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Methods: Four pillars of labour statistics

Labour Statistics: Concepts, Sources and Methods
Reference period
2021
Released
15/02/2022

ABS labour statistics are drawn from four key types of data sources, or “pillars” of data, which provide complementary insights into the labour market. These are:

  • household surveys - individual households answer labour market questions about their individual, family or household circumstances (e.g. the monthly Labour Force Survey)
  • business surveys - collect a broad range of information from businesses about jobs and employees (e.g. the Survey of Employee Earnings and Hours, Job Vacancies Survey)
  • administrative data - information maintained by governments (such as taxation data) and other entities made available to the ABS for statistical purposes (e.g. as published in Weekly Payroll Jobs and Wages)
  • accounts compilation - bringing together data from separate administrative, business, and household sources to produce an Australian Labour Account)
Shows the four pillars that underpin Australian labour market statistics: Household Surveys, Business Surveys. Administrative Data and Labour Accounts
Shows the four pillars that underpin Australian labour market statistics: Household Surveys, Business Surveys. Administrative Data and Labour Accounts

Sample surveys versus censuses

The ABS uses both sample surveys and censuses to collect information from a population about characteristics of interest. In the field of labour statistics, the ABS uses sample surveys of households and businesses, as well as censuses (such as the Industrial Disputes collection).

Censuses involve the collection of information from all units in the target population, while sample surveys involve the collection of information from only a part (sample) of the target population.

Sample surveys have both advantages and disadvantages when compared with censuses. Some advantages are reduced costs (as less time is needed to collect, process and produce data), possible reductions in non-sampling error (this concept is discussed in further detail later in this chapter), improved timeliness, and the potential to gather more detailed information from each respondent.

A disadvantage of sample surveys is that estimates are subject to sampling error, which occurs because data were obtained from only a sample rather than the entire population (this concept is discussed in further detail later in this chapter). Also, as a result of obtaining only a small number of observations in particular geographical areas and sub-populations, detailed cross-tabulations may be subject to high levels of error and be of limited use.

Censuses are generally used when broad level information is sought for many fine sub-groups of the population, whereas sample surveys are used to collect detailed information to estimate for broader levels of the population.

Sample design and sampling techniques

ABS labour-related household and business sample surveys use probability sampling techniques, drawing their samples from a population frame. This section briefly defines and explains key concepts and terms related to survey design. See the household and business surveys sections for more detail on aspects of survey design that are particular to these types of surveys.

Population

A survey is concerned with two types of population: the target population, and the survey population. The target population is the group of units about which information is sought, and is also known as the scope of the survey. It is the population at which the survey is aimed. The scope should state clearly the units from which data are required and the extent and time covered, e.g. households (units) in Australia (extent) in August 2020 (time).

However, the target population is a theoretical population, as there are usually a number of units in the target population which cannot be surveyed. These include units which are difficult to contact and units which are missing from the frame. The survey population is that part of the population that is able to be surveyed, and is also called the coverage population.

Statistical units

Statistical units are used in the design, collection, analysis and dissemination of statistical data. There are several types of units, including: sampling units (the units selected in the sample survey), collection units (the units from which data are collected), reporting units (the units about which data are collected), and analysis units (the units used for analysis of the data). The units used in a survey may change at various stages in the survey cycle. For example, the Labour Force Survey uses a sample of households (sampling unit) from which information is collected from any responsible adult (collection unit) about each person in the household in scope of the survey (reporting units). The results of the survey may then be analysed for families (analysis unit).

Frames

The frame comprises a list of statistical units (e.g. persons, households or businesses) in the population, together with auxiliary information about each unit. It serves as a basis for selecting the sample. Two types of frames are used in ABS labour-related surveys:

  • List based frames - List based frames comprise a list of all sampling units in the survey population. List based frames are commonly used in surveys of businesses. ABS business surveys currently draw their list frames from the ABS Business Register.
  • Area based frames - Area based frames comprise a list of non-overlapping geographic areas. These areas may be defined by geographical features such as rivers and streets. They are usually used in household surveys. Once an area is selected, a list is made of the households in the area, and a sample of households selected from the list. Examples of geographic areas that may be used to create area frames include: local government areas; census collection districts; and postcodes.

Auxiliary variables are characteristics of each unit for which information is known on the frame prior to the survey. Auxiliary variables can be used in the sample design to better target the population of interest, if the information on the frame is of sufficiently high quality and is correlated with the variables of interest in the survey. They can also be used in the estimation process in conjunction with the survey data: for example, industry of businesses.

For most sampling methodologies, it is desirable to have a complete list from which to select a sample. However, in practice it can be difficult to compile such a complete list and therefore frame bias may be introduced. Frame bias occurs when an inappropriate frame is used or there are problems with the composition of the frame, with the result that the frame is not representative of the target population. Frames become inaccurate for many reasons. One of the most common problems is that populations change continuously, causing frames to become out of date. Frames may also be inaccurate if they are compiled from inaccurate sources. The following are some of the problems that can occur in the composition of frames.

Under coverage occurs when some units in the target population that should appear on the frame do not. These units may have different characteristics from those units which appear on the frame, and therefore results from the survey will not be representative of the target population.

Out of scope units are units that appear on the frame but are not elements of the target population. Selection of a number of out of scope units in the sample reduces the effective sample size, and increases sampling error. Furthermore, out of scope units appearing on the frame may be incorrectly accounted for in the estimation process, which may lead to bias in survey estimates.

Duplicates are units that appear more than once on the frame. The occurrence of duplicates means that the probability of selection of the units on the frame is not as it should be for the respective sample design. In particular, the duplicate units will have more than the correct chance of selection, introducing bias towards the characteristics of these units. Duplicates also increase sampling error.

Deaths are units that no longer exist in the population but are still on the frame. Deaths have the same impact on survey results as out of scope units.

The quality of auxiliary variables can affect the survey estimates of the variables of interest, through both the survey design and the estimation process.

The ABS attempts to minimise frame problems and uses standardised sample and frame maintenance procedures across collections. Some of the approaches taken are to adjust estimates using new business provisions, and to standardise across surveys the systems for handling estimation, imputation and outliers.

Probability samples

Probability samples are samples drawn from populations such that every unit in the population has a known, or calculable, non-zero probability of selection which can be obtained prior to selection. In order to calculate the probability of selection, a population frame must be available. The sample is then drawn from this frame. Alternatives to probability samples are samples formed without a frame, such as phone-in polls.

Probability sampling is the preferred ABS method of conducting major surveys, especially when a population frame is available. Probability samples allow estimates of the accuracy of the survey estimates to be calculated. They are also used in ABS surveys as a means of avoiding bias in survey results. Bias is avoided when either the probability of selection is equal for all units in the target population or, where this is not the case, the effect of non-equal probabilities is allowed for in estimation.

Stratified sampling

Stratified sampling is a technique which uses auxiliary information available for every unit on the frame to increase the efficiency of a sample design. Stratified sampling involves the division (stratification) of the population frame into non-overlapping, homogeneous (similar) groups called strata, which can be treated as totally separate populations. A sample is then selected independently from each of these groups, and can therefore be selected in different ways for different strata, e.g. some strata may be sampled using 'simple random sampling' while others may be 'completely enumerated'. These terms are explained below. Stratification variables may be geographical (e.g. State, capital city/balance of State) or non-geographical (e.g. number of employees, industry, turnover).

All surveys conducted by the ABS use stratification. Household surveys use mainly geographic strata. Business surveys typically use strata which are related to the economic activity undertaken by the business, for example industry and size of the business (the latter based on employment size).

Completely enumerated strata

Completely enumerated strata are strata in which information is obtained from all units. Strata that are completely enumerated tend to be those where: each population unit within the stratum is likely to contribute significantly to the estimate being produced (such as strata containing large employers where the estimate being produced is employment); or there is significant variability across the population units within the stratum.

Simple random sampling

Simple random sampling is a probability sampling scheme in which each possible sample of the required size has the same chance of selection. It follows that each unit of the population has an equal chance of selection.

Simple random sampling can involve units being selected either with or without replacement. Replacement sampling allows the units to be selected multiple times, whereas without replacement sampling allows a unit to be selected only once. In general, simple random sampling without replacement produces more accurate results as it does not allow sample to be 'wasted' on duplicate selections. All ABS surveys that use simple random sampling use the 'without replacement' variant. Simple random sampling without replacement is used in most ABS business surveys.

Systematic sampling

Systematic sampling is used in most ABS household surveys, and provides a simple method of selecting the sample. It involves choosing a random starting point within the frame and then applying a fixed interval (referred to as the 'skip') to select members from a frame.

Information on auxiliary variables can be used in systematic sampling to improve the efficiency of the sample. The units in the frame can be ordered with respect to auxiliary variables prior to calculating the skip interval and starting point. This approach ensures that the sample is spread throughout the range of units on the frame, ensuring a more representative sample with respect to the auxiliary variable.

Systematic sampling with ordering by auxiliary variables is only useful if the frame contains auxiliary variables about each of the units in the population, and if these variables are related to the variables of interest. The relationship between the variables of interest and the auxiliary variables is often not uniform across strata. Consequently, it is possible to design a sample survey with only some of the strata making use of auxiliary variables.

Probability proportional to size sampling

Probability proportional to size sampling is a selection scheme in which units in the population do not all have the same chance of selection. With this method, the larger the unit with respect to some measure of size, the greater the probability that unit will be selected in the sample. Probability proportional to size sampling will lead to unbiased estimates, provided the different probabilities of selection are accounted for in estimation.

Cluster sampling

Cluster sampling involves the units in the population being grouped into convenient clusters, usually occurring naturally. These clusters are non-overlapping, well-defined groups which usually represent geographical areas. The sample is selected by selecting a number of clusters, rather than directly selecting units. All units in a selected cluster are included in the sample.

Multi-stage sampling

Multi-stage sampling is an extension of cluster sampling. It involves selecting a sample of clusters (first-stage sample), and then selecting a sample of population units within each selected cluster (second-stage sample). The sampling unit changes at each stage of selection. Any number of stages can be employed. The sampling units for any given stage of selection each form clusters of the next-stage sampling units. Units selected in the final stage of sampling are called final-stage units (or ultimate sampling units). The Survey of Employee Earnings and Hours uses multi-stage sampling - businesses (the first-stage units) selected in the survey are asked to select a sample of 'employees' (the final-stage units) using employee payrolls. Household surveys also use multi-stage sampling.

Multi-phase sampling

Multi-phase sampling involves collecting basic information from a sample of population units, then taking a sub-sample of these units (the second-phase sample) to collect more detailed information. The second-phase sample is selected using the information collected in the first phase, and allows the second-phase sample to be targeted to the specific population of interest. Population totals for auxiliary variables, and values from the first-phase sample, are used to weight the second-phase sample for the estimation of population totals.

Multi-phase sampling aims to reduce sample size and the respondent burden and collection costs, while ensuring that a representative sample is still selected from the population of interest. It is often used when the population of interest is small and difficult to isolate in advance, or when detailed information is required. Multi-phase sampling is also useful when auxiliary information is not known for all of the frame units, as it enables the collection of data for auxiliary variables in the first-phase sample.

The first-phase sample is designed to be large to ensure sufficient coverage of the population of interest, but only basic information is collected. The basic information is then used to identify those first-phase sample units which are part of the population of interest. A sample of these units is then selected for the second-phase sample. Therefore, the sampling unit remains the same for each phase of selection. If multi-phase sampling was not used, detailed information would need to be collected from all first-phase sample units to ensure reasonable survey estimates. In this way, multi-phase sampling reduces the overall respondent burden.

Weighting and estimation

Sample survey data only relate to the units in the sample. Therefore, the sample estimates need to be inflated to represent the whole population of interest. Estimation is the means by which this inflation occurs.

The following section outlines various methods of calculating the population estimates from the sample survey data. It then describes various editing procedures used in labour-related statistics to improve the population estimates.

Estimation is essentially the application of weights to the individual survey, and summing these weighted records to estimate totals. The value of these weights is determined with respect to one or more of the following three factors:

  • the probability of selection for each survey unit (probability weighting);
  • adjustment for non-response to correct for imbalances in the characteristics of responding sample units (post-stratification); and
  • adjustments to agree with known population totals for auxiliary variables - to correct for further imbalances in the characteristics of the selected sampled units (post-stratification, ratio estimation, calibration).

Weights are determined using formulae (estimators) of varying complexity.

Number-raised estimation

Number-raised weights are given by Nh/nh (where Nh is the total number of units in the population for the stratum, and nh is the number of responding units in the sample for that stratum). The weight assigned to each survey unit indicates the number of units in the target population that the survey unit is meant to represent. For example, a survey unit with a weight of 100 represents 100 units in the population. Each survey unit in a stratum is given the same weight. Number-raised weights can only be used to weight simple random samples.

Advantages of number-raised estimation are: it does not require auxiliary data; it is unbiased; and the accuracy of the estimates can be calculated relatively simply. However, number-raised estimation is not as accurate as some other methods with the same overall sample size.

Ratio estimation

Ratio estimation involves the use of known population totals for auxiliary variables to improve the weighting from sample values to population estimates. It operates by comparing the survey sample estimate for an auxiliary variable with the known population total for the same variable on the frame. The ratio of the sample estimate of the auxiliary variable to its population total on the frame is used to adjust the sample estimate for the variable of interest.

The ratio weights are given by X/x (where X is the known population total for the auxiliary variable, and x is the corresponding estimate of the total based on all responding units in the sample). These weights assume that the population total for the variable of interest will be estimated by the sample equally as well (or poorly) as the population total for the auxiliary variable is estimated by the sample.

Ratio estimation can be more accurate than number-raised estimation if the auxiliary variable is highly correlated with the variable of interest. However, it is subject to bias, with the bias increasing for smaller sample sizes and where there is lower correlation between the auxiliary variable and the variable of interest.

Post-stratification

Post-stratification estimation also involves the use of auxiliary information to improve the weighting from sample values to population estimates. Subgroups of the survey sample units are formed based on auxiliary variables after the survey data have been collected. Estimates of subgroup population sizes (based on probability weighting) are compared with known subgroup population sizes from independent sources. The ratio of the two population sizes for each subgroup is used to adjust the original estimate for the variable of interest (based on probability sampling).

Post-stratification is used to refine the estimation weighting process by correcting for sample imbalance and, assuming that the survey respondents are representative of missing units, correcting for non-response. For example, in the LFS, the sample is post-stratified by age, sex, capital city/rest of State, and State/Territory of usual residence. Estimates of the number of persons in these subgroups based on Census/Estimated Resident Population data are then compared to the estimates based on the survey sample to give the post-stratification weights.

Calibration

Calibration essentially uses all available auxiliary information to iteratively modify the original weights (based on number-raised weights). The new weights ensure that the sample estimates are consistent with known auxiliary information. Both post-stratification and ratio estimation can be used as part of the calibration weighting process. Calibration is useful if the survey sample estimates need to match the unit totals for a number of different subgroups, or for more than one auxiliary variable. It is mostly used in Special Social Surveys. For example, the Survey of Employment and Unemployment Patterns was weighted so that the survey estimates aligned with both population estimates based on Census data and estimates of the number of persons 'employed', 'unemployed' and 'not in the labour force' from the LFS.

Editing

Editing is the process of correcting data suspected of being wrong, in order to allow the production of reliable statistics. The aims of editing are:

  • to ensure that outputs from the collection are mutually consistent: for example, two different methods of deriving the same value should give the same answer;
  • to correct for any missing data;
  • to detect major errors, which could have a significant effect on the outputs; and
  • to find any unusual output values and their causes.

The purpose of editing is to correct non-sampling errors, such as those introduced by misunderstanding of questions or instructions, interviewer bias, miscoding, non-availability of data, incorrect transcription, non-response, and non-contact. Non-response occurs when all (total non-response) or part (partial non-response) of a questionnaire is not completed by the respondent. High levels of non-response can cause bias in the sample based estimates.

Editing is also used to identify outliers. The statistical term 'outlier' has several definitions, depending on the context in which it is used. Here it is used loosely to describe extreme values that are verified as being correct, but are very different from the values reported by similar units, and are expected to occur only very rarely in the population as a whole. In practice, an outlier is usually considered to be a unit that has a large effect on survey estimates of level, on estimates of movement, or on the sampling variance. This may occur because the unit is not similar to other units in the stratum - for example, if its’ true employment is much greater than the frame employment. It may also occur when an extreme value is recorded for some variable from an otherwise ordinary sampling unit.

Certain types of non-response, and the presence of outliers in the sample, may be addressed using a variety of statistical techniques.

Imputation involves supplying a value for a non-responding unit, or to replace 'suspect' data. Imputation methods fall into three groups:

  • the imputed value may be derived from other information supplied by the respondent;
  • the imputed value may be derived from information supplied by other similar respondents in the current survey; and
  • the values supplied by the respondent in previous surveys may be modified to derive a value.

The following imputation methods are used in labour-related surveys:

  • Deductive imputation involves correcting a missing or erroneous value by using other information that reveals the correct answer. For example, a response of 18,000 has been given where respondents have been asked to reply in '$000s' and where the expected range of responses is 13-21. A quick examination of other parts of the form shows that $18,000 is very likely the amount actually spent by the respondent, so 18,000 is 'corrected' to 18.
  • Central-value imputation involves replacing a missing or erroneous item with a value considered to be 'typical' of the sample or sub-sample concerned. Live respondent mean is an example of central-value imputation. This technique involves calculating the average stratum value for the data item of interest across all responding live units in the stratum, and assigning this value to all live non-responding units in the stratum.
  • Hot-deck imputation is similar to central-value imputation, but takes the absolute value from a donor unit: for example, earnings per hour for a given combination of occupation, location and industry in Characteristics of Employment.
  • Cold-deck imputation involves using previous survey data to amend items which fail edits. It may involve copying data from the previous survey cycle to the current cycle. One specific example of this type of imputation is Beta imputation, which involves estimating missing values by applying an imputed growth rate to the most recently reported data for these units, provided that data have been reported in either of the two previous periods.

When adjusting for outliers, a compromise is always necessary between the variability and bias associated with an estimate. There are two methods available for dealing with outliers. Historically the ABS has used the 'surprise outlier' approach for most business surveys, but over time has gradually changed to using 'winsorization'.

  • Surprise outlier approach - Generally, this technique is used to deal with a selected unit which is grossly extreme for a number of variables. The approach treats each outlier as if it were the only extreme unit in the stratum population. The outlier is given a weight of one, as if it had been selected in a CE stratum. As a result of the outlier's movement to the CE stratum, the weight for units in the outlier's selection stratum has to be recalculated, as the population and sample size have effectively been reduced by one. This has the effect that the other population units which would have been represented by the outlier are now represented by the average of the other units in the stratum. Therefore, the choice of treatments for a suspected outlier using the surprise outlier approach are either for it to represent all of the units it would normally represent, or to represent no units other than itself. It is preferable to set a maximum number of surprise outliers which can be identified in any one survey.
  • Winsorization technique - This technique is a more flexible approach. Here a value is considered to be an outlier if it is greater than a predetermined cut off. The effect of the outlier on the estimates is reduced by modifying its reported value. On application of the winsorization formula, sample values greater than the cut off are replaced by the cut off plus a small additional amount. The additional amount is the difference between the sample value and the cut off, multiplied by the stratum sampling fraction. Thus winsorization has most impact in strata with low sampling fractions, and the impact decreases as sampling fractions increase. Effectively, winsorization results in the outlier only representing itself, with the remaining population units that would have been represented by the outlier being instead represented by the cut off.

Time series estimates

Time series are statistical records of various activities measured at regular intervals of time, over relatively long periods. Data collected in irregular surveys do not form time series. The following section outlines the various elements of time series, and describes the ABS method of calculating seasonally adjusted and trend estimates.

ABS time series statistics are published in three forms: original, seasonally adjusted and trend.

Original estimates are the actual estimates the ABS derives from the survey data or other non-survey sources. Original estimates are comprised of trend behaviour, systematic calendar related influences, and irregular influences.

Systematic calendar related influences operate in a sustained and systematic manner that is calendar related. The two most common of these influences are seasonal influences and trading day influences.

Seasonal influences occur for a variety of reasons:

  • They may simply be related to the seasons and related weather conditions, such as warmth in summer and cold in winter. Weather conditions that are out of character for a particular season, such as snow in summer, would appear as irregular, not seasonal, influences.
  • They may reflect traditional behaviour associated with various social events (e.g. Christmas and the associated holiday season).
  • They may reflect the effects of administrative procedures (e.g. quarterly provisional tax payments and end of financial year activity).

Trading day influences refer to activity associated with the number and types of days in a particular month, as different days of the week often have different levels of activity. For instance, a calendar month typically comprises four weeks (28 days) plus an extra two or three days. If these extra days are associated with high activity, then activity for the month overall will tend to be higher.

Seasonal and trading day factors are estimates of the effect that the main systematic calendar related influences have on ABS time series. These evolve to reflect changes in seasonal and trading patterns of activity over the life of the time series, and are used to remove the effect of seasonal and trading day influences from the original estimates.

Seasonally adjusted estimates are derived by removing the systematic calendar related influences from the original estimates. Seasonally adjusted estimates capture trend behaviour, but still contain irregular influences that can mask the underlying month to month or quarter to quarter movement in a series. Seasonally adjusted estimates by themselves are only relevant for sub-annual collections.

Irregular influences are short term fluctuations which are unpredictable, and hence are not systematic or calendar related. Examples of irregular influences are those caused by one-off effects such as major industrial disputes or abnormal weather patterns. Sampling and non-sampling errors that behave in an irregular or erratic fashion with no noticeable systematic pattern are also irregular influences.

Trend estimates are derived by removing irregular influences from the seasonally adjusted estimates. As they do not include systematic, calendar related influences or irregular influences, trend estimates are the best measure of the underlying behaviour of the series, and the labour market.

Trend estimates are produced by smoothing the seasonally adjusted series using a statistical procedure based on Henderson moving averages. At each survey cycle, the trend estimates are calculated using a centred x-term Henderson moving average of the seasonally adjusted series. The moving averages are centred on the point in time at which the trend is being estimated. The number of terms used to calculate the trend estimates varies across surveys. Generally, ABS monthly surveys use a 13-term Henderson moving average, and quarterly surveys use a 7-term Henderson moving average.

Estimates for the most recent survey cycles cannot be directly calculated using the centred moving average method, as there are insufficient data to do so. Instead, alternative approaches that approximate the smoothing properties of the Henderson moving average are used - such as asymmetric averages. This can lead to revisions in the trend estimates for the most recent survey cycles, until sufficient data are available to calculate the trend using the centred Henderson moving average. Revisions of trend estimates will also occur with revisions to the original data and re-estimation of seasonal adjustment factors.

Reliability of estimates

The accuracy of an estimate refers to how close that estimate is to the true population value. Where there is a discrepancy between the value of the sample estimate and the true population value, the difference between the two is referred to as the 'error of the sampling estimate'. The total error of the survey estimate results from two types of error:

  • sampling error - errors which occur because data were obtained from only a sample rather than the entire population, and
  • non-sampling error - errors which occur at any stage of a survey, and can also occur in censuses.

Sampling error

    Sampling error equals the difference between the estimate obtained from a particular sample, and the value that would be obtained if the whole survey population were enumerated. It is important to consider sampling error when publishing survey results as it gives an indication of the accuracy of the estimate, and therefore reflects the importance that can be placed on interpretations. For a given estimator and sample design, the expected size of the sampling error is affected by how similar the units in the target population are and the sample size.

    Variance

    Variance is a measure of sampling error that is defined as the average of the squares of the deviation of each possible estimate (based on all possible samples for the same design) from the expected value. It gives an indication of how accurate the survey estimate is likely to be, by measuring the spread of estimates around the expected value. For probability sampling, an estimate of the variance can be calculated from the data values in the particular sample that is generated.

    Methods used to calculate estimates of variance in ABS labour-related surveys are outlined below.

    • Jack-knife: This method starts by dividing the survey sample into a number of equally sized groups (replicate groups), containing one or more units. Pseudo-estimates of the population total are then calculated from the sample by excluding each replicate group in turn. The jack-knife variance is derived from the variation of the respective pseudo-estimates around the estimate based on the whole sample. This method is used in a number of household surveys, including the LFS (from November 2002), supplementary surveys (from August 2005), the Multipurpose Household Survey (MPHS) and some labour-related business surveys.
    • Bootstrap: The Bootstrap is a variance estimation method which relies on the use of replicate samples, essentially sampling from within the main sample. Each of these replicate samples is then used to calculate a replicate estimate and the variation in these replicate estimates is used to calculate the variance of a particular estimate.
    • Ultimate cluster variance: This method is used in some multi-stage sampling, and involves using the variation in estimates derived from the first-stage units to estimate the variance of the total estimate. This method is used in the Survey of Employee Earnings and Hours.
    • Split halves: This method involves dividing the sample into half and, from each half, obtaining an independent estimate of the total. The variance estimate is produced using the square of the difference of these estimates. Variations of the split halves method for calculating variance estimates were used in a number of household surveys, including the LFS prior to November 2002 and supplementary surveys prior to August 2005.

    The variances indicated in ABS household survey publications are generally based on models of each survey's variance. The variances for a range of estimates are calculated using one of the above methods, and a curve is fitted to the results. This curve indicates the level of variance which could be expected for a particular size of estimate.

    Standard Error (SE)

    The most commonly used measure of sampling error is called the standard error (SE). The SE is equal to the square root of the variance. An estimate of the SE can be derived from either the population variance (if known) or the estimated variance from the sample units. Any estimate derived from a probability based sample survey has an SE associated with it (called the SE of the estimate). The main features of SEs are set out below.

    • SEs indicate how close survey estimates are likely to be to the expected population values that would be obtained from a census conducted under the same procedures and processes;
    • SEs provide measures of variation in estimates obtained from all possible samples under a given design;
    • Small SEs indicate that variation in estimates from repeated samples is small, and it is likely that sample estimates will be close to the true population values, regardless of the sample selected;
    • Estimates of SEs can be obtained from any probability sample - different random samples will produce different estimates of SEs;
    • SEs calculated from survey samples are themselves estimates, and thus also subject to SEs;
    • When comparing survey estimates, statements should be made about the SEs of those estimates; and
    • SEs can be used to work out confidence intervals. This concept is explained below.

    Confidence Interval (CI)

    A confidence interval (CI) is defined as an interval, centred on the estimate, with a prescribed level of probability that it includes the true population value (if the estimator is unbiased), or the mean of the sampling distribution (if the estimator is biased). Estimates from ABS surveys are usually unbiased.

    Estimates are often presented in terms of a CI. Most commonly, CIs are constructed for 66%, 95%, and 99% levels of probability. The true value is said to have a given probability of lying within the constructed interval. For example:

    • 66% chance that the true value lies within 1 standard error of the estimate (2 chances in 3);
    • 95% chance that the true value lies within 2 standard errors of the estimate (19 chances in 20); and
    • 99% chance that the true value lies within 3 standard errors of the estimate (99 chances in 100).

    CIs are constructed using the standard error associated with an estimate. For example, a 95% CI is equivalent to the survey estimate, plus or minus two times the standard error of the estimate. For example, the originally published LFS estimate of employment (seasonally adjusted) for September 2017 was 12,290,200 persons, and the estimate had a standard error of 44,400. The 95% CI could be expressed: "we are 95% confident that the true value for employment lies between 12,201,400 and 12,379,000".

    Relative Standard Error (RSE)

    Another measure of sampling error is the relative standard error (RSE). This is the standard error expressed as a percentage of the estimate. Since the standard error of an estimate is generally related to the size of the estimate, it is not possible to deduce the accuracy of the estimate from the standard error without also referring to the size of the estimate. The relative standard error avoids the need to refer to the estimate, since the standard error is expressed as a proportion of the estimate. RSEs are useful when comparing the variability of population estimates of different sizes. They are commonly expressed as percentages.

    Very small estimates are subject to high RSEs, which detract from their usefulness. In some ABS labour-related statistical publications, estimates with an RSE greater than 25% but less than 50% have an asterisk (*) displayed beside the estimate, indicating they should be used with caution. Estimates with an RSE greater than 50% have two asterisks (**) displayed beside the estimate, indicating they are so unreliable as to detract seriously from their value for most reasonable uses. All cells in a Data Cube with RSEs greater than 25% contain a comment indicating the size of the RSE. These cells are identified by a red indicator in the corner of the cell. The comment appears when the mouse pointer hovers over the cell.

    Non-sampling error

    Non-sampling error refers to all other errors in the estimate. Non-sampling error can be caused by non-response, badly designed questionnaires, respondent bias, interviewer bias, collection bias, frame deficiencies and processing errors. It is often difficult and expensive to quantify non-sampling error.

    Non-sampling errors can occur at any stage of the process, and in both censuses and sample surveys. Non-sampling errors can be grouped into two main types: systematic and variable. Systematic error (called bias) makes survey results unrepresentative of the population value by systematically distorting the survey estimates. Variable error can distort the results on any given occasion, but tends to balance out on average over time.

    Every effort is made to minimise non-sampling error in ABS surveys at every stage of the survey, through careful design of collections, and the use of rigorous editing and quality control procedures in the compilation of data. Some of the approaches adopted are listed below.

    • Reducing frame deficiencies.
    • Reducing non-response - Non-response results in bias in the estimate because it is possible the non-respondents have different characteristics to respondents, leading to an under-representation of the characteristics of non-respondents in the sample survey estimate. The ABS pursues a policy of intensive follow up of non-respondents. This includes multiple visits or telephone calls in an attempt to contact respondents, and letters requesting compliance with the survey. Partial non-response is also followed up with respondents.
    • Reducing instrument errors - These errors relate to poor questionnaire design, leading to questions which are not easily understood by respondents, and hence incorrect responses. This is particularly relevant for household surveys. The ABS ensures that all household survey questionnaires are carefully tested using cognitive testing and dress rehearsals of the survey before it is officially conducted. New business survey questionnaires and additional questions in business surveys are also rigorously tested before they are introduced.

    Measures of non-sampling error

    Non-sampling error is difficult to quantify; however, an indication of the level of non-sampling error can be determined from a number of quality measures. These include:

    • Response rates: The number of responding units in a survey expressed as a proportion of the total number of units selected (excluding deaths). Response rates can also be calculated for individual questions within a survey.
    • Imputation rates: The number of responses which need to be imputed expressed as a proportion of the total number of responses
    • Coverage rates: An estimate of the proportion of units in the target population which are not covered by the frame
    • Any Responsible Adult rates: The number of responding units in a survey for which information was supplied by a responsible adult rather than personally, expressed as a proportion of the total number of responding units. Any Responsible Adult rates can only be calculated for household surveys.

