Microdata: Work-related injuries
Enables detailed analysis of people who experienced a work-related illness or injury
Work-related Injuries microdata in DataLab
Work-related Injuries microdata is now available in ABS DataLab, released as a supplementary file for the Longitudinal Labour Force (LLFS) microdata. All existing users of the LLFS microdata will automatically get access to the additional file (use of the file may require an updated project proposal) and new users can apply for access to both files.
A detailed data item list for the Work-related Injuries microdata is available in Data downloads.
Work-related Injuries in Tablebuilder
Work-related injuries microdata is now available in TableBuilder for the period 2021-22. The previously released and unrevised issues of Work-related Injuries in Tablebuilder for the periods 2013-14 and 2017-18 will continue to remain available.
This product provides a range of information about the release of microdata relating to people who experienced a work-related injury or illness in the last 12 months.
Microdata are the most detailed information available from a survey and are generally the responses to individual questions on the questionnaire or data derived from two or more questions.
About the survey
The Work-related Injuries (WRI) survey is collected monthly as a module of the Multi-Purpose Household Survey (MPHS) with the sample pooled across a financial year. The survey has been run every fourth year since 2005-06.
The survey is designed to provide a large range of statistics on work-related injuries across the following conceptual groups:
- Cultural diversity
- Education and Qualifications
- Participation and Job attachment
- Characteristics of employment
- Characteristics of main job
- Employment arrangements
- Income and Earnings
- Work-related injury or illness
- Characteristics of job where work-related injury occurred
- Outcomes and financial assistance for work-related injury
Microdata from the Work-related Injuries survey is released in both Tablebuilder and DataLab for the following years:
- TableBuilder: 2013-14, 2017-18 (as originally published) and 2021-22.
- DataLab: September 2000, 2005-06, 2009-10, 2013-14, 2017-18, and 2021-22 (revised data using updated population benchmarks)
TableBuilder is an online tool for creating tables and graphs from underlying microdata. Refer to TableBuilder for more information.
DataLab is the analysis solution for high-end users who want to undertake real time complex analysis of detailed microdata in a secure environment. Refer to DataLab for more information.
Accessing the data
You can use this data in:
- TableBuilder - online tool for creating tables and graphs.
- DataLab - analyse detailed microdata
Further information about these products, and other information to assist users in understanding and accessing microdata in general, is available from the Microdata and TableBuilder Entry Page.
Further information about the survey and the microdata can be found in the various sections associated with this product, including:
- Detailed lists of data items are available in Data downloads.
Data available on request
Data collected in the survey but not included in DataLab or TableBuilder may be available from the ABS, on request, as statistics in tabulated form.
Subject to confidentiality and sampling variability constraints, special tabulations can be produced incorporating data items, populations and geographic areas selected to meet individual requirements. These are available, on request, on a fee for service basis. For more information, contact the ABS by visiting www.abs.gov.au/about/contact-us or email the the Labour Statistics Branch at email@example.com.
Data and file structure
General information about the Work-Related Injuries (WRI) survey, including summary results, are available in Work-Related Injuries.
Detailed information about the survey including scope and coverage, survey design, data collection methodology, weighting, estimation and benchmarking, estimate reliability and a glossary can be accessed from the Methodology page of the publication.
The data items included in the WRI TableBuilder are grouped under broad headings and subheadings as shown in the image below. A complete data items list can be found in Data downloads.
The underlying format of the WRI file is structured at a single person level. This person level contains general demographic information such as age, sex, and country of birth as well as status of employment, weekly income, qualifications and the details of most recent work-related injury or illness.
When tabulating data from TableBuilder, person weights are automatically applied to the underlying sample counts to provide the survey's population estimates.
Not applicable categories
Most data items included in the TableBuilder file include a 'Not applicable' category. This category generally represents the number of people who were not asked a particular question or the number of people excluded from the population for a data item when that item was derived (e.g. Hours usually worked in main job is not applicable for unemployed persons or persons not in the labour force).
The population relevant to each data item should be kept in mind when extracting and analysing data. The actual population count for each data item is equal to the total cumulative frequency minus the 'Not applicable' category.
Generally, some populations can be 'filtered' using other relevant data items. For example, if the population of interest is 'Employed', any data item with that population (excluding the 'Not applicable' category) could be used.
