8155.0 - Australian Industry, 2018-19 Quality Declaration 
Latest ISSUE Released at 11:30 AM (CANBERRA TIME) 29/05/2020   
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QUALITY DECLARATION - SUMMARY

INSTITUTIONAL ENVIRONMENT

The statistics presented in this release were derived using a combination of data collected directly by the Australian Bureau of Statistics (ABS), the Economic Activity Survey (EAS), under the authority of the Census and Statistics Act 1905 and Business Activity Statement (BAS) data collected by the Australian Taxation Office (ATO). The Income Tax Assessment Act 1936 provides for the ATO to pass information to the Australian Statistician for the purposes of the Census and Statistics Act 1905.

Please refer to ABS Institutional Environment for more information about the institutional environment of the ABS, including its legislative obligations, financing and governance arrangements and mechanisms for scrutiny of ABS operations. For more information about the institutional environment of the ATO, please refer to Part 4 Management and accountability in the Commissioner of Taxation Annual Report 2018–19.


RELEVANCE

The main purpose of the EAS is to measure the size, structure and performance of Australian industry for use in the compilation of national accounts. The estimates permit analysis not only for a single reference period (2018–19) but also over time (annually from 2006–07).

The information is also used by government departments and economic analysts to assist in policy formulation and evaluation. Financial estimates include income, expenses, industry value added, and capital expenditure.

A range of performance measures, usually referred to as ratios, can be produced from the data available from businesses' financial statements. The performance measures presented in this publication comprise:

  • profitability ratios, which measure rates of profit on sales
  • debt ratios, which indicate the ability of businesses to meet the cost of debt financing
  • investment ratios, which indicate the capacity of business to invest in capital assets
  • labour measures, which relate to output, labour costs and employment.

The businesses that contribute to the statistics in this publication are classified by:
The scope of the EAS included all businesses operating in the Australian economy during 2018-19, except for:
  • In most industries, entities classified to the SISCA Sector 3 General government institutional sector. This exclusion particularly affects data presented for Public administration and safety, Education and training and Health care and social assistance (ANZSIC Divisions O, P and Q, respectively), in that the estimates relate only to private sector businesses. However, SISCA Sector 3 General government businesses classified to Water supply, sewerage and drainage services (ANZSIC Subdivision 28, within Division D) are included, so that the estimates include data (for example) for relevant local government organisations.
  • Entities classified to ANZSIC Division K Financial and insurance services, with the exception of Subdivision 64 Auxiliary finance and insurance services, which is now included as an experimental series, see the data cube 'Experimental estimates for Auxiliary finance and insurance services' in Australian Industry (cat. no. 8155.0). Note that estimates included in this publication for Total selected industries exclude Division K Financial and insurance services.
    Government owned or controlled Non-Financial Corporations are included.

    The EAS was designed primarily to deliver national estimates for all in-scope industry divisions. State data were compiled for a restricted set of data items using a combination of data collected directly by the ABS and BAS data collected by the ATO.

Businesses reporting for periods other than the year ending June

Where businesses were unable to supply data for the 12 months ended 30 June 2019, an accounting period for which data can be provided was used for data other than those relating to employment. All businesses were asked to report employment for the last pay period ending in June 2019.

Estimates of financial data in some industries, such as Mining and Manufacturing, are heavily impacted by fluctuating commodity prices. In these industries the reporting by businesses for an accounting period that is not for the period ended 30 June, can result in different estimates compared with what they would have been, had the businesses reported for an accounting period ended 30 June. Estimates of wages and salaries, total income, total expenses and industry value added which have been adjusted for the effects of off-June year reporting are presented in the 'Off-June Year adjusted estimates by industry subdivision' data cube.


TIMELINESS

The EAS is conducted annually with estimates generally available within twelve months of the reference period to which they relate. For the 2018–19 reference period, questionnaires were despatched by ABS in August 2019 and BAS data were received from the ATO in September 2019. The estimates are scheduled for release in May 2020, almost eleven months after the end of the reference period.


ACCURACY

The ABS aims to produce high quality data from its industry collections while minimising the reporting burden on businesses. To achieve this, extensive effort is put into survey and questionnaire design, collection procedures and processing. The 2018-19 survey used generalised regression estimation. Generalised regression estimation is a form of ratio estimation which makes use of auxiliary data items which are strongly correlated with key data items directly collected by the ABS from businesses. The auxiliary variables used in this survey were turnover and wages from data sourced from the ATO. Use of this methodology allowed high quality statistics to be produced from a small, direct collect sample of 19,109 businesses.

Two types of error can occur in estimates that are based on a sample survey: sampling error and non-sampling error.

Sampling error occurs when a sample, rather than the entire population, is surveyed. It reflects the difference between estimates based on a sample and those that would have been obtained had a census been conducted. One measure of this difference is the standard error. There are about two chances in three that a sample estimate will differ by less than one standard error from the figure that would have been obtained if all businesses had been included in the survey, and about nineteen chances in twenty that the difference will be less than two standard errors.

Another measure of sampling error is the relative standard error (RSE), which is obtained by expressing the standard error as a percentage of the estimate to which it refers. The relative standard error is a useful measure in that it provides an immediate indication of the sampling error in percentage terms, and this avoids the need to refer also to the size of the estimate. Selected data item RSEs at the industry division level for Australia are shown in the table below. Detailed relative standard errors are available on request.

To illustrate, the estimate of total income for Mining in 2018-19 was $317,628m. The RSE of this estimate is shown as 0.3%, giving a standard error of approximately $953m. Therefore, there are two chances in three that, if all units had been included in the survey, a figure in the range of $316,675m to $318,581m would have been obtained, and nineteen chances in twenty (i.e. a confidence interval of 95%) that the figure would have been within the range of $315,722m to $319,534m.

