Participation, Job Search and Mobility, Australia methodology

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Reference period
February 2020
Released
10/08/2020

Explanatory notes

Introduction

The statistics in this release were compiled from the 2020 iteration of the Participation, Job Search and Mobility (PJSM) survey conducted throughout Australia in February of each year as a supplement to the Australian Bureau of Statistics (ABS) monthly Labour Force Survey (LFS).

This survey provides a comprehensive and coherent dataset on persons experiences relating to job search, job change and labour market participation, combining key elements from previous separate collections:

It informs on the following broad labour market issues: job mobility; job search; participation and increasing participation; underemployment; and marginal attachment. This enables analysis of persons experiences relating to job search, job change and increasing participation, all of which can be cross classified by other employment characteristics such as hours worked, industry, occupation and sector of job, as well as personal characteristics.

The explanatory notes relate to key aspects of the supplementary survey, however, information about survey design, scope, coverage and population benchmarks relevant to the monthly LFS, which also applies to supplementary surveys, can be found in the publication Labour Force, Australia (cat. no. 6202.0).

Reference period

Interviews for the Labour Force Survey are generally conducted during the two weeks beginning on the Sunday between the 5th and 11th of each month. The information obtained relates to the week before the interview (i.e. the reference week). For Participation, Job Search and Mobility (PJSM), the reference period will be the labour force weeks of the month of February. While data is collected within those weeks, the reference period in PJSM will sometimes differ according to the subject matter. Where they differ from the reference week, these will be explicitly stated within the data item label or definitions (for example, in the last 3 months, in the last four weeks).

Scope and coverage

The scope of this survey is all people aged 15 years and over, excluding:

  • members of the permanent defence forces
  • certain diplomatic personnel of overseas governments, customarily excluded from the Census and estimated populations
  • members of non-Australian defence forces (and their dependants)
  • overseas residents in Australia.
     

The following exclusions also apply:

  • people living in remote parts of Australia
  • people living in non-private households such as hotels, university residences, students at boarding schools, patients in hospitals, inmates of prisons and residents of other institutions (e.g. retirement homes, homes for people with disabilities)
  • people living in Aboriginal and Torres Strait Islander communities.
     

Coverage rules are applied which aim to ensure that each person is associated with only one household and hence has only one chance of selection in the survey. See the Explanatory Notes of Labour Force, Australia (cat. no. 6202.0) for more details.

Collection method

The collection methodology for the labour supplementary surveys, including Participation, Job Search and Mobility (PJSM), is generally the same as for the Labour Force Survey. Interviews are conducted at the same time as interviews for the LFS, most interviews are conducted by telephone and online electronic collection with some conducted face to face. Information about each household member in scope of the supplementary survey is generally collected from one adult using the 'Any Responsible Adult' (ARA) methodology.

Response rates for the supplementary surveys are generally slightly lower than for the LFS.

Sample design

In February of each year, 7/8ths of the sample for the Labour Force Survey are included within the responding sample of Participation, Job Search and Mobility (PJSM). People in the outgoing rotation group in the LFS are excluded from all supplementary surveys.

For details on the sample design for LFS, see the Explanatory Notes in Labour Force, Australia (cat. no. 6202.0).

Sample size

The sample size for the February 2020 Participation, Job Search and Mobility survey (after taking into account scope, coverage and sub sampling exclusions) is approximately 21,100 households.

Weighting, benchmarking and estimation

Survey weights are calibrated against population benchmarks to ensure that the survey estimates conform to the independently estimated distribution of the population, rather than the distribution within the sample itself.

When calibrating the weights, the survey sample is grouped into categories based on the following characteristics:

  • State or territory
  • Capital city or rest of state
  • Sex
  • Age
  • Employed, underemployed, unemployed or not in the labour force.
     

The PJSM survey benchmarks were based on the most recently available release of Estimated Resident Population (ERP) data. Estimates from previous years are also revised using this method. Each year estimates are revised for the previous three years as updated benchmarks become available.

To reduce the impact of seasonality on the different estimates of labour force status, the estimates have been adjusted by factors based on trend LFS estimates. These factors were applied at the State and territory, Sex, employment, underemployment, unemployment and residual not in the labour force levels, based on the trend LFS series as published in the March 2020 issue of Labour Force, Australia (cat. no. 6202.0). This adjustment accounts for February seasonality and irregular effects.