    Confidentiality

    All releases of data from the ABS are confidentialised to ensure that no unit (e.g. person or business) is able to be identified. The ABS applies a set of rules, concerning the minimum number of responses required to contribute to each data cell of a table, and the maximum proportion that any one respondent can contribute to a table cell, to ensure that information about specific units cannot be derived from published survey results.

    In some instances it is not possible to confidentialise responses from businesses that contribute substantially to a data cell. In this case, agreement is sought from the business for their data to still be published. If agreement is not reached, all affected data cells are suppressed.

    Under the Census and Statistics Act, 1905 it is an offence to release any information collected under the Act that is likely to enable identification of any particular individual or organisation. Introduced random error is used to ensure that no data are released which could risk the identification of individuals in the statistics.

    A technique, known as perturbation, has been developed to randomly adjust cell values. Random adjustment of the data is considered to be the most satisfactory technique for avoiding the release of identifiable data. When the technique is applied, all cells are slightly adjusted to prevent any identifiable data being exposed. These adjustments result in small introduced random errors. However, the information value of the table as a whole is not impaired.

    These adjustments may cause the sum of rows or columns to differ by small amounts from table totals. The counts are adjusted independently in a controlled manner, so the same information is adjusted by the same amount. However, tables at higher geographic levels may not be equal to the sum of the tables for the component geographic units.

    It is not possible to determine which individual figures have been affected by random error adjustments, but the small variance which may be associated with derived totals can, for the most part, be ignored.

    Household surveys

    Household surveys and population censuses constitute the primary sources of ABS labour statistics on people and households. In addition to information about current and previous labour force participation, information collected also includes demographic data, such as age, sex, family characteristics and country of birth. Labour statistics collected about people provide insight into the supply of labour to the Australian labour market.

    Household surveys falling within the labour statistics program include:

    The ABS household survey program also includes other social surveys that contain a labour force status module. Other labour-related data include:

    For specific information on each of these surveys, refer to the relevant methodology pages for each statistical release.

    Scope and coverage

    The scope of ABS household surveys varies from survey to survey. The Census of Population and Housing has the broadest scope of all ABS household collections, and aims to collect information from all persons residing in Australia on Census night. The scope of the Labour Force Survey (LFS) is the civilian population aged 15 years and over, and while the Labour Force Supplementary Surveys (LFSSs) vary, their scope is generally narrower than that of the LFS. The target populations of Special Social Surveys (SSS) also vary.

    Practical collection difficulties, low levels of response, high levels of sample loss and the small numbers involved have resulted in the exclusion of persons living in remote and sparsely settled parts of Australia from a number of household surveys (exceptions include: the Census of Population and Housing; the LFS; and some SSSs). The exclusion of these persons has only a minor impact on any estimates produced for individual states and territories, with the exception of the Northern Territory.

    Some household surveys exclude all persons living in special dwellings from their scope. Special dwellings include hotels, motels, hospitals, prisons and boarding houses. Other household surveys exclude certain types of persons living in special dwellings: for example, institutionalised persons and boarding school pupils are excluded from the scope of most supplementary surveys. 

    Institutionalised persons are people selected in institutions such as hospitals and homes (including general homes, other hospitals, convalescent homes, homes for the aged, retirement homes, homes for the handicapped and orphanages), and prisons, apart from live-in staff that do not usually live in a private dwelling.

    Coverage rules are generally applied in all household surveys to ensure that each person is associated with only one dwelling, and hence has only one chance of selection. The chance of a person being enumerated at two separate dwellings in the one survey is considered to be negligible. Some surveys remove certain dwellings from coverage but not from scope; the estimates still are intended to include these excluded dwellings. The estimation method used for the survey makes an adjustment to include these dwellings and persons in the final outputs.

    Collection methodologies

    A number of methods are used by the ABS for collecting data. Those most commonly used in labour-related surveys can be categorised into three basic groups: 

    • interview;
    • self-enumeration; and
    • documentary sources.

    Historically, these collection methods have been manual, paper-based methods. Each of these methods has a corresponding electronic method, generally referred to as 'computer assisted'.

    Interview

    The interview method of data collection involves an interviewer contacting data providers, asking the questions, and recording the responses. Interviews can be personal, where the data provider is interviewed personally, or involving Any Responsible Adult (ARA), where the ARA responds on behalf of other survey units. Interviews can be conducted either face to face or over the telephone. Interviews are most commonly used in household surveys.

    Personal interviewing involves each provider being questioned about his or her own details. The Any Responsible Adult (ARA), or proxy, method of interviewing is used in a number of ABS household surveys as an alternative to personal interviewing. This involves obtaining information about all the persons in a selected household who are in scope of the survey, from the first responsible adult with whom the interviewer makes contact (rather than speaking to each individual personally). The method is only used for collecting information on topics where other members of the household are likely to be able to answer the question. If the ARA is unable to supply all of the details for another individual in the household, a personal interview is conducted with that particular individual.

    Face to face interview (CAPI - Computer Assisted Personal Interviewing)

    When performing a computer-assisted personal interview (CAPI), the interviewer takes a laptop computer to the interview and codes the data into the computer as it is provided. Advantages of this method of data collection are:

    • more flexibility to move around the form and skip questions;
    • higher response rates;
    • interviewers are able to help respondents understand the questions, thereby allowing for the collection of more complex data;
    • some edit checks are carried out at the time of the interview, thus improving data quality; and
    • the overall timeliness of the survey is improved.

    However, face to face interviews are expensive. Face to face interviews involve a trained interviewer visiting the provider to conduct the survey. There are costs involved in time and travel to reach the respondents; maintenance of the computer equipment; in the recruitment, training in the use of CAPI; management of an interviewer work force; and the actual interview time increases as responses are coded and edited at the time of the interview. Other disadvantages are that data can possibly be subject to bias caused by the interviewer's appearance and attitude, and that respondents may not feel free to disclose sensitive or private information to an interviewer.

    Telephone interview (CATI - Computer Assisted Telephone Interviewing)

    Computer Assisted Telephone Interviewing (CATI) involves responses being keyed directly into a computer by the interviewer as the providers are asked the survey questions over the telephone. This technique allows for:

    • reduced costs compared to face to face interviews, as fewer interviewers are needed and there are no travel costs involved;
    • telephone interviews potentially producing more timely results;
    • some editing to be carried out immediately (which improves the data quality and decreases processing time);
    • 'call scheduling' to take place. Respondents can be called at convenient times or when data is available. Also, if the phone is engaged, the system will reschedule the call, and follow-ups for additional information are relatively quick and inexpensive;
    • questions to be sequenced so that only relevant questions are visible to the interviewer (therefore reducing interviewer errors); and
    • monitoring of interviewing staff so that consistency of performance is higher.

    As with other methods of data collection, there are some drawbacks associated with this approach. There are limits on the number and complexity of questions that can be asked and, because of the ease with which the respondent can terminate the interview, non-response and partial non-response can be higher than with face to face interviews.

    Telephone interviewing is used in both ABS household and business surveys, and may be used in conjunction with face to face interviews. For example, in the Labour Force Survey (LFS) the first interview is generally conducted face to face and the remaining interviews are conducted by telephone if the provider agrees.

    Online self-completion (CAWI – Computer Assisted Web Interviewing)

    Online self-completion of surveys was introduced in December 2012. Respondents were offered the option of self-completing the survey online, in place of a face-to-face or telephone interview. The online self-completion offer was later expanded to all private households. Interviewer collection (both face-to-face and via telephone) continues to be available for those respondents where it is inappropriate for operational, technological or personal reasons.

    The use of electronic returns produces a faster response than other self-enumeration methods. Questions can also be sequenced so that only the questions relevant to the respondent are visible. The disadvantages are: increased cost for development of the forms, maintenance of the related systems and security, and help-desk staff to support the use of the form. Also, this technique requires respondents to have computer access.

    Household surveys and collection methods
     Respondent modesRespondent selection
    Labour Force Survey and associated Supplementary SurveysPredominantly interviewer administered – first month often face-to-face, with telephone interview thereafter. Online self-enumeration offered as the primary response mode.Any responsible adult.
    Multipurpose Household SurveyPredominantly interviewer administered – first month often face-to-face, with telephone interview thereafter. Online self-enumeration offered as the primary response mode.Personal interview – self-reporting.
    Special Social SurveyInterviewer administered – face to face or telephone interviewing.Personal interview – self-reporting.
    Census of Population and HousingSelf-enumeration – either pen and paper or on-line.Any responsible adult.

    Intensive follow up procedures for non-response are in place for household surveys. Interviewers make a number of attempts to contact households at different times of the day and on different days during the week. For households unable to be contacted by telephone, a face-to-face visit is attempted. If the household can still not be contacted within the survey period after repeated attempts, and the dwelling has been verified as not vacant, the household is listed as a non-contact.

    Sample design

    With the exception of the Census of Population and Housing, most ABS household surveys use probability sample designs, drawing their sample from the Monthly Population Survey (MPS) and the SSS samples, which are drawn from a ‘Master Sample’. These household surveys all use a multi-stage, stratified sample design. Typically three stages are used; the first stage units (FSUs) are randomly selected areas the size of Statistical Area Level 1’s (SA1s) - about 200 dwellings. The Master Sample consists of these FSUs.

    The Master Sample is drawn from the Population Survey Framework, which is composed of three components: the private dwelling framework, the special dwelling framework, and the Aboriginal and Torres Strait Islander Communities framework. These three frames are generally non-overlapping, and therefore enable the selection of samples that represent the Australian population. The overlap occurs as there are some special dwellings within the Aboriginal and Torres Strait Islander Communities framework.

    For more information about sample design and method of estimation, see the LFS methodology page.

    Private dwelling framework

    In general, private dwellings are structures built specifically for living purposes, such as houses, flats, home units, and any other structures used as private places of residence. A private dwelling can also be a caravan, a houseboat, a house attached to an office, or rooms above a shop. In practice, some dwellings such as caravan parks and marinas are listed on the special dwelling list.

    In most areas of Australia, private dwelling sample selection is structured around the selection of fine geographic regions defined by the aggregation of mesh blocks. Mesh blocks are the finest unit in the Australian Statistical Geography Standard (ASGS), the ABS Geography Standard which replaced the previous standard in 2012. For more information about mesh blocks, see Australian Statistical Geography Standard (ASGS): Volume 1 - Main Structure and Greater Capital City Statistical Areas (cat. no. 1270.0.55.001).

    The key geographic sampling unit in the new framework is called the Base Frame Unit. These Base Frame Units were created by combining contiguous mesh blocks in nearly all regions of Australia, and were created solely for the purpose of household survey sampling. Their intended role is to define the geographic area within which dwellings are organised into groups which are selected in a sample together. These selected dwellings within the selected Base Frame Units are termed the “cluster”. The clusters vary in size from 5-15, reflecting the cost of enumeration. If an area is remote and costly to enumerate, it will have a cluster size at the upper-end of this range of cluster sizes.

    Three special strata are adopted: Secure Apartment Buildings, Pre-Determined Growth, and Indigenous geography strata. There is a single special stratum of each type within a State/Territory (at most), so the sample in these strata can cut across the area unit boundaries

    Each area selection unit in the master sample is assigned an "area type" class based on the geography of Australia. A variety of geographic classifications defined by different sources are combined to derive the area type classes:

    • ASGS: Greater Capital City Statistical Area (GCCSA);
    • ABS Geography classifications: Remoteness area (RA), Section of state (SoS), Urban centre or locality (UCL); and
    • Household Survey Methodology (HSM): Self representing Area (SRA) / non-SRA (based on estimated population density).

    Special dwelling framework

    The special dwelling household framework is a list of 'special' dwellings, from which samples of special dwellings and their residents can be selected. Special dwellings are establishments which provide predominantly short-term accommodation for communal or group living, and often provide common eating facilities. They include hotels, motels, hostels, hospitals, religious institutions providing accommodation, educational institutions providing accommodation, prisons, boarding houses, short-stay caravan parks, and may include some Aboriginal and Torres Strait Islander communities that are not on the Aboriginal and Torres Strait Islander Community Frame. Some special dwellings are designed for a particular purpose (e.g. hospitals) and, as such, provide accommodation for specific groups of persons. Special dwellings each comprise a number of dwelling units. Currently, there are around 26,000 special dwellings on the frame.

    The framework contains information about the occupancy of each special dwelling as it was on Census night.

    The special dwelling framework is also stratified geographically, though at a broader level than the private dwelling framework. In many cases the demographic, social and labour force characteristics of the occupants of special dwellings are not typical of the population residing in private dwellings, and therefore it is necessary to sample special dwellings separately by placing them in separate strata within each geographic (sample design) region. This provides for more effective samples of persons within special dwellings and private dwellings, and the flexibility to select some samples which exclude all or some special dwellings, or to select samples in which special attention is paid to persons residing in particular special dwellings.

    Aboriginal and Torres Strait Islander Community Frame

    The Aboriginal and Torres Strait Islander Community frame is a tool used to ensure adequate sample selection for this population. It can be thought of as an extension of the private dwelling frame. A Mesh Block is classified as a discrete community mesh block if it is deemed to have an Aboriginal or Torres Strait Islander community population of 75% or more, and lies in the non-metropolitan area of Queensland, South Australia and Western Australia or Northern Territory. This frame is constructed using information from the Census of Population and Housing and other information covering the communities.

    There are two sample groups included on this frame. Discrete Aboriginal and Torres Strait Islander communities (including any out-stations associated with them) are referred to as the 'community sample'. Dwellings in areas not covered by the community sample are referred to as 'non-community sample'. Information on the Aboriginal and Torres Strait Islander Community frame, community and non-community sample is contained in the ABS publication National Aboriginal and Torres Strait Islander Social Survey: User Guide, 2014–15 (cat. no. 4720.0).

    The Aboriginal and Torres Strait Islander Communities frame is stratified geographically by State/Territory, with Torres Strait Islander communities in Queensland separately stratified.

    MPS and SSS master samples

    From July 2018, there will be a single Master Sample covering the sample requirements for both the Monthly Population Survey (MPS) and the Special Social Surveys (SSS)’s. The 2018 Master Sample will be the first to make use of the Address Register, which is now also used to support the enumeration of the Census of Population and Housing. In addition, a new method of selection (known as Conditional Selection) will also operate from 2018 onward, which will support more flexible sampling methods. Conditional selection is a method of selecting survey samples that allows the ABS to effectively manage overlap between different surveys, to prevent any household from being selected for two or more surveys, while also allowing survey samples to be located nearby to each other in order to reduce survey costs.

    The MPS sample and the SSS samples comprise Base Frame Units taken from the private dwelling framework, special dwellings, and Indigenous communities (IC) from the ICF. Most household surveys conducted by the ABS use samples drawn from the Master Sample.

    The MPS consists of monthly LFS, the Multipurpose Household Survey (MPHS), and also various supplementary surveys conducted in conjunction with the LFS. Dwellings selected in the LFS sample remain in sample for eight consecutive months. The program of SSSs consists of large-scale periodic surveys covering a wide variety of topics.

    Most SSSs have similar (though slightly smaller) survey scope to the MPS, so the requirements and structure of the samples are also similar. In terms of the geographic scope of MPS and SSSs, a key difference is that most SSSs exclude very remote areas. Most SSSs do not obtain sample from discrete Indigenous communities, or select persons in special dwellings.

    To date, the SSS Base Frame Units do not include any Base Frame Units selected in the MPS sample, thereby preventing households selected for the MPS from also being selected for a SSS during the life of a specific sample design.
    It has traditionally been the practice that the Master Sample is re-selected and redesigned every five years following the Census of Population and Housing. The move from Census-based master samples to Address Register-based designs enables more frequent updates, with the first Address Register-based sample expected to be in use for 3 years, from July 2018 to June 2021.

    Sample selection

    From 2018, the ABS is using an Address Register in the sample selection process for all of its household surveys.

    The Address Register, which is also now used to support the enumeration of the Census of Population and Housing, is a list of all physical addresses (both residential and non-residential) in Australia. The main input to the register is the Geocoded National Address File (G-NAF), with continuing supplementation from other available address sources and from field work undertaken by ABS officers.

    The ABS has developed this register as the central source of addresses used in the collection of information in response to the need for more efficient and effective household survey designs, including:

    • the creation of a dwelling frame for the mail out areas of the 2016 Census; and
    • the creation of quarterly frames for ABS household surveys.
    • The Address Register Common Frame is a trusted and comprehensive data set of Australian address information. It contains current address text details, coordinate reference (or “geocode”), and address use information for addresses in Australia.

    Stages of selection

    There are three stages of selection:

    • First Stage Units; then
    • Base Frame Units (consisting of aggregates of Mesh Blocks); then
    • Dwellings.

    The Mesh Block is the finest ASGS 2016 geographical unit, typically containing 30-60 dwellings. First Stage Units are typically a set of contiguous Mesh Blocks. These stages of selection within a stratum are illustrated in Figure 17.1 below.

    Three stages of selection

    Three stages of selection
    Outlines the three stages of selection in Household Surveys. In 2015 a comprehensive list of all physical addresses in Australia was created for use in household survey designs, and is known as the Address Register. It contains current address details, coordinate reference (or "geocode") and address use information for addresses in Australia. Usage of the Address Register as the Labour Force Survey sampling frame forms a three stage selection process made up of first stage units; then base frame units (consisting of aggregate of mesh blocks); and lastly dwellings.

    Benchmarks

    Changes to the LFS population benchmarks impact primarily on the magnitude of the LFS estimates (i.e. employment and unemployment) that are directly related to the underlying size of the population.

    Estimates of the population produced from household surveys are calculated in such a way as to add up to independently estimated counts (benchmarks) of the population. For the LFS, these benchmarks are based on Census of Population and Housing data, adjusted for under-enumeration and updated for births, deaths, interstate migration, and net permanent and long term migration. Benchmarks have been developed for state/territory of usual residence, part of state of usual residence (for example, capital city, rest of state), age and sex. Each cross-classification of these benchmark variables is known as a benchmark cell. Revisions are made to benchmarks after each Census of Population and Housing, and when the bases for estimating the population are reviewed.

    Other household surveys use various combinations of benchmark variables to produce benchmark cells. Some surveys use supplementary information (such as LFS estimates), referred to in this context as pseudo-benchmarks, to supplement independent demographic benchmarks based on Census of Population and Housing data. Household surveys may use calibration methods to incorporate other auxiliary information on target populations into estimates - for instance, benchmarks for the Indigenous population or the population of private households.

    Non-response

    Non-response arises when no information is collected from one or more occupants of a selected dwelling.

    Interviewers make a number of attempts to contact households at different times of the day and on different days during the week. For households and persons unable to be contacted by telephone, face-to-face visits are attempted. If the household still cannot be contacted within the survey period after repeated attempts (and if the dwelling has been verified as not vacant), it is listed as a non-contact. Non-contact is the most common form of non-response.

    The response rate commonly quoted for ABS household surveys refers to the number of fully responding dwellings expressed as a percentage of the total number of selected dwellings excluding sample loss. Examples of sample loss for the LFS include:

    • households where all persons are out of scope and/or coverage;
    • vacant dwellings;
    • dwellings under construction;
    • dwellings converted to non-dwellings;
    • derelict dwellings; and
    • demolished dwellings.

    For most household surveys, a non-response adjustment is performed implicitly by the estimation system, which effectively imputes for each non-responding person on the basis of all responding persons in the same post-stratum. This adjustment accounts for both full non-response and non-response for individual questions.

    Labour Force Survey

    The Labour Force Survey (LFS) provides Australia's official measure of employment, unemployment and labour force participation. The data captured in this survey are some of Australia’s key economic statistics, providing insight into the Australian economy and Australian people.

    About the Labour Force Survey

    The Labour Force Survey
    FrequencyMonthly
    Responding sample sizeApprox. 26,000 households (52,000 people)
    ScopeUsual resident civilians 15 years and over
    Response rateApprox. 93%
    PublicationLabour Force, Australia; Labour Force, Australia - Detailed; Labour Force Status of Families, Australia
    Data availabilityQuarterly from 1966-1977 and monthly from 1978 onwards

    Each month, The LFS collects data on the labour force activity of persons around 52,000 people in 26,000 households. The information is collected through a household sample survey conducted by trained interviewers either face-to-face or over the phone, or via online self-completion form. The survey is detailed, including around 70 questions.

    The scope of the LFS is limited to the usually resident civilian population of Australia, aged 15 years and over. As such, the survey includes residents who are temporarily overseas (less than 6 weeks), but excludes members of the permanent defence forces. The ABS then weights the people in the survey sample to the most recent population figures, to provide a representative picture of the whole population.

    In addition to data on employment and unemployment, the LFS also collects information on underutilisation, hours worked, job searching and retrenchments, as well as socio-demographic characteristics.

    The Labour Force Survey Standard Products and Data Item Guide is a useful reference for users seeking different data variables from the Labour Force Survey. The guide is divided into two sections:

    • Section 1: Labour Force Survey standard product data - The first section alphabetically lists and explains the data items in Labour Force Survey standard products and where to find them.
    • Section 2: Detailed information on Labour Force Survey standard products - The second section lists the Labour Force Survey standard products, and specifies the data items contained within each spreadsheet and data cube.

    Sample rotation

    The LFS sample can be thought of as comprising eight sub-samples (or rotation groups), with each subsample remaining in the survey for eight months. A new rotation group is introduced each month to replace an outgoing rotation group, generally from the same geographic area.

    Sample rotation enables reliable measures of monthly change in labour force statistics to be compiled, while ensuring the sample reflects changes in the household population.

    Figure 1: Sample rotation

    Figure 1: Sample rotation
    As shown in Figure 1, the LFS sample can be thought of as comprising eight sub-samples (or rotation groups), with each subsample remaining in the survey for eight months. A new rotation group is introduced each month to replace an outgoing rotation group, generally from the same geographic area. Sample rotation enables reliable measures of monthly change in labour force statistics to be compiled, while ensuring the sample reflects changes in the household population.

    For more information about the Labour Force Survey, see the Labour Force, Australia methodology page.

    Using labour force data

    Time-series data

    Data collected regularly over time may display seasonal and irregular patterns. This raw data, known as the original series, can be very volatile, making it difficult to identify underlying trends. The ABS therefore publishes two additional data series to aid time-series analysis: seasonally adjusted and trend data in addition to the original (unadjusted) survey estimates.

    Trend data helps to determine the underlying path of the series, by smoothing out any irregularities. It is calculated as a 13 month moving average, using data from 6 months prior to and following the reference period.

    Seasonally adjusted data has been modified to remove any patterns caused by regularly repeating cycles in the real world, such as the Christmas period, harvesting season, and school holidays. This series aids in short-term forecasting and allows series to be compared between periods; however, can still be volatile.

    Reliability

    As the LFS is a sample survey, the data are subject to sampling and non-sampling error. The ABS takes data quality seriously and makes every effort to minimise error where possible, achieving a response rate of 93%. While the sample is designed to ensure sampling error is as low as possible at the national and state/territory level, it can be higher for labour force regions or for detailed population breakdowns.

    International comparisons of Labour Force Surveys

    International comparisons of labour statistics are essential in providing a global context to economic analysis, social research and policy formation and evaluation. When comparing data across countries, consideration must be given to the differences in how labour concepts are measured. Since 1919, the International Labour Organisation (ILO) has maintained and developed a system of international labour standards. The ABS provides data about the Australian labour for to groups such as the Organisation for Economic Co-operation and Development (OECD) and ILO, who collate data from multiple countries on a similar basis to allow such cross-country comparisons to occur.

    When comparing data across countries, consideration should also be given to differences in the collection methodologies of each country’s labour force survey.

    Comparison of Labour Force Surveys
     AustraliaCanadaNew ZealandUnited KingdomUnited States
    SurveyLabour Force SurveyLabour Force SurveyHousehold Labour Force SurveyLabour Force SurveyCurrent Population Survey
    OrganisationAustralian Bureau of StatisticsStatistics CanadaStatistics New ZealandOffice for National StatisticsBureau of Labour Statistics
    FrequencyMonthlyMonthlyQuarterlyQuarterlyMonthly
    ScopeUsually resident, civilians aged 15+Civilian non-institutionalised population aged 15+Usually resident, civilian non-institutional populations aged 15+Permanent residents aged 16-74 yearsUsually resident, civilian non-institutional populations aged 16+
    Sample SizeApprox. 26 000 dwellings (52 000 persons)Approx. 56 000 dwellings (100 000 persons)Approx. 15 000 dwellings (30 000 persons)Approx. 40 000 dwellings (100 000 persons)Approx. 60 000 dwellings (112 000 persons)
    Population (June, 2016)24.21 mill.36.71 mill.4.70 mill.58.38 mill.325.34 mill.
    % population in survey0.21%0.27%0.64%0.17%0.03%
    Working age population (2016)0.33%0.42%0.98%0.24%0.05%
    Sample rotationRotating panel sample design. Selected households remain in the survey for eight consecutive months. A new rotation group is introduced each month to replace an outgoing group (one-eighth of the sample).Rotating panel sample design. Selected households remain in the survey for six consecutive months. A new rotation group is introduced each month to replace an outgoing group (one-sixth of the sample).Rotating panel sample design. Selected households remain in the survey for eight consecutive quarters. A new rotation group is introduced each quarter, from the same Primary Sampling Unit, to replace an outgoing group (one-eighth of the sample).Rotating panel sample design. Selected households respondents are questioned five times at 13 week intervals (consecutive) and one-fifth of the sample is replaced each quarter.Eight representative rotation groups, each in the sample for eight months total. Each rotation group is included in the sample for two four month periods, separated by an eight month period not in the sample. 75% of the sample is common from month-to-month and 50% one year apart for the same month.
    Collection methodologyPersonal interview, telephone interview, and online form. Data is collected for each in-scope household member from 'Any Responsible Adult'.Personal or telephone interview (in English or French). Data collected from a knowledgeable household respondent. Proxy reporting accounts for 65% of collected information.First interview conducted in person with subsequent interviews via telephone, unless personal interview requested by respondent.First interview conducted in person, with subsequent interviews via telephone. 35.0% of the interviews in 2015 were carried out by proxy.Personal interview conducted in first and fifth month (after 8 month dormant period). Other interviews via telephone. Data collected from a responsible adult household respondent.
    Response ratesApprox. 93%Approx. 90%Approx. 78%Approx. 49%Approx. 87%
    TopicsEmployment, unemployment, underemployment, labour underutilisation, participation, working time, job search, last job and economic inactivity by socio-demographic groups and by region.Employment, unemployment, underemployment, labour underutilisation, working time, weekly earnings and economic inactivity by socio-demographic groups and by Provinces.Employment, unemployment, underemployment, labour underutilisation, working time and economic inactivity by socio-demographic groups and by region.Employment, unemployment, underemployment, labour underutilisation and economic inactivity by socio-demographic groups.Employment, unemployment, underemployment, labour underutilisation, working time and economic inactivity by socio-demographic groups and by States.

    For more information on Labour Force Surveys in other countries, see:

    For further information, please email labour.statistics@abs.gov.au

    Labour force supplementary surveys

    A supplementary topic was included with the Labour Force Survey (LFS) for the first time in November 1961, and this concept was gradually extended so that the majority of months in each year included supplementary questions on one or more topics.

    Each Labour Force Supplementary Survey (LFSS) comprises a series of additional questions asked at the end of each LFS interview. The survey methodology does not differ greatly among the supplementary surveys, and in many aspects is the same as the LFS methodology (outlined in the section: Labour Force Survey). This section describes the broad survey methodology of the supplementary surveys. They should be used in conjunction with the subsections of this section, which outline elements of the methodology which are unique to each supplementary survey.

    From July 2014, the ABS improved and consolidated the content of the LFS and labour supplementary surveys. See Information Paper: Outcomes of the Labour Household Surveys Content Review, 2012 and Forthcoming changes to ABS Labour Force and Supplementary Surveys for more information.

    The Characteristics of Employment Survey (COE) combines the key elements from the previous separate Employee Earnings, Benefits and Trade Union Membership Survey (EEBTUM), Forms of Employment Survey (FOES) and Working Time Arrangements Survey, to provide a comprehensive and coherent dataset on characteristics of persons' employment.

    The Participation, Job Search and Mobility (PJSM) Survey combines the key elements from the previous separate Persons Not in the Labour Force Survey (PNILF), Underemployed Workers Survey (UEW), Job Search Experience Survey (JSE) and Labour Mobility Survey (LM), to provide a comprehensive and coherent dataset on persons' experiences relating to job search, job change and increasing participation.

    Objectives of the labour force supplementary surveys

    The LFSSs form an important component of the ABS's household surveys program, which aims: 

    • to provide a range of statistics required to monitor the social and economic wellbeing of Australians, with particular reference to important sub-groups of the population; and
    • to support the development, implementation and evaluation of policies and programs of key Commonwealth and State government agencies.

    The information requirements of ABS household surveys are determined on the basis of submissions from users on their needs for and uses of household survey data. They also reflect ABS deliberations on what is required of a national statistics program in the various subject fields, based on user contact and consultation.

    In the field of labour statistics, supplementary surveys provide detailed information on a range of labour topics and interest groups such as:

    • labour force - labour force experience,
    • employment - underemployment; multiple job holding; forms of employment,
    • employees - earnings; trade union membership; benefits; and working arrangements,
    • unemployment - job search experience; successful and unsuccessful job search,
    • persons not in the labour force - discouraged job seekers; other persons with marginal attachment to the labour force, and
    • persons retrenched or made redundant from work.