The size of the RSE may be a misleading indicator of the reliability of the estimates for (a) operating profit before tax, (b) earnings before interest, tax, depreciation and amortisation and (c) industry value added. It is possible for an estimate to legitimately include positive and negative values, reflecting the financial performance of individual businesses. In this case, the aggregated estimate can be small relative to the contribution of individual businesses, resulting in a standard error which is large relative to the estimate.

Relative Standard Errors

Employment
Wages & Salaries
Sales & service income
Total income
Total expenses
OPBT
EBITDA
IVA
2018-19
%
%
%
%
%
%
%
%

Agriculture, forestry and fishing
2.3
2.6
2.6
2.6
2.6
10.4
8.2
5.3
Mining
0.7
0.5
0.2
0.3
0.4
0.7
0.3
0.2
Manufacturing
1.1
1.2
0.6
0.6
0.7
3.1
2.7
1.1
Electricity, gas, water and waste services
1.5
1.8
0.8
0.8
1.1
3.5
1.7
1.3
Construction
1.8
1.5
1.6
1.6
1.9
6.0
5.0
2.1
Wholesale trade
2.5
1.4
1.0
1.0
1.1
8.4
7.0
2.6
Retail trade
2.2
1.0
1.4
1.4
1.5
13.3
7.9
2.5
Accommodation and food services
3.9
3.4
2.3
2.3
2.7
18.3
13.5
4.1
Transport, postal and warehousing
2.1
1.8
1.5
1.5
1.4
8.5
3.6
1.9
Information media and telecommunications
1.9
1.5
2.4
2.3
2.2
12.6
2.4
1.5
Rental, hiring and real estate services
3.0
3.1
2.1
3.2
2.6
6.9
3.9
2.8
Professional, scientific and technical services
2.0
1.0
1.4
1.4
1.6
5.5
7.6
2.0
Administrative and support services
2.8
1.8
2.2
2.2
2.2
13.3
10.4
2.1
Public administration and safety (private)
5.4
3.3
5.9
5.8
5.8
22.8
22.1
4.7
Education and training (private)
2.4
1.8
2.1
1.6
1.6
5.8
6.5
1.9
Health care and social assistance (private)
1.6
1.0
1.6
1.1
1.2
4.3
3.8
1.1
Arts and recreation services
3.9
1.4
1.8
1.7
1.7
5.5
6.9
2.6
Other services
3.0
1.9
2.3
2.3
2.2
10.6
36.1
2.6
Total selected industries(a)
0.6
0.4
0.4
0.4
0.4
1.7
1.2
0.6

(a) Excludes Division K Financial and insurance services.


Non-sampling error arises from inaccuracies in collecting, recording and processing the data. Every effort was made to minimise reporting error, by the careful design of questionnaires, intensive training of survey analysts, and efficient data processing procedures. The imprecision due to non-sampling variability cannot be quantified and should not be confused with sampling variability, which is measured by the standard error.

Differences in accounting policy and practices across businesses and industries can also lead to some inconsistencies in the data provided to compile the estimates. Although much of the accounting process is subject to standards, there remains a great deal of flexibility available to individual businesses in the accounting policies and practices they adopt.

The above limitations are not meant to imply that analysis based on these data should be avoided, only that the limitations should be considered when interpreting the data. This publication presents a wide range of data that can be used to analyse business and industry performance. It is important that any analysis be based upon the range of data presented rather than focusing on one variable.

Non-sampling error also occurs when information cannot be obtained from all businesses selected in the survey. For the 2018-19 EAS, there was an 85.7% response rate from all businesses that were surveyed and found to be operating during the reference period. Data were imputed for the remaining 14.3% of operating businesses. This imputation contributed 11.0% to the estimate of total income for Total selected industries.


COHERENCE

The ABS has been conducting the EAS annually since 1990–91, collecting a core set of data items each year. The definitions of these are reviewed each year and are refined or respecified as needed. Additional data items are collected in different years, in response to demand and priorities.

Since the 2006–07 reference year the survey has been conducted using ANZSIC 2006 and new methodologies. As a result, a new series of these estimates commenced from 2006–07.

Improved quality for Health care and social assistance estimates

In 2018-19, the EAS question wording was improved for the Health care and social assistance industry to provide explicit instructions on how businesses should report government payments. This resulted in improved estimates of sales and service income, funding from government for operational costs, profit margin and sales and service income per person employed for 2018-19. Caution should be exercised when comparing 2018-19 estimates for these items (which can be seen in the 'Australian industry by division' and 'Australian industry by subdivision' data cubes), with estimates from previous years. The improved question wording will be continued in future EAS collections and subsequent reporting for the Health care and social assistance division is expected to be consistent with the 2018-19 EAS. Further details can be found in the 'Changes in this release' section of the 2018-19 release of Australian Industry (cat. no. 8155.0).


INTERPRETABILITY

Estimates from the EAS are available as original series only, and are neither seasonally nor trend adjusted.

Although financial estimates in this release relate to the full twelve months, employment estimates relate to the last pay period ending in June of the given year.

Further information about terminology and other technical aspects associated with these statistics can be found in the publication Australian Industry (cat. no. 8155.0), which contains detailed Explanatory Notes, a technical note on Estimation Methodology, a technical note on State and Territory Estimates, a technical note on Finer Level Manufacturing Industry Estimates, a technical note on Off-June Year Adjusted Estimates, and a Glossary.


ACCESSIBILITY

Data from the 2018-19 EAS are available free of charge on the ABS website.