Participation, Job Search and Mobility, Australia methodology

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Reference period
February 2019
Released
8/07/2019

Explanatory notes

Introduction

1 The statistics in this release were compiled from the Participation, Job Search and Mobility (PJSM) survey conducted throughout Australia in February 2019, as a supplement to the Australian Bureau of Statistics (ABS) monthly Labour Force Survey (LFS).

2 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).

Concepts, sources and methods

3 The conceptual frameworks used in the monthly LFS align closely with the standards and guidelines set out in Resolutions of the International Conference of Labour Statisticians. Descriptions of the underlying concepts and structure of Australia's labour force statistics, and the sources and methods used in compiling these estimates, are presented in Labour Statistics: Concepts, Sources and Methods (cat. no. 6102.0.55.001).

Labour Force Survey scope

4 The scope of the LFS is restricted to persons aged 15 years and over and excludes the following persons:

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

Participation, job search and mobility scope

5 In addition to the LFS scope exclusions, PJSM also excludes students at boarding schools, patients in hospitals, residents of homes (e.g. retirement homes, homes for persons with disabilities), and inmates of prisons.

6 PJSM was conducted in both urban and rural areas in all states and territories, but excluded persons living in Aboriginal and Torres Strait Islander communities.

Coverage

7 The estimates in this publication relate to persons included in the survey in February 2019. In the LFS, coverage rules are applied, which aim to ensure that each person is associated with only one dwelling, and hence has only one chance of selection in the survey. See Labour Force, Australia (cat. no. 6202.0) for more details.

Sample size

8 Supplementary surveys are not always conducted on the full LFS sample. Since August 1994 the sample for supplementary surveys has been restricted to no more than seven-eighths of the LFS sample.

9 This survey is fully based on the sample introduced after the 2016 Census of Population and Housing, using the Address Register as the sampling frame for unit selection. For more information, see the Information Paper: Labour Force Survey Sample Design (cat. no. 6269.0)

Reliability of the estimates

10 Estimates in this publication are subject to sampling and non-sampling errors:

  • Sampling error is the difference between the published estimate and the value that would have been produced if all dwellings had been included in the survey. For more information, see the Technical Note.
  • Non-sampling errors are inaccuracies that occur because of imperfections in reporting by respondents and interviewers, and errors made in coding and processing data. These inaccuracies may occur in any enumeration, whether it be a full count or a sample. Every effort is made to reduce the non-sampling error to a minimum by careful design of questionnaires, intensive training and effective processing procedures.
     

Seasonality

    11 The estimates are based on information collected in the survey month (February) and, due to seasonality, may not be representative of other months of the year.

    12 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 2019 issue of Labour Force, Australia (cat. no. 6202.0). This adjustment accounts for February seasonality and irregular effects.

    13 Historical estimates re-published in this issue from surveys conducted in different survey months (e.g. March, June and September) will be subject to different seasonal impacts, which may result in an observable break in series between the historical data and data collected in PJSM. Trend factors have also been applied to these historical estimates to reduce the impact of seasonality on the estimates.

    Classifications used

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

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

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

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

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

    Comparability of time series

    19 The LFS estimates and estimates from the supplementary surveys, (e.g. PJSM) are calculated in such a way as to sum to the independent estimates of the civilian population aged 15 years and over (population benchmarks). Generally, revisions are made to population benchmarks for the LFS following the final rebasing of population estimates to the latest five-yearly Census of Population and Housing. These population benchmarks are updated quarterly based on Estimated Resident Population (ERP) data. However, the estimates from previously published supplementary surveys were not normally revised to reflect the latest benchmarks.

    20 From August 2015, Labour Force Estimates have been compiled using population benchmarks based on the most recently available release of ERP data, continually revised on a quarterly basis. At the time of publication, this issue's estimates are comparable with the published labour force estimates for March 2019.

    21 From last issue of PJSM (February 2018), the estimates in this publication have moved to regular rebenchmarking to reflect the latest revisions to ERP data and updated trend LFS estimates.

    22 Caution should be exercised when comparing results from the 2019 PJSM to earlier issues of PJSM and previously published supplementary surveys as the populations used in each may not be directly comparable. For this reason, results from previous PJSM surveys have been revised and republished in this issue based on the labour force estimates for March 2019.

    Comparability with previous surveys

    23 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

    24 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.

    25 Results of previous surveys were published in Persons Not in the Labour Force, Australia (cat. no. 6220.0); and the standard data service Persons Not in the Labour Force, Australia (cat. no. 6220.0.40.001) for 1994 and 1995 (available in hardcopy only).

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

    Underemployed Workers

    27 UEW was conducted in May 1985, 1988 and 1991. In 1994, the survey became an annual survey and until 2013 was collected each September.

    28 Results of previous surveys were published in Underemployed Workers, Australia (cat. no. 6265.0); and the standard data service Underemployed Workers, Australia (cat. no. 6265.0.40.001) for 1994 and 1995 (available in hardcopy only).

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

    Job Search Experience

    30 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).