    Current and historical supplementary survey statistical releases

    PublicationCatalogue NumberFrequencyData Availability
    Characteristics of Employment, Australia6333.0AnnualThis product replaces the publications: Employee Earnings, Benefits and Trade Union Membership, Australia; Forms of Employment, Australia; Working Time Arrangements; and Locations of Work.
    Characteristics of Recent Migrants, Australia6250.0IrregularThis product replaces the publication Labour Force Status and Other Characteristics of Migrants, Australia.
    Child Employment, Australia6211.0IrregularCurrent
    Education and Work, Australia6227.0AnnualCurrent
    Employee Earnings, Benefits and Trade Union Membership, Australia6310.0AnnualThis product has been replaced by Characteristics of Employment
    Forms of Employment, Australia6359.0AnnualThis product has been replaced by Characteristics of Employment
    Job Search Experience, Australia6222.0AnnualThis product has been replaced by Participation, Job Search and Mobility, Australia
    Labour Force Experience, Australia6206.0BiennialDiscontinued
    Labour Mobility, Australia6209.0BiennialThis product has been replaced by Participation, Job Search and Mobility, Australia
    Locations of Work, Australia6275.0IrregularThis product has been replaced by Characteristics of Employment
    Multiple Jobholding, Australia6216.0IrregularLatest data available on request July 2001
    Participation, Job Search and Mobility, Australia6226.0AnnualThis product replaces Persons Not in the Labour Force; Underemployed Workers and Job Search Experience, Australia; and Labour Mobility, Australia
    Persons Not in the Labour Force, Australia6220.0AnnualThis product has been replaced by Participation, Job Search and Mobility, Australia
    Persons Not in the Labour Force, Underemployed Workers and Job Search Experience, Australia6226.0.55.001One-off (Bridge)This product has been replaced by Participation, Job Search and Mobility, Australia. Originally, this product replaced the publications: Persons Not in the Labour Force, Australia; Underemployed Workers, Australia; and Job Search Experience, Australia
    Pregnancy and Employment Transitions, Australia4913.0IrregularCurrent
    Underemployed Workers, Australia6265.0AnnualThis product has been replaced by Participation, Job Search and Mobility, Australia
    Working Time Arrangements, Australia6342.0IrregularThis product has been replaced by Characteristics of Employment

    Labour Multipurpose Household Survey topics

    The Multipurpose Household Survey (MPHS) was introduced in 2004–05. The MPHS is conducted each financial year throughout Australia as a supplement to the ABS' monthly Labour Force Survey (LFS). The MPHS topic questions are asked each month from July to June in a personal interview. The MPHS is designed to provide statistics annually for a number of small, self-contained topics.

    The MPHS collects detailed information on a number of labour market issues, including:

    In addition to the labour-related topics mentioned above, the MPHS program also includes other social topics not related to labour statistics, such as:

    • Crime victimisation;
    • Participation in sport and physical activity;
    • Environmental views and behaviours;
    • Patient experiences in Australia; and
    • Household use of information technology.

    For all MPHS topics, general demographic information such as age, sex, labour force characteristics, education and income are also available.

    This section describes the broad survey methodology of the MPHS. For information on the four labour related MPHS topics that have been conducted to date, see the following sub sections: Barriers and Incentives to Labour Force Participation; Retirement and Retirement Intentions; Work-Related Injuries; and Qualifications and Work.

    Objectives of the Multipurpose Household Survey

    The MPHS topics are an important part of the ABS household surveys program, which aims to:

    • provide a range of statistics needed to monitor the social and economic wellbeing of Australians, with particular reference to important sub-groups of the population; and
    • support the development, implementation and evaluation of policies and programs of key Commonwealth and State government agencies.

    The information requirements of MPHS topics are determined on the basis of submissions from users on their needs for, and uses of, household survey data. They also reflect ABS deliberations on what is required of a national statistics program in the various subject fields, based on user contact and consultation.

    The MPHS is a flexible multi-topic survey vehicle, which is used to collect and output data in a timely fashion. The MPHS includes a number of topics that require personal interview (rather than using the any responsible adult (ARA) method), and are therefore unsuited to the monthly supplementary survey program. The MPHS has a shorter development and output time than the special social surveys to achieve flexibility in responding to user demands as they arise, and to allow the ABS to respond to emerging demand and contemporary priorities in a timely way (publications are usually available within six months of the completion of data collection).

    The MPHS is conducted as a supplement to the monthly LFS. Each month one eighth of the households in the LFS sample are rotated out of the survey. Generally, around 80% of these rotating-out households are then selected for the MPHS each month. In these households, after the LFS has been fully completed for each person in scope and coverage, a person aged 15 years and over is selected at random (based on a computer algorithm) and asked the additional MPHS topic questions in a personal interview. In cases where the MPHS topic is age sensitive, permission is sought from a parent or guardian before conducting the personal interview with a person aged 15 – 17. If permission is not given, the parent or guardian may be asked on behalf of the 15 – 17 year old, but are not asked questions relating to opinions/perceptions.

    Unlike LFS which collects information from all members of the household from any responsible adult, the MPHS uses a randomly selected member of the household to answer questions about themselves.

    Data are collected using Computer Assisted Interviewing (CAI), whereby responses are recorded directly onto an electronic questionnaire in a notebook during a telephone, face-to-face personal interview or online self-completion.

    Data for MPHS topics are collected each month over a financial year. This reduces the impact of any seasonal effects on the data.

    Census of Population and Housing

    The Census of Population and Housing is conducted every five years to measure the number of people in Australia on Census night, their key characteristics and the households and dwellings in which they live. By collecting lots of information in a standardised way, from the country as a whole, the Census provides a rich and detailed snapshot of Australia. Australia's seventeenth (and most recent) national Census was conducted on 10 August 2021.

    The Census and the Labour Force Survey (LFS) both measure information about the labour market activity of persons aged 15 years and over. While both collections measure the same concepts surrounding the labour force in Australia, there are a number of differences between the two that should be considered when comparing the data, as statistics produced from these collections are not the same.

    Labour-related topics on the 2016 Census include: labour force status, status in employment, employment type, occupation, industry of employment, hours worked, place of work and method of travel to work. For unemployed persons, information is collected on whether looking for full-time or part-time work.

    Purpose of Census and the Labour Force Survey

    The Census provides a rich snapshot of all people living in the country on Census night. It is the leading source of information for small population groups and areas, and allows for the analysis of labour market activities and industry and occupation data at a more detailed level. The Census also collects information about a range of characteristics of people, including, but not limited to, their labour force status, enabling analyses across a broader range of socioeconomic dimensions.

    However, the Labour Force Survey produces the most authoritative and recent estimates of labour market information, including employment and unemployment. Labour force statistics are published monthly by the ABS in Labour Force, Australia (cat no. 6202.0). The Labour Force Survey is designed specifically to measure changes over time in the Australian labour force, and to provide a high quality measure for use in international comparisons. It provides a highly accurate estimate of key labour force statistics of the Australian economy, including employment, unemployment and underemployment, as well as a range of more detailed labour market-specific data. The Labour Force Survey is the leading source of data for monitoring Australia’s labour market conditions.

    Understanding differences between the LFS and Census

    Scope and coverage

    The Census includes everyone who is in Australia on Census night, regardless of age, with the exception of foreign diplomats and their families. Visitors to Australia are counted regardless of how long they have been in the country, or how long they plan to stay. Persons present in Australian offshore territories (Jarvis Bay, Christmas Island and Cocos Keeling Islands and Norfolk Island) are included in the Census. Thus, babies and children under the age of 15, Australian defence force members, tourists, students, working holiday makers and other temporary residents are counted in the Census. However, the Census excludes Australian residents who are out of the country on Census night.

    In contrast, the scope of the LFS is limited to all persons aged 15 years and over, excluding members of the permanent defence forces, certain diplomatic personnel of overseas governments, overseas residents in Australia, short term visitors, short term students and members of non-Australian defence forces (and their dependants) stationed in Australia. However, the LFS includes resident who are temporarily overseas for less than 6 weeks.

    Collection methodology

    The greatest difference in collection methodology between the Census and the Labour Force Survey is the questionnaires that are used. The Census questionnaire covers a broad range of topics across a range of social and economic domains, while the Labour Force Survey is specifically designed to produce labour statistics.

    In addition to having a greater multi-topic focus to its design, the Census must also necessarily use as few questions as possible for each individual topic, to a much greater extent than is the case with the Labour Force Survey and other household surveys. For example, the data item ‘Labour Force Status’ is derived in the Census based on only four questions, while the Labour Force Survey questionnaire includes an extensive range of questions to measure Labour Force Status with a much greater level of precision. In particular, the Census count of unemployed people is higher than the corresponding Labour Force estimate, given it is not possible to measure the distinction between someone who is unemployed and not in the labour force with the same level of precision.

    Lastly, the Census, by necessity, given its size, involves all households self-completing online or paper questionnaires. In contrast, the Labour Force Survey is able to offer a mixture of modes to respondents, including interviews by highly trained interviewers (either over the telephone or face-to-face) or self-completion online questionnaires, according to their preference, to elicit the most precise responses to the detailed questions that are asked.

    Treatment of non-response

    To account for unreturned Census forms, demographic characteristics of persons in non-responding households are either imputed or included in the 'not stated' category. However, Labour Force Status is not imputed and data are not adjusted for non-responding households.

    Issues with response or coverage in the Census are identified through the Post Enumeration Survey, which is conducted a few weeks after the Census to estimate the number and characteristics of people either not counted or counted multiple times on Census night.

    In contrast, only fully responding households contribute to Labour Force Survey estimates. Non-responding households are treated as 'not stated' and excluded and adjusted for through the weighting process. As a sample survey, it is then weighted to an independent population benchmark based on the Estimated Resident Population (ERP), which ensures estimates add up to an independently estimated distribution of the usual resident civilian population aged 15 years and over, regardless of any sample lost due to non-response.

    Business surveys

    This section provides an overview of the survey methodology used in ABS labour-related business surveys. Business surveys are the primary source of data on labour costs, earnings, job vacancies and industrial disputes, all of which provide insight into the demand for labour in the Australian labour market.

    Business surveys falling within the labour statistics program collect information from employing businesses on a range of topics. The program includes:

    For specific information each of these surveys, refer to the methodology pages for each statistical release.

    Scope and coverage

    The scope of ABS labour-related surveys varies across collections. Most ABS labour-related business surveys draw upon the ABS Business Register (ABSBR), which is sourced from the Australian Taxation Office's Australian Business Register (ABR). The scope of surveys which use the business register is restricted by the scope and coverage of the register itself (as outlined in the next section). Surveys with broader or different scope are required to either supplement the business register, or use a sample that has been composed independently of the register by using relevant alternative data sources.

    The following groups are generally excluded from labour-related business surveys:

    • Employing businesses in the Agriculture, Forestry and Fishing industry (Australian and New Zealand Standard Industrial Classification (ANZSIC) Division A), in line with the International Labour Organisation (ILO) Resolution from the Twelfth International Conference of Labour Statisticians 1973. Given that "hired labour constitutes only a minor part of total labour input" in this industry, it would be disproportionately costly to survey a sufficient number of these businesses to obtain a sample of employees to adequately represent this industry.
    • Private households employing staff (ANZSIC subdivision 96). Not all private households employing staff are required to register with the Australian Taxation Office (ATO), and as a result of this there is incomplete coverage on the business register and these units are excluded.
    • Foreign government representation in Australia (ANZSIC class 7552). Practical collection difficulties and the low numbers of Australian employees involved have resulted in the exclusion of this industry group from the labour-related business surveys.
    • Members of Australian permanent defence forces.
    • Employing organisations located outside Australia.

    ABS business register

    The ABSBR is a list of businesses and organisations operating in Australia, and is based on the ABR. Organisations are included on the ABR when they register for an Australian Business Number (ABN). The ABSBR is used to create frames for the various business surveys run by the ABS and consists of two populations; the profiled population, and the non-profiled population. Organisations which are considered sufficiently complex and significant are included in the profiled population. They are structured according to the ABS Economic Units Model (ABSEUM), using information provided by the organisations. Organisations in the non-profiled population have less complex structures, and are based largely on ABR information.

    Statistical units

    Statistical units are used to represent one member of the population being surveyed. Statistical units on the ABSBR are based on the ABSEUM. The ABSEUM (see Figure 23.1) has four statistical unit levels that are commonly applied in collections: the Enterprise Group; the Legal Entities that belong to that group; the Type of Activity Units that these legal entities carry out; and the location where these economic activities take place.

    • Enterprise Group (EG) covers all the operations within Australia's economic territory of legal entities under common control.
    • Legal Entity (LE) covers all the operations in Australia of an entity which possesses some or all of the rights and obligations of individual persons or corporations, or which behaves as such in respect of those matters of concern for economic statistics. Examples of legal entities include companies, partnerships, trusts, sole (business) proprietorships, government departments and statutory authorities. In most cases the LE is equivalent to a single ABR registration.
    • Type of Activity Unit (TAU) comprises one or more legal entities, sub-entities or branches of a legal entity that can report productive and employment activities. TAUs are created if accounts sufficient to approximate Industry Value Added (IVA) are available at the ANZSIC subdivision level.
    • Location is a single, unbroken physical area from which an organisation is engaged in productive activity on a relatively permanent basis, or at which the organisation is undertaking capital expenditure with the intention of commencing productive activity on a relatively permanent basis at some time in the future.

    ABS Economics units model

    ABS Economics units model
    For the compilation of statistics, the ABS has developed an Economics Units Model to further describe and categorise enterprises and their components. This is displayed in Figure 23.1. The Units Model is a tiered structure, containing four levels: Enterprise Group which is an institutional unit which contains one or more legal entities under common control and covers all of their collective activities in Australia; Legal Entities: Is an institutional unit which covers all activities in Australia of a single entity which possesses some or all of the rights and obligations of individual persons or corporations, or which behaves as such in respect of those matters of concern for economic units. In most case the LE is equivalent to a single Australian Business Number (ABN) registration; Type of Activity Units: Is a producing unit comprised of one ore more legal entities that can report productive and employment activities, and are homogenous in their activity; and Location Units: The Location Unit is comprised of a single, unbroken physical area from which an organisation is engaged in productive activity on a relatively permanent basis.

    Sample design and selection

    Business surveys undertaken by the ABS fall under two categories: probability sample surveys (information is collected from a random sample of units on the frame), and censuses (information is collected from all units on the frame). With the exception of the Industrial Disputes collection, all labour-related business surveys are probability sample surveys which construct their frame from the ABSBR. The Industrial Disputes collection aims to be a census of all stoppages, and businesses involved in these stoppages are identified through media monitoring and observation of disputes from multiple sources (see the section: Industrial Disputes Collection for more details).

    When a sample is selected for an ABS business survey, a survey frame must first be drawn from the ABSBR. From that point, the survey frame is then divided (stratified) into groups with similar characteristics, known as strata. The stratification variables typically used in the labour-related business surveys include: state, industry and employment size. The sector (public/private) stratification variable may also be used in some collections. After this, a small number of strata containing large or highly variable units are completely enumerated (CE). For each of the remaining strata, a simple random sample of units is selected. Some strata with a small population are also CE.

    The Survey of Employee Earnings and Hours (EEH) uses an additional step in its sample selection that involves asking businesses to select a random sample of employees from their payrolls using instructions provided by the ABS (see the section Survey of Employee Earnings and Hours for more details).

    There are various constraints placed on sample selection. For most labour-related business surveys, sample selection is constrained by ensuring that a portion of the sample that is not CE is rotated, and that small businesses will be in the sample for no more than 12 successive quarters. Some surveys are further constrained by ensuring that there is either minimal or maximal overlap with other surveys.

    Sample sizes vary across ABS labour-related business surveys. In determining the required sample size for each survey, factors such as required accuracy level, expected level of non-response and total cost are taken into consideration.

    Collection methods

    Most ABS labour-related business surveys use an electronic collection methodology, using internet based survey forms. Data from some surveys are collected through the mail-out/mail-back or the telephone interview collection methodology.

    In the event of non-response, intensive follow-up procedures that involve reminder letters and telephone contact are undertaken. 'Priority' intensive follow-up is used for a number of surveys; this involves targeting the following types of non-responding units:

    • Units that contribute significantly to estimates;
    • Newly selected units (e.g. in ongoing surveys); and
    • Units that did not respond in the previous survey cycle.

    Estimation

    The estimation procedure is the application of weights to individual survey records so that the whole target population is represented (see the section: Overview of Survey Methods for more information). For ABS business surveys, the values of these weights are determined by one or more of the following factors:

    • Probability weighting: the probability of selection for each survey unit.
    • Ratio estimation: adjustments to agree with population benchmarks to correct for imbalances in the characteristics of the selected sampled units.
    • New business provisions: adjustments to account for deficiencies with the survey frame, such as missing units.
    • Adjustment for non-response: to correct for further imbalances in the characteristics of responding sample units.

    Number-raised estimation and ratio estimation are the two main techniques used in surveys constructing their frame from the ABSBR. The labour-related business surveys use stratum-by-stratum ratio estimation in strata where the population benchmark is known, and sampling efficiencies achievable are greater than with number-raised estimation. For strata where benchmark information is not available, number-raised estimation is used. See the section: Overview of Survey Methods for more information.

    New business provisions are used in the estimation process to allow for births of businesses that have occurred up to the end of the survey reference period, but are not reflected on the survey frame. The calculation of the contribution of a new business is based on the average contribution of estimates of like units already on the frame. In the case of labour-related business surveys, the annual Survey of Employment and Earnings (Public Sector) does not allow for new business provisions, as data is collected from public sector units only. The Industrial Disputes collection is a census collection, and does not use weighting.

    Editing and non-response adjustment

    Editing is used in ABS business surveys to correct a number of non-sampling errors such as misunderstanding of questions or instructions, miscoding, non-availability of data, incorrect transcription, non-response and non-contact. Editing and further investigation is performed on estimates where anomalies have been detected. Significance editing is used by some labour-related business surveys, and reduces the overall editing load for the survey while maximising the effectiveness of editing on survey estimates. Significance editing involves assessing each survey value that requires editing against how greatly the survey estimate will be affected by using the unedited value. Only those values which will significantly affect the survey estimate are then edited.

    Adjustments for non-response are made in the estimation process for all business surveys. There are two categories of non-response for ABS business surveys: partial non-response, and complete non-response. The extent to which values are imputed depends upon the amount and the quality of data already provided.

    Imputed values can be derived for business surveys from three sources. The first source is data provided by the particular unit to be imputed for, which may be in the form of data previously provided or current data with partial response. The second source is similar to the first and involves the use of auxiliary information known about the unit, such as tax data from the frame. The third source is data provided by other units believed to have similar responses to the missing data. For complete non-responses and refusals in completely enumerated strata, all data items for the unit are imputed, preferably from previously provided data. Alternatively, where no useful information exists to use in imputation, the weights may be adjusted to account for non-response.

    Two main methods for the treatment of outliers are used in ABS business surveys: Surprise Outliering and Winsorisation.

    Administrative data

    The ABS regularly uses administrative data to support the collection of data in business and household surveys. The ABS also uses combined data assets, such as those supplied by the Australian Tax Office (ATO), to develop labour statistics to provide unique insights into the Australian labour market from both a jobholder and employer perspective:

    More granular demographic and employer characteristics are available in these statistics than in survey based outputs, providing detailed insights into jobs, jobholders, wages and income.

    ATO administrative data assets used in the generation of these labour statistics include:

    • Australian Business Register
    • Business taxation information (BIT) for owner managers of un-incorporated enterprises
    • Client Register (CR)
    • PAYG payment summary
    • Personal Income Tax return (PIT)
    • Single Touch Payroll (STP)

    Differences from traditional collection methods

    Administrative datasets are not typically designed with statistical production in mind. The underlying concepts relate to administrative policy or procedures, rather than statistical constructs. This can result in coverage, definition and quality differences compared to survey based outputs.

    Administrative data can cover large populations in more detail and therefore provide different levels of insight than traditional collection methods. However, administrative data can capture very specific populations or sub-populations, compared to surveys which collect information from a representative sample of the population.

    While more detailed statistics are available, the estimates may present unique views of the population, particularly where adjustments are not applied to broaden the population represented.

    In addition to more variable reporting quality, administrative datasets are significantly larger than those obtained from business and household surveys. The timeliness of outputs is weighed against the quality assurance of individual records. As such, the increased speed of statistical production may require macro level adjustments to address anomalous reporting (such as in WPJW). Where more time and information are available to resolve anomalous records, micro level adjustments may be in use.

    Producing statistics from administrative data requires an alternative approach to processing and quality assurance than those used in survey based statistics. However, administrative datasets are an increasingly valuable source of new data, providing a rich variety of alternative insights into the labour market.

    More information on the methodologies supporting the current suite of Labour statistics derived from administrative data can be found in the respective statistical releases: Jobs in Australia; Personal Income in Australia; and Weekly Payroll Jobs and Wages in Australia.

    Linked Employer-Employee Dataset (LEED)

    The Linked Employer Employee Dataset (LEED) is a cross-sectional database which is built using Australian Tax Office (ATO) administrative data linked to ABS Business Longitudinal Analytical Data Environment (BLADE).

    The LEED enables simultaneous analysis of met supply and demand in the Australian labour market, through:

    • providing supplementary labour statistics and facilitates labour market research at industry and regional levels.
    • enabling analysis of the Australian labour market at macro and micro levels;
    • enabling analysis of how specific events impact employees and employers;
    • helping to understand structural changes in the labour market.

    The LEED consists of three cross-sectional files:

    • a person file;
    • a jobs file; and
    • an employer file.

      The LEED associates information about a person with information about their employing business. This is done by establishing the existence of a job. An employed person can have one or more jobs throughout the year with one or more employers, some of which may be held concurrently with others. A job can be created either by an employing business or the personal enterprise of the individual (an owner manager).

      LEED overview

      Scope

      The LEED contains information for all persons who interacted with the Australian taxation system with reference to financial years after 2011-12. The LEED includes data for all persons who either:

      • submitted an individual tax return (ITR); or
      • individuals who had a Pay As you Go (PAYG) payment summary issued by an employer and then remitted to the ATO.

      Employees who did not submit a tax return and have not provided their Tax File Number to their employer will not appear in the LEED. Owner managers of unincorporated enterprises (OMUEs) who did not submit an ITR are also excluded.

      The LEED includes all employers present on the BLADE who have at least one employee linked to them. Some small businesses are excluded from the BLADE (e.g. those that do not meet the turnover threshold at which they must register for Goods and Services Tax) and do not appear on the LEED. Synthetic records are created for these records where they are both unincorporated and owned by an Owner Manager of an Unincorporated Enterprise present on the LEED.

      The LEED includes all sources of income, regardless of whether the income provider resides within Australia's economic territory.

      Integration methodology

      Initial data cleaning is undertaken to remove duplicate and erroneous records. In particular, job records are repaired to minimise the impact of administrative noise on output statistics, such as annual payment summaries issued in two separate parts.

      Before the linkage takes place, an input job level file is created largely based on the PAYG payment summary file. This file is also enhanced with job records derived using ITR information, to cover jobs without payment summary information, such as OMUE jobs. Data quality is enhanced by using occupation information from ITR, and the best available age, sex, and geographic information between the PAYG, ITR and CR data.      

      Jobs are integrated with the employer by one of two methods. The method is dependent on which ABS Business register population the employer is grouped into.

      Non-profiled population (businesses with a simple structure): a deterministic approach using the Australian Business Number (ABN).

      Profiled population (businesses with a complex structure): a more detailed approach to linking is used, detailed below.

      Where an employer is part of the profiled population, the relevant jobs are assigned to the type of activity units based on a logistic regression model developed using 2016 Census data. The model references independent variables common to both Census and personal income tax data, including sex, age, occupation, and region of usual residence. These are used to predict the industry of employment, which conceptually aligns to a type of activity unit. 

      Where an employee has multiple job relationships with the same reporting ABN in an enterprise group, each job relationship is assigned to the same type of activity unit.

      Based on the model, each job record is assigned a probability of being in any of the type of activity units present in the employing enterprise group. Iterative random assignment is undertaken using these probabilities until employment benchmarks are met. Benchmarks are based on Quarterly Business Indicators Survey (QBIS) data where a unit is in scope. Otherwise, BLADE employment levels are substituted where possible, otherwise no benchmarking is done.

      The above process is applied to link the different input datasets for each financial year. Records have not been integrated across years and therefore, the LEED is a cross-sectional database and is not longitudinal.

      Legislative environment

      The LEED incorporates:

      • person level ITR data, job level PAYG payment summary data and Client Register data supplied by the ATO to the ABS under the Taxation Administration Act 1953 - which requires that such data is only used for the purpose of administering the Census and Statistics Act 1905; and
      • employer level data that include the ABS's BLADE data and the ABS Business Register data supplied by the Registrar of Australian Business Register (ABR) to the ABS under A New Tax System (Australian Business Number) Act 1999 - which requires that such data is only used for the purpose of carrying out the functions of the ABS. 

      The data limitations or weakness outlined here are in the context of using the data for statistical purposes, and not related to the ability of the data to support the ATO's or ABR's core operational requirements.

      Legislative requirements to ensure privacy and secrecy of these data have been followed. In accordance with the Census and Statistics Act 1905, results have been confidentialised to ensure they are not likely to enable identification of a particular person or organisation. All personal information is handled in accordance with the Australian Privacy Principles contained in the Privacy Act 1988.

      ABS data integration practices comply with the High Level Principles for Data Integration Involving Commonwealth Data for Statistical and Research Purposes.

      The LEED is comprised of a person file, a job file and an employer file

      The LEED is comprised of a person file, a job file and an employer file
      Figure 1 gives an overview of the LEED and shows how it is cross-sectional database. It is comprised of a person file, a job file and a business (employer) file. The LEED includes person and employer level information provided to the ABS by the ATO and the Registrar of the Australian Business Register (ABR). LEED uses this data via the Business Longitudinal Analysis Data Environment (BLADE). The persons filed classifies persons as either not employed or employed. The jobs file is a complete list of the job relationships held at any time during the reference year. Whilst the employer file contains all employers in a job relationship with someone on the person file at any point during the reference year.

      Person file

      Each person file contains data for all persons who either submitted an Individual Tax Return (ITR) or who were identifiable on a payment summary in the reference year. Each record includes de-identified demographic and geographic data, and aggregate income information.

      Employed persons may be either employees (including Owner Manager of Incorporated Enterprises or OMIEs), Owner Managers of Unincorporated Enterprises (OMUEs), or both. Employees are identified by the presence of aggregate employee income and at least one linked employee job.

      Employees who have not submitted an ITR but who have provided their Tax File Number to their employer are imputed from Pay As You Go payment summary data.

      OMUEs are identified by the presence of any of the own unincorporated business income types and a linked OMUE job.

      Tax lodgers who are not employees or owner managers (such as persons with only investment incomes) are included on the person file to support statistical analysis that requires a more complete view of the tax lodger population.

      Jobs file

      The jobs file is a complete list of the job relationships held at any time during the reference year. It is constructed primarily from Pay As You Go (PAYG) payment summary data. PAYG payment summaries describe the payments made to an individual by an employer within a financial year. Conceptually, payment summary data should include most employee/employer job relationships. OMUE jobs are derived from ITR data and are added to the jobs file, some of these link to businesses in the Business Longitudinal Analysis Data Environment (BLADE).

      The LEED jobs file does not capture voluntary jobs and unpaid contributing family worker jobs.

      In some cases a synthetic employee job record has been created based on information in the person file. This has occurred when a person has recorded wage or salary information that cannot be identified in payment summary data. Sometimes, an employee job may not be able to be linked to an employing organisation due to reporting errors or missing information.

      A person can hold several jobs during the year, either concurrently (as a multiple job-holder) or consecutively. For a person who is an employee of several employers, each relationship is listed as a separate job. Due to data limitations, only one self-employment job can be recorded for any OMUE even if a person owns and manage more than one enterprise. An OMUE can hold other jobs as an employee.

      Data on multiple job holders can also be found in the Labour Account Australia, however there are a number of differences between the two sources.

      PAYG payment summary start and end dates are used to

      • determine the start and end of a job relationship,
      • identify concurrent job-holding, and
      • determine the duration of the job.

      These dates are known to have high measurement error rates, which are likely to inflate job and concurrent job counts. Some of this error may be due to misinterpretation and recording errors, but it is also expected that payroll system and report design have an influence.

      Some treatments have been applied to address over counts of jobs or concurrent job-holding, including:

      • In cases where a person has received several PAYG payment summaries from the same employer, and the time between the end of the first payment summary and the start of the next payment summary is 31 days or less, this is counted as a single job.
      • In cases where a person has received several PAYG payment summaries from different employers, they are only considered to be concurrent if they overlap by more than 31 days.
      • In cases where a person has more than 10 jobs, those within the same industry sub-division (2-digit ANZSIC industry) are counted as a single job in the 2011-12 to 2016-17 data. From 2017-18 reference year, a lower level of industry classification - those within the same industry class (4-digit ANZSIC industry), was used to collapse jobs. This change has improved data quality  but has brought in a negligible increase to the number of total jobs reported compared with reported numbers in the old approach.

      These treatments are aimed at minimising the impact of administrative errors while also reflecting a reasonably accurate view of differing job structures.

      Employer file

      In the LEED, an employer is identified when a job has been linked to any legal entity in the non-profiled population or any type of activity unit in the profiled population.

      The employer file contains business units present in BLADE that could be linked to a job, as well as unincorporated entities. Some unincorporated entities are identified in personal income tax data and are not otherwise included in BLADE or cannot be identified in BLADE. Industry and several other employer variables are not available for these unincorporated entities.

      LEED outputs

      Key outputs

      The LEED provides cross-sectional information relating to employees and owner managers of unincorporated enterprises

      Key data/series include:

      • Employed persons and their jobs (employees and owner managers of unincorporated enterprises)
      • Multiple job holders
      • Income at job and person levels
      • Regional spotlights of jobs and employed persons

      Other data includes (but is not limited to):

      For people with income:

      • Income types: Total, Employee, Investment, Own unincorporated business, Superannuation
      • Counts of earners
      • Distributional information: mean, median, quartiles, percentile ratios, gini coefficient, income share
      • Geography - region of residence (at State and Territory, Local Government Area, Statistical Area 4, 3, and 2 levels)
      • Demographic information: age, sex

      In addition, for persons with jobs:

      • Counts: Employed persons, Jobs, Employees, Owner-Managers of Unincorporated Enterprises, Multiple job holders
      • Status in employment: Employee, Owner-manager of Unincorporated Enterprise
      • Income: Employment, Employee, Own Unincorporated Business, Duration adjusted income per job (annualised)
      • detailed occupation and skill levels of persons
      • detailed industry of job
      • Sector (public/private)
      • Number of jobs held (employee jobs and owner manager of unincorporated enterprise jobs)
      • Duration of jobs
      • Concurrent and non-concurrent jobs

      Information relating to employers:

      • employment size
      • detailed industry of business activity
      • type of legal organisation (TOLO)
      • institutional sector (SISCA)

      Statistical releases

      LEED data is disseminated through the publications listed below. Additional data is available through Customised Data Requests.

