2018-19 WGEA Data Quality Report

 

 

image is decorative and depicts three people working on devices to analyse data

 

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.

Timeliness

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
Construction 236 209 88.6
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
Manufacturing 665 598 89.9
Mining 163 150 92.0
Other services 159 145 91.2
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
Retail trade 349 296 84.8
Transport, postal and warehousing 224 192 85.7
Wholesale trade 273 250 91.6
No Industry specified 3 - -
Total 5,336 4,841 90.7

Based on ANZSIC code provided by the reporting organisation

 

Coverage/Relevance

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
  ('000) ('000) (%)
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
Construction 754.2 130.0 17.2
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
Manufacturing 838.8 352.8 42.1
Mining 228.3 161.9 70.9
Other services 342.3 58.9 17.2
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
Retail trade 1,129.1 694.2 61.5
Transport, postal and warehousing 494.7 201.9 40.8
Wholesale trade 302.7 132.9 43.9
Total 10,485.6 4,341.3 41.4

 

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. 

Excluded reports

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%
Construction 1 209 0.48%
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%
Manufacturing 2 598 0.33%
Mining 0 150 0.00%
Other Services 1 145 0.68%
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%
Retail Trade 1 296 0.34%
Transport, Postal and Warehousing 0 192 0.00%
Wholesale Trade 1 250 0.40%
Total 21 4,841 0.43%

 

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
Full-time $33,418 $42,715
Part-time $11,623 $14,417
Casual $14,052 $15,951

 

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

Division 2013-14 2014-15 2015-16 2016-17 2017-18 2018-19
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
Construction 192 203 195 202 202 209
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
Manufacturing 633 663 636 613 583 598
Mining 162 169 154 135 140 150
Other services 130 149 142 142 140 145
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
Retail trade 293 315 303 294 305 296
Transport, postal and warehousing 181 196 190 186 187 192
Wholesale trade 208 222 225 229 231 250
Total 4,354 4,670 4,697 4,621 4,644 4,841

 

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
0-249 1,962 2,181 2,163 2,096 2,045 2,137
250-499 1,032 1,091 1,125 1,125 1,129 1,158
500-999 644 656 648 648 655 713
1000-4999 716 742 632 615 688 692
5000+ 131 127 129 129 127 141
Total 4,354 4,670 4,697 4,621 4,644 4,841

 

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

Division 2013-14 2014-15 2015-16 2016-17 2017-18 2018-19
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
Construction 143,259 132,805 117,004 121,141 124,862 129,979
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
Manufacturing 371,937 366,111 345,539 338,569 344,270 352,754
Mining 190,171 177,639 148,724 136,545 142,411 161,870
Other services 50,627 59,080 55,940 62,048 53,079 58,865
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
Retail trade 648,558 653,173 681,384 666,328 682,834 694,211
Transport, postal and warehousing 207,845 208,998 199,019 195,557 192,749 201,892
Wholesale trade 100,261 106,331 111,101 115,990 121,106 132,948
Total 3,891,900 3,974,822 4,025,304 4,052,105 4,156,340 4,341,295

 

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
0-249 299,257 328,561 330,555 323,755 312,720 327,714
250-499 360,654 380,351 394,934 387,497 393,243 405,576
500-999 450,894 455,943 448,712 452,048 457,310 500,763
1000-4999 1,223,448 1,242,618 1,264,610 1,311,176 1,430,303 1,428,474
5000+ 1,557,647 1,567,349 1,586,493 1,577,629 1,562,764 1,678,768
Total 3,891,900 3,974,822 4,025,304 4,052,105 4,156,340 4,341,295