    31 Information on persons 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).

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

    Labour Mobility

    33 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.

    34 Results of previous surveys were published in Labour Mobility, Australia (cat. no. 6209.0).

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

    Comparability with monthly LFS statistics

    36 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.

    37 For example, PJSM provides data on the main reason for leaving or losing a person’s last job in the previous 12 months, such as retrenchment. PJSM provides a micro analysis understanding of retrenchment dynamics for the Labour Force. To observe the frequency of the number of persons retrenched, users should refer to the Labour Force quarterly retrenchment data (see Labour Force, Australia, Detailed, Quarterly cat. no. 6291.0.55.003).

    38 The ABS underemployment framework uses a threshold (35 hours in the reference week) based on the boundary between full-time and part-time work. This is common to the estimates from both LFS and PJSM.

    Products and services

    39 A number of Datacubes (spreadsheets) containing all tables produced for this publication are available from the Data downloads section of the publication. The Datacubes present tables of estimates and their corresponding Relative Standard Errors (RSEs).

    40 For users who wish to undertake a more detailed analysis of the data, the survey microdata will be released through the TableBuilder product. For more details, refer to the TableBuilder information, Microdata, Participation, Job Search and Mobility, Australia (cat. no. 6226.0.00.001). For more information see About TableBuilder.

    41 Special tabulations are available on request. Subject to confidentiality and sampling variability constraints, tabulations can be produced from the survey incorporating data items, populations and geographic area selections to meet individual requirements. These will be provided in electronic form. All enquiries should be made to the National Information and Referral Service on 1300 135 070.

    Next survey

    42 This survey is to be conducted next in February 2020.

    Acknowledgement

    43 ABS surveys draw extensively on information provided freely by individuals, businesses, governments and other organisations. Their continued cooperation is very much appreciated: without it, the wide range of statistics published by the ABS would not be available. Information received by the ABS is treated in strict confidence as required by the Census and Statistics Act, 1905.

    Related publications

    44 Current publications and other products released by the ABS are available from the Statistics Page on the ABS website. The ABS also issues a daily Release Advice on the website which details products to be released in the week ahead.

    Technical note - data quality

    Reliability of the estimates

    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. In addition, the reliability of estimates based on sample surveys are also subject to sampling variability. That is, the estimates may differ from those that would have been produced had all persons in the population been included in the survey.

    Non-sampling error

    2 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.

    Sampling error

    3 Sampling error is the difference between the published estimates, derived from a sample of persons, and the value that would have been produced if the total population (as defined by the scope of the survey) had been included in the survey. One measure of the sampling error 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 persons 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 persons had been surveyed, and about 19 chances in 20 (95%) that the difference will be less than two SEs.

    4 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.

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

    5 RSEs for count estimates have been calculated using the Jackknife method of variance estimation. This involves the calculation of 30 'replicate' estimates based on 30 different subsamples of the obtained sample. The variability of estimates obtained from these subsamples is used to estimate the sample variability surrounding the count estimate.

    6 The Excel spreadsheets in the Data downloads section contain all the tables produced for this release and the calculated RSEs for each of the estimates.

    7 Only estimates (numbers or percentages) with RSEs less than 25% are considered sufficiently reliable for most analytical 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 Excel spreadsheets 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.

    8 Another measure is the Margin of Error (MOE), which shows the largest possible difference that could be between the estimate due to sampling error and what would have been produced had all persons been included in the survey with a given level of confidence. It is useful for understanding and comparing the accuracy of proportion estimates.

    9 Where provided, MOEs for estimates are calculated at the 95% confidence level. At this level, there are 19 chances in 20 that the estimate will differ from the population value by less than the provided MOE. The 95% MOE is obtained by multiplying the SE by 1.96.

    \(\large MOE = SE\times 1.96\)

    Calculation of standard error

    10 Standard errors can be calculated using the estimates (counts or percentages) and the corresponding RSEs. See What is a Standard Error and Relative Standard Error, Reliability of estimates for Labour Force data for more details.

    Proportions and percentages

    11 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 RSE of a proportion is given below. This formula is only valid when x is a subset of y:

    \(\large {RSE}\left(\frac{x}{y}\right) \approx \sqrt{[R S E(x)]^{2}-[R S E(y)]^{2}}\)

    Differences

    12 The difference between two survey estimates (counts or percentages) can also be calculated from published estimates. Such an estimate is also subject to sampling error. 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:

    \(\large S E(x-y) \approx \sqrt{[S E(x)]^{2}+[S E(y)]^{2}}\)

    13 While this formula will only be exact for differences between separate and uncorrelated characteristics or sub populations, it provides a good approximation for the differences likely to be of interest in this publication.

    Significance testing

    14 A statistical significance test for a comparison between estimates can be performed to determine whether it is likely that there is a difference between the corresponding population characteristics. The SE of the difference between two corresponding estimates (x and y) can be calculated using the formula shown above in the Differences section. This SE is then used to calculate the following test statistic:

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

    15 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 real difference between the populations with respect to that characteristic.

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