      Jobs in Australia
      Frequency: Annual, from 2011-12
      Jobs in Australia (JIA) provides aggregate statistics from the Linked Employer-Employee Dataset. It provides information about filled jobs in Australia, the people who hold them, and their employers. JIA provides data across 2,288 Statistical Areas as well as Local Government Areas.

      Personal Income in Australia
      Frequency: Annual, from 2011-12
      Formerly Estimates of Personal Income for Small Areas, Personal Income in Australia (PIiA) provides a comprehensive range of income indicators across small geographic areas. PIiA is now based on the LEED, ensuring better consistency with Jobs in Australia.

      Tablebuilder: Jobs in Australia
      Frequency: Annual, from 2011-12
      Release of Jobs in Australia data through TableBuilder. This enables users to build their own customised tables from the Linked Employer-Employee Dataset microdata, including for State and Commonwealth Electoral Divisions.

      Single Touch Payroll (STP)

      The Australian Taxation Office (ATO) receives payroll information from employers through Single Touch Payroll (STP) enabled payroll and accounting software each time the employer runs its payroll. The ATO provides selected employer and job level data items from the STP system to the ABS for the production of official statistics. STP replaces the need for businesses to provide a payment summary annual report or 'group certificates.'

      STP data is used to produce near real-time weekly indexes of payroll jobs and wages, including weekly changes in the number of payroll jobs, changes in wages and average weekly wages by jobs at the national, state and territory and ANZSIC industry level by selected personal characteristics, including sex and age group.

      Scope and coverage

      The scope and coverage of these estimates are largely defined and constrained by the characteristics of the data sources from which these estimates are produced. As such, users should note that not all jobs and wages in the Australian labour market are captured within these estimates.

      Payroll jobs

      Payroll jobs as reported to the ATO through STP are in scope of these estimates. All payroll jobholders regardless of age or Australian residency status are included. Persons reported via STP must hold either a Tax File Number (TFN) or an Australian Business Number (ABN).

      A payroll job is a relationship between an employee and their employing enterprise, where the employee is paid in the reference week through STP-enabled payroll or accounting software and reported to the ATO. Where an employee is paid other than weekly, the established payment pattern is used to include payroll jobs paid in weeks outside the reference week.

      Payroll jobs reported via STP exclude owner managers of unincorporated enterprises (OMUEs), which are more prevalent in the Construction and Agriculture, forestry and fishing industries. 

      Employers with 20 or more employees (large employers) commenced transition to STP reporting on 1 July 2018. Employers with less than 20 employees (small employers) began transitioning to STP on 1 July 2019. Any reporting concessions that were made available for small employers ended on 30 June 2021. At the time of this release, almost all large employers and eligible small employers are reporting through STP.

      In addition, payroll jobs reported in the Defence Industry (ANZSIC Class 7600) are excluded from these estimates by the ABS to better align with other Labour estimates.

      Wages

      The STP reported wages associated with each payroll job are in scope of these estimates. Wages are gross amounts, prior to taxation and deductions and include:

      • salary payments and allowances,
      • labour hire payments and foreign income,
      • the value of payments in kind (where a fringe benefit amount is recorded),
      • bonuses where they are reported in the same field as normal payments.

      The total wages concept broadly aligns with the Australian System of National Accounts (ASNA) definition of wages and salaries, with the exception of payments to employee's superannuation and severance and termination payments which are excluded.

      More specifically, the following STP reported income items are included in the production of wages estimates;

      • gross income amount (including bonuses),
      • allowance income,
      • fringe benefit amount (reportable, taxable),
      • fringe benefit amount (reportable, tax exempt),
      • other income (not specified),
      • foreign income amount including tax exempt income,
      • Community Development Employment Project income.

      Other data sources

      The STP data are enhanced through combining other administrative data held by the ABS (also sourced from the Australian taxation system).

      Sex, age and residential geography variables are primarily sourced from Client Register data (supplied by ATO to the ABS as part of the transfer of Personal Income Tax data). Sex can only be sourced from Client Register data. When age and residential geography are not available from Client Register data, they are sourced from STP data. The ABS receives annual snapshots of de-identified Client Register data from the ATO, for use in the production of statistics.

      Industry of activity, sector and employment size variables of the employing business are sourced from the ABS Business Register (ABSBR).  

      STP outputs

      STP data are compiled into weekly indexes of payroll jobs and wages. These are published in Weekly Payroll jobs and Wages in Australia on a monthly basis.

      Each release contains both payroll jobs and total wages indexes and percentage change movements. Estimates are available at the national, state and territory and Australian and New Zealand Standard Industry Classification (ANZSIC) division by selected jobholder and employer attributes. Australian Statistical Geography Standard sub-state regions (Statistical Area 4, Statistical Area 3 and Greater Capital City Statistical Area) and ANZSIC subdivision estimates are also updated in each release. 

      Levels for jobs and wages are not available for release at this time. The payroll jobs index provides a measure of changes in jobs over time since the week ending 14 March 2020. Information on levels for jobs are best sourced from estimates of filled jobs from Labour Account Australia and estimates of employed persons from Labour Force, Australia. More information on the relationship between payroll jobs and LFS employment is included in the Weekly Payroll Jobs and Wages methodology page.

      The data underlying these estimates are revised in each release and reflected in percentage change movements and indexes.

      The estimates are presented as an original series only. Seasonally adjusted and trend estimates are not yet available. A number of years of data will be required before seasonal patterns can be observed and adjusted for.

      Australian Labour Account

      The Australian Labour Account provides a conceptual framework for integrating data from a number of sources including household survey, business survey and administrative data to produce a coherent and internally consistent set of aggregate estimates of key labour market variables, which more effectively enable the description and analysis of the state and dynamics of the Australian labour market. These core variables can help users make sense of seemingly inconsistent labour related data, which are often based on different reference periods, populations, concepts, definitions and methodologies.

      The Australian Labour Account is macro-economic in scope, building on the International Labour Organisation fundamentals and expanding them to ensure consistency with the Australian System of National Accounts. It aims to extend the analytical capacity of national accounts data by providing a labour-specific lens. The Australian Labour Account framework has been designed to conceptually align with the System of National Accounts production boundary (see Institutional Units and the Economically Active Population). This ensures direct compatibility with National Accounts and productivity estimates, as well as providing a mechanism for bringing together conceptually related aggregate data from business, household and administrative sources.

      The Labour Account provides a time series of estimates of the number of employed persons, the number of jobs, hours worked and the income earned for each industry in one coherent framework. Historically, published estimates of employed persons in each industry have only been available for industry of main job. The expanded scope and additional data sources used in the Labour Account include data for multiple job holders by their industry of second, third and fourth job. The Australian Labour Account is published on a quarterly basis in Labour Account Australia.

      The Australian Labour Account framework incorporates four quadrants: Jobs, Persons, Labour Volume and Labour Payments.

      Australian Labour Account quadrants

      Australian Labour Account quadrants
      The four quadrants of Labour Account made up Jobs; Persons; Labour (volume) and Payment. The jobs quadrant provides data on numbers of filled jobs, vacant jobs and total number of jobs in the economy. The persons quadrant provides data on numbers of employed persons, together with data on numbers of unemployed and underemployed persons. The labour volume quadrant provides data on the relationship between hours of labour supplied by individuals, and hours of labour used by businesses. Lastly, the labour payments quadrant provides data on the relationship between total labour costs by businesses and total labour income by workers.

      International context

      There are currently no international standards regarding the production of a labour account, however a four step process has been documented by the ILO and was followed (to varying degrees) by the National Statistical Organisations in Denmark, the Netherlands and Switzerland in compiling their own labour accounts. The ILO process has been used as a guide in compiling the Australian Labour Account. For further information on the four step process, refer to Labour Accounts: A Step Forward to a Coherent and Timely Description of the Labour Market.

      The ILO describes two approaches to compiling a labour account: a cross-sectional approach involving confrontation and reconciliation of key labour market measures, and a longitudinal approach which incorporates changes to population and labour force via births, deaths, and net migration, and includes measures such as duration of employment. The Australian Labour Account focuses on the cross-sectional approach (since this is the approach that supports data confrontation and reconciliation), and also provides a time-series dimension.

      The ILO lists six central elements in labour statistics:

      • employed persons and jobs;
      • unemployed and underemployed persons;
      • job vacancies;
      • hours of work and full-time equivalents;
      • income from employment and labour costs; and
      • organisation of the labour market (i.e. statistics on collective labour agreements, industrial disputes and trade-union memberships).

      No country has yet compiled a labour account that measures all of these elements. The Australian Labour Account covers most elements listed in the ILO approach, with the exception of data on full-time equivalents and statistics on labour market organisation. The Australian Labour Account also includes measures of underutilised labour (an estimate of the hours of work sought by the unemployed, plus additional hours preferred by the underemployed).

      The Australian Labour Account, in particular the quarterly information disaggregated by industry division, provides an opportunity to significantly improve the quality of aggregates such as the number of jobs occupied and total number of persons employed within each industry, measures of hours worked, and changes in labour productivity.

      Uses of the Labour Account

      The Australian Labour Account is an enhancement to the broader set of Australia’s National Accounts. It aims to provide a set of labour related statistics on employed persons, filled jobs, hours and payments that is consistent with the concepts, definitions and scope of the Australian National Accounts.

      Australian Labour Account data are likely to be of most value to people engaged in the use of labour statistics in macro-economic analysis, forecasting and in policy related research. They should also assist economic journalists and public commentators in informing public understanding of labour statistics.

      The Australian Labour Account should be used for industry analysis of labour growth and performance in terms of people, jobs, hours, labour costs and income. For example, Labour Force Survey data for employed persons by industry has historically only been available for industry of main job. The expanded scope and additional data sources of the Australian Labour Account includes data for the total number of all secondary jobs (including second, third and fourth job etc.), allocated to industry of main and secondary job. This allows for an industry perspective of the number of people employed in each industry in a time series. These data can be used by researchers and policy makers to better model how the number of people employed could be impacted by shocks to industry or changes to policy.

      The Australian Labour Account is a complement to the existing suite of labour statistics. Users should continue to use the Labour Force, Australia for headline employment, unemployment and persons not in the labour force estimates, as this is the data suite that is internationally comparable and aligned with International Labour Organisation (ILO) conventions.

      Macro-economic analysis

      The Australian Labour Account draws on the macro-economic framework and statistical techniques used in the Australian National Accounts to help address the inconsistencies, scope gaps, frequency and timeliness shortcomings of labour data drawn from a variety of business and household surveys and other administrative sources.

      The Australian Labour Account tables are designed for use in macro-economic analysis. They provide annual and quarterly data on a similar timetable and at a similar level of industry detail as the national accounts.

      An important use the Australian Labour Account is expected to be in the analysis of productivity, where the Australian Labour Account will provide data on hours worked at an industry level that is more coherent with industry output than data currently available from the household Labour Force Survey.

      The Australian Labour Account should assist users in understanding the employment implications, at a macro-economic scale, of developments such as globalisation, new technologies, growth of services and the changing pattern of global demand for resources.

      The Australian Labour Account will also help users understand the economic contribution of groups who fall outside the scope of official Labour Force Survey statistics, particularly the role of short-term working visa holders.

      Micro-economic analysis

      The Australian Labour Account tables do not incorporate detailed data on employment by age, gender, income, earnings, employment arrangements, union membership, occupation, educational qualifications or region.

      If users require detailed dynamics essential for analysis of individual or household characteristics, they should continue to rely on the Census, household and business surveys, and on exploiting the potential of tax and other administrative transaction records. The Australian Labour Account nevertheless provides a macro-economic context within which to understand and interpret micro-economic labour data.

      International comparisons

      To enable the international comparison of labour statistics, especially data on employment and un-employment, Australia (along with most countries) follows guidelines and standards established by the ILO. Australia’s official labour force data, derived from the household Labour Force Survey, remains the source of internationally comparable statistics on the labour force, employment and unemployment.

      Due to practical difficulties in consistently measuring work undertaken by certain population groups, particularly children, transient workers and defence force personnel, ILO standards exclude these groups, despite the fact their labour activities contribute to national production. The Australian Labour Account shows that persons excluded from the scope of official Labour Force Survey statistics account for about 5% of all persons employed in production in Australia. The Australian Labour Account, based on 2008 System of National Accounts (2008 SNA) standards, should assist in making more reliable and transparent comparison of productivity statistics and other data that relate labour inputs to production, earnings and expenditure.

      Limitations to be aware of in the use of the Labour Account are described below.

      Conceptual limitations

      The purpose of the Australian Labour Account is to support macro-economic analysis requiring data on the participation of the population in paid employment and related economic production. In addition, the Australian Labour Account is designed to be consistent in concept and scope with the Australian System of National Accounts (ASNA). For this reason, work which falls outside the ASNA definition of economic activity such as cleaning, cooking and child care produced and consumed within households, and voluntary work undertaken outside institutional settings such as coaching children’s sports teams, are excluded from the scope of the Australian Labour Account. Estimates of numbers of persons engaged, and hours spent, in unpaid work are available from other sources, e.g. How Australians Use Their Time, 2006.

      Content limitations

      The macro-economic emphasis is again reflected in the level of disaggregation of Australian Labour Account data. The focus is on the national economy, with data disaggregated by industry at the Australian and New Zealand Standard Industrial Classification (ANZSIC) division and subdivision levels. Data are available both quarterly and annually, with quarterly data published in close succession to the Australian National Accounts. The development of a state level component, in line with the state component of the Australian National Accounts, would be a potential further extension of the Australian Labour Account.

      Scope limitations

      Some types of activity conceptually falling within the scope of the Australian Labour Account may be excluded from, or not well measured in, the available data sources. These are summarised below.

      Scope limitations impacting both household and business estimates include:

      • jobs associated with illegal or hidden activities (the non-observed economy) are likely to be under-reported in both business and household surveys;
      • positions that are voluntary, with no remuneration at all, not even in kind, but working within a recognised institutional unit, are outside the scope of both business and household collections;
      • non-salaried directors are not included in business or household sources;
      • child workers under the age of five are outside the scope of business collections (those who are self-employed or contributing family workers) and household collections (all employed children under five); and
      • there is no good source of data on jobs that are filled by two or more people under a job sharing arrangement. On both the business and the household sides, a position that is filled by a job sharing arrangement would be counted as multiple filled jobs, not a single job held by multiple employed persons.

      Scope limitations impacting household side estimates include:

      • data on hours worked are calculated for a particular reference week each month, and are assumed to be representative of weeks for which data are not collected;
      • industry estimates for the unemployed population are based on industry of last job worked (within the past two years) from the Labour Force Survey, and do not necessarily equate to the industries in which the unemployed are currently seeking work, nor do they include those unemployed persons who have never held a job previously;
      • no adjustments have been made to align the Labour Force Survey unemployed persons or hours sought with the 2008 SNA residency and production boundaries, as there is no reliable information to derive estimates of additional hours of work sought by short term working visa holders. It is also assumed that defence force personnel and child workers are fully employed. The Labour Account should not be used to derive proportional measures such as an unemployment rate or participation rate, as the numerator and denominator are not strictly comparable;
      • illegal non-resident job holders: the estimated number of short term (less than 12 months) visitors to Australia who work for Australian resident enterprises is based on numbers of working visa holders. No estimate is made for those working without an appropriate visa; and
      • Australian residents living in Australia employed by overseas resident enterprises: an estimate of the number of jobs filled by these people has been deducted from household side estimates, based on data supplied by the Department of Home Affairs. This estimate only represents persons working in diplomatic or consular related jobs.

      Scope limitations impacting business side estimates include:

      • domestic staff employed by private households are outside the scope of business surveys used in compiling business sources estimates of filled jobs, but would be in scope of the Labour Force Survey;
      • jobs held by self-employed persons operating their business without a registered ABN fall outside the scope of business surveys, but would be in scope of household surveys;
      • employees on workers’ compensation who are not paid through the payroll are not included in business side sources;
      • estimates for employment subsidies in the Labour Payments quadrant are based on Commonwealth data sourced from the Department of Finance. No adjustment has been made for employment subsidies paid under State or Local government schemes. Employment subsidies can be difficult to classify, particularly state or local government schemes for which information is often limited;
      • no adjustments have been made to labour payments for unpaid employed persons (both adult and child workers) working on a farm or in a family business (contributing family workers). It is likely that these employed persons are paid in-kind, but this is impossible to estimate with any degree of confidence;
      • no adjustment has been made for payments made to child workers under self-employment arrangements in the Labour Payments quadrant. It is possible that self-employed child workers are not being captured in labour payment estimates, as they are likely to not have an ABN and therefore be out of scope of ABS business surveys. One of the most common occupations from the 2006 Child Employment Survey was Leaflet or Newspaper Deliverer. It is likely that an employed child delivering leaflets would be treated as an independent contractor by their employer, and not an employee. In this situation, if the employed child does not have an ABN, they are unable to be selected for ABS business surveys.
      • job vacancies data does not include vacancies available in the non-observed economy (jobs associated with illegal or hidden activities), private households employing staff, foreign embassies and consulates, and Australian permanent defence forces.
      • the National Skills Commission Internet Vacancy Index, used to supplement ABS Job Vacancy Survey data for the Agriculture, Forestry and Fishing Division, only includes job advertisements listed on the internet. Job advertisements listed only in newspapers, on notice boards and other mediums (other than the internet) are not included;
      • there is no known data source relating to hours worked but not paid, or hours paid but not worked; and
      • the survey of Employee Earnings and Hours, which is used as a source for calculating hours paid, excludes employees in certain industries and in certain employment categories (e.g. employees on leave without pay, on strike, or casuals not rostered to work during the survey reference period, managerial employees where there is no link between pay and hours worked, and employees on workers’ compensation who are not paid through the payroll).

      Other limitations

      Timeliness:

      • Annual industry statistics compiled from the annual EAS are not available at the time required for compiling the latest annual Australian Labour Account estimates, requiring the extrapolation of Labour Account filled jobs (and related) data for up to seven quarters.
      • There is a time lag between the current reference period and the release of data in Government Finance Statistics, Australia. Therefore, data for employment subsidies in the Australian Labour Account are extrapolated forwarded based on the movement of previous data.

      Data availability:

      • Data on numbers of child workers has not been collected since 2006. In modelling current estimates of numbers of child workers, assumptions are made about the proportion of children working, the industries in which they work and their propensity to hold secondary jobs.
      • Data are not available for earlier parts of some series of the Australian Labour Account, and missing data have been estimated through applying movements or proportional distribution from a conceptually related series to observed Australian Labour Account data. Data estimated in this way should not be considered to be as statistically robust as data based on observed and comparable survey estimates.

      Accuracy

      • As noted in the discussion of Balanced Tables, there are several sources of statistical error in source data which are reflected in internal discrepancies within the Australian Labour Account, most notably between household and business side estimates of numbers of filled jobs.

      Methodological limitations

      • Methods used in compiling Australian Labour Account statistics are constrained by the robustness of their assumptions. Assumptions made in the Australian Labour Account include:

      Jobs quadrant:

      • quarterly estimates of private sector business sources filled jobs assume that movement in numbers of jobs reported are indicative of changes in benchmarked employment numbers reported in Australian Industry;
      • that short term student visa holders have similar levels of employment to other resident students aged 15-24 years;
      • that short term visa holders other than students and sponsored visa holders have similar levels of employment to the broader resident population;
      • that permanent defence force personnel and employed children under 15 years do not hold secondary jobs; and
      • that average proportions of multiple job holders with second, third and fourth jobs apply to time periods prior to 2014. While data collected prior to 2014 can identify whether an employed person is a multiple job holder, numbers of secondary jobs were not collected from the LFS prior to 2014.

      Labour Volume quadrant:

      • that derived weekly averages sourced from the Survey of Employee Earnings and Hours (used in computing hours paid for) are equally applicable to employees who are not covered by the survey, including:
      • employees on leave without pay, on strike, or casuals not rostered to work during the survey reference period;
      • persons engaged in the Agriculture, Forestry and Fishing industry;
      • employees on workers’ compensation who are not paid through the payroll; and
      • members of the Australian permanent defence forces.

       

       

      Labour Account framework

      The Australian Labour Account framework has been designed to conceptually align with the accounting conventions of the United Nations System of National Accounts (2008 SNA), as applied in the Australian System of National Accounts (ASNA). In particular, the Australian Labour Account aligns with production and residency boundaries of the ASNA. This ensures direct compatibility with national accounts and productivity estimates, as well as providing a mechanism for bringing together conceptually related aggregate data from business, household and administrative sources. The scope of the Australian Labour Account is consistent with that of the national economy, as defined in the Australian System of National Accounts (ASNA), which follows the international standard set out in the United Nations System of National Accounts.

      The Labour Account consists of four quadrants: Jobs; Persons; Labour Volume and Labour Payments.

      • The Jobs Quadrant provides data on numbers of filled jobs derived separately from business and household sources, plus data on vacant jobs to provide a total number of jobs in the economy.
      • The Persons Quadrant includes data on numbers of employed persons, together with data on numbers of unemployed and underemployed persons (derived from household sources).
      • The Labour Volume Quadrant provides data on hours paid for (derived from business sources) and hours worked (from household sources), plus data on additional hours of work sought by unemployed and underemployed persons (from household sources).
      • The Labour Payments Quadrant provides data on labour income and employment costs (from business sources).

      The Labour Account combines data from the persons, jobs, labour volume and labour payments tables to calculate average hours worked, average remuneration (per person and per job), and average labour cost per hour worked.

      The four quadrants are linked by a set of identity relationships, which the aggregate statistics must satisfy. These identities are shown below. The identities used in the Australian Labour Account are consistent with the identities used in other countries. Some relationships are direct, such as employed persons in the total economy is equal to the number of main jobs, while other relationships are considered indirect or derived, such that the relationship is based on an average or ratio measure such as average hours worked per job, or average labour income per employed person.

      Identity relationship diagram

      Identity relationship diagram
      The Labour Account identity relationship diagram displays the four quadrants (jobs, persons, volume and payments) and identifies the relationships between each of these quadrants. These four quadrants are linked by a set of identity relationships which is represented in this identity relationship diagram. Some relationships are direct, such as employed persons in the total economy is equal to the number of main jobs, while other relationships are considered indirect or derived, such as average labour income per employed persons.

      Conceptual framework

      Labour Account conceptual framework

      Labour Account conceptual framework

       

       

      Labour Account concepts

      The supply of labour relates to the quantum of labour services offered by people (i.e. the number of hours actually worked by employed persons, plus the number of additional hours being sought by those who are either unemployed or underemployed). Household surveys are the primary source of data on the supply of labour, supplemented by related administrative data.

      Labour demand relates to the quantum of labour services sought by companies and other institutional units engaged in economic activity, within the scope of the 2008 SNA production boundary. It includes the numbers of hours actually paid for in filled jobs, plus the unmet labour demand by employing units measured through vacant jobs. Surveys of businesses, government and not-for-profit institutions and relevant administrative data sets are the main sources of information on labour demand.

      Production boundary

      Accounts compilation uses some important boundaries to define the scope and treatment of events that occur within the economy. These boundaries are:

      • the production boundary defining the scope of productive economic activity; the asset boundary distinguishing transactions in assets from income and expenditure; and
      • the boundary between current and capital transfers (IMF, 2007, The system of macroeconomic accounts statistics: an overview, Pamphlet series no. 56).

      The definition of the production boundary used in the Australian Labour Account determines the scope of activities covered, and the size of the economy measured in the account.

      The Australian Labour Account includes all persons employed in economic activity as defined by the 2008 SNA. Economic activity is the production of goods and services falling within the 2008 SNA production boundary by institutional units resident in the Australian Economic Territory. In the 2008 SNA, production is viewed as a physical process in which labour and assets (capital) are used to transform inputs of energy, materials and services into outputs of other goods and services.

      In its simplest form, economic activity is the production of goods and services, and in the 2008 SNA is always a result of production (ASNA, 2.8).

      Economic activity covers all market production and certain types of non-market production, including the production and processing of primary produce by households for their own consumption (e.g. vegetable gardens, fruit trees or eggs from chickens), the construction of dwellings and structures for own use, the production of fixed assets for own use and the production of dwelling services from owner occupied homes.

      Scope – economic activity in terms of 2008 SNA concept of goods and services production

      Scope – economic activity in terms of 2008 SNA concept of goods and services production
      The diagram shows there are economic activities (i.e. related to the production of goods and services) and non-economic activities. Economic activities are either market production or non-market production. Market production is the production of goods and services normally intended for sale on the market; and production of other goods and services such as government activities. Non-market production is the production of primary products for own consumption; processing of primary commodities for own consumption by the producers of these items; production of fixed assets for own use; and production for own consumption of other commodities by persons who also produce them for the market.

      While the 2008 SNA definition of the production of goods and services covers a wide range of activities, many other activities still remain outside its scope. For example, the production of domestic and personal services for consumption within the same household (such as preparing meals and caring for children) is excluded. The production of most domestic and personal services is excluded, as the decision to consume these services within the household is made even before the service is provided, and because of the adverse effects their inclusion would have on the usefulness of the accounts for policy purposes and analysis of inflation and unemployment. The extension of the production boundary to include own account household services would result in virtually the whole adult population being defined as 'economically active', unemployment under the existing International Labour Organisation (ILO) definition would cease to exist, and employment statistics would become meaningless (2008 SNA, 1.42, 6.31; ASNA, 8.3).

      One exception is the production of dwelling services from owner occupied housing. This is a pragmatic compromise required to allow comparison of economic activity between countries with significant differences in rates of home ownership. However, no labour input is associated with this activity.

      Unpaid work and volunteer services

      A distinction can be made between those who have an agreement to provide labour for token remuneration or income in kind, those for whom there is explicitly no remuneration, and those where there is apparently no remuneration but the workers benefit directly from the output to which they contribute. In ILO statistics, all three types of worker are included in the economically active population as employees.

      In the 2008 SNA, the remuneration of those working for token amounts or only income in kind is measured by these costs. No imputation for an additional element of remuneration is included. For example, if doctors or teachers work for only food and lodging, the value of this as income in kind is the only remuneration imputed to them. Such instances may arise in religious institutions, or in the wake of natural disasters. If the unit employing these staff is responsible for whatever little remuneration is received, these people are classed as employees and included in the scope of the Australian Labour Account.

      If staff are purely voluntary, with no remuneration at all, not even in kind, but are working in a recognised institutional unit (business, government agency, not-for-profit organisation) engaged in economic activity, then these individuals are still regarded as being employed in 2008 SNA terms. As they are not paid, there is no related compensation of employees recorded for them. Individuals providing services to groups of other individuals, such as coaching a children’s sports team, without any associated infrastructure, are not regarded as employed but rather engaging in a leisure pursuit (2008 SNA, 19.37 - 19.39).

      Although they fall within scope of the 2008 SNA, the Australian Labour Account does not include estimates of numbers of persons engaged by institutional units on a purely voluntary basis. This is consistent with the current treatment in the ASNA, which unlike the 2008 SNA does not allow for the measurement of voluntary contributions of labour.

      If family members contribute to the output of an unincorporated enterprise, the estimate of mixed income is assumed to include an element of remuneration for them, and thus they are all treated as being in the economically active population from a 2008 SNA point of view (2008 SNA, 19.40). The Australian Labour Account includes estimates for contributing family members, consistent with the 2008 SNA.

      In scope activities with the ASNA

      In scope activities with the ASNA
      The diagram describes economic production and non-productive activities. Economic production is the activities in units that produce goods and services included in the general production boundary of the System of National Accounts. These are further categorised according to whether they are within scope of the Australian System of National Accounts (ASNA). Activities within scope of the ASNA are: paid employment; self employment in market enterprises; subsistence work; and reported illegal activities. Activities outside of the scope of the ASNA (but within the general production boundary) are: unreported illegal activities; volunteer work; and unpaid household work. Non-productive activities include participating in: education, training and study; leisure and culture; and self care (eating, sleeping, grooming etc.).

      Treatment of illegal activities

      The 2008 SNA treats illegal actions that conform to the characteristics of transactions (notably the characteristic that there is mutual agreement between the parties) in the same way as legal actions. Thus, although the production or consumption of certain goods such as narcotics may be illegal, market transactions in such goods should, in principle, be recorded in the national accounts.

      As such, the work done by people working illegally on a farm (i.e. visa holders working in breach of visa conditions), working in the construction industry without a permit, selling merchandise without a licence, or working ‘cash-in-hand’ for tax evasion purposes or for fear of being reported to immigration officials, falls within the scope of economic activity.

      However, many illegal actions are crimes against persons or property that cannot be construed as transactions. For example, theft is not an action into which two units enter by mutual agreement. Conceptually, theft or violence is an extreme form of externality in which damage is inflicted on a household or another institutional unit deliberately, and not merely accidentally or casually. Thus, thefts of goods from households, for example, are not treated as transactions and estimated values are not recorded for them under household expenditures (2008 SNA 3.97; ASNA 3.22-3.23).

      Due to reluctance in reporting illegal activity on the part of those engaged, it is likely that employment related costs, remuneration, employment, jobs and hours worked related to these activities are under-reported in both business and household surveys and administrative records used in compiling both Australian National Accounts and Australian Labour Account statistics.

      Although some illegal activity is within the 2008 SNA production boundary and may be reported to some extent by businesses, Australia does not specifically adjust for employment relating to illegal activity in the ASNA. Similarly, illegal activity is not adjusted for in the Australian Labour Account.

      Scope of the population

      Economically active population

      The Australian Labour Account contains information about the economically active population who provide labour for economic production. The economically active population is defined as all persons who, during a specified time, contribute to or are available to contribute to the production of economic goods and services as defined by the 2008 SNA.

      Population age

      The scope of the population in the Australian Labour Account includes all persons who contribute to Australian economic activity, irrespective of age. This scope is consistent with the 2008 SNA.

      The ILO standards and guidelines defining the labour force recognise the need to exclude persons below a certain age from the measures, without specifying a particular age limit. The responsibility for setting such limits lies with individual countries. Examples of factors influencing the age limit are:

      • legislation governing the minimum school leaving age;
      • labour laws setting the minimum age for entering paid employment;
      • the extent of the contribution to economic activity by young people; and
      • the cost and feasibility of accurately measuring this contribution in household surveys.