Zero value cells
Tables generated from sample surveys will sometimes contain cells with zero values because no respondents that satisfied the parameters of a particular cell in a table were in the survey. This is despite there being people in the general population with those characteristics. This is an example of sampling variability which occurs with all sample surveys. Relative Standard Errors cannot be generated for zero cells.
The WRI TableBuilder file contains income items as continuous variables. Where a valid response was not recorded for a person, their income items are allocated a special code (e.g. 99999998 = 'Not stated'). When using the summation options section to analyse income variables TableBuilder will automatically exclude these codes.
When analysing income variables in this particular file, it is important to note that all details relate to a person. This includes continuous household income variables which describe the income of the household in which the person resides, and applies that person's weight only (not a household weight). When using this file to examine continuous income variables, it is important to note that only a person weight is applied. Therefore, analysis of total gross household income will display the mean household income of people with selected characteristics, and not the mean household income of all houses with any characteristics.
For example, a table returning mean total gross household income estimates for NSW is showing the mean household income for people living in NSW, and not the mean household income for all households in NSW.
More specific information applicable to Work-related Injuries (WRI) survey in TableBuilder, which should enable users to understand, interpret and tabulate the data, is outlined below.
Confidentiality features in TableBuilder
In accordance with the Census and Statistics Act 1905, all the data in TableBuilder are subjected to a confidentiality process before release. This confidentiality process is undertaken to avoid releasing information that may allow the identification of particular individuals, families, households, dwellings or businesses.
Processes used in TableBuilder to confidentialise records include the following:
- perturbation of data; and
- table suppression
To minimise the risk of identifying individuals in aggregate statistics, a technique is used to randomly adjust cell values. This technique is called perturbation. Perturbation involves small random adjustments of the statistics and is considered the most satisfactory technique for avoiding the release of identifiable statistics while maximising the range of information that can be released. These adjustments have a negligible impact on the underlying pattern of the statistics.
The introduction of these random adjustments result in tables not adding up. As a result, randomly adjusted individual cells will be consistent across tables, but the totals in any table will not be the sum of the individual cell values. The size of the difference between summed cells and the relevant total will generally be very small.
Please be aware that the effects of perturbing the data may result in components being larger than their totals. This includes determining proportions.
Some tables generated within TableBuilder may contain a substantial proportion of very low counts within cells (excluding cells that have counts of zero). When this occurs, all values within the table are suppressed in order to preserve confidentiality. The following error message below is displayed (in red) at the bottom of the table when table suppression has occurred.
ERROR: The table has been suppressed as it is too sparse
ERROR: table cell values have been suppressed
Counting units and weights
Weighting is the process of adjusting results from a sample survey to infer results for the total population. To do this, a 'weight' is allocated to each record. The weight is the value that indicates how many population units are represented by each sample unit.
To produce estimates for the in-scope population you must use a weight field in your tables. In TableBuilder they can be found under the 'Summation Options' category in the left hand pane under the applicable level. If you do not select a weight field, TableBuilder will apply 'Person weight' by default. This will give you estimates of the number of persons.
If you are estimating the number of persons with certain characteristics (e.g. 'Number of work-related injuries or illnesses in the last 12 months') the weight listed under the category heading 'Person level weighting' must be used.
Selecting data items for cross-tabulation
The Person level contains a range of data items detailing the characteristics of respondents including variables related to demographics, education, participation, characteristics of employment, and the details of the work-related injury or illness.
Populations and data items
When adding a data item to a table, it should be noted that not all respondents to the survey may be associated with that data item. For example, the data item “Location where work-related injury or illness occurred” only applies to people who experienced a work-related injury or illness. The “Not applicable” category is used to indicate people who did not experience a work-related injury or illness in the last 12 months as only those who experienced an injury were asked about the location where the injury occurred.
Similarly, if users want to add multiple data items to a table they should ensure that these data items are applicable to the same population group.
For more information about data items, users should refer to the Data Items List available from Data downloads.
Cross-tabulating data items on the same level
Cross-tabulating data from the person level with other data items from the same level will produce data about people. For example, cross-tabulating the geographic variable 'State or territory of usual residence' by the 'Hours usually worked in all jobs' produces a table showing the number of people in each region by the hours that they usually work each week in all jobs.
