Characteristics of Australian Business methodology

The BCS is an annual survey and is the vehicle for estimates in business use of information technology; innovation; and a broad range of other non-financial business characteristics. A more detailed set of items for either business use of information technology or innovation are collected every second year (i.e. in alternating years). The 2018-19 BCS collected detailed information relating to innovation.

The Economics Unit Model is used by the ABS to determine the structure of Australian businesses and other organisations. The model consists of:

The Enterprise Group (EG)
Legal Entities (LEs)
Type of Activity Units (TAUs)
Location Units

Businesses contributing to the estimates in this publication are sourced from the ABS Business Register (ABSBR), and are selected at either the Australian Business Number (ABN) unit or the Type of Activity Unit (TAU) level, as described below.

In the BCS, the statistical unit used to represent the majority of businesses, and for which statistics are reported, is the ABN unit. The ABN unit is the business unit which has registered for an ABN, and thus appears on the Australian Tax Office (ATO) administered Australian Business Register (ABR). These units are suitable for ABS statistical needs when the business is simple in structure, and are generally referred to as the non-profiled population. In these instances, one ABN equates to one statistical unit.

For more significant and diverse businesses where the ABN unit is not suitable for ABS statistical needs, the ABS maintains its own unit's structure through direct contact with the business, and the statistical unit used is the TAU. A TAU comprises one or more business entities, sub-entities or branches of a business entity within an Enterprise Group that can report production and employment activities. When a minimum set of data items is available, a TAU is created which covers all the operations within an industry subdivision (and the TAU is classified to the relevant subdivision of the Australian and New Zealand Standard Industrial Classification (ANZSIC)). These units are generally referred to as the profiled population.

ANZSIC is used to classify the industry in which the TAU or ABN has productive activity. Further information on this classification can be found in Australian and New Zealand Standard Industrial Classification (ANZSIC), 2006 (Revision 2.0) (cat. no. 1292.0).

The Standard Institutional Sector Classification of Australia (SISCA) provides a framework for dividing the Australian economy into institutional sectors. Further information on this classification can be found in Standard Economic Sector Classifications of Australia (SESCA), 2008 (Version 1.1) (cat. no. 1218.0).

The scope of the estimates in this publication consists of all employing business entities in the Australian economy, except for:

SISCA 3000 General government
SISCA 6000 Rest of the world
ANZSIC06 Division O Public administration and safety
ANZSIC06 Division P Education and training
ANZSIC06 Groups 624 (Financial asset investing) and 633 (Superannuation funds)
ANZSIC06 Groups 954 (Religious services) and 955 (Civic, professional and other interest group services)
ANZSIC06 Subdivision 96 Private households employing staff

The frame for the BCS is a subset of the ABSBR and includes employing businesses only. These are defined as those businesses which register for the ATO's Pay As You Go Withholding (PAYGW) scheme. It is not unusual for some of these 'employing businesses' to have zero employment at various times during the reporting period. The BCS frame is updated to take account of new businesses, businesses which have ceased employing, changes in employment levels, changes in industry and other general business changes. Businesses which have ceased employing are identified when the ATO cancels their ABN and/or PAYGW registration. In addition, businesses with less than 50 employees which did not remit under the PAYGW scheme in each of the previous five quarters, are removed from the frame. The estimates in this publication include an allowance for the time it takes a newly registered business to be included in the survey frame.

Collection of data included in this release was undertaken based on a random sample of approximately 7,000 businesses via online forms or mail-out questionnaires. The sample was stratified by industry and an employment-based size indicator. All businesses on the ABSBR identified as having 1,000 or more employees were included in the sample.

The reference period for most of the characteristics items included in the 2018-19 BCS is during the year ended 30 June 2019 or as at 30 June 2019. Financial data relates to the most recent financial year ended on or before 30 September 2019.

The BCS draws on the conceptual definitions and guidelines included in the 'Oslo Manual: Guidelines for Collecting and Interpreting Innovation Data' (Fourth Edition, 2018). This manual provides a framework for the collection of innovation statistics and specifies the definitions of innovating businesses and innovation-active businesses that are used by the ABS. The BCS draws on this manual for the questions used in the BCS and in the presentation of outputs from the survey.

Key indicators of innovation include: measures of business innovation (innovating, innovation-active); types of innovation (goods or services, process); and status of innovation (introduced, still in development, abandoned). Definitions for each of these measures of business innovation are provided in the Glossary.

BCS innovation data released previously, in cat. no. 8166.0, 8158.0 and 8167.0, used 2005 'Oslo Manual: Guidelines for Collecting and Interpreting Innovation Data, Third edition ' concepts.

The BCS applied the fourth edition of the OSLO definitions for the 2018-19 collection, resulting in:

• reduction in types of innovation from four to two 
  • previously there were four types of innovation introduced by businesses: goods and/or services, operational processes, organisational/managerial processes and marketing methods.
  • starting with this cycle there are two types of innovation introduced by business: goods and/or services; and process.
     
