also publish the total response rate i.e., the percentage of firms in the sample that we received usable data for.

Understanding inter-quarter variability in the Quarterly Employment Survey

Stratified sample design divides a population into smaller mutually exclusive groups, called strata. Random samples are drawn within these strata. The goal of stratification is to group similar units, by industry and employment count for QES. Reducing variability in output variables between units within each stratum allows a more efficient and representative selection of units to be surveyed over the population.

Different strata have different probabilities of selection and sample weights, according to the expected level of variability and contribution to the population’s outputs. For example, the very largest businesses may have a 100% chance of selection and carry a weight of 1, so that they represent only themselves. Meanwhile, very small businesses have a low chance of selection, and those sampled carry a large weight to represent many units.

Each quarter, units in QES are allocated to strata based on ANZSIC division and specific cut-offs in employment count. A sample is drawn according to the probabilities of selection in each stratum. Typically, a similar group of businesses is sampled each quarter since selection at the strata level is based on a fixed span of permanent random numbers, to which each business is assigned. Similarity of the sample between quarters promotes accuracy in inter-quarter movements. Conversely, sample changes due to business births, deaths and movements between strata reduce accuracy in inter-quarter movements.

Prior to March 2021, the old sample design maintained a constant strata allocation but still experienced some changes in the sample due to business births and deaths. The new sample design reallocates strata each quarter to maintain an efficient and representative sample. The downside of regular reallocation is larger variability in inter-quarter movements than previously experienced, especially at the industry level.

Data based on full-coverage administrative sources may be more suitable than sample data such as QES for studying inter-quarter movements at lower levels. Please refer to the Monthly Employment Indicator (MEI) or Business Employment Data (BED) series for more information.

LCI

The LCI measures changes in salary and wage rates for a fixed quantity and quality of labour. LCI data is collected by postal and electronic surveys.

For the December 2022 quarter, respondents were asked to report pay rates on the reference date of 15 November 2022. The response rate was 86.3 percent, finishing below the target response rate of 94 percent. The response rate for key firms was 94.4 percent, below the target of 100 percent.

The lower response rate has required higher levels of imputation where we carried forward previous wages for the relevant position that did not reply. The LCI data remains fit for purpose.

Labour cost index – DataInfo+ has more information about LCI methodology.

Quarter	Response rate	Target
December 2022	86.3	94
September 2022	87.4	94
June 2022	93.4	94
March 2022	91.3	94
December 2021	92.1	94
September 2021	91.4	94
June 2021	96.3	94
March 2021	95.6	94
December 2020	95.8	94
September 2020	94.1	94
June 2020	95.2	94
March 2020	95.0	94
December 2019	95.1	94
September 2019	94.8	94
June 2019	95.3	94
March 2019	95.0	94
December 2018	94.8	94
September 2018	95.2	94
June 2018	95.2	94
March 2018	95.4	94
December 2017	95.8	94

General information and methodology

For general information and methodology on the specific surveys within the labour market statistics release, please see the following Datainfo+ pages:

• Household Labour Force Survey
• Labour Cost Index
• Quarterly Employment Survey

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