This report communicates the processes that were followed to assess the quality of the 2018-19 employee census data. For the 2018-19 reporting period:
- The cut-off date for inclusion in the dataset was on 17 September 2019
- The dataset and gender equality indicator (GEI) scorecard were released on 19 November 2019
Data quality is defined as a measure of how fit for purpose a given data set is, and that there exists many aspects of data quality known as dimensions which contribute to how fit for purpose data is. In line with the literature and national data agencies and departments, the Agency uses the six data quality dimensions: relevance, accuracy, timeliness, accessibility, interpretability, and coherence.
There were 4,841 reporting organisations* (covering 9,388 businesses) included the 2018 – 19 dataset. This represents organisations who have submitted valid data to the Agency as at the cut-off date (90.7% of all relevant reporting organisations for the 2018 – 19 reporting period). Table 1 below shows that reporting timeliness by industry ranges from 98% (Electricity, gas, water and waste services sector) to 84% (Accommodation and food services sector).
* Relevant employers that submit reports to the Agency, sometimes on behalf of other subsidiary entities within their corporate structure.
Table 1: The 2018-19 WGEA dataset – timeliness by industry
|Industry (ANZSIC Division)*||Number of reporting organisations||Number of reporting organisations||Timeliness rate (%)|
|Accommodation and food services||306||257||84.0|
|Administrative and support services||296||267||90.2|
|Agriculture, forestry and fishing||55||52||94.5|
|Arts and recreation services||116||107||92.2|
|Education and training||566||534||94.3|
|Electricity, gas, water and waste services||50||49||98.0|
|Financial and insurance services||269||254||94.4|
|Health care and social assistance||729||668||91.6|
|Information media and telecommunications||155||150||96.8|
|Professional, scientific and technical services||600||550||91.7|
|Public administration and safety||32||30||93.8|
|Rental, hiring and real estate services||90||83||92.2|
|Transport, postal and warehousing||224||192||85.7|
|No Industry specified||3||-||-|
* Based on ANZSIC code provided by the reporting organisation
The 2018 – 19 dataset covers 4,341,295 employee positions in the non-public sector. This is 41.4% of the estimated overall Australian workforce as at May 2018. Table 2 below shows that the WGEA dataset covers over 60% of the workforce in the administrative and support services, financial and insurance services, information media and communications, and retail trade sectors. The dataset has lower coverage in public administration and safety (where the public sector is a dominant employer), other services (where small businesses dominate) and agriculture, forestry and fishing and construction (where small to medium businesses are common).
Table 2: The 2018-19 WGEA dataset - coverage of all Australian employees
|Industry (ANZSIC Division)||Employees in the workforce - (ABS Labour Force Survey)||Employees in the dataset||The WGEA coverage of total employees in Australia 2017-18|
|Accommodation and food services||812.9||226.6||27.9|
|Administrative and support services*||295.3||309.2||104.7|
|Agriculture, forestry and fishing||161.6||23.6||14.6|
|Arts and recreation services||197.0||91.8||46.6|
|Education and training||951.1||441.6||46.4|
|Electricity, gas, water and waste services||141.2||50.3||35.6|
|Financial and insurance services||399.6||274.6||68.7|
|Health care and social assistance||1,511.0||682.5||45.2|
|Information media and telecommunications||202.7||122.5||60.4|
|Professional, scientific and technical services||784.1||301.8||38.5|
|Public administration and safety||772.6||37.1||4.8|
|Rental, hiring and real estate services||166.2||47.2||28.4|
|Transport, postal and warehousing||494.7||201.9||40.8|
Data quality checks
Each submission undergoes a series of automated quality checks. Appendix I lists the data quality checks that were applied for the 2018 – 19 reporting period. Organisations with potential errors were sent an email with a request to correct the data and resubmit their reports or contact the Agency. The Agency accepts anomalies if the employer provides a legitimate reason. Examples of legitimate reasons are listed on Appendix I.
There were 21 organisations with legitimate anomalies that were excluded from the dataset to prevent the distortion of benchmark results. These organisations tended to have less than 10 employees and/or had no managers in their workforce profile.
Table 3: Industry breakdown of 21 reports excluded from the 2018 - 19 dataset
|Division||Number of reports excluded from dataset||Number of reports included in dataset||Proportion of reports excluded from dataset|
|Accommodation and Food Services||1||257||0.39%|
|Administrative and Support Services||2||267||0.74%|
|Agriculture, Forestry and Fishing||1||52||1.89%|
|Arts and Recreation Services||0||107||0.00%|
|Education and Training||0||534||0.00%|
|Electricity, Gas, Water and Waste Services||0||49||0.00%|
|Financial and Insurance Services||3||254||1.17%|
|Health Care and Social Assistance||2||668||0.30%|
|Information Media and Telecommunications||0||150||0.00%|
|Professional, Scientific and Technical Services||6||550||1.08%|
|Public Administration and Safety||0||30||0.00%|
|Rental, Hiring and Real Estate Services||0||83||0.00%|
|Transport, Postal and Warehousing||0||192||0.00%|
Data quality checks - Questionnaire data
The most common anomalies that were accepted by the Agency as legitimate and are included in the dataset relate to the governing bodies data. Table 4 shoes that these anomalies affect less than 2% of organisations in the dataset. This is an insignificant impact.