      A maximum age limit is not a feature of the international guidelines but, for practical reasons, some countries do use a maximum age limit. The international guidelines also recognise the possible need, in the survey context, to exclude other population groups such as persons living permanently or semi-permanently in institutions.

      Australia has adopted an age definition of 15 years and over in the Labour Force Survey, as is allowed within ILO standards and guidelines. Australian labour and compulsory schooling legislation have resulted in low numbers of young persons below this age being involved in economic activity. While such legislation varies from state to state, the net result is that age 15 is the lowest practical limit at which it is feasible and cost-effective to measure the participation of young people in economic activity with acceptable accuracy in a household based collection (i.e. the Labour Force Survey).

      Employment data collected in ABS surveys of businesses relate to all persons employed in economic activity falling within the scope of the survey, regardless of age.

      Scope differences in ABS surveys are adjusted for in the Australian Labour Account.

      Australian Defence Forces

      The Australian Labour Account includes permanent members of the Australian Defence Forces (ADF). This is consistent with the scope of the 2008 SNA.

      The ILO international standards require that members of the armed forces be classified as employed and recommends that, for analytical purposes, the economically active population be divided into two parts: the armed forces and the economically active civilian population. The guidelines recognise that there may be difficulties in obtaining information about membership in the armed forces from labour force surveys, and that separate use of administrative counts may be necessary.

      As a result of these recognised difficulties in obtaining data, Australia excludes permanent members of the armed forces from the Labour Force Survey and the related labour force estimates. Similarly, ANZSIC Class 7600 (Defence) is out of scope of relevant business surveys. Data on Australian defence force members are included in the Australian Labour Account to adjust for differences in scope between survey data and the ASNA.

      Australian Defence Forces Reservists

      ADF reservists are included in the current collection of the Labour Force Survey, and in the Australian Labour Account. Reservist jobs are considered as secondary jobs, should the reservist have a main job elsewhere.

      Non-private dwellings

      While some household surveys exclude all persons living in non-private dwellings, these persons are included in the Labour Force Survey and therefore in the Australian Labour Account.

      Persons living in non-private dwellings include persons living in correctional and penal institutions, dormitories of schools and universities, religious institutions, hospitals, boarding houses, hotels and motels and so on. The exclusion of the institutional population in some household surveys is largely due to practical considerations of sampling.

      Institutional units and sectors

      The 2008 SNA defines an institutional unit as an economic entity that is capable, in its own right, of owning assets, incurring liabilities and engaging in economic activities and in transactions with other entities (2008 SNA, 4.2; ASNA, 4.3). There are two types of institutional units: Households and Legal or Social Entities (ASNA 4.6).

      Households

      A household is defined as a group of persons who share the same living accommodation, who pool some or all of their income and wealth, and who consume certain types of goods and services collectively, mainly housing and food (2008 SNA, 4.4; ANSA, 4.7). Households are providers of labour services.

      Legal or social entities

      A legal or social entity is defined as one whose existence is recognised by law or society independently of the persons or entities that may own or control it (2008 SNA, 4.6; ASNA, 4.10). In the Australian system, the legal entity unit is closest to the 2008 SNA concept of the institutional unit. However, in the ASNA, the unit used is the enterprise, which can be a single legal entity or a group of related legal entities that belong to the same institutional subsector. Four main types of institutional units are recognised in the 2008 SNA and the ASNA: households, non-profit institutions, government units and corporations (including quasi-corporations) (ANSA, 2.3).

      The ASNA recognises corporations (incorporated and unincorporated), co-operatives, non-profit institutions, quasi-corporations and unincorporated government units (departments and agencies) as types of legal or social entity.

      An enterprise is a view of an institutional unit as a producer of goods and services. The term enterprise may refer to a corporation, a quasi-corporation, a non-profit institution or an unincorporated enterprise (2008 SNA, 5.1).

      Most enterprises consist of individual legal or social entities, or in some instances combinations of unincorporated legal or social entities. A household can constitute an enterprise where it undertakes economic activity that falls within the 2008 SNA production boundary.

      An enterprise can be further subdivided into component production units where it engages in distinctive types of productive activity (multiple industries), at separate locations, e.g. a manufacturing plant and a wholesale outlet (2008 SNA, 5.2).

      By creating jobs, enterprises generate demand for labour services.

      The ABS has implemented these principles in the ABS Economic Units Model, which is used to determine the productive structure of Australian institutional units (ASNA, 4.31). The model consists of:

      • The Enterprise Group (EG) - essentially equivalent to the 2008 SNA enterprise concept (2008 SNA, 5.1). The group dimension recognises the reality that enterprises can consist of multiple legal or social entities under common control.
      • Legal Entities (LEs) - approximate the 2008 SNA concept of legal and social entities, but is extended to include households engaged in productive economic activity. 
      • Type of Activity Units (TAUs) - incorporate the industry homogeneity element of the 2008 SNA establishment, recognising that distinct activities such as manufacturing and retailing can be co-located. 
      • Location Units - incorporate the location element of the 2008 SNA establishment.

      The Enterprise Group (EG) is an institutional unit that covers all the operations within Australia's economic territory of legal entities under common control. Control is defined in Corporations legislation. Majority ownership is not required for control to be exercised. 

      The Legal Entity (LE) is an institutional unit covering all the operations in Australia of an entity which possesses some or all of the rights and obligations of individual persons or corporations, or which behaves as such in respect of those matters of concern for economic statistics. Examples of legal entities include companies, partnerships, trusts, sole (business) proprietorships, government departments and statutory authorities. Legal entities are institutional units. In most cases, the LE is equivalent to a single Australian Business Number (ABN) registration. 

      The Type of Activity Unit (TAU) comprises one or more legal entities, sub-entities or branches of a legal entity that can report productive and employment activities. Type of Activity Units are created if accounts sufficient to approximate Gross Value Added are available at the Australian and New Zealand Standard Industrial Classification (ANZSIC) subdivision level. 

      A Location is a producing unit comprised of a single, unbroken physical area from which an organisation is engaged in productive activity on a relatively permanent basis, or at which the organisation is undertaking capital expenditure with the intention of commencing productive activity on a relatively permanent basis at some time in the future. 

      Institutional sectors

      The institutional sectors of the 2008 SNA group together similar kinds of institutional units. Corporations, non-profit institutions, government units and households are intrinsically different from each other in that their economic objectives, functions and behaviour are different. Likewise, institutional units are allocated to sector according to the nature of the economic activity they undertake (2008 SNA, 4.16-4.17). 2008 SNA defines the following institutional sectors: 

      1. Financial Corporations;
      2. Non-financial Corporations;
      3. General government;
      4. Non-profit institutions serving households (NPISH);
      5. Households; and
      6. Rest of the World.

      In the ASNA, the NPISH sector is combined with the household sector.

      Industry

      An industry consists of all establishments (in the Australian context, Type of Activity Units) in the economy engaged in the same, or similar, types of activity (2008 SNA, 5.2; ASNA, 2.10-2.14). Units in the same industry are generally characterised by common production functions, use of similar types of assets, intermediate inputs or the production of outputs sharing common characteristics (ASNA, 5.1). Typically, goods producing industries are distinguished from service producing industries; extractive industries (agriculture, forestry, fishing and mining) are distinguished from transformative industries (manufacturing and construction) and from distributive industries (transportation, wholesaling and retailing).

      Type of Activity Units are classified to an industry using the Australian and New Zealand Standard Industrial Classification (ANZSIC, 2006 version), which is based on the current International Standard Industrial Classification (ISIC, revision 4).

      In business surveys, data about jobs, both vacant and filled, hours paid for, labour costs and remuneration are collected at the Type of Activity Unit level, and are classified to the industry of the unit. This is also the unit level at which data are collected for compiling production (Gross Value Added) and generation of income accounts.

      The Australian Labour Account provides data for each of the 19 industry divisions that represent the highest level of the ANZSIC and a subset of data for each of the 86 subdivisions. ANZSIC division codes and titles are:

      A Agriculture, Forestry and Fishing
      B Mining
      C Manufacturing
      D Electricity, Gas, Water and Waste Services
      E Construction
      F Wholesale Trade
      G Retail Trade
      H Accommodation and Food Services
      I Transport, Postal and Warehousing
      J Information Media and Telecommunications
      K Financial and Insurance Services
      L Rental, Hiring and Real Estate Services
      M Professional, Scientific and Technical Services
      N Administrative and Support Services
      O Public Administration and Safety
      P Education and Training
      Q Health Care and Social Assistance
      R Arts and Recreation Services
      S Other Services

      Economic territory and residency

      The production of meaningful statistics about the economically active population requires that the economic territory to which the population relates is accurately defined.

      The concept of economic territory in the 2008 SNA is not identical to the concept of country. The most commonly used definition is a territory under the effective economic control of a single government, and as such usually approximates the geographic borders of a country.

      In principal, the economic territory of Australia as defined in the ASNA includes the geographic territory under the effective control of the Australian government, including:

      • any islands belonging to Australia which are subject to the same fiscal and monetary authorities as the mainland;
      • the land area, airspace, territorial waters, and continental shelf lying in international waters over which Australia enjoys exclusive rights or over which it has, or claims to have, jurisdiction in respect of the right to fish or to exploit fuels or minerals below the sea bed; and
      • territorial enclaves in the rest of the world (that is, geographic territories situated in the rest of the world and used, under international treaties or agreements, by general government agencies of the country). Territorial enclaves include embassies or consulates, military bases, scientific stations, etc. It follows that the economic territory of Australia does not include the territorial enclaves used by foreign governments which are physically located within Australia’s geographical boundaries.

      Specifically, the economic territory of Australia consists of geographic Australia including Cocos (Keeling) Islands, Christmas Island, Norfolk Island, Jarvis Bay, Australian Antarctic Territory, Heard Island and McDonald Islands, Territory of Ashmore Reef and Cartier Island, and the Coral Sea Islands.

      Within the Australian labour household surveys context, a distinction must be made between: the territories which determine the estimated resident population of Australia; those which are covered by household survey collection procedures; and those used to benchmark or ‘weight’ household survey estimates (i.e., the population benchmarks). See Information Paper: Improved Methods for Estimating Net Overseas Migration, 2006.

      • The “other territories” of Australia, namely Jervis Bay, Christmas Island, Cocos (Keeling) Island, and Norfolk Island after the 2016 Census, are included in the estimated resident population of Australia, but excluded from household survey collection procedures and population benchmarks.
      • The “external territories” of Australia, namely Territory of Ashmore and Cartier Islands, Coral Sea Islands Territory, Australian Antarctic Territory, and Territory of Heard and McDonald Islands, are not included in the estimated resident population, household survey collection procedures or the population benchmarks.

      Within the Australian labour business surveys context, no further geographical restrictions are imposed. Samples for business surveys are typically selected from the ABS Business Register, and therefore all businesses within the economic territory of Australia may be included, providing they meet other relevant scope restrictions.

      Residency

      Within the 2008 SNA, residency is defined as the economic territory with which an institutional unit or individual has the strongest connection - in other words, its centre of predominant economic interest. Each institutional unit or individual is a resident of one and only one economic territory.

      Actual or intended residence for one year or more is used as an operational definition in many countries (including Australia) to facilitate international comparability.

      Residence of individuals and households

      Persons are considered to have the strongest connection with the economic territory in which they physically reside. In the broadest sense, the total population consists of either all usual residents of the country (the usually resident or de jure population) or all persons present in the country (the de facto population) at a particular time.

      Household surveys use the first population category, the usually resident population. All persons who are usually resident in Australia are considered part of the usually resident population, regardless of nationality, citizenship or legal status.

      To determine whether a person is usually resident, Australia has adopted a 12 in 16 month rule. This rule specifies that, to be considered a usual resident, a person must have been (or expect to be) residing in Australia for 12 months or more in a 16 month period. This 12 month period does not need to be consecutive.

      The application of the 12 in 16 month rule in the labour household survey context cannot be so precise. A screening question asks if the respondent is a short term resident and, if so, they are excluded from the survey. Labour household surveys also include residents who are temporarily overseas for less than six weeks. However, the 12 in 16 month rule is explicitly applied in the estimated resident population, and the population benchmarks used to weight the LFS. For more information regarding the 12 in 16 month rule, refer to Information Paper: Improved Methods for Estimating Net Overseas Migration, 2006 (cat. no. 3107.0.55.003).

      Residence of students

      In the 2008 SNA, the residence of students is described as:

      "…people who go abroad for full-time study generally continue to be resident in the territory in which they were resident prior to studying abroad. This treatment is adopted even though their course of study may exceed a year. However, students become residents of the territory in which they are studying when they develop an intention to continue their presence in the territory of study after the completion of the studies."

      Within the Australian labour household survey context, there is no special treatment for students and they are treated using the same 12 in 16 month rule. Within the Australian business survey context, there is no distinction made between students and other persons, such that they are included if they are an employee, irrespective of their length of stay in the country.

      Residence of enterprises

      Within the labour business survey context, the de facto population is used, that is, all employees are included irrespective of their length of stay in the country. This is consistent with the SNA production boundary.

      As a general principle, an enterprise is resident in an economic territory when it is engaged in a significant amount of production of goods or services from a location in the territory.

      An enterprise is resident in an economic territory when there exists, within the economic territory, some location, dwelling, place of production, or other premises on which or from which the unit engages and intends to continue engaging, either indefinitely or over a finite but long period of time, in economic activities and transactions on a significant scale. The location need not be fixed, so long as it remains within the economic territory.

      Corporations and non-profit institutions normally may be expected to have a centre of economic interest in the economy in which they are legally constituted and registered. Corporations may be resident in economies different from their shareholders, and subsidiaries may be resident in different economies from their parent corporations. When a corporation, or unincorporated enterprise, maintains a branch, office, or production site in another territory to engage in a significant amount of production over a long period of time (usually one year or more) but without creating a corporation for the purpose, the branch, office, or site is considered to be a quasi-corporation (i.e., a separate institutional unit) resident in the territory in which it is located.

      Within the Australian business survey context, residency is determined by deriving the sample selection of business frames from the Australian Business Register, which is an administrative data source maintained by the Australian Taxation Office (ATO). The registration of a business by the ATO is deemed to be a demonstration that the business has a centre of economic interest within Australia.

      Residency in the Australian Context

      Applying residency concepts to survey collections:

      Business surveys:

      • include non-residents living in Australia employed by Australian companies, such as short-term foreign students studying in Australia for periods of less than 12 months.
      • include estimates of non-resident persons engaged by Australian businesses operating overseas that have no intention to stay in the non-resident country for more than 12 months.

      Household surveys:

      • include Australian residents living in Australia employed by non-resident enterprises, for example Australians engaged by foreign embassies and consulates and by overseas companies that have no intention of staying in Australia for more than 12 months.
      • do not include estimates of non-resident persons engaged by Australian businesses operating overseas, that have no intention to stay in the non-resident country for more than 12 months.

      Applying residency concepts in practice, the Australian Labour Account makes the following scope adjustments to household survey estimates:

      • add: non-residents living in Australia employed by Australian companies. Non-residents such as short-term foreign students studying in Australia for periods of less than 12 months, short-term migrants and working tourists are included because they contribute to Australia’s economic production and are included in the Compensation of Employees component of Gross Domestic Product (GDP).
      • less: Australian residents living in Australia employed by non-resident enterprises, for example Australians engaged by foreign embassies and consulates and by overseas companies that have no intention of staying in Australia for more than 12 months.

      The Australian Labour Account does not include estimated numbers of non-resident persons engaged by Australian businesses operating overseas, but with no intention to stay in the non-resident country for more than 12 months. While conceptually included in the scope of the Australian Labour Account, due to lack of data no estimate has been included for the foreign workers they may employ.

      The economic territory used in the Australian Labour Account is summarised below.

      Australian Labour Account economic territory

      Australian Labour Account economic territory
      The diagram shows that resident households (all usual residents of Australia as measured through the Estimate Resident Population), PLUS non-residents living in Australia employed by Australia companies, LESS Australian residents living in Australia employed by non-resident enterprises, EQUALS Australian Labour Account economic territory (household side). Resident Australian institutions - those with an active ABN registered in Australia EQUALS Australian Labour Account economic territory (business side).

      Labour Account sources

      Different data sources have been used in compiling the four quadrants of the Australian Labour Account. In general, the same data sources have been used to compile both quarterly and annual labour account estimates. Quarterly survey estimates have also been benchmarked to annual survey estimates where possible.

      Australian Labour Account data at an industry level are derived where possible from data classified by industry reported in both business and household surveys. Where Australian Labour Account data at an industry level are not reported in surveys, the industry detail has been modelled using alternative sources.

      The Australian Labour Account uses both published and unpublished data from various sources. These are detailed below.

      SourceQuadrantData ItemData item detailPublication Status
      Job Vacancies SurveyJobsJob VacanciesJob vacanciesPublished data
      Internet Vacancy Index (Department of Employment, Skills, Small and Family Business)JobsJob VacanciesJob vacanciesUnpublished data
      Economic Activity Survey (EAS)JobsFilled Jobs (Business Sources)Private sectorPublished data
      Quarterly Business Indicators Survey (QBIS)JobsFilled Jobs (Business Sources)Private sectorUnpublished data
      Survey of Employment and Earnings (SEE)JobsFilled Jobs (Business Sources)Public sectorUnpublished data for industry
      Wage and Salary Earners, AustraliaJobsFilled Jobs (Business Sources)Used for backcastingPublished data
      Quarterly Business Indicators Survey (QBIS)JobsAdjustments to Filled Jobs (Business Sources)Industry scope adjustmentUnpublished data
      Business Register Unit (ABS)JobsAdjustments to Filled Jobs (Business Sources)Industry scope adjustmentUnpublished data
      National AccountsJobsAdjustments to Filled Jobs (Business Sources and Household Sources)Defence personnelUnpublished data
      Labour Force Survey (LFS), monthly, detailedJobsAdjustments to Filled Jobs (Business Sources)Contributing Family WorkersPublished data
      Child Employment Survey (2006)JobsAdjustments to Filled Jobs (Business Sources and Household Sources)Child workersPublished and Unpublished data
      Labour Force Survey (LFS), monthly, detailedJobsFilled Jobs (Household Sources)Base numberPublished data
      Labour Force Survey (LFS), quarterlyJobsFilled Jobs (Household Sources)Industry distributionPublished data
      Labour Force Survey (LFS), monthlyJobsFilled Jobs (Household Sources)Labour Force Survey Main JobPublished data
      National AccountsJobsAdjustments to Main JobDefence personnelUnpublished data
      Migration, AustraliaJobsAdjustments to Main JobNon-residents living in Australia employed by Australian companies/business entities : Main job students and Main job non-studentsUnpublished data
      Overseas Arrivals and Departures, AustraliaJobsAdjustments to Main JobNon-residents living in Australia employed by Australian companies/business entities : Main job students and Main job non-studentsUnpublished data
      Balance of Payments (ABS)JobsAdjustments to Main JobAustralian residents living in Australia and employed by oversees companies/business entitiesUnpublished data
      Child Employment Survey (2006)JobsAdjustments to Main JobChild workersPublished and Unpublished data
      Labour Force Survey (LFS), monthlyJobsLabour Force Survey Secondary JobLabour Force Survey Secondary JobUnpublished data
      Migration, AustraliaJobsAdjustments to Secondary JobNon-residents living in Australia employed by Australian companies/business entities - secondary jobUnpublished data
      Overseas Arrivals and Departures, AustraliaJobsAdjustments to Secondary JobNon-residents living in Australia employed by Australian companies/business entities - secondary jobUnpublished data
      Linked Employer Employee Database (LEED)JobsSecondary jobsIndustry of employment, secondary jobsPublished and Unpublished data
      Labour Force Survey (LFS), monthlyPersonsLabour Force Survey Employed PersonsLabour Force Survey Employed PersonsPublished data
      National AccountsPersonsAdjustments to Employed PersonsDefence personnelUnpublished data
      Migration, AustraliaPersonsAdjustments to Employed PersonsNon-residents living in Australia employed by Australian companies/business entitiesUnpublished data
      Overseas Arrivals and Departures, AustraliaPersonsAdjustments to Employed PersonsNon-residents living in Australia employed by Australian companies/business entitiesUnpublished data
      Balance of Payments (ABS)PersonsAdjustments to Employed PersonsAustralian residents living in Australia employed by overseas companies/business entitiesUnpublished data
      Child Employment Survey (2006)PersonsAdjustments to Employed PersonsChild WorkersPublished and Unpublished data
      Labour Force Survey (LFS), monthlyPersonsLabour Force Survey UnemployedLabour Force Survey UnemployedPublished data
      Labour Force Survey (LFS), monthlyPersonsLabour Force Survey Underemployed PersonsLabour Force Survey Underemployed PersonsPublished data
      Labour Force Survey (LFS), monthlyPersonsLabour Force Survey Underutilised PersonsLabour Force Survey Underutilised PersonsPublished data
      Labour Force Survey (LFS), monthlyPersonsLabour Force Survey Not in the Labour ForceLabour Force Survey Not in the Labour ForcePublished data
      Labour Force Survey (LFS), monthlyVolumeLabour Account Hours Actually Worked in All JobsHours actually worked in all jobsUnpublished data
      Survey of Employee Earnings and Hours (EEH)VolumeLabour Account Hours Paid ForHours paid forUnpublished data
      Survey of Employee Earnings and Hours (EEH)VolumeLabour Account Ordinary HoursOrdinary hoursUnpublished data
      Survey of Employee Earnings and Hours (EEH)VolumeLabour Account Overtime HoursOvertime hoursUnpublished data
      National AccountsVolumeAdjustments to hours actually worked in all jobsHours actually worked by Defence personnelUnpublished data
      Migration, AustraliaVolumeAdjustments to hours actually worked in all jobsHours actually worked by non-residents living in Australia employed in AustraliaUnpublished data
      Overseas Arrivals and Departures, AustraliaVolumeAdjustments to hours actually worked in all jobsHours actually worked by non-residents living in Australia employed in AustraliaUnpublished data
      Child Employment Survey (2006)VolumeAdjustments to hours actually worked in all jobsHours actually worked by child workersPublished and Unpublished data
      Balance of Payments (ABS)VolumeAdjustments to hours actually worked in all jobsHours actually worked by Australian residents living in Australia employed by overseas companies/business entitiesUnpublished data
      Labour Force Survey (LFS), quarterlyVolumeHours Sought by UnemployedHours sought by UnemployedPublished and Unpublished data
      Labour Force Survey (LFS), quarterlyVolumeAdditional Hours Sought by UnderemployedAdditional hours sought by UnderemployedPublished and Unpublished data
      National AccountsPaymentsCompensation of EmployeesCompensation of EmployeesUnpublished data
      National AccountsPaymentsWages and SalariesWages and SalariesUnpublished data
      National AccountsPaymentsEmployers' Social ContributionsEmployers' Social ContributionsUnpublished data
      Wage and Salary Earners, AustraliaPaymentsCompensation of EmployeesUsed to backcast Compensation of EmployeesPublished data
      National AccountsPaymentsEmployers' Payroll taxesEmployers' Payroll taxesUnpublished data
      National AccountsPaymentsRecruitment CostsRecruitment CostsPublished and Unpublished data
      Job Vacancies SurveyPaymentsRecruitment CostsRecruitment CostsPublished data
      National AccountsPaymentsTraining CostsTraining CostsPublished and Unpublished data
      Quarterly Business Indicator SurveysPaymentsTraining CostsTraining CostsPublished data
      Government Finance Statistics (GFS)PaymentsEmployment subsidiesEmployment subsidiesUnpublished data
      National AccountsPaymentsLabour income from self-employmentLabour income from self-employmentUnpublished data

      Labour Account methods

      Compilation methods

      The Australian Labour Account data tables are compiled using different methods, namely interpolation, extrapolation, backcasting and benchmarking. Methods chosen are based on two factors: the context in which the data were originally collected, and ability to fill data gaps between collection points or reference periods.

      Interpolation

      Interpolation is a method of constructing new data points within the range of a discrete set of known data points. Where interpolation is used in the Australian Labour Account, it is generally designed to create a quarterly series between two annual data points when data with a quarterly frequency are not available. An example of this is measuring the number of public sector jobs, where quarterly data are estimated from two annual data points.

      Extrapolation

      Extrapolation is the process of estimating values of a variable beyond its original observed range. Some estimates in the labour account are derived by extrapolating data using various indicators, as information necessary to compile a comprehensive and complete account may not be sufficiently timely. For example, as there is a time lag between the current reference period and the release of Government Finance Statistics (GFS), data for employment subsidies in the Australian Labour Account are extrapolated forwarded based on the movements of previously observed data.

      Backcasting

      Backcasting is the process of estimating values of a variable prior to its original observed range, usually through analysing the growth rates or proportional distribution of a conceptually related series. In addition, some estimates for earlier time periods in the Australian Labour Account are backcast from partially observed information. For example, data from the Job Vacancies Survey are not available on the current industry classification prior to 2009, however the total number of job vacancies is known. Data on the current industry classification for earlier time periods have been backcast using by applying a concordance between the previous and current industry classifications, with the additional constraint that the known total number of job vacancies must be maintained.

      Benchmarking

      Benchmarking is the processes of combining sub-annual (quarterly) indicator data and annual data, and aligning them to produce quarterly economic data that combine the robustness of the annual ‘benchmark’ source while reflecting the pattern of sub-annual movement. Benchmarks (or annual data) are usually of higher quality because they come from annual surveys, which draw on more complete source data (e.g. balanced and audited company financial accounts), conduct more detailed enquiries, and generally have larger sample sizes. To create a quarterly series, the annual data provides the overall levels, to which a conceptually related quarterly indicator series is benchmarked. An example of this in the Australian Labour Account is estimating private sector filled jobs by benchmarking quarterly jobs data to annual data.

      There are a number of methods used to benchmark flow data, depending on the type of data to be benchmarked. The method used the majority of the time, due to its accuracy and ease of implementation, is the ‘Proportional Denton Method’. This method preserves the movement of the quarterly data by minimising the absolute difference in the relative adjustments of two neighbouring quarters (i.e. keeping the benchmarked data to indicator data ratio as constant as possible over the time series), under the constraint that the sum of the quarters is equal to the annual data for each benchmark year.

      The Australian Labour Account uses a modified Proportional Denton Method to benchmark the Quarterly Business Indicators Survey (QBIS) industry data to the annual industry data from the Economic Activity Survey (EAS).

      The standard Proportional Denton Method is modified for use in the Australian Labour Account in the following ways:

      • the Proportional Denton Method is generally used only in relation to flow data. In the Australian Labour Account, the mathematics underlying the Proportional Denton method have been modified to apply to stock data;
      • the Proportional Denton Method is generally not used to extrapolate data series beyond their observed range. In the Australian Labour Account, annual industry data from the EAS, which are not yet available, have been extrapolated for the latest year as part of the modified Proportional Denton Method by assuming a benchmark data to indicator data ratio of one;
      • in the context of flow data, the annual benchmark data measures a variable over an entire year and so should (theoretically) be equal to the sum of the four indicator data points for that year. In contrast, stock data measure a variable at a single point in time, and the annual stock benchmark data could simply be considered a more accurate measure of the indicator data of that quarter. The modified Proportional Denton Method used in the Australian Labour Account imposes an additional constraint for stock estimates, that the benchmarked quarterly data must be equal to the annual benchmark data in the June quarter of each year while maintaining, as much as possible, the quarterly movements of the indicator data.

      For more information regarding the Proportional Denton Method, refer to paragraph 7.40 in the Australian System of National Accounts: Concepts, Sources and Methods.

      Annual Australian Labour Account data

      Data in the Australian Labour Account are compiled with quarterly estimates as the primary level of data compilation, with annual estimates subsequently produced from quarterly data. The method used to annualise data varies for each quadrant, depending on whether data are stock or flow estimates.

      Stock data

      The Jobs and Persons quadrants contain stock data, which are data that measure certain attributes at a point in time. Data in these quadrants are annualised using a simple arithmetic average of the four quarterly estimates. While these average annual levels are not true stock values, in the sense that they are not measured at a specific point in time, the purpose of presenting annual estimates as an arithmetic average is to minimise issues with using any particular quarterly observation to represent an annual stock, as any particular quarterly observation may under or over represent “usual” stock levels for a particular year. This is particularly relevant for industries which exhibit strongly seasonal employment levels, for example retail trade.

      For example, consider the example below of two industries which exhibit the following patterns in employed persons over a one year period.

      Time periodIndustry A – employed persons (000’s)Industry B – employed persons (000’s)
      Sep-15115220
      Dec-15120300
      Mar-16125230
      Jun-16130220
      2015-16 annual average123243

      The annual average stock level for 2015-16 for Industry A is 123 thousand employed persons. The choice of using an annual average, an end of year stock level (of 130 thousand employed persons) or a mid-point stock level (of 120 thousand employed persons) for this industry does not significantly change the annual level of employed persons.

      For Industry B, which shows a strong cyclical increase in employed persons each December, the choice of annual stock level is more significant. If an annual average stock level (of 243 thousand employed persons in 2015-16) or end of year stock level (of 220 thousand employed persons) were chosen, a much lower annual stock level would result than if a mid-point stock level (of 300 thousand employed persons) were used.

      Flow data

      The Labour Volume and Labour Payments quadrants contain flow data, which represent a measure of activity over a given period. Data in these quadrants are annualised as the sum of the four quarterly estimates.

      Seasonal adjustment

      Any original time series can be thought of as a combination of three broad and distinctly different types of behaviour, each representing the impact of certain types of real world events on the information being collected: systematic calendar related events, short-term irregular fluctuations and long-term cyclical behaviour.

      Seasonal adjustment is a statistical technique that attempts to measure and remove the effects of systematic calendar related patterns including seasonal variation to reveal how a series changes from period to period. Seasonal adjustment does not aim to remove the irregular or non-seasonal influences, which may be present in any particular data series. This means that movements of the seasonally adjusted estimates may not be reliable indicators of trend behaviour.

      The ABS software for seasonal adjustment is the SEASABS (SEASonal analysis, ABS standards) package, a knowledge based seasonal analysis and adjustment tool. The seasonal adjustment algorithm used by SEASABS is based on the X-11 Variant seasonal adjustment software from the U.S. Census Bureau.