Revised time series are available from the Data download section of this publication. All data from 2016 to 2020 are comparable with estimates, benchmarks and trend factors published in the March 2020 issue of Labour Force, Australia (cat. no. 6202.0).

Survey output

Data on participation, job search and mobility for people aged 15 years and older, and includes:

  • Socio-demographic information.
  • Employed persons - Status in employment; hours actually worked; hours usually worked; full-time or part-time status; whether worked and reason worked less hours than usual; whether available to start work; continuous duration with current employer/business; sector; occupation; industry; whether entitled to paid leave; whether retrenched; whether available and/or looking for work; whether promoted and/or transferred; previous occupation; and whether changed industry or occupation.
  • Unemployed persons - Duration of job search; whether looked for full-time or part-time work; whether checked or registered with a Job Services Australia/jobactive provider; number of employment offers; whether turned down job offers; reasons for turning down job offers; and whether would move interstate or intrastate.
  • Underemployed persons - Underemployment status; whether available and/or looking for work; duration of current period of insufficient work; whether would prefer to change employer to work more hours; whether would prefer to change occupation to work more hours; and whether would move interstate or intrastate.
  • Ceased a job - Continuous duration of last job; occupation of last job; industry of last job; status in employment of last job; hours usually work each week in last job; reason for ceasing last job; when began last job; whether entitled to paid leave in last job; and whether changed industry or occupation.
  • Persons not in the labour force - Main activity when not in the labour force; time since last job; whether had a job in the last 10 or 20 years; reasons not actively looking for work; whether available to start work; whether preferred full-time or part-time work; intention to enter the labour force; whether wanted to work; whether would move interstate or intrastate; continuous duration of last job; occupation of last job; industry of last job; and whether entitled to paid leave in last job.
     

Data are also available in TableBuilder, see Microdata: Participation, Job Search and Mobility (cat. no. 6226.0.00.001).

Standards and classifications

Country of birth data are classified according to the Standard Australian Classification of Countries (SACC), 2016 (cat. no. 1269.0).

Occupation data are classified according to ANZSCO - Australian and New Zealand Standard Classification of Occupations, 2013, Version 1.2 (cat. no. 1220.0).

Industry data are classified according to the Australian and New Zealand Standard Industrial Classification (ANZSIC), 2006 (Revision 2.0) (cat. no. 1292.0).

Education data are classified according to the Australian Standard Classification of Education (ASCED), 2001 (cat. no. 1272.0).

Geography data are classified according to the Australian Statistical Geography Standard (ASGS), 2016 (cat. no. 1270.0.55.001).

Comparability with Labour Force estimates

Due to differences in the scope, sample size and reference period of this supplementary survey and that of the monthly LFS, the estimation procedure may lead to some small variations between labour force estimates from this survey and those from the LFS.

Comparability with previous surveys

The move to create a consolidated supplementary survey involved a change in collection month for a number of the existing supplementary survey topics, namely JSE, UEW and PNILF moved from the July or September months, to February.

In order to better understand the impacts that the change in timing had, all three surveys (JSE, PNILF and UEW) were conducted in February 2014, in a format that was similar to their current format. This meant that JSE was conducted in July 2013 and PNILF and UEW were conducted in September 2013, and then again in February 2014 in Persons Not In the Labour Force, Underemployed Workers and Job Search Experience, Australia, February 2014 (cat. no. 6226.0.55.001).

Care should be taken when comparing the estimates from PJSM with previous supplementary surveys as Persons Not in the Labour Force (PNILF) and Underemployed Workers (UEW) were previously collected in September, Job Search Experience (JSE) in July, and Labour Mobility (LMOB) was collected in February. Collection of data from the combined PJSM survey was undertaken in February.

Persons Not In the Labour Force (PNILF)

PNILF was first conducted in May 1975 and again in May 1977. From 1979 to 1987 the survey was collected twice a year (March and September). From 1988 to 2013 it was conducted annually in September. Results of previous surveys were published in Persons Not in the Labour Force, Australia (cat. no. 6220.0).

For more information on the history of changes to PNILF, see the Explanatory Notes (cat. no. 6220.0).

Underemployed Workers (UEW)

UEW was conducted in May 1985, 1988 and 1991. In 1994, the survey became an annual survey and until 2013 was collected each September. Results of previous surveys were published in Underemployed Workers, Australia (cat. no. 6265.0).

For more information on the history of changes to UEW, see the Explanatory Notes (cat. no. 6265.0).