Multi-response data items
A number of the survey's data items allow respondents to report more than one response. These are referred to as 'multi–response data items'. An example of such a data item is pictured below. For this data item respondents can report all of the people in the workplace who were told about the most recent work-related injury.
When a multi–response data item is tabulated, a person is counted against each response they have provided (e.g. a person who responds 'Colleague' and 'Supervisor/Line Manager' will be counted once in both of these categories).
As a result, each person in the appropriate population is counted at least once, and some people are counted multiple times. Therefore, the total for a multi–response data item will be less than or equal to the sum of its components.
DataLab allows real time access to detailed microdata files through a portal to a secure ABS environment. Using detailed microdata in DataLab allows users to run advanced statistical analyses using recent analytical software.
For information about the data items available on the detailed microdata files, see the Data Item Lists in Data downloads.
Detailed microdata files in DataLab can be accessed on-site at ABS offices or in a secure virtual environment from your own computer. All unit record data remains in DataLab, and any analysis results or tables are checked by the ABS before being provided to users.
Refer to DataLab for more information, including prerequisites for DataLab access.
Work-related Injuries microdata in DataLab, 2000 to 2021-22
Work-related Injuries (WRI) microdata is now available in ABS DataLab, released as a supplementary file for the Longitudinal Labour Force (LLFS) microdata. All existing users of the LLFS microdata will automatically get access to the additional file (use of the file may require an updated project proposal) and new users can apply for access to both files.
This release of Work-related Injuries microdata features financial year data for the years 2005-06, 2009-10, 2013-14, 2017-18 and 2021-22. It also includes microdata for the single month WRI survey run in September 2000.
The record identifiers used in the WRI and LLFS microdata are consistent across both files. This is to facilitate data linkage between the two files and enable further analysis.
The WRI survey is collected by personal interview from a random person aged 15 years and over in private dwellings from 1/8th of the Labour Force Survey (LFS) sample (selected from the 'outgoing rotation group' - people who are in their final month of selection in the LFS). This means that most records on the LLFS will not have a corresponding WRI record.
The WRI survey is collected monthly, with the sample pooled over a financial year. The survey month identifier (ABSMID), which indicates the month of collection, has been included to facilitate data linkage to the LLFS. While this can provide monthly sub-samples, it is recommended that in most cases the microdata should be analysed using the whole financial year sample, using the 'Reference year' (REFYEAR) identifier to mark different points in time.
The WRI survey run in September 2000 was collected from private dwellings in 7/8th of the Labour Force Survey (LFS) sample.
More details on these records and the formatting of record identifiers can be found in the Data Item List in Data downloads.
Person level weights (and replicate weights for calculating standard errors) are provided on the WRI file. These differ from the weights provided on the LLFS file, as the weights are recalibrated for WRI due to the reduced sample size compared to the LFS. Aggregate estimates from both sets of weights will align closely, as the WRI survey data is benchmarked to match the corresponding 12-month average of estimates from the LFS, but care should be taken when performing micro analysis.
Weights and replicate weights have been calibrated for the surveys whole financial year sample and should be used in conjunction with the Reference year (REFYEAR) identifier.
Similarly, the weights and replicate weights provided for the September 2000 WRI survey are recalibrated to account for the reduced sample size and have been benchmarked to match trend estimates from the LFS.
WRI weights are recommended for cross-sectional analysis of WRI data items, but when linking WRI and LLFS data for longitudinal analysis, new weights should be calculated based on the population benchmarks provided on the LLFS file. Care should be taken to account for attrition bias by adjusting the weights appropriately (increasing the weights for those more likely to leave the LFS). More information on using benchmarks and weights for longitudinal analysis is provided in Longitudinal Labour Force.
Data item list
History of changes
- Release of 2021-22 WRI microdata in TableBuilder.
- Release of 2021-22 WRI microdata in DataLab.
- First release of WRI microdata in DataLab. Includes revised microdata for Sep 2000 & 2005-06 to 2021-22 surveys.
- Release of 2017-18 WRI microdata in TableBuilder.
Previous catalogue number
This release previously used catalogue number 6324.0.30.001.