Table 3: Common questionnaire anomalies – 2018 -19 dataset
|Anomaly||Number of organisations affected||% of organisations in the dataset (4,841)|
|Target (%) is less than or equal to percentage of women on governing body||87||1.8|
|Organisation has no chair on its governing body||46||1.0|
|Too many chairs for orgs||32||0.7|
|Too many board members for orgs||30||0.6|
Data quality - remuneration data
Organisations are able to provide remuneration data in unit level or aggregated format. Unit level data enables a richer analysis of remuneration data.
In the 2018 – 19 reporting year, the unit level dataset accounted for 2,297,561 records (53% of the 4,341,295 employee records).
Known limitations of the benchmarks remuneration data provided by employers in general are summarised below:
- Approximately 0.7% of employee salaries are below $13,000, which is the minimum wage for 15 year olds. Most of these salaries are legitimate as some employees are under 15 years of age or are on a disability scheme payment in this dataset. There are legitimate cases where an employee has no salary (for example, in some religious organisations; and when an employee works on commission only).
- The data for casual employees includes a ‘casual loading’ and cannot be compared to non-casual employee remuneration data.
- Some non-executive board directors have been incorrectly inputted as key management personnel in the workplace profile, which means that some salaries are particularly low for this category.
- Approximately 1.6% of employers reported the same base salary and total remuneration amounts for some employees (noting that this situation can be legitimate under certain circumstances – for example, employees who are under 18 and work less than 30 hours a week, or employees that earn less than $450 a month).
- It is possible that salaries of some part-time or casual employees have not been annualised and/or converted to full-time equivalent amounts, which could lead to more variance in the salary data.
- Table 4 shows that there is more variance in the salary and remuneration unit level data submitted for Full-time employees compared to Part-time and Casual employees. Median absolute deviation (MAD) is used as a measure of variance as it is robust against outlier records.
Table 4: Median absolute deviation (MAD) of annualised salaries 2018 – 19 unit level dataset
|Employment status||Annualised salary MAD||Annualised remuneration MAD|
WGEA census data - changes over time in reporting organisations
The overall size of the comparison group may have changed from last year. Although the most recent year-on-year change was minimal, with an overall increase of 23 reporting organisations, the composition of the comparison group may be affected (Table 5).
Table 5: Industry breakdown of reporting organisations in WGEA’s benchmark dataset over time
|Accommodation and food services||248||258||260||233||236||257|
|Administrative and support services||227||239||253||253||254||267|
|Agriculture, forestry and fishing||42||46||47||47||49||52|
|Arts and recreation services||98||98||106||100||102||107|
|Education and training||491||520||526||512||514||534|
|Electricity, gas, water and waste services||51||53||52||47||46||49|
|Financial and insurance services||225||238||232||238||254||254|
|Health care and social assistance||539||613||652||652||648||668|
|Information media and telecommunications||119||125||134||132||136||150|
|Professional, scientific and technical services||433||472||488||513||514||550|
|Public administration and safety||19||19||22||17||21||30|
|Rental, hiring and real estate services||63||72||80||76||82||83|
|Transport, postal and warehousing||181||196||190||186||187||192|
Organisations may have changed size due to restructure or downsizing (Table 6).
- Organisations may have modified their ownership structure.
- Organisations may have chosen to report in a different way this year (e.g. as a collective last year and as separate subsidiaries this year).
Table 6: Organisation size breakdown of reporting organisations in WGEA’s benchmark dataset over time
|Organisation size category (number of employees)||2013-14||2014-15||2015-16||2016-17||2017-18||2018-19|
WGEA census – changes over time in employee numbers
- The longitudinal census dataset shows a growth in employee numbers.
- Table 7 and 8 show that employee coverage has been consistent since 2013 – 14 and have grown steadily across industries
Table 7: Industry breakdown of employee records in WGEA’s benchmark dataset over time
|Accommodation and food services||173,653||177,140||190,167||202,871||203,434||226,641|
|Administrative and support services||196,917||211,735||237,001||276,728||305,937||309,210|
|Agriculture, forestry and fishing||22,379||25,082||27,480||27,716||21,424||23,599|
|Arts and recreation services||95,105||93,460||95,579||87,645||89,102||91,770|
|Education and training||381,484||396,159||413,532||408,027||420,626||441,565|
|Electricity, gas, water and waste services||45,454||47,646||44,226||42,387||43,279||50,321|
|Financial and insurance services||267,363||275,319||273,307||272,757||273,038||274,570|
|Health care and social assistance||515,176||559,088||593,819||627,746||655,949||682,519|
|Information media and telecommunications||131,697||131,798||131,647||128,702||120,508||122,453|
|Professional, scientific and technical services||288,272||291,561||289,332||276,852||283,413||301,848|
|Public administration and safety||27,405||25,247||29,569||22,721||34,475||37,115|
|Rental, hiring and real estate services||34,337||36,450||40,934||41,775||43,844||47,165|
|Transport, postal and warehousing||207,845||208,998||199,019||195,557||192,749||201,892|
Table 8: Organisation size breakdown of employee records in WGEA’s benchmark dataset over time
|Organisation size category (number of employees)||2013-14||2014-15||2015-16||2016-17||2017-18||2018-19|