Job Search Experience (JSE)

JSE was conducted annually in July from 2002 to 2013. Results of similar surveys on the job search experience of unemployed persons conducted in July 1984, July 1985, June 1986, July 1988, July 1990, June 1991, and annually from July 1992 to July 2001 were published in various issues of Job Search Experience of Unemployed Persons, Australia (cat. no. 6222.0). Information on people who had started work for an employer for wages or salary during the 12 months up to the end of the reference week was collected in June 1986 and two-yearly from July 1990 to July 2000 and was published in Successful and Unsuccessful Job Search Experience, Australia (cat. no. 6245.0).

For more information on the history of changes to JSE, see the Explanatory Notes (cat. no. 6222.0).

Labour Mobility

Labour Mobility and similar surveys were conducted in November 1972, February 1975, February 1976, annually from February 1979 to February 1992 and biennially from February 1994 to February 2012 and most recently in February 2013. Results of previous surveys were published in Labour Mobility, Australia (cat. no. 6209.0).

For more information on the history of changes to LMOB, see the Explanatory Notes (cat. no. 6209.0).

Related publications

ABS publications which may also be of interest include:

Technical note - data quality

Introduction

1  The estimates in this publication are based on information obtained from a sample survey. Any data collection may encounter factors, known as non-sampling error, which can impact on the reliability of the resulting statistics. Non-sampling error may occur in any collection, whether it is based on a sample or a full count such as a census. Sources of non-sampling error include non-response, errors in reporting by respondents or recording of answers by interviewers and errors in coding and processing data. Every effort is made to reduce non-sampling error by careful design and testing of questionnaires, training and supervision of interviewers, and extensive editing and quality control procedures at all stages of data processing. The reliability of estimates based on sample surveys are also subject to sampling error.

Sampling variability and sampling error

2 Since the estimates in this publication are based on information obtained from occupants of a sample of dwellings, and not an entire population, they are subject to sampling variability. That is, they may differ from those estimates that would have been produced if all dwellings had been included in the survey. One measure of the likely difference is given by the standard error (SE), which indicates the extent to which an estimate might have varied by chance because only a sample of dwellings was included. There are about two chances in three (67%) that a sample estimate will differ by less than one SE from the number that would have been obtained if all dwellings had been included, and about 19 chances in 20 (95%) that the difference will be less than two SEs.

3 Another measure of the likely difference is the relative standard error (RSE), which is obtained by expressing the SE as a percentage of the estimate.

\(\large R S E \%=\left(\frac{S E}{e s t i m a t e}\right) \times 100\)

4 RSEs for Participation, Job Search and Mobility estimates have been calculated using the Jackknife method of variance estimation. This process involves the calculation of 30 'replicate' estimates based on 30 different sub-samples of the original sample. The variability of estimates obtained from these sub-samples is used to estimate the sample variability surrounding the main estimate.

5 The data cubes in the Data download section contain all the tables produced for this release and the calculated RSEs for each of the estimates. The RSEs for estimates other than medians have been calculated using the Jackknife method, and RSEs for the medians have been calculated using the Woodruff method.

6 In the tables in this publication, only estimates (numbers, percentages, means and medians) with RSEs less than 25% are considered sufficiently reliable for most purposes. However, estimates with larger RSEs have been included. Estimates with an RSE in the range 25% to 50% should be used with caution while estimates with RSEs greater than 50% are considered too unreliable for general use. All cells in the data cubes with RSEs greater than 25% contain a comment indicating the size of the RSE. These cells can be identified by a red indicator in the corner of the cell. The comment appears when the mouse pointer hovers over the cell.

Calculation of standard error

7 RSEs are routinely presented as the measure of sampling error in this publication and related products. SEs can be calculated using the estimates (counts or means) and the corresponding RSEs.

8 An example of the calculation of the SE from an RSE follows. An estimate of underemployed males for February 2020 was 491,100, which has an RSE of 3.1%. The SE is:
 

\(\large{\begin{array}{l} \text{ SE of estimate} \\ = \left(\frac{\text{RSE}}{100} \times \text{estimate} \right) \\ = 0.031 \times 491,100 \\ = 15,200\left(\text{rounded to the nearest 100} \right) \end{array}} \)


9 Therefore, there are about two chances in three that the value that would have been produced if all dwellings had been included in the survey would fall within the range 475,900 to 506,300 and about 19 chances in 20 that the value would fall within the range 460,700 to 521,500. This example is illustrated in the following diagram.