      Trend estimates

      In cases where the removal of only the seasonal element from an original series (resulting in the seasonally adjusted series) may not be sufficient to allow identification of changes in its trend, a statistical technique is used to dampen the irregular element. This technique is known as smoothing, and the resulting smoothed series are known as trend series.

      Smoothing, to derive trend estimates, is achieved by applying moving averages to seasonally adjusted series. A number of different types of moving averages may be used; for quarterly series a seven term Henderson moving average is generally applied by the ABS. The use of Henderson moving averages leads to smoother data series relative to series that have been seasonally adjusted only. The Henderson moving average is symmetric, but asymmetric forms of the average may be applied as the end of a time series is approached. The application of asymmetric weights is guided by an end weight parameter, which is based on the calculation of a noise-to-signal ratio (i.e. the average movement in the irregular component, divided by the average movement in the trend component). While the asymmetric weights enable trend estimates for recent periods to be produced, they result in revisions to the estimates when subsequent observations are available.

      Revisions to trend series may arise from:

      • the availability of subsequent data;
      • revisions to the underlying data;
      • identification of and adjustment for extreme values, seasonal breaks and/or trend breaks;
      • re-estimation of seasonal factors; and
      • changes to the end weight parameter.

      For more information about ABS procedures for deriving trend estimates and an analysis of the advantage of using them over alternative techniques for monitoring trends, see Information Paper: A Guide to Interpreting Time Series - Monitoring Trends.

      In the Australian Labour Account, quarterly tables are produced in original, seasonally adjusted and trend terms. For the purpose of deriving the annual average level from quarterly stocks of jobs and employed persons using an arithmetic average, original quarterly series are used.

      Balanced tables

      After adjusting for conceptual and scope differences between data sources, a statistical discrepancy remains between the number of filled jobs as reported by businesses and the number of filled jobs as reported by households.

      These discrepancies represent the cumulative impact of data source error, including survey error, and modelling error. Survey error includes both sampling error and non-sampling error. Sampling error is the predictable variability arising from the use of samples, rather than a complete enumeration of the populations of enterprises and households. Non-sampling error is all other error present in an estimate, and includes:

      • Error arising from the reliability of the survey population and related benchmark data, e.g. the accuracy, completeness and timeliness of the Business Register from which business survey samples are drawn, or the reliability of Estimated Resident Population data used in benchmarking the Labour Force Survey;
      • Error arising from data used in the estimation and imputation procedures applied in both business and household surveys;
      • Error embedded in the estimation and imputation models used in surveys, for example incorrect assumption that missing firm data is similar to that of reporting firms of comparable size in the same industry; and
      • Error made by respondents in reporting data - for example, the Labour Force Survey relies on one responsible adult in each household to accurately report on the employment status of all other adults in the household, including industry of employment and hours worked in the survey reference week. Industry can be misreported where people are employed by labour hire firms, but actually work in other industries such as Mining, Construction or Manufacturing.

      Error can occur in non-survey data sources, such as missing data or misclassification in government administrative records used directly in the Australian Labour Account. For example, error could occur in the industry classification of sponsored visa holders, or in the reported number of persons in the permanent defence forces.

      Modelling error reflects errors embedded in the modelling assumptions used in the Australian Labour Account, for example in assuming that the proportion of children aged under 15 years who work has remained constant since 2006, or in assuming that Quarterly Business Indicators Survey employment movements accurately reflect quarterly change in the latest available annual data.

      The balanced Australian Labour Account estimates apply knowledge of the known strengths and weaknesses of data sources and methodologies, to derive a single estimate of the number of filled jobs.

      The balanced estimate of numbers of filled jobs impacts on other data in the Australian Labour Account that incorporate that estimate in their calculation. This includes balanced estimates of numbers of persons employed, hours paid for and hours worked.

      Two general observations about data source quality are relevant in deriving a balanced estimate of numbers of filled jobs:

      • Household estimates of numbers of filled jobs are considered more reliable at a total economy level. Household data are mainly sourced from the Labour Force Survey, which applies a consistent methodology and asks a consistent set of questions of a statistically robust sample of persons about the number of jobs held by employed persons in their household. By contrast, no single business survey covers the whole economy. Estimates of the total number of filled jobs from the business side are derived from three separate surveys (Economic Activity Survey, Survey of Employment and Earnings, and Quarterly Business Indicators Survey), supplemented by data obtained from the Australian Business Register. Each source has a different methodology, a different sample, and asks different questions. Adjustments are required to counter overlap. Growth in household side filled jobs is more consistent over time with growth in related economic data (Gross Domestic Product and Compensation of Employees) at a total economy level than growth in business side data.
      • Business sources are considered more reliable in estimating the distribution of jobs across industries. The numbers of filled jobs reported by each business survey respondent are automatically coded to the industry classification of that business. This implies that labour input is correctly linked to related production, employment related costs and compensation.

      Whilst additional considerations are taken into account at the industry level, the balanced estimate of filled jobs generally incorporates the advantage of the industry distribution derived from business side data, within a total economy estimate sourced from household side data.

      Revisions in the Australian Labour Account

      Revisions are a change in the value of a published estimate. Revisions arise from the correction of errors, the incorporation of more up-to-date data, reassessment of seasonal factors, and from time to time the introduction of new concepts or improved data sources and methods.

      Revisions are an inevitable consequence of the process of producing the Australian Labour Account. Revisions reflect both the complexity of measurement, and the need to trade off some level of precision in order to provide timely estimates, to maximise their use in analysis of current economic conditions.

      Quarterly revisions

      • Updates to the Estimated Resident Population (ERP), usually affecting the latest eight quarters of data, resulting in quarterly revisions to the Labour Force Survey statistics on persons, jobs and hours worked;
      • Revisions to Quarterly Business Indicator Survey statistics on filled jobs, arising from replacement of imputed data with actual responses following late receipt of survey questionnaires; and
      • Revisions to previously published seasonally adjusted and trend series, which will be revised to incorporate the seasonal effects of the latest quarterly data. This process is referred to as concurrent seasonal adjustment.

      Annual revisons

      • Revisions which reflect the cumulative impact of previous revisions to quarterly data;
      • Revisions to Economic Activity Survey statistics on filled jobs, arising from replacement of imputed data with actual responses following late receipt of survey questionnaires;
      • Revisions to Compensation of Employees and Gross Mixed Income following annual benchmarking of the Australian National Accounts, usually affecting the latest three years of quarterly data; and
      • Revisions to expenditure on recruitment services and training, following release of updated Input-Output Tables.

      Other periodic revisions

      • Five yearly post-Census benchmarking of ERP, resulting in revisions to the household Labour Force Survey statistics on persons, jobs and hours worked; and
      • Revisions to Compensation of Employees and Gross Mixed Income arising from scheduled National Accounts historical revisions, potentially affecting quarterly data back to 1960.

      Ad hoc revisions

      • All data sources can be subject to revisions arising from the correction of errors. These can include data capture and compilation errors, mistakes in classification, or respondent misreporting; and
      • Australian Labour Account data are also subject to revision arising from internal compilation errors.

      ABS and international data quality assessment frameworks include revisions history as one of the indicators of quality. A revisions history assists users in assessing the probability and potential scale of change to published data. The ABS publishes revisions to previously published data with each quarterly update of the Australian Labour Account.

      Jobs quadrant

      The Jobs quadrant provides data on the number of jobs, both filled and vacant. Estimates from business surveys are balanced with estimates from household surveys.

      Jobs quadrant

      Jobs quadrant
      The diagram shows that in the Jobs quadrant: Number of main jobs plus Number of secondary jobs equals Filled jobs. Filled jobs plus Job vacancies equals Total jobs.

      Jobs concepts

      The concept of a “job” is central to the Australian Labour Account. It is the mechanism through which people engage in production.

      The Oxford English Dictionary has multiple meanings for the word, one of which approximates the concept as it is applied in the Australian Labour Account and the 2008 System of National Accounts (2008 SNA) – “a task or piece of work, especially one that is paid”.

      The 2008 SNA does not explicitly define a job. Rather, it observes the agreement between an employee and the employer defines a job, and each self-employed person has a job (2008 SNA, 19.30). In application, a self-employed person is both the employer and employee. A job is position held by a person that involves work, duties or responsibilities; it may or may not provide returns of compensation or benefits to the individual.

      As the dictionary definition implies, not all jobs are paid, either in money or in kind. People can be engaged in productive economic activity within an institutional unit for no apparent reward, in which case they are contributing to output but receiving no compensation. The 2008 SNA concept of a job includes these people as volunteer labour (2008 SNA, 19.39).

      Jobs are created by enterprises. In the case of the self-employed person, the International Labour Organisation (ILO) defines these jobs as those where the remuneration is directly dependent upon the profits (or the potential for profits) derived from the goods and services produced (where own consumption is considered to be part of profits). The incumbents make the operational decisions affecting the enterprise, or delegate such decisions while retaining responsibility for the welfare of the enterprise. In this context, "enterprise" includes one person operations.

      In summary, and in the context of the Australian Labour Account, a job is a set of production related tasks that can be assigned to and undertaken by a person, and for which they are usually, but not necessarily, remunerated either in money or in kind.

      Production related tasks are constrained to economic activity within the 2008 SNA production boundary, and jobs are created and maintained by institutional units (Type of Activity Units within Enterprise Groups in the Australian context).

      The Australian Labour Account includes all jobs created and maintained by institutional units (that is, households, legal entities and social entities) resident in Australian economic territory, involving economic activity within the Australian application of the 2008 SNA production boundary.

      Estimates of movements in the number of jobs in the economy provide a measure of labour market performance and capacity.

      Jobs characteristics

      Jobs can be classified according to:

      • inherent job characteristics (e.g. whether the job is full-time or part-time),
      • characteristics of the person holding the job (e.g. whether the job is filled by a self-employed person or an employee), or
      • characteristics of the enterprise creating the job (e.g. the industry or institutional sector to which the job relates).

      Status in employment

      In the Australian context, self-employment according to the ILO definition is not separately identified. Rather, jobs are distinguished according to the status in employment categories of the people filling the job.

      These categories include:

      • Employee;
      • Owner manager of incorporated enterprise with employees;
      • Owner manager of incorporated enterprise without employees;
      • Owner manager of unincorporated enterprise with employees;
      • Owner manager of unincorporated enterprise without employees; and
      • Contributing family workers.

      The closest approximation to the ILO concept of self-employment in the Australian context is the aggregation of the four “owner manager” status in employment categories.

      Employees

      Employees are those employed persons who do not operate their own incorporated or unincorporated enterprise. An employee works for a public or private employer and receives remuneration in wages, salary, on a commission basis (with or without a retainer), tips, piece rates, or payment in kind.

      Owner managers of incorporated enterprises

      An owner manager of an incorporated enterprise is a person who operates his or her own incorporated enterprise, that is, a business entity which is registered as a separate legal entity to its members or owners (also known as limited liability company).

      An owner manager of an incorporated enterprise (an OMIE) may or may not hire one or more employees in addition to themselves and/or other owners of that business.

      Owner managers of unincorporated enterprises

      In the Australian Labour Account, own-account workers and employers employed in their own enterprises are referred to as Owner Managers of Unincorporated Enterprises (OMUEs). OMUEs are persons who operate their own unincorporated enterprise, or engage independently in a profession or trade. An owner manager of an unincorporated enterprise may or may not hire one or more employees in addition to themselves and/or other owners of that business.

      Contributing family workers

      A contributing family worker is a person who works without pay in an economic enterprise operated by a relative. Contributing family workers, including those working without pay in unincorporated enterprises engaged wholly or partly in market production, are also treated as self-employed (2008 SNA, 7.30b).

      The ILO defines a contributing family worker as a person who holds a self-employment job in an enterprise operated by a related person, and who cannot be regarded as a partner because the degree of his or her commitment to the operation of the enterprise, in terms of the working time or other factors to be determined by national circumstances, is not at a level comparable with that of the head of the establishment.

      Internationally the concept is restricted to those living in the same household, however Australia has not applied the same criteria of cohabitation in its implementation. For example, an adult child who makes unpaid contributions of labour to a family business operated by their parents, and does not live in the same household as the parents, is still considered to be a contributing family worker.

      Own-account workers engaged in the production of goods exclusively for own final use by their household (such as subsistence farming or do-it-yourself construction of own dwellings), are considered employed according to the definition of employment adopted by Thirteenth International Convention of Labour Statisticians (ICLS). Households producing unpaid domestic or personal services (e.g., housework, caring for family members) for their own final consumption are excluded, as such activities fall outside the 2008 SNA production boundary and are not considered employment.

      Jobs in the Australian Labour Account

      Jobs which are in and out of scope of the Australian Labour Account are summarised in the table below.

      Jobs in scopeJobs out of scope
      Paid employment with formal work agreements – i.e. an employer/employee relationship.Positions which are purely voluntary and no remuneration is received, either in cash or in kind.
      Owner managers of businesses – i.e. self-employed persons.Activities relating to the production of unpaid domestic services.
      Unpaid contributions of labour to a family business or farm – i.e. contributing family workers.Activities and positions outside of Australia’s economic territory.
      Activities relating to the production of goods for own consumption.Activities relating to unreported illegal transactions.

      Jobs and persons

      The number of jobs in the economy exceeds the number of persons employed, to the extent that some employed persons have more than one job in the same period. An individual with more than one job may do these successively, as when the person works for part of the week in one job and the rest of the week in another, or in parallel, as when the person has an evening job as well as a daytime job. In addition, the number of jobs in the economy may be reduced when compared to the number of persons employed in instances of formal job sharing arrangements.

      Employers may not be aware of, and in any case are not asked to provide information on, secondary jobs undertaken by their employees. When employers supply information on the number of employees, they actually provide information on the number of jobs they hold. This is because the same employee would be reported separately by each employer. The distinction between the number of jobs and the number of employed persons is one issue that is informed by the Australian Labour Account.

      The Australian Labour Account recognises this difference by accounting for multiple job holding, and reports the number of jobs in the Jobs quadrant and employed persons in the Persons quadrant. However, the Australian Labour Account does not compile estimates of formal job sharing, as there is currently no available data source to measure this, and it is particularly unlikely to be reported accurately by businesses.

      The statistics derived from the Labour Force Survey are designed to produce estimates of the number of people engaged in economic activity. The statistics derived from ABS business surveys count the number of jobs in which people are employed. For example, a person holding multiple jobs with different employers would be counted once in ABS household surveys as an employed person, but in ABS business surveys would be counted multiple times, once by each employer for each job that they held.

      A number of examples illustrate this:

      • if an unemployed person became employed full-time (by starting one full-time job), then the full-time employment estimate from the Labour Force Survey would increase by one (in a business survey, or a 'filled jobs' count, this would lead to an increase in the filled jobs estimate by one);
      • if an unemployed person became employed full-time (by starting two part-time jobs with a total of 35 hours of work or more per week), then the full-time employment estimate from the Labour Force Survey would increase by one (however, in a business survey, or a 'filled jobs' count, this would lead to an increase in the filled jobs estimate by two);
      • if a person who was already employed in one part-time job took on another part-time job, this would have differing impacts on the employment estimates from the Labour Force Survey depending on the total number of hours worked: if the sum of hours worked in the two part-time jobs was fewer than 35 hours per week, the employment estimates from the Labour Force Survey would remain unchanged, but if the sum of hours worked was 35 hours or more, the employment estimates from the Labour Force Survey would show a decrease of one in part-time employment and an increase of one in full-time employment (however, in both cases this would lead to an increase of one in the filled jobs estimate from a business survey);
      • if a person who was employed in three part-time jobs (working a total of more than 35 hours per week) resigned from these and assumed one full-time job, this would have no impact on the employment estimates from the Labour Force Survey (however, this would lead to a decrease of two in the filled jobs estimate - the number of part-time filled jobs would decrease by three while the number of full-time filled jobs would increase by one); and
      • if a person employed in two part-time jobs became unemployed, the employment estimate from the Labour Force Survey would decrease by one (however, this would lead to a decrease of two in the filled jobs estimate from a business survey).

      The Proportion of Secondary Jobs presents the number of secondary jobs as a proportion of the total number of filled jobs for each industry and the total economy. This measure provides insight into the relative number of secondary jobs in each industry, and enables comparison across industries and with each industry to an economy wide average.

      Proportion of Vacant Jobs

      The development of the Australian Labour Account has made it possible to produce an important new labour market measure – the Proportion of Vacant Jobs (PVJ).

      The PVJ provides a useful labour demand-side view of relative labour demand, at the industry level, presenting the relationship between unmet demand (job vacancies) and met demand (filled jobs) within the Australian Labour Account.

      The PVJ is calculated as the number of vacant jobs as a proportion of total jobs. This derived measure is a function of filled jobs and job vacancies. By bringing together met demand and unmet demand, the PVJ provides new insights into changes in the labour market.

      In addition to providing insights into cyclical labour demand and employment, changes in the PVJ over time can also highlight that some of the following may be occurring:

      • Changing employment capacity – there may be indications that the industry is nearing its full employment potential or, conversely, that there is the possibility of future employment growth;
      • Job churn – the industry may not be maintaining long term employment, resulting in a high number of job vacancies without long term growth in employment;
      • Skill mismatch – current availability of skills may not be able to satisfy employer requirements, resulting in an extended search for appropriately skilled staff; and/or
      • Changing employment conditions or arrangements - the industry may be transitioning from full-time to part-time roles, or a greater use of contractors or use of labour hire firms.

      Understanding changes in the PVJ (and analysing the underlying factors contributing to these changes) will enable Australia to better understand its labour market.

      Jobs sources

      Source data for quarterly and industry estimates of jobs

      Numbers of filled jobs, from the business sources side, are sourced from the following ABS data:

      • Quarterly estimates of private sector jobs are estimated from underlying data from the Quarterly Business Indicators Survey (QBIS), from Business Indicators, Australia.
      • Quarterly estimates of private sector jobs for out of scope ANZSIC Divisions in QBIS are estimated from the Economic Activity Survey (EAS), published in Australian Industry for ANZSIC Division A (Agriculture, Forestry and Fishing) and Division O (Public Administration and Safety), using quarterly Compensation of Employees as a quarterly indicator series; and
      • Quarterly data for the public sector are estimated using underlying data from the Survey of Employment and Earnings (SEE), from Employment and Earnings, Public Sector, using quarterly public sector Compensation of Employees as a quarterly indicator series.

      Business survey data are supplemented by ABS business register information, defence force information, child workers information and estimates from the ABS Labour Force Survey for contributing family workers.

      The number of filled jobs, from the household survey side, is the aggregate of the number of main jobs and secondary jobs, less jobs with formal job sharing arrangements. Estimates for main jobs and secondary jobs are sourced from underlying data from Labour Force, Australia. Survey based data are supplemented with defence force information, child workers information, information on non-residents working in Australia, and Australian residents living in Australia employed by overseas companies/business entities to account for survey scope restrictions. There is no information currently available on the number of jobs with formal job sharing arrangements.

      Numbers of job vacancies are sourced from Job Vacancies, Australia. Data from the Internet Vacancy Index, published by the Department of Employment, Skills, Small and Family Business, are used to supplement ABS survey data for the out of scope ANZSIC Division A (Agriculture, Forestry and Fishing).

      The table below summarises data sources used in compiling quarterly and industry estimates of jobs.

      Source dataUse in compiling quarterly data
      Australian IndustryUsed to benchmark quarterly data from Business Indicators, Australia of Employees as a quarterly indicator series. Also used to compile estimates of private sector filled jobs (business sources) for out of scope ANZSIC Divisions in QBIS, using quarterly Compensation.
      Business Indicators, AustraliaUsed to compile quarterly estimates of private sector filled jobs (business sources).
      Employment and Earnings, Public SectorUsed to compile estimates of public sector filled jobs (business sources), using quarterly Compensation of Employees as a quarterly indicator series.
      Business register information (ABS Business Register Unit)Used for scope adjustments to private sector filled jobs (business sources).
      Defence force information (ABS National Accounts)Used to estimate out of scope defence jobs for both filled jobs (business sources) and filled jobs (household sources).
      Labour Force, AustraliaUsed to estimate filled jobs (household sources), both main and secondary jobs. Also used to estimate jobs held by out of scope non-residents working in Australia, and unemployment.
      Child Employment, Australia, 2006Used to estimate out of scope child employment for both filled jobs (business sources) and filled jobs (household sources).
      Migration, Australia and Overseas Arrivals and Departures, AustraliaUsed to estimate jobs held by out of scope non-residents working in Australia.
      Balance of PaymentsUsed to estimate out of scope Australian residents living in Australia employed by overseas companies/business entities.
      Job Vacancies, AustraliaUsed to compile job vacancies, and total jobs.
      Internet Vacancy Index (Department of Employment, Skills, Small and Family Business)Used to compile jobs vacancies, and total jobs, for the out of scope Agriculture, Forestry and Fishing ANZSIC Division A.

      Source data for annual estimates of jobs

      The number of annual filled jobs, from both the business and household side, and the number of annual job vacancies, are compiled from the same data sources as the quarterly estimates.

      Jobs methods

      The Jobs quadrant provides data on the number of jobs (filled and vacant) as at the end of the quarter. Job statistics are compiled for each ANZSIC industry subdivision and division, and for the economy as a whole. Unless otherwise stated, the methods described apply to both levels of aggregation.

      Total jobs

      Total jobs is the sum of filled jobs, plus job vacancies.

      Filled jobs

      Filled jobs (business sources)

      The number of filled jobs, from the business sources side, is equivalent to the number of people employed in enterprises resident in the Australian Economic Territory and engaged in economic activity within the scope of the National Accounts production boundary. People counted include employees, working proprietors and partners, employees absent on paid or prepaid leave, employees on workers' compensation who continue to be paid through the payroll, and contract workers paid through the payroll.

      Filled jobs (business sources), for each quarter, is estimated by aggregating:

      • For the private sector, the number of employees as at the end of each quarter, sourced from the annual Economic Activity Survey (EAS) and published in Australian Industry;
      • For the public sector, the number of employees as at the end of each quarter, derived using underlying data from the Survey of Employment and Earnings (SEE). Public sector SEE data used in the Australian Labour Account exclude units in the non-financial and financial sectors, as they are also in scope of the EAS; and
      • Quarterly estimates of underlying Quarterly Business Indicator Survey (QBIS) data from Business Indicators, Australia to represent private sector employment in ANZSIC Division K (Finance and Insurance Services), which is out of scope of the EAS.

      These three surveys cover most of the ANZSIC industries, except for:

      • Class 6310 Life Insurance;
      • Class 6330 Superannuation Funds; and
      • Class 7600 Defence.

      Units in ANZSIC Class 6330 Superannuation Funds are funds set up to provide retirement benefits. Conceptually they are considered to be non-employing units, and therefore would not contribute to Australian Labour Account estimates. As such, no estimate for employment in this industry has been included.

      Scope adjustments are made for the following sectors and populations:

      Add:

      • The number of persons employed (at the end of each quarter) in ANZSIC Class 6310 (Life Insurance), sourced from underlying data from the ABS Business Register. This industry is not included in the EAS or QBIS.
      • The number of persons employed in the permanent defence forces as at the end of each quarter, sourced from underlying ABS National Accounts data. Defence force personnel fall outside the scope of the SEE. All defence force personnel in Class 7600 (Defence) are assumed to work in the Public Administration and Safety industry (ANZSIC Division O).
      • The number of unpaid contributing family workers for the quarter, sourced from the Labour Force Survey and published in Labour Force, Australia, as unpaid employees are out of scope of ABS business surveys.
      • An estimate of the number of child workers (persons aged 5 to 14) who are self-employed, working on a farm, or as a contributing family worker. These data are sourced from ABS household survey data, using underlying data from Child Employment, Australia, 2006. Population estimates from Australian Demographic Statistics are used to extrapolate the number of child workers from the 2006 benchmark level, by assuming that the proportion of the age group working has not changed. Industry proportions are based on underlying Labour Force Survey data on employed persons aged 15 years old. No adjustments are made for child workers who are employees, as these persons are in scope of both EAS and QBIS.

      Deduct:

      • The number of persons engaged in ANZSIC subdivision 28 Water Supply, Sewerage and Drainage (Employment and Earnings, Public Sector, Australia) as this subdivision is included in the Australian Industry. ABS Business Register data are available from June 2007. For earlier time periods, the movement in filled jobs for the Electricity, Gas, Water and Waste Services industry is applied.
      Calculation of filled jobs (business sources) by industry

      Data derived from an annual survey are generally considered to be of higher quality than quarterly data due to the larger sample sizes, and are generally subject to less volatility than quarterly run surveys. Annual source data provide overall levels, known as annual benchmarks, from which quarterly estimates are compiled. This ensures consistency between the quarterly and annual labour accounts.

      For all ANZSIC industry divisions except A (Agriculture, Forestry and Fishing); K (Financial and Insurance Services) and O (Public Administration and Safety), a mathematical technique (the modified Proportional Denton Method) is used to benchmark quarterly stocks of private sector jobs reported in QBIS to annual data from EAS. This ensures the benchmarked quarterly levels are identical each June quarter, while maintaining the observed quarterly pattern from QBIS as much as possible.

      For the most recent quarters, for which EAS year-end data are not available, the previous year-end EAS numbers are extrapolated, also using the modified Proportional Denton Method. Extrapolated data are calculated for up to 6 quarters, due to the 18 month lag in the delivery of EAS data.

      For Division A (Agriculture, Forestry and Fishing) and Division O (Public Administration and Safety), for which QBIS data are not available, EAS estimates of the number of jobs is used as an annual benchmark, with quarterly Compensation of Employees used as a quarterly indicator series.

      For Division K (Finance and Insurance Services) for which EAS data are not available, employment data reported in QBIS are used directly as the quarterly estimate of private sector job holding.

      To calculate the number of public sector filled jobs, underlying data from the Survey of Employment and Earnings (SEE) are used as an annual benchmark, with quarterly public sector Compensation of Employees used as a quarterly indicator series.

      EAS data are not available on a consistent industry classification prior to 2009-10. For time periods prior to June 2010, filled jobs as measured from business sources are derived as follows: 

      • From December quarter 2001 to June quarter 2010: seasonally adjusted movements in Compensation of Employees (which have been price deflated using the Wage Price Index), are applied to the June 2010 level.
      • From September quarter 1994 to December quarter 2001, movements in the number of employees from Wage and Salary Earners, Australia are applied to the December 2001 level. These data relate to both the public and private sectors for each industry division except for Division A (Agriculture, Forestry and Fishing), which is limited to the public sector only. Applying movements from the Agriculture industry based on the public sector data produces large movements, given the small level associated with the indicator series. Movements from the Transport and storage industry are instead used as a proxy, given the strong links in production and supply chains between agriculture and transport. As the data are also on a historical industry classification basis, conversion factors based on employees from the Labour Force Survey are applied to approximate the current industry classification.

      Filled jobs (household sources)

      The number of filled jobs, from the household side, is equal to the number of people employed in main jobs and secondary jobs sourced from the household Labour Force Survey.

      Filled jobs (household sources), for each quarter, is estimated by aggregating:

      • The number of main jobs reported in the end of quarter reference month (i.e. March, June, September and December) in the household Labour Force Survey and published in Labour Force Australia, and
      • The number of secondary jobs reported in the end of quarter reference month in the household Labour Force Survey.

      The following scope adjustments are made:

      Add:

      • The number of persons employed in the permanent defence forces as at the end of each quarter, to the estimate of main jobs. Defence force personnel are not included in the Labour Force Survey, and these data are sourced from underlying ABS National Accounts data. All defence force personnel are assumed to work in ANZSIC Division O (Public Administration and Safety). Permanent defence force personnel are also assumed to work solely in their main job and not have multiple jobs.
      • An estimate of the number of child job holders who are aged between 5 to 14 years as at the end of each quarter, to the estimate of main jobs. It is assumed that child workers do not hold secondary jobs. The estimate covers all child workers, regardless of employment status, as all children less than 15 years of age are excluded from the scope of the Labour Force Survey. The estimate is derived from data collected in the 2006 household survey Child Employment, Australia, 2006. Population estimates from Australian Demographic Statistics have been used to extrapolate the number of child workers from the 2006 benchmark level, by assuming the proportion of children in the 5-14 year age cohort who work has remained the same as that recorded in 2006. Industry allocations are based on underlying Labour Force Survey data on the industry of employment of 15 year old persons.
      • An estimate of the number of main jobs held by non-resident visitors to Australia employed by Australian resident enterprises to the estimate of main jobs (see Non-resident visitors section below).
      • An estimate of the number of secondary jobs held by non-resident visitors employed by Australian resident enterprises to the number of secondary jobs.

      Non-resident visitors

      Time periods from March 2006 onwards

      The Labour Force Survey excludes from its scope non-resident visitors who intend spending less than 12 months in Australia, some of whom are employed during their stay by Australian resident enterprises. As non-resident visitors are included in the scope of business surveys (EAS and QBIS), only household side labour force data are adjusted to include non-resident visitors who are employed.

      Data are sourced from short term visitor arrivals statistics from Overseas Arrivals and Departures, Australia and overseas migration data from Overseas migration. Data are obtained for the number of short term visitors who are present in Australia at the end of the reference quarter but who are not included in the Estimated Resident Population. Of interest are those people who have entered the country with a visa that includes working rights. Information on the main reason for journey is also collected. These visa classes and reasons for journey are detailed below.