Calculation of standard error and relative standard error

Proportions and percentages

10 Proportions and percentages formed from the ratio of two estimates are also subject to sampling errors. The size of the error depends on the accuracy of both the numerator and the denominator. A formula to approximate the RSEs of proportions not provided in the spreadsheets is given below. This formula is only valid when x is a subset of y.

\(\large{RSE \left( \frac{x}{y} \right)= \sqrt{[RSE \left(x \right)]^2 - [RSE \left(y \right)]^2}}\)

11 Considering an estimate of 12,981,500 employed persons, 1,176,000 or 9.06% were underemployed. The RSE for 1,176,000 is 2.0% and the RSE for 12,981,500 is 0.4%. Applying the above formula, the RSE for the proportion who were underemployed is:

\(\large{RSE=\sqrt{\left(2.0\right)^2-\left(0.4\right)^2}=1.96\%}\)

12 Therefore, the SE for the proportion who were underemployed was .18 percentage points (= (1.96/100) x .09). Therefore, there are about two chances in three that the proportion of underemployed workers is between 8.9% and 9.2%, and 19 chances in 20 that the proportion was within the range 8.7% to 9.4%.

Sums or differences between estimates

13 Published estimates may also be used to calculate the sum of two or more estimates, or the difference between two survey estimates (of numbers, means or percentages) where these are not provided in the spreadsheets. Such estimates are also subject to sampling error.

14 The sampling error of the difference between two estimates depends on their SEs and the relationship (correlation) between them. An approximate SE of the difference between two estimates (x–y) may be calculated by the following formula:

\(\large{RSE \left( \frac{x}{y} \right)= \sqrt{[RSE \left(x \right)]^2 - [RSE \left(y \right)]^2}}\)

15 The sampling error of the sum of two estimates is calculated in a similar way. An approximate SE of the sum of two estimates (x+y) may be calculated by the following formula:

\(\large{SE(x+y) =\sqrt{[RSE(x)]^{2}+[RSE(y)]^{2}}}\)

16 For example, an estimate of males aged 55–64 years who were underemployed part-time workers was 47,000, and the SE for this estimate was 5,600 (rounded to the nearest 100). For males aged 65 years and over, the number who were underemployed part-time workers was 13,800 and the SE was 3,100. The estimate of the combined age group i.e. males aged 55 years and over who were underemployed part-time workers is:

\(\large{47,000 + 13,800 = 60,800}\)

17 The SE of the estimate of males aged 55 years and over who were underemployed part-time workers is:

\(\large{SE =\sqrt{(5,600)^{2}+(3,100)^{2}}=6,400}\)

18 Therefore, there are about 2 chances in 3 that the value that would have been produced if all dwellings had been included in the survey would fall within the range 54,400 to 67,200 and about 19 chances in 20 that the value would fall within the range 48,000 to 73,600.

19 While these formulae will only be exact for sums of, or differences between, separate and uncorrelated characteristics or subpopulations, it is expected to provide a good approximation for all sums or differences likely to be of interest in this publication.

Standard errors of means and sums

20 The estimates of means and sums of continuous variables are subject to sampling variability and random adjustment. As for population estimates, the variability due to sampling and random adjustment is combined into the calculated standard error, and the relative standard error is reported. The component of variability arising from sampling is calculated using the Jackknife method.

Standard errors of quantiles

21 The estimates of quantiles such as medians, quartiles, quintiles and deciles are subject to sampling variability and random adjustment. As for population estimates, the variability due to sampling and random adjustment is combined into the calculated standard error, and the relative standard error is reported. The component of variability arising from sampling is calculated using the Woodruff method. This is also true for equal distribution quantiles.

Significance testing

22 A statistical test for any comparisons between estimates can be performed to determine whether it is likely that there is a significant difference between two corresponding population characteristics. The standard error of the difference between two corresponding estimates (x and y) can be calculated using the formula in paragraph 10. This standard error is then used to calculate the following test statistic:

\(\Large{\left ( \frac{x-y}{SE \left(x-y \right)} \right)}\)

23 If the value of this test statistic is greater than 1.96 then there is evidence, with a 95% level of confidence, of a statistically significant difference in the two populations with respect to that characteristic. Otherwise, it cannot be stated with confidence that there is a difference between the populations with respect to that characteristic.

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