      Visa subclasses and Reasons for journey used in the Australian Labour Account

      Visa subclass

      400 Temporary Work (Short Stay Activity) (from 23/3/13)
      401 Temporary Work (Long Stay Activity) (from 24/11/12)
      402 Training and Research (from 24/11/12)
      403 Temporary Work (International Relations) (from 24/11/12)
      405 Investor Retirement (from 1/11/04)
      410 Retirement
      416 Special Program
      417 Working Holiday
      419 Visiting Academic
      420 Entertainment
      421 Sport
      422 Medical Practitioner
      423 Media and Film Staff
      424 Public Lecturer
      426 Diplomatic or Consular
      427 Domestic Worker Overseas Executive
      428 Religious Worker
      430 Supported Dependent of Australian or NZ Citizen Temp in Australia
      442 Occupational Trainee
      444 Special Category - New Zealand Citizen
      456 Business (Short Stay) (from 1/8/96)
      457 Temporary Work (Skilled) (from 24/11/12) previously Business (Long Stay) (from 1/8/96)
      459 Sponsored Business Visitor (short stay) (from 1/7/00)
      461 New Zealand Citizen (Family Relationship) Temporary Visa (from 26/2/01)
      462 Work and Holiday
      470 Professional Development (from 1/7/03)
      476 Skilled - Graduate (from 1/9/07)
      482 Temporary Skill Shortage (from 18/03/2018)
      485 Temporary Graduate (from 23/3/13) previously Skilled - Graduate (from 1/9/07) (replaced 497)
      500 Student (Temporary) (from 01/07/16)
      570 Independent ELICOS Sector(from 1/7/01)
      571 Schools Sector (from 1/7/01)
      572 Vocational Education and Training Sector (from 1/7/01)
      573 Higher Education Sector (from 1/7/01)
      574 Postgraduate Research Sector (from 1/7/01)
      575 Non-Award Foundation/Other Sector (from 1/7/01)
      576 Ausaid/Defence Sponsored Sector (from 1/7/01)
      995 Diplomatic

      Reason for journey 

      • Business
      • Convention/conference
      • Education
      • Employment
      • Exhibition – Other/Not Stated/Not Applicable
      • Holiday
      • Visiting friends and relatives
      Visa subclass and reason for journey - used in calculating short term visitor arrivals

      400 Employment
      401 Employment; Education
      402 Employment; Education
      403 Employment; Education
      405 Employment; Education
      410 Employment; Education
      416Employment; Education
      417 Employment; Education; Holiday; Business; Visiting friends and relatives
      419 Employment; Education
      420 Employment; Education
      421 Employment; Education
      422 Employment; Education
      423 Employment; Education
      424 Employment; Education
      426 Employment; Education
      427 Employment; Education
      428 Employment; Education
      430 Employment; Education
      442 Employment; Education
      444 Employment; Education
      456 Employment; Education
      457 Employment; Education; Business; Visiting friends and relatives
      459 Employment; Education
      461 Employment; Education
      462 Employment; Education; Holiday
      470 Employment; Education
      476 Employment; Education
      482 Employment; Education; Business; Visiting friends and relatives
      485 Employment; Education
      500 Employment; Education
      570 Employment; Education
      571 Employment; Education
      572 Employment; Education
      573 Employment; Education
      574 Employment; Education
      575 Employment; Education
      576 Employment; Education
      995 Employment; Education


      Visa classes are aggregated into three main groups: short term visitors (students); short term visitors (sponsored visa holders); and short term visitors (other).

      To estimate the number of main jobs held by students who short term visitors, the quarterly average employment rate of resident persons attending tertiary education, obtained from the Labour Force Survey, is multiplied by the estimated number of short term student visa holders. The Labour Force Survey data used in the calculation of employed short term students is limited to those persons aged 15-24 years old, who are currently undertaking full-time tertiary education. The method assumes that similar employment rates apply to short term visitors on student visas as for full-time Australian resident tertiary students, and that all short term student visa holders are in the labour force (either employed or unemployed).

      To estimate the number of main jobs held by other short term visitors, the quarterly average employment rate for all residents is multiplied by the number of visa holders (other than sponsored visa holders) with working rights. This method assumes that all temporary entrants with a visa that had working rights (other than 400, 457 and 482 visa holders) were in the labour force (either employed or unemployed), and that similar rates of employment for this group apply when compared with the resident population

      To estimate the number of main jobs held by short term visitors who are sponsored visa holders, the total number of short term arrivals with this type of visa is used. As these visa types require that the holder remains employed for the duration of the visa, an employment rate of 100% is assumed.

      To estimate the number of secondary jobs held by other non-resident short-term visitors, the estimated number of non-resident main job holders (excluding students and sponsored visa holders) is multiplied by the proportion of resident employed persons who hold multiple jobs sourced from the Labour Force Survey. Students and sponsored visa holders are assumed to only hold main jobs, due to the restrictions associated with these types of visa. This method assumes that the same proportion of short term visitors hold multiple jobs as for the resident employed population.

      There is a time lag in the estimation of Net Overseas Migration (NOM) data. Consequently, estimates of short term visitors for the latest quarters are extrapolated by applying movements in Overseas Arrivals and Departures (OAD) data to estimates of NOM. The movements are applied after matching visa codes and reasons for journey between the NOM and OAD series.

      Time periods from September 1994 to December 2005 

      OAD and NOM data with both visa type and reason for journey are not available for the entire time series of the Australian Labour Account. For earlier time periods, the following data are available:

      • NOM data classified by reason for journey by visa type is available from March 2006
      • OAD data classified by reason for journey by visa type is available from September 2004
      • OAD data classified by reason for journey only is available from September 1993.

      As with the current end of the NOM series, estimates for the periods prior to March 2006 are modelled from OAD data by applying movements with matching visa codes and reasons for journey category to the estimates of March 2006.

      The resulting series are aggregated to students, non-students and sponsored visa holders in the same way as for the rest of the time series.

      Disaggregation to industry

      Jobs held by short term visitors are disaggregated to industry in the following ways:

      • Main jobs held by short term visitors (students) are disaggregated to industry using an underlying Labour Force Survey series of persons aged 15-24 attending full-time educational institutions.
      • For main jobs held by other short term visitors, underlying data from Labour Force Survey supplementary surveys which approximates tenuous employment, namely part-time employment with no leave entitlements, are used.
      • For main jobs held by short term visitors (sponsored visa holders), data from the Department of Home Affairs on the industry of the employer sponsoring the visa are used to distribute the total to industry division. Division level totals are further disaggregated to subdivision, using the tenuous employment data described above. Data from the Department of Home Affairs are not available prior to the 2005-06 financial year. For time periods prior to this, 2005-06 industry proportions are assumed to apply. 
      • Data for short term visitors on “working holiday visas” (417 and 462) is distributed to industry using published information on employers of these visa types from the Australian Taxation Office.
      • For secondary jobs held by sponsored visa holders) is assumed to apply. 

      Deduct:

      • the number of jobs held by Australian residents living in Australia employed by non-resident enterprises, sourced from underlying Balance of Payments data. As most of the people involved are employed by agencies of foreign governments (consulates, embassies etc.), the deductions are made from ANZSIC subdivision 75 (Public Administration) within Division O (Public Administration and Safety). Although the Labour Force Survey would include people over the age of 15 years in this category, they are not contributing to economic activity within Australian economic territory as measured in the Australian National Accounts.
      Calculation of filled jobs (household sources) by industry

      The Labour Force Survey collects quarterly data on the industry of the main job held by employed persons. For each employed person, it also collects the number of secondary jobs held (second, third, fourth or more). The Labour Force Survey does not record the industry of secondary jobs. To calculate the number of filled jobs and people employed at an industry level requires the allocation of each secondary job to an industry.

      This is done in the Australian Labour Account by first obtaining the total number of multiple job holders and the number of second, third and fourth jobs from the Labour Force Survey. Employed persons who indicate they hold more than four jobs are assumed to hold only four jobs, as no further information on the number of jobs actually held is available. At this stage of compilation, multiple job holders and second, third and fourth jobs are classified by the industry of main job for each employed person.

      Data from the ABS Linked Employer Employee Dataset (LEED) are then used to determine the proportions of the industry of employment of second, third and fourth jobs for multiple job holders, and applied to industry of main job Labour Force Survey data. These proportions are extracted as at the end date for each quarter from the LEED, and are updated as new data points become available. Industry proportions from the earliest available LEED are applied to earlier time periods in the Australian Labour Account, and similarly the latest available proportions are applied to subsequent time periods where necessary.

      Where relevant, data are sourced from information collected in the Labour Force Survey in the last month of the relevant quarter, and apportioned across the industries using the related quarterly labour force industry data. For example, estimates in the September quarter Australian Labour Account are sourced from September month Labour Force data, which are then distributed across industry divisions from the industry distribution of quarterly data captured in the August Labour Force Survey published in Labour Force, Australia, Detailed, Quarterly.

      Sector of Filled Jobs

      One commonly used sector classification in labour statistics is the public and private sector classification. In this classification, the public sector includes all government units, such as government departments, non-market non-profit institutions that are controlled and mainly financed by government, and corporations and quasi-corporations that are controlled by government. The private sector refers to enterprises that are not controlled by Commonwealth, state/territory or local governments (that is, any enterprise that is not part of the public sector).

      The Australian Labour Account publishes estimates of private and public sector filled jobs. These are compiled by applying proportions from business sources (with data from the Economic Activity Survey representing the private sector, and data from the Survey of Employment and Earnings representing the public sector) to balanced numbers of filled jobs for each industry.

      Job sharing

      There is currently no household side information available on the number of jobs with job sharing arrangements. As a result, the total number of filled jobs is equivalent to the sum of reported main jobs and secondary jobs, plus scope adjustments. As with the business side, shared jobs on the household side would be counted as many times as there are people engaged in such arrangements.

      Annual jobs methods

      The Jobs quadrant contains stock data, which are data that measure certain attributes at a point in time. To determine an annual estimate of jobs in this quadrant, an average level is derived using a simple arithmetic average of the four quarterly estimates. Refer to Labour Account Methods for an example of this method.

      The annual estimate of jobs is an approximate estimate of the number of jobs at any point in time during the year.

      Job vacancies

      A job vacancy is a job available for immediate filling on the survey reference date and for which recruitment action has been taken. Recruitment action includes efforts to fill vacancies by advertising, by on site or online notices, by notifying employment agencies or trade unions and by contacting, interviewing or selecting applicants already registered with the enterprise or organisation.

      Estimates of job vacancies exclude:

      • jobs not available for immediate filling on the survey reference date;
      • jobs for which no recruitment action has been taken;
      • jobs which became vacant on the survey date and were filled on the same day;
      • jobs of less than one day's duration;
      • jobs only available to be filled by internal applicants within an organisation;
      • jobs to be filled by employees returning from paid or unpaid leave or after industrial disputes;
      • vacancies for work to be carried out by contractors; and
      • jobs for which a person has been appointed but has not yet commenced duty.


      Total quarterly job vacancies are calculated as:

      • the sum of the number of vacant positions reported in the ABS Job Vacancies Survey for the relevant quarterly reference date/month (3rd Friday of February, May, August and November) and published in Job Vacancies, Australia (ABS cat. no. 6354.0); plus
      • the number of job advertisements from the Department of Employment, Skills, Small and Family Business Internet Vacancy Index (as at the 1st day of the third month of the reference quarter; i.e. 1 March, 1 June, 1 September, 1 December), for the following Australian and New Zealand Standard Classification of Occupations (ANZSCO) occupation codes: 12 Farmers and Managers; 36 Skilled Animal and Horticultural Workers; and 84 Farm, Forestry and Garden Workers.

      Internet Vacancy Index data are added to capture vacancies available in employing enterprises primarily engaged in Agriculture, Forestry and Fishing, which are out of scope of the quarterly ABS Job Vacancies Survey.

      Industry detail at the ANZSIC subdivision level is not available directly from either the ABS Job Vacancies Survey or the Department of Employment, Skills, Small and Family Business Internet Vacancy Index, and is modelled in the Australian Labour Account using the following methods:

      • For subdivisions within Division A (Agriculture, Forestry and Fishing), information from the Department of Employment, Skills, Small and Family Business Internet Vacancy Index for agricultural occupations at four digit ANZSCO level are aggregated to approximate these ANZSIC subdivisions; and
      • For all remaining subdivisions, ANZSIC division level information from the Job Vacancies Survey is disaggregated to subdivision level using data from the Labour Force Survey relating to employees by subdivision (excluding Owner Managers of Unincorporated Enterprises).

      Data from the ABS Job Vacancies Survey are available on the current ANZSIC 2006 industry classification from November 2009 onwards, and data on an ANZSIC 1993 basis and the total number of job vacancies are available for earlier time periods. Data for each ANZSIC 2006 industry division for earlier time periods are estimated by applying a concordance between the ANZSIC 1993 and ANZSIC 2006 industry classifications. The known total number of job vacancies is maintained using this approach. Data at the industry division level are then distributed to industry subdivision by applying proportions from the LFS employees (excluding Owner Managers of Unincorporated Enterprises) series.

      The Job Vacancies Survey was suspended for five periods between August 2008 and August 2009 inclusive, as a result of a series of cuts to the ABS forward work program. The ABS has used econometric modelling techniques using a full-time equivalent flow series to estimate total job vacancies for the missing period. It should be noted that the modelled data are not part of the Job Vacancies Survey series and are not available in the related publication or the Australian Labour Account. However, modelled data for the gap period have been used in the production of seasonally adjusted and trend time series data.

      Job vacancies for each industry for the period September 2008 and September 2009 have been estimated by applying the movement from the LFS number of employees (excluding Owner Managers of Unincorporated Enterprises) to subdivision level job vacancies data on an ANZSIC 2006 basis from December 2009. These industry estimates are constrained to the modelled total number of job vacancies for this period.

      Data from the Department of Employment, Skills, Small and Family Business are available from January 2006 onwards. Data for earlier time periods are estimated by applying the movement in the number of employees (excluding Owner Managers of Unincorporated Enterprises) for each Agriculture subdivision from the LFS to the 2006 level.

      Jobs quadrant calculations

      Jobs quadrant sources and calculations

      Jobs quadrant sources and calculations
      The diagram shows that: Job Vacancy Survey (ABS) data plus Internet Vacancy Index (DoE) data - Div A equals Total job vacancies QBIS data (Div K) plus EAS data (Div A and O with COE as quarterly indicator series) plus QBIS data (for remaining Divs, benchmarked to annual EAS data) equals Total filled jobs (business sources), private sector SEE data (with COE as quarterly indicator series) plus Total filled jobs (busines sources) private sector equals Total filled jobs (business sources) Industry scope adjustments (ABS) plus Defence personnel (NAB) plus Contributing family workers (LFS) plus Child workers (not working for an employer) equals Adjustments to filled jobs (business sources) Total filled jobs (business sources) plus Adjustments to filled jobs (business sources) equals Labour Account filled jobs (business sources) Labour Account filled jobs (business sources) plus Total job vacancies equals Labour Account total jobs (business sources) Labour Force Survey (LFS) main jobs (monthly) multiplied by Labour Force Survey (LFS) main jobs (quarterly industry proportions) equals Labour Account Labour Force Survey main jobs Labour Force Survey (LFS) secondary jobs (monthly) multiplied by secondary jobs proportions (LEED) equals Labour Account Labour Force Survey secondary jobs Defence personnel (NAB) plus Non residents employed in Australia adjustment (ABS) plus Child workers (all employed children) less Residents employed overseas adjustment (BoP) equals Adjustments to filled jobs (household surveys) Labour Account Labour Force Survey main jobs plus Labour Account Labour Force Survey secondary jobs plus Adjustments to filled jobs (household surveys) equals Labour Account filled jobs (household surveys) Labour Account filled jobs (business sources) less Labour Account filled jobs (household sources) equals Statistical discrepancy (jobs)

      Persons quadrant

      The Persons quadrant provides statistics on employed people, people looking for and available for employment (unemployed people), and underemployed people.

      Persons quadrant

      Persons quadrant
      The diagram shows that: Employed persons equals Number of main jobs (Total economy level). Unemployed persons plus Underemployed persons equals Underutilised persons. Employed persons plus Unemployed persons equals Labour Force.

      Persons concepts

      The official measure of the population of Australia is based on the concept of usual residence. It refers to all people, regardless of nationality, citizenship or legal status, who usually live in Australia, with the exception of foreign diplomatic personnel and their families.

      The Australian Labour Account uses a practical application of the ‘12/16’ rule to establish usual resident status for non-resident visa holders with working rights. A person is regarded as a usual resident if they have been (or expect to be) residing in Australia for a period of 12 months or more. This 12 month period does not have to be continuous and is measured over a 16 month period. For more information on the ‘12/16 month rule’ methodology, see the Technical Note in Migration, Australia, 2008-09.

      The scope of the population in the Australian Labour Account includes all persons who contribute to Australian economic activity, irrespective of age.

      Persons sources

      Source data for quarterly and industry estimates of persons

      Labour statistics represented in the Persons quadrant are mostly sourced from estimates calculated from the monthly Labour Force Survey. Data from the monthly Labour Force Survey are released in two stages: Labour Force, Australia, and Labour Force, Australia, Detailed. Labour Force Survey data are supplemented with defence force information, child workers information and information on non-residents. 

      Data from the ABS Linked Employer Employee Dataset (LEED) are used to determine industry of employment of secondary job holders, and applied to Labour Force Survey data to calculate total jobs in each industry. This information is used to adjust the Labour Force Survey estimate of employed persons in each industry, by excluding multiple job holding within the same industry from the total number of filled jobs.

      The table below summarises data sources used in compiling quarterly and industry estimates of persons.

      Source dataUse in compiling quarterly data
      Labour Force, Australia and Labour Force, Australia, DetailedUsed to compile estimates of employed persons, unemployed persons, underemployed persons, not in the labour force and civilian population
      Defence force information (National Accounts)Used to estimate employed defence personnel.
      Child Employment, Australia, 2006Used to estimate employed children.
      Migration, Australia and Overseas Arrivals and Departures, AustraliaUsed to estimate short-term non-residents working in Australia.
      Balance of PaymentsUsed to estimate employed Australian residents living in Australia employed by overseas companies/business entities.
      Australian Demographic StatisticsUsed for the total estimated resident population.

      Source data for annual estimates of persons

      The same source data are used in compiling annual estimates in the Persons quadrant.

      Persons methods

      The Persons quadrant provides data on the number of employed, unemployed and underemployed persons for each quarter. Persons statistics are compiled for all industries (at both the division and subdivision level) and for the economy as a whole. Unless otherwise stated, the methods described apply to both levels of aggregation.

      Labour Account employed persons

      Similar adjustments to those made in compiling the Jobs quadrant are made to adjust the employed persons estimate from the Labour Force Survey to align with 2008 SNA production and residence concepts. These include calculating estimates for:

      • permanent defence force personnel;
      • employed persons under 15 years of age (child workers);
      • non-residents employed in Australia by Australian businesses; and
      • Australian residents employed working overseas.

      At an industry level, similar assumptions are made with respect to multiple job holding for these groups as for employed persons generally, with the exception of the following groups:

      • permanent defence forces, whose employment conditions are presumed to exclude secondary jobs;
      • short term arrival students and sponsored visa holders are assumed to only hold main jobs, due to the restrictions associated with these types of visa; and
      • employed children under 15 years, who are also assumed to not hold secondary jobs.

      Please refer to the Jobs Quadrant Methods for more detail regarding these adjustments.

      Similar to the Jobs quadrant, the Persons quadrant, where relevant, uses data sourced from information collected in the Labour Force Survey in the last month of the relevant quarter, and apportions this across the industries using the related quarterly labour force industry data. For example, estimates in the September quarter labour account are sourced from September month Labour Force data, which are then distributed across industry divisions from the industry distribution of quarterly data captured in the August Labour Force Survey published in Labour Force, Australia, Detailed.

      Calculation of employed persons by industry

      At an industry level, the number of employed persons is the sum of those holding main jobs in the industry, plus those holding secondary jobs after adjusting for double counting (i.e. for persons holding multiple jobs in the same industry). The Labour Force Survey captures data quarterly on the industry of the main job held by employed persons. For each employed person, it also records the number of secondary jobs held (second, third, fourth or more). The Labour Force Survey does not record the industry of secondary jobs.

      Data from the ABS Linked Employer Employee Dataset (LEED) are then used to determine the proportions of the industry of employment of second, third and fourth jobs for multiple job holders, and applied to industry of main job Labour Force Survey data. These proportions are used to allocate the relevant quarterly Labour Force Survey secondary job holdings to each industry, to estimate the total number of filled jobs in each industry.

      These proportions are extracted as at the end date for each quarter from the LEED, and are updated as new data points become available. Industry proportions from the earliest available LEED are applied to earlier time periods in the Australian Labour Account, and similarly the latest available proportions are applied to subsequent time periods where necessary.

      To estimate the number of people employed in each industry, instances where the industry of second job is the same as the industry of main job are identified. These jobs are removed to derive a count of the number of additional people employed in each industry, and added to LFS main job data. 

      The Labour Force Survey provides an estimate of employed persons in each industry of main job. The Australian Labour Account produces the total number of people employed in each industry from an industry perspective. As a result, the sum of employed persons in the Australian Labour Account across industry divisions does not equal the total number of people employed in the whole economy. 

      The purpose of adjusting the Labour Force Survey number of people employed in each industry of main job is to provide information on the total number of people employed in each industry in a time series. This could be used to assess training programs or policy changes targeting a particular industry, to provide a more realistic picture of the number of people who may be impacted by any such change. 

      Multiple Job Holders 

      The Labour Force Survey identifies multiple job holders as employed persons who, during the reference week, worked in more than one job and that was not the result of changing jobs. Multiple job holding is the main reason why estimates of employment from the Labour Force Survey cannot be equated to estimates of jobs. Also, under the Labour Force Survey, industry classification for multiple job holders is based on main job, with this main industry identified using hours actually worked.

      In the Linked Employer Employee Dataset (LEED), multiple job holders are persons who have two or more concurrent jobs at any point during the financial year. Industry information is available for each individual job.

      The Australian Labour Account incorporates both Labour Force Survey and LEED data, and can use this information to provide data on the number of multiple job holders. This is distinct from the number of secondary jobs for each industry, which is presented in the Jobs quadrant.

      Estimates of multiple job holders in the Australian Labour Account are compiled by applying proportions from business/ administrative data sources (the LEED) to balanced numbers of main jobs for each industry, while controlling to the proportion of multiple job holding at the total economy level taken from the Labour Force Survey. 

      Additional estimates of persons

      The Persons quadrant includes additional related estimates at both total economy and industry levels for:

      • Unemployed Persons;
      • Underemployed Persons;
      • Underutilised Persons; and
      • Persons not in the Labour Force (total economy only).

      It should be noted that industry estimates for the unemployed population are based on industry of last job worked (within the past two years) from the Labour Force Survey, and do not necessarily equate to the industries in which the unemployed are currently seeking work, nor do they include those who have never held a job previously. As such, care should be exercised when interpreting estimates of unemployed persons (and therefore underutilised persons and the total labour force) on an industry basis.

      Annual estimates of persons

      The Persons quadrant contains stock data, which are data that measure certain attributes at a point in time. To determine an annual estimate of persons in this quadrant, an average level is derived using a simple arithmetic average of the four quarterly estimates. Refer to Labour Account Methods for an example of this method.

      The annual estimate of employed persons is an approximate estimate of the number of persons employed at any point in time during the year.

      Persons quadrant calculations

      Persons quadrant sources and calculations

      Persons quadrant sources and calculations
      The diagram shows that: Labour Force Survey (LFS) main jobs by status in employment (monthly) multiplied by Labour Force Survey (LFS) main jobs by status in employment (quarterly industry proportions) equals Labour Force Survey employed persons by industry by quarter. Defence personnel (NAB) plus Non residents employed in Australia adjustment (ABS) plus Child workers (all employed children) plus Secondary employment (Jobs quadrant) less Residents employed overseas adjustment (BoP) equals Adjustments to employed persons. Labour Force Survey (LFS) employed persons by industry by quarter plus Adjustments to employed persons equals Labour Account employed persons by industry by quarter. Labour Force Survey (LFS) unemployed (monthly) multiplied by Labour Force Survey (LFS) unemployed by industry of last job (quarterly industry proportions) equals Labour Force Survey unemployed persons by industry of last job by quarter. Labour Force Survey (LFS) Underemployed (monthly) multiplied by Labour Force Survey (LFS) underemployed by industry of main job (quarterly industry proportions) equals Labour Force Survey underemployed persons by industry of main job by quarter. Labour Force Survey unemployed persons by industry of last job by quarter plus Labour Force Survey underemployed persons by industry of main job by quarter equals Labour Force Survey underutilised persons by industry by quarter. Labour Force Survey unemployed persons by industry of last job by quarter plus Labour Account employed persons by industry by quarter equals Labour Account Labour Force total by industry by quarter.

      Hours quadrant

      The Hours (Labour Volume) quadrant describes the relationship between the hours of labour that are supplied by individuals, and the hours of labour that are used or demanded by businesses. These data have a direct link to Australian National Accounts and productivity statistics.

      Hours quadrant

      Hours quadrant
      The diagram shows that: Hours actually worked plus Hours sought but not worked equals Available hours of labour supply. Hours paid for equals Ordinary time hours paid for plus Overtime hours paid for. Hours sought but not worked equals Hours sought by unemployed plus Additional hours sought by underemployed. Hours actually worked divided by Filled jobs equals Average hours worked per job.

      Hours concepts

      Labour volume is expressed as hours worked, and has been defined in International Labour Organisation (ILO) conventions in terms of the time when (paid) employees were at the disposal of an employer; that is, when available to receive work orders from an employer or person in authority, with hours worked covering all jobs. During such periods of availability, workers are expected to be ready to work if work is possible, requested or necessary. This general concept is made meaningful for the self-employed if it is taken to mean time when the self-employed are available to do their work, such as being at the disposal of clients, ready to receive purchase orders or available to make sales, etc. Further information is available in the ILO Resolution concerning the measurement of working time (Eighteenth International Conference of Labour Statisticians, 2008).

      Measuring the levels and trends of hours worked for different groups of employed persons is important in order to monitor working and living conditions, as well as analysing economic cycles. Information on hours of work enables various analytical insights such as: classification of employed persons into full-time and part-time status; the identification of underemployed persons; and the creation of aggregate monthly hours worked estimates. The general notion of hours of work encompasses a number of related concepts: hours usually worked; hours actually worked; hours paid for; and normal hours of work. 

      Usual hours worked and actual hours worked

      Usual hours worked and actual hours worked
      The diagram shows that Usual hours worked less Holidays, sick leave, rostered time off etc. plus Overtime, extra shifts etc. equals Actual hours worked.

      Hours usually worked

      Hours usually worked is the typical number of hours worked in a job for a short reference period (such as one week) that is representative of a longer reference period (e.g. a month, quarter, season or year). Usual hours may differ from actual hours worked at a given time if employed persons are away from work due to illness, vacation, strike, a change of job or other reasons, or are at work for more hours than normal due to overtime, extra shifts and so on (ILO, Surveys of Economically Active Population, Ch.5).

      Hours actually worked

      International resolutions relating to actual hours worked adopted by the Eighteenth International Conference of Labour Statisticians (ICLS) in 2008 refer to wage and salaried employees. There are no international recommendations relating to actual hours worked for all categories of the employed population. However the ILO, in its manual Surveys of Economically Active Population, Employment, Unemployment and Underemployment, suggests that actual hours worked in a given job should be defined to cover all types of employment in labour force surveys. Hours actually worked is the time spent in a job for the performance of activities that contribute to the production of goods and services during a specified short or long reference period.

      According to the ILO resolution, actual hours of work measured within the 2008 SNA production boundary includes all time spent directly on, and in relation to, productive activities; down time; and resting time such as: 

      • time spent in addition to hours worked during normal periods of work (including overtime);
      • time spent at the place of work on activities such as the preparation of the workplace, repairs and maintenance, preparation and cleaning of tools, and the preparation of receipts, time sheets and reports;
      • time spent at the place of work waiting or standing by due to machinery or process breakdown, accident, lack of supplies or power or internet access, etc.; and
      • time corresponding to short rest periods (resting time) including tea and coffee breaks or prayer breaks.

      Excluded are:

      • hours paid for but not worked such as paid annual leave, public holidays or paid sick leave;
      • meal breaks; and
      • in respect of paid employment, time spent on travel to and from work when no productive activity for the job is performed (even when paid by the employer).

      Monthly hours worked in all jobs

      Monthly hours worked in all jobs is a measure of the total number of hours worked by employed persons in a calendar month. Monthly hours worked in all jobs are modelled estimates.

      Seasonally adjusted monthly hours worked in all jobs estimates are produced by combining two series.

      The first series is the seasonally adjusted actual hours worked in the reference week, adjusted for holiday timing. These estimates provide an indication of movements across months. 

      The second series is an annual benchmark series containing original estimates of actual hours worked in each financial year. The annual actual hours worked original estimates are calculated by determining the actual hours worked for each week of the financial year. As actual hours worked are only collected in respect of the reference week of the Labour Force Survey, actual hours worked for weeks not covered by the Labour Force Survey are imputed based on the actual hours worked for the adjacent reference weeks. The imputation accounts for, amongst other things, the effect of public holidays on hours worked; that is, it accounts for holidays that occur in the reference week of the Labour Force Survey as well as holidays that occur in weeks other than the reference week. 

      These two series are then combined to produce the seasonally adjusted monthly hours worked in all jobs series. A trend series is also subsequently produced. This approach ensures that: 

      • The level of the monthly hours worked in all jobs (seasonally adjusted) series is consistent with the level of the annual benchmarks; and
      • The movements in the series are consistent with the movements in the seasonally adjusted actual hours worked in the reference week series.

      Estimates of monthly hours worked in all jobs are available from the Labour Force Survey. For more information on monthly hours worked in all jobs, refer to Information Paper: Expansion of Hours Worked Estimates from the Labour Force Survey.

      Actual and aggregate hours worked

      Actual and aggregate hours worked
      The diagram shows that the Aggregate number of actual hours worked are scaled from the reference week to the calendar month, and adjusted to account for trading days, holidays and seasonality to give Aggregate monthyl hours worked.

      Hours paid for

      Hours paid for applies to a paid-employment job and to a self-employment job paid on the basis of time units. For a paid-employment job, hours paid for is the time for which payment has been received from the employer (at normal rates, in cash or in kind) during a specified short or long reference period, regardless of whether the hours were actually worked or not.

      Hours paid for:

      • includes time paid but not worked such as paid annual leave, paid public holidays and certain absences such as paid sick leave; and
      • excludes time worked but not paid by the employer, such as unpaid overtime, and absences that are not paid by the employer, such as unpaid educational leave or maternity leave that is paid through transfers by government from social security systems.

      As such, hours paid for will differ from the number of hours actually worked if an employee works more or less hours than their paid hours. Hours paid for will also differ from usual hours in some cases, for example if an employee performs long hours in some weeks to have rostered days or weeks off.

      Measures of hours paid for are collected from business payroll records in the ABS Survey of Employee Earnings and Hours (EEH). The EEH also collects information on the following components:

      • ordinary time hours paid for - defined as the award, standard or agreed hours of work paid for at the ordinary rate. Ordinary hours paid for include: stand-by or reporting time hours, which are part of standard hours of work, and hours of paid annual leave, paid sick leave and long service leave taken during the reference period (ASNA, 23.167). Ordinary time hours paid for at penalty rates (e.g. for shift work) are not converted to their ordinary time equivalent; and
      • overtime hours paid for - defined as hours paid for in excess of award, standard or agreed hours of work, at both standard and penalty rates.

      Applying the concept in practice, the Australian Labour Account makes no estimate for hours paid and not worked, or hours worked but not paid for, as this is currently a known data gap.

      Actual hours worked and hours paid for

      Actual hours worked and hours paid for
      The diagram shows that Actual hours worked less unpaid overtime and extra shifts and including Paid overtime and extra shifts, plus paid holidays, sick leave etc. equals Hours paid for.

      Normal hours of work

      Normal hours of work is defined in a 2008 ICLS resolution as ‘the hours fixed by or in pursuance of laws or regulations, collective agreements or arbitral awards to be performed in specified paid-employment jobs over a specified reference period, such as per day, week, month or year (within the 2008 SNA production boundary). Normal hours of work may also apply to a job in self-employment when the hours are in accordance with the hours fixed for all jobs in a specific industry or occupation (such as for drivers to ensure public safety)’ (ICLS 2008, 13(1)).

      Measures of normal hours of work are not produced by the ABS. However, the concept is used to assist in allocating respondents in the full-time/part-time status classification in ABS business surveys.

      Hours sources

      Source data for quarterly and industry estimates of labour volume

      All statistics used to populate the Labour Volume quadrant are derived based on calculations involving the average weekly hours paid for rate sourced from underlying data from the publication Employee Earnings and Hours, Australia. The Survey of Employee Earnings and Hours (EEH) is conducted every two years.

      No adjustments have been made to the average weekly hours paid for rate, as the necessary adjustments to correct for survey data scope limitations are included in the filled jobs estimate used in the calculations to derive hours paid for estimates. See the Jobs section for an explanation of the scope adjustments made to filled jobs estimates.

      The number of hours actually worked, on the household side, is sourced from underlying data from Labour Force, Australia. The Australian National Accounts uses the same underlying source data to derive a quarterly hours actually worked estimate, while also including an estimate for hours worked by defence force personnel. The same adjustment for defence hours is used in the Australian Labour Account, ensuring consistency across both accounts, as well as creating a direct link to the labour productivity statistics published in the Australian System of National Accounts.

      For the Australian Labour Account, the hours actually worked data are further adjusted for the number of hours worked by child workers, non-residents living in Australia employed by Australian companies, and Australian residents living in Australia employed by overseas companies.

      The number of hours sought by unemployed persons is sourced from Labour Force, Australia, Detailed from 2014 onwards. For earlier periods, a derived average number of hours sought per unemployed person is applied to the relevant number of unemployed people. A similar methodology is applied to derive the number of additional hours sought by underemployed persons.

      The table below summarises data sources used in compiling quarterly estimates in the hours quadrant.

      Source dataUse in compiling quarterly data
      Employee Earnings and Hours, AustraliaUsed in compiling estimates of hours paid for.
      Labour Force, AustraliaUsed in compiling estimates of hours actually worked.
      Hours worked by defence personnel (Australian National Accounts)Used in compiling estimates of hours actually worked.
      Labour Force, Australia, DetailedUsed in compiling estimates of hours sought by unemployed persons, and additional hours sought by underemployed persons.
      Child Employment, Australia, 2006Used to estimate the number of hours worked by employed children.
      Migration, Australia and Overseas Arrivals and Departures, AustraliaUsed to estimate hours worked by out of scope non-residents working in Australia.
      Balance of PaymentsUsed to estimate hours worked by out of scope Australian residents living in Australia employed by overseas companies/business entities.

      Source data for annual estimates of labour volume

      Source data for the annual estimates of labour volume are the same as those described above for quarterly estimates.

      Hours methods

      Methods for the compilation of quarterly estimates of labour volume

      Hours actually worked

      Hours actually worked are collected in the Labour Force Survey. Respondents report the hours worked in their main job and the hours worked in all their jobs in the survey reference week. The aggregate number of hours worked by all employed persons in all jobs (including secondary employment) and main jobs, classified by industry of main job, is calculated for the reference week.

      Hours actually worked during the reference week are used to derive modelled estimates of total hours worked by industry of main job across a quarter. The results are published in Labour Force, Australia, and are combined with an estimate of hours worked by permanent defence personnel in the hours actually worked series published in quarterly Australian National Accounts data.

      In the hours worked series published in Labour Force, Australia and quarterly Australian National Accounts data, hours worked are allocated to industry on the basis of an employed persons industry of main job. The Australian Labour Account, while maintaining consistency with the total number of hours worked published in Labour Force, Australia, reallocates hours worked among industries to account for instances of secondary job holding.

      Permanent defence force personnel hours are sourced from quarterly Australian National Accounts data and are allocated to Australian and New Zealand Standard Industrial Classification (ANZSIC) subdivision 76 (Defence) within Public Administration and Safety (Division O), as conditions of employment assume that secondary jobs are not allowed.

      There is no single source of information to determine the industry allocation of hours worked in secondary jobs. Estimates of hours worked in secondary jobs by industry of secondary job are determined by combining information form the Labour Force Survey (LFS), and the Linked Employer-Employee Dataset (LEED). The method used is detailed below:

      Step 1: The aggregate hours worked estimates from the LFS are apportioned between hours worked in main jobs and hours worked in secondary jobs, based on LFS estimates of hours actually worked in the reference week of the mid-quarter month. The calculations are performed by industry subdivision of main job, so produce final estimates of hours worked in main job. However, the industry classification of hours worked in secondary jobs is still determined by the industry of main job.

      Step 2: The industry classification of those hours worked in secondary jobs is then rederived by the industry of secondary job according to the following process:

      • Take the total hours worked in secondary jobs from step 1. As noted, these series are available by industry of main job.
      • For each industry of main job m, take the hours worked in secondary jobs (by workers whose main job is in industry m) and multiply it by the proportion of job holders who hold a secondary job in industry s, to get an estimate of hours worked in secondary jobs in industry s by workers whose main job is in industry m. These proportions are taken from the LEED.
      • Sum the values from step 2b to get estimates of all hours worked in secondary jobs by industry of secondary job.

      The exception to the above is for estimates of hours worked by permanent defence force personnel, which are sourced from quarterly Australian National Accounts data. All hours worked by those personnel are allocated to Australian and New Zealand Standard Industrial Classification (ANZSIC) subdivision 76 (Defence) within Public Administration and Safety (Division O), as conditions of employment assume that secondary jobs are not allowed.

      Scope adjustments

      Hours actually worked in all jobs derived from the Labour Force Survey are adjusted to align with the production and residency boundaries of the Australian System of National Accounts (ASNA) by including estimates of hours worked by child workers, non-residents living in Australia employed by Australian resident enterprises and members of the permanent defence forces, and excluding hours worked by Australian residents employed by non-resident enterprises. The estimated numbers of jobs held by persons in each category are taken from the Jobs quadrant.

      Estimates for the number of hours actually worked by non-residents living in Australia employed by Australian resident enterprises are based on visa type. For short term students, the number of hours is capped at twenty hours per week as this is a work condition of student visas during university/school semesters. For other short term arrivals (excluding students), an average hours actually worked per job is estimated at half (50%) of the hours actually worked by the general resident population. While half is a crude estimate, it is assumed that non-residents would work less than the average hours worked by residents, to account for a holidaying component of their trip to Australia. Quarterly hours actually worked by Australian residents living in Australia employed by non-resident enterprises are also based on the quarterly average hours worked per job estimates.

      Hours worked by child workers are derived based on data from the 2006 Survey of Child Workers. Quarterly hours actually worked by child workers are calculated by multiplying the relevant quarterly estimate of employed children by the average number of hours worked from the 2006 Survey of Child Workers.

      Hours worked by permanent defence force personnel are not specifically adjusted for in the Australian Labour Account, as the underlying Australian National Accounts estimates used in the Australian Labour Account include an adjustment for hours worked by permanent defence personnel. The Australian National Accounts estimate of hours worked assumes that permanent defence personnel work the same number of hours in their jobs as average hours worked in main jobs by the general population.

      Hours worked by the adjusted scope populations are allocated to industry as described in the table below.

      Scope adjustmentAllocation to industry
      Australian residents working in Australia employed by non-resident enterprisesHours worked are deducted from the Public Administration and Safety (ANZSIC Division O) industry, as most people in this category are locally engaged by foreign embassies, consulates and so on.
      Students on short term visasHours allocated in the same proportions as the calculated estimates of main jobs held by short term students, i.e. based on resident full-time tertiary students aged 15-24 years.
      Short term working visa holdersHours allocated in the same proportions as the calculated estimates of main and secondary jobs held by short term non-students.
      Child workers under 15 yearsHours allocated in the same proportions as the calculated estimates of employed children, i.e. based on 15 year old employed persons from the LFS. Child workers under 15 years are assumed to hold only main jobs.

      Hours sought but not worked

      Hours sought but not worked are estimated by aggregating hours sought by the unemployed and additional hours preferred by the underemployed. Hours sought by unemployed persons are the hours unemployed persons could work if they were employed. Additional hours preferred by underemployed persons are the potential hours of employed people that are not fully utilised. It includes people employed part-time who want to and are available to work more hours, as well as people employed full-time who worked part-time hours in the survey reference week for economic reasons.

      Both series are sourced from Labour Force, Australia, Detailed, Quarterly. Input data from the Labour Force Survey are not available prior to 2014. For earlier time periods, an average hours sought based on data from 2014 to 2017 is multiplied by the number of unemployed and underemployed persons. Data are further multiplied by 13 to derive a quarterly estimate from the weekly data representative of the Labour Force Survey reference week.

      It should be noted that industry estimates for the unemployed population (and therefore the hours sought by those unemployed persons) are based on industry of last job worked (within the past two years) from the Labour Force Survey. This does not necessarily equate to the industries in which unemployed persons are currently seeking work, nor do they include those who have never held a job previously. Similarly, it is assumed that any additional hours sought by the underemployed are sought in the same industry as the main job of each underemployed person. As such, care should be exercised when interpreting estimates of hours sought on an industry basis.

      No adjustments have been made to align the Labour Force Survey hours sought with the ASNA residency and production boundaries, as there is no reliable information to derive estimates of additional hours of work sought by short term working visa holders. It is also assumed that defence force personnel and child workers are fully employed.

      Available hours of labour supply

      Available hours of labour supply are the total number of hours for which people in the labour force are prepared to make themselves available for work. It is the sum of hours actually worked in all jobs, including adjustments for scope, and hours sought but not worked.

      Hours paid for

      Total hours paid for, at both an industry and total economy level, is calculated by adding quarterly estimates of ordinary and overtime hours paid. In addition, ordinary time hours paid is calculated separately for Owner Managers of Unincorporated Enterprises to other Status in Employment types.

      Hours paid for – Owner Managers of Unincorporated Enterprises

      To calculate hours paid for Owner Managers of Unincorporated Enterprises, it is assumed that hours paid for in this group are equivalent to the number of hours actually worked, as they would generally have no entitlement to any form of paid leave.

      As such, the total number of hours paid for Owner Managers of Unincorporated Enterprises are calculated for each industry by taking the average number of hours actually worked in the reference week by this group from the Labour Force Survey, and multiplying the weekly average by the number of Owner Managers of Unincorporated Enterprises in that industry. The result is then further multiplied by 13 weeks to derive a quarterly estimate. These figures, estimated at an industry level, are summed to produce a ‘whole of economy’ total.

      Hours paid for – Other Status in Employment types

      In calculating hours paid for other Status in Employment types, average weekly ordinary time hours paid and average weekly overtime hours paid for each industry are derived from underlying data from the EEH. To calculate both overtime and ordinary hours paid for, average weekly measures are multiplied by the number of filled jobs in each industry, less Owner Managers of Unincorporated Enterprises. The filled jobs data are taken from the Jobs quadrant, while the number of Owner Managers of Unincorporated Enterprises is taken from the Persons quadrant. As the survey data reflects a ‘typical week’, quarterly estimates of total ordinary and overtime hours paid for are derived by multiplying the average weekly data by 13 weeks. Similar to the hours paid for Owner Managers of Unincorporated Enterprises, figures estimated at an industry level are summed to produce a ‘whole of economy’ total.

      Prior to 2014, the two average weekly hours series for ordinary time hours paid and paid overtime were only available for non-managerial employees (refer to Labour Payments Concepts for a definition). From the 2014 release of the publication Employee Earnings and Hours, Australia (ABS cat. no. 6306.0), these series are available for all employees, which includes managerial employees where there is a link between pay and hours worked. The all employees series are used in Australian Labour Account hours paid for estimates where available. Internal analysis conducted during the development of the Australian Labour Account showed that the all employees series did not differ noticeably from the non-managerial employees series, therefore no adjustments have been made for scope for years prior to 2014.

      In addition, as the EEH is a biennial survey, average weekly hours paid data for years where EEH survey data are not available are estimated as the average of the two neighbouring years. For example, average weekly hours paid data for 2013 are calculated as the average of EEH data for 2012 and 2014. EEH data are also not available on the current industry classification basis prior to 2008. Data for earlier time periods have been estimated by matching current and historical industry classifications, as much as possible, at the industry subdivision level.

      As Division A is out of scope of the Survey of Employee Earnings and Hours, the calculation of hours paid for the Agriculture Forestry and Fishing Industry (ANZSIC Division A) applies the average hours paid for Division I (Transport, Postal and Warehousing).

      Annual labour volume methods

      As all data contained in the Labour Volume quadrant are flow data, which represent a measure of activity over a given period, data across time periods are additive. Therefore, annual data in the Labour Volume quadrant are derived as the sum of the four quarterly estimates.

      It should be noted that the Labour Volume quadrant includes derived measures such as Average hours worked per job and Average hours worked per Labour Account employed person. These are calculated using a flow as the numerator (e.g. Hours actually worked), divided by a stock for the denominator (e.g. Filled jobs). Where these data are presented in annual terms, caution must be exercised when comparing this result with other estimates measured at the same point in time. These data are intended for comparison across time and industries within the Australian Labour Account, and to provide a link between the Jobs and Labour Volume quadrants.

      Hours quadrant calculations

      Hours quadrant sources and calculations

      Hours quadrant sources and calculations
      The diagram shows that: Average weekly ordinary hours paid for by industry (EEH) multiplied by 13 weeks per quarter equals Ordinary hours paid by industry (non OMUEs). Average weekly hours worked by OMUEs by industry (LFS) multiplied by Number of OMUEs per industry (LFS) multiplied by 13 weeks per quarter equals Ordinary hours paid by industry (OMUEs). Ordinary hours paid by industry (non OMUEs) plus Ordinary hours paid by industry (OMUEs) equals Labour Account total ordinary hours paid by industry. Average weekly overtime hours paid by industry (EEH) multiplied by Labour Account filled jobs (business sources) by industry (less OMUEs from LFS) multiplied by 13 weeks per quarter equals Labour Account overtime hours paid by industry. Labour Account total ordinary hours paid by industry plus Labour Account overtime hours paid by industry equals Labour Account total hours paid by industry.
      Hours quadrant sources and calculations
      The diagram shows that: Weekly hours worked in main jobs by industry (LFS) plus Weekly hours worked in secondary jobs by industry (LFS and LEED) equals Industry proportions of hours worked (LFS and LEED) multiplied by Total hours actually worked (LFS) equals Hours actually worked by industry. Hours worked by short-term arrivals (non students) plus Hours worked by short-term arrivals (students) plus hours worked by children less Hours worked by residents overseas equals Adjustment to hours worked. Hours actually worked by industry plus Adjustment to hours worked equals Labour Account hours worked by industry. Labour Account total hours paid less Labour Account hours worked equals Residual - Hours quadrant.
      Hours quadrant sources and calculations
      The diagram shows that: Average weekly hours sought by unemployed persons by industry (LFS) multiplied by Number of unemployed persons multiplied by 13 weeks per quarter equals Hours sought by unemployed by industry. Average weekly additional hours sought by underemployed persons by industry (LFS) multiplied by Number of underemployed persons multiplied by 13 weeks per quarter equals Additional hours sought by underemployed by industry. Hours sought by unemployed plus Additional hours sought by underemployed equals Available hours of labour supply.

      Payments quadrant

      The Labour Payments quadrant accounts for the costs incurred by enterprises in employing labour and the incomes received by people from its provision.

      Payments quadrant

      Payments quadrant
      The diagram shows that: Total Labour cost divided by Hours worked equals Average cost per hour worked. Total Labour cost divided by Hours paid equals Average cost per hour paid. Total Labour cost equals Total labour income plus Employment related costs plus Payroll tax less Employment subsidies. Compensation of employees plus Labour income from self-employment equals Total labour income. Total labour income divided by Employed persons equals Average labour income per employed person.

      Payment concepts

      The conceptual framework for statistical measures of employee remuneration in Australia (in the context of the broader concept of labour costs) are discussed in the Earnings chapter. The narrowest concept outlined in the international guidelines is that of 'Earnings'. Concepts of 'Wages and salaries', 'Employee income', 'Compensation of Employees' and 'Labour costs' all include and extend upon the concept of 'Earnings'.

      The statistical measure of labour costs is based on the concept of labour as a cost to the employer and relates to:

      • all cash and in-kind payments of wage and salaries to employees;
      • all contributions by employers in respect of their employees to social security, private pension, casualty insurance, life insurance and similar schemes; and
      • all other costs borne by employers in the employment of labour that are not related to employee compensation (such as costs of training, welfare services to employees, payroll taxes etc.).

      Measures of labour costs should be net of any subsidies, rebates or allowances from governments for wage and salary payments to employees, or for other labour costs borne by employers.

      The definition of labour costs from the 1966 International Conference of Labour Statisticians, paragraph 39 is ‘...remuneration for work performed, payments in respect of time paid for but not worked, bonuses and gratuities, the cost of food, drink and other payments in kind, cost of workers' housing borne by employers, employers' social security expenditures, cost to the employer for vocational training, welfare services and miscellaneous items, such as transport of workers, work clothes and recruitment together with taxes...’.

      Payments sources

      Source data for quarterly estimates of labour payments

      Labour payments data are primarily sourced from underlying data from two ABS National Accounts publications: Australian System of National Accounts and the Australian National Accounts: National Income, Expenditure and Product. Please refer to Chapter 11 of the Australian System of National Accounts: Concepts, Sources and Methods for details on how data are compiled for National Accounts.

      Data components of other labour related costs to employers are sourced from the Australian National Accounts: Input-Output Tables, Product Details and underlying information from ABS Supply-Use tables.

      The table below summarises data sources used in compiling quarterly estimates in the Labour Payments quadrant.

      Source dataUse in compiling quarterly data
      Australian System of National AccountsUsed in compiling estimates of labour income from self-employment.
      Australian National Accounts: National Income, Expenditure and ProductUsed in compiling estimates of Compensation of employees, payroll taxes and labour income from self-employment.
      Australian National Accounts: Input-Output Tables, Product DetailsUsed in compiling estimates of training costs and recruitment costs.
      ABS Supply-Use tablesUsed in compiling estimates of employment subsidies, training costs and recruitment costs.
      Government Finance Statistics, AustraliaUsed in compiling estimates of employment subsidies.
      Job Vacancies, AustraliaUsed in compiling quarterly estimates of Recruitment costs.
      Business Indicators, AustraliaUsed in compiling quarterly estimates of Training costs.

      Source data for annual estimates of labour payments

      Source data for the annual estimates of labour payments are the same as those described above for quarterly estimates.

      Payments methods

      Methods for the compilation of quarterly and industry estimates of labour payments

      Total labour income

      Total labour income is the sum of:

      • Compensation of employees; and
      • Labour income from self-employment.

      Total labour costs

      Total labour costs is the sum of:

      • Total labour income; and
      • Other employment related costs.

      Estimates of Compensation of employees at a total economy and industry division level are derived from underlying Australian National Accounts data. Division level data from the Australian National Accounts is further disaggregated to industry subdivision, using Compensation of employees information from the ABS Supply-Use tables for most industries. For some industries, the Supply-Use industries are more aggregated than industry subdivision. For these industries, information from the annual Economic Activity Survey or the proportion of filled jobs from business sources is used to disaggregate data to industry subdivision. One exception is Division S (Other Services), which uses information relating to earnings in all jobs from the household Characteristics of Employment Survey to disaggregate data to industry subdivision, as subdivision 96 (Private Households Employing Staff) is out of scope of all business collections.

      Quarterly Compensation of Employees data are not available prior to September 2002. For earlier time periods, data at industry division level are backcast by applying movement in gross earnings from Wage and Salary Earners, Australia to the September 2002 level. These data relate to both the public and private sectors for each industry division except for Division A (Agriculture, Forestry and Fishing), which is limited to the public sector only. As the data are also on a historical industry classification basis, conversion factors (based on annual Australian National Accounts Compensation of Employees benchmark data) are also applied to approximate the current industry classification. These backcast quarterly data are then benchmarked to published annual levels.

      Labour income from self-employment is an estimate of the share of Gross Mixed Income (GMI) attributable to the provision of labour. GMI is the surplus or deficit accruing from production by unincorporated enterprises that includes both the return on labour and return on capital.

      The calculation of the labour share of GMI on an annual basis for each industry follows the method described in compiling Productivity Statistics outlined in Chapter 19 (Productivity Measures) of the Australian System of National Accounts: Concepts, Sources and Methods. This method assumes that self-employed proprietors receive the same average compensation per hour as wage and salary earners, and can be summarised as comprising the following steps:

      1. Average hourly income of wage and salary earners in each industry is calculated by dividing Compensation of Employees by the estimated number of hours worked in all jobs by employees in the industry (excluding the self-employed).
      2. This hourly rate is then multiplied by the estimated number of hours worked by self-employed persons (OMUEs) whose main job is classified to the industry. This information is derived by expanding the average number of hours worked in the reference weeks recorded in the Labour Force Survey by the number of weeks in the quarter and aggregating for the year.
      3. This estimate is then multiplied by a scaling factor, to constrain to total industry GMI reported in the National Accounts. The scaling factor represents the ratio of the sum of the independently calculated labour and capital shares of GMI, for each industry, to the independently calculated estimate of total industry GMI reported in the National Accounts. This difference can arise from the use of different sources and methods to derive estimates of returns to labour and capital, to the method used by national accounts in calculating total GMI.
      4. As productivity statistics are not compiled for industries with significant “non-market” components, no GMI scaling factor is applied to estimated self-employed labour income for Division P (Education and Training) and Division Q (Health Care and Social Assistance).
      5. No GMI is estimated for Division D (Electricity, Gas, Water and Waste Services), Division K (Financial and Insurance Services) and Division O (Public Administration and Safety), as there are no owner managed unincorporated enterprises (OMUEs) classified to these industries.

      The Australian Labour Account calculates quarterly labour income from self-employment for each industry division by taking the scaled labour share of GMI from underlying Australian National Accounts productivity data, as calculated using the steps described above, and applying this share to the total level of quarterly GMI for each industry division. This approach ensures consistency between Australian Labour Account estimates of labour income from self-employment and Australian National Accounts GMI data.

      As productivity statistics are not compiled for Division P (Education and Training) and Division Q (Health Care and Social Assistance), the scaled labour share of GMI for Division M (Professional, Scientific and Technical Services) is applied to total quarterly GMI for these industries. In addition, the scaled labour share of GMI for Division I (Transport, Postal and Warehousing) is used to represent Division A (Agriculture, Forestry and Fishing) while the scaled labour share of GMI for Division A is further investigated.

      As industry productivity statistics are only compiled annually, the same annual scaled labour share of GMI is applied to each quarterly GMI measure for the financial year.

      To disaggregate estimates of labour income from self-employment for each industry division to subdivision level, Gross Operating Surplus information from the ABS Supply-Use tables is used for most industries. For some industries, the Supply-Use industries are more aggregated than industry subdivision. For these industries, information from the annual Economic Activity Survey is used.

      Quarterly GMI data are not available prior to September 2001. For earlier time periods, data at the industry division level are backcast by applying movements in original Gross Value Added (chain volumes) to the September 2001 level. These backcast data are then benchmarked to annual scaled GMI. For Division P (Education and Training) and Division Q (Health Care and Social Assistance), labour income from self-employment is backcast directly by applying movements in Gross Value Added (chain volumes).

      Other employment costs

      Other employment costs are the sum of

      • Employers payroll taxes;
      • Payment for recruitment services;
      • Training costs; less
      • Employment subsidies.
      Employers payroll taxes

      Estimates for employers’ payroll taxes at industry division level are taken from underlying Australian National Accounts estimates. Division level data from the Australian National Accounts is further disaggregated to industry subdivision, using Compensation of Employees information from the ABS Supply-Use tables for most industries. For some industries, the Supply-Use industries are more aggregated than industry subdivision. For these industries, information from the annual Economic Activity Survey is used.

      Payment for Recruitment services and Training costs

      Estimates of annual total expenditure on recruitment services are calculated as the sum of Intermediate Use (purchase price) and Government Final Consumption Expenditure sourced from the Australian National Accounts: Input-Output Tables, Product Details for Input-Output Product Classification (IOPC) 72110010 (Employment placement and recruitment services). Total quarterly job vacancies from Job Vacancies, Australia (ABS cat. no. 6354.0) are used as a quarterly indicator series to distribute this annual total across the four financial year quarters.

      Training Costs are similarly derived and sourced from the Input-Output tables, using the following IOPC codes:

      • IOPC 81010010 Technical, vocational and other non-tertiary education services;
      • IOPC 81020010 Tertiary higher education services (including undergraduate and postgraduate);
      • IOPC 82120010 Arts education services (excluding vocational);
      • IOPC 82190011 Adult, community and other education services; and
      • IOPC 82200010 Education support services.

      Total wages and salaries for Division P (Education and Training) from Business Indicators, Australia are used as a quarterly indicator series to distribute this annual total across the four financial year quarters. As these data are not available prior to March 2001, data for earlier time periods are backcast by applying movements in private sector gross earnings from Wage and Salary Earners, Australia to the March 2001 level.

      As Input-Output tables are only available infrequently for earlier periods and with a significant time lag for more recent periods, estimates of total annual expenditure on recruitment services and training costs for the intervening and out years are compiled using underlying data from the Supply-Use tables, based on applying movements in the following Supply-Use Product Classification (SUPC) codes:

      • SUPC 72005 Employment placement and recruitment services;
      • SUPC 80205 Technical, vocational and tertiary education services; and
      • SUPC 80310 Arts, adult and other education services.

      Supply-Use tables also provide proportions used to allocate total quarterly expenditure on recruitment services and training costs to industry subdivision. These proportions are based on total intermediate use of these products for each Supply-Use industry, with information from the Economic Activity Survey used for those industries where Supply-Use industries are more aggregated than industry subdivision.

      Employment subsidies

      Employment subsidies are payments made by government, typically to employers. They may be based on the size of the total workforce, the employment of particular types of people such as those who are physically handicapped or who have been unemployed for long periods. These subsidies may also be intended to cover some or all of the costs of training schemes organised or financed by employers.

      Information on employment subsidies is sourced from data provided by the Department of Finance to compile estimates for the publication Government Finance Statistics, Australia – specifically, data relating to “labour market assistance to jobseekers”. As data for the current year employment subsidies estimate is not available at the time of publication of the Australian Labour Account, annual data for the current year are modelled based on previous years’ movements.

      Typically, only annual data are available for estimates of employment subsidies. Therefore, quarterly estimates of employment subsidies are derived by evenly distributing the annual estimate across the four quarters. However, additional subsidies payable for a specific purpose may be added in targeted quarters to specific industries. 

      Employment subsidies data from Government Finance Statistics, Australia are not available prior to 2010-11. Estimates for earlier time periods are modelled based on movements in a similar GFS data item, namely “Commonwealth subsidies paid to other”, where “other” refers to other than public trading enterprises.

      To allocate employment subsidies to industry subdivision, data from the Supply-Use tables for subsidies on production by Supply-Use industry are used to derive industry proportions, with information from the Economic Activity Survey used for those industries where Supply-Use industries are more aggregated than industry subdivision.

      Method for the compilation of annual estimates of labour payments

      As all data contained in the Labour Payments quadrant are flow data, which represent a measure of activity over a given period, data across time periods are additive. Therefore, annual data in the Labour Payments quadrant are derived as the sum of the four quarterly estimates.

      It should be noted that the Labour Payments quadrant includes derived measures such as Average labour Income per employed Person. These are calculated using a flow as the numerator (e.g. Labour income), divided by a stock for the denominator (e.g. Labour Account employed persons). Where these data are presented in annual terms, caution must be exercised when comparing this result with other estimates measured at the same point in time, such as estimates of Average Weekly Earnings. This data is intended for comparison across time and industries within the Australian Labour Account, and to provide a link between the Persons and Labour Payments quadrants.

      Payments quadrant calculations

      Payments quadrant sources and calculations

      Payments quadrant sources and calculations
      The diagram shows that: Placement and recruitment services costs (IO tables) multiplied by Movements in related supply use products (non 10 years) equals Annual placement and recruitment services split by Using job vacancies as a quarterly indicator series equals Placement and recruitment services costs by quarter. Placement and recruitment services costs multiplied by Industry weights from annual supply use tables equals Placement and recruitment services costs by industry. Training costs (IO tables) multiplied by Movements in related supply use products (non 10 years) equals Annual training costs split by Using QBIS education outputs as a quarterly quarterly indicator series equals Training costs by quarter. Training costs multiplied by Industry weights from annual supply use tables equals Training costs by industry. Annual labour market assistance to jobseekers (GFS) divided by Four quarters equals Employment subsidies by quarter multiplied by Industry weights - subsidies on production (supply use tables) equals Employment subsidies by industry. Placement and recruitment services costs plus Training costs plus Employers payroll taxes (NAB) less Employment subsidies equals Other related costs to employers. Labour share of GMI (NAB) multiplied by Quarterly industry GMI (NAB) equals Labour income from self-employment. Compensation of employees (NAB) plus Other related costs to employers plus Labour income from self-employment equals Total labour costs. Labour income from self-employment plus Compensation of employees (NAB) equals Total labour income. Total labour costs less Total labour income equals Residual - Payments quadrant.