• Goods and/or services innovation definition change
  • starting with this cycle, changes to aesthetic design or packaging is included with goods and/or services innovation. This was previously reported under marketing methods innovation.
     
• new innovation type - Process innovation
  • this includes all innovation collected previously under: operational processes; organisational/managerial processes; and marketing methods. The exception is aesthetic design or packaging which is now part of goods and/or services innovation.
     
• flow on impacts of these changes to abandoned and ongoing innovation to reflect the two types of innovation from this cycle.
   

Refer to the Appendix to view all changes to the BCS. A copy of the 2018-19 BCS questionnaire can also be found via the Data downloads section.

For comparative purposes, 2017-18 BCS data for both the 2018 and 2005 versions of the OSLO manual can be found in datacube 11 (via the Data downloads section).

Innovation-active businesses were asked to estimate their expenditure (using ranges) on the development or introduction of all new goods, services and processes during the reference period.

Using this data and running multiple simulations, the estimated total expenditure spent on innovation by Australian businesses in 2018-19 is between $33 billion and $37 billion. Approximately one third of total innovation expenditure is derived from businesses that reported actual dollar values $5 million or greater.

Further expenditure information can be found in datacube 8 (via the Data downloads section).

Estimates of the number of businesses operating in Australia can be derived from a number of sources within the ABS. They may relate to a particular point in time or may be presented as an average annual figure. However, these estimates will not always show the same results. Variations will occur because of differing data sources, differing scope and coverage definitions between surveys, as well as variations due to sampling and non-sampling error. More information about business counts can be found in the Information Paper: A Statistical View of Counts of Businesses in Australia (cat. no. 8162.0).

The BCS is not designed to provide high quality estimates of numbers of businesses for any of the output classifications (for example, employment size or industry) and the number of businesses in this publication are only included to provide contextual information for the user. The estimate of the total number of businesses may not be equal to the sum of each employment size range due to rounding of business counts to the nearest thousand. A more robust source of counts of Australian businesses is available from Counts of Australian Businesses, including Entries and Exits, Jun 2015 to Jun 2019 (cat. no. 8165.0).

For output purposes, businesses are classified to employment size ranges based on actual data reported in the survey. For the first time outputs will be available for some data items in the 0-19 employment size range (small businesses). For industry output, the classification is drawn from information held about the business on the ABSBR.

The sample is designed to produce efficient estimates for industry and employment size at the national level; therefore it does not provide quality estimates for states/territories. As estimates may not reflect change over time for a selected state/territory or adequately enable comparison between states/territories, they are not available.

Estimates of proportions have been calculated using unrounded figures, but are shown in the tables rounded to one tenth of a percentage point. Where figures have been rounded, discrepancies may occur between the sum of the component items and the total. Figures presented in the commentary have been rounded to the whole percentage.

ABS publications draw extensively on information provided freely by individuals, businesses, governments and other organisations. Their continued cooperation is very much appreciated: without it, the wide range of statistics published by the ABS would not be available. Information received by the ABS is treated in strict confidence as required by the Census and Statistics Act 1905.

When interpreting the results of a survey, it is important to take into account factors that may affect the reliability of the estimates. Estimates in this publication are subject to both non-sampling and sampling errors.

Non-sampling errors may arise as a result of errors in the reporting, recording or processing of the data and can occur even if there is a complete enumeration of the population. These errors can be introduced through inadequacies in the questionnaire, treatment of non-response, inaccurate reporting by respondents, errors in the application of survey procedures, and incorrect recording of answers and errors in data capture and processing.

The extent to which non-sampling error affects the results of the survey is difficult to measure. Every effort is made to reduce non-sampling error by careful design and testing of the questionnaire, efficient operating procedures and systems, and the use of appropriate methodology.

Some of the items collected in the BCS are dynamic in nature and the concepts measured are subject to evolution and refinement over time; it is not possible to measure the impact of these changes on data quality.

The approach to quality assurance for the BCS aims to make the best use of ABS resources to meet user prioritised requirements - both in terms of data quality and timing of release. The approach specifies the level and degree to which each data item is quality assured, noting that only some of the total output from the BCS is able to be quality assured to the highest standards. Different priorities are assigned to groups of data items, with highest priority being assigned to key point in time data on business use of IT and innovation.

The 2018-19 BCS had a response rate of 90%.

The difference between estimates obtained from a sample of businesses, and the estimates that would have been produced if the information had been obtained from all businesses, is called sampling error. The expected magnitude of the sampling error associated with any estimate can be estimated from the sample results. One measure of sampling error is given by the standard error (SE), which indicates the degree to which an estimate may vary from the value that would have been obtained from a full enumeration (the 'true' figure). There are about two chances in three that a sample estimate differs from the true value by less than one standard error, and about nineteen chances in twenty that the difference will be less than two standard errors.

The following is an example of the use of standard error on the estimated proportion of businesses with a web presence. If the estimated proportion of businesses with a web presence was 54.4% and the standard error of this estimate was 1.0. There would be approximately two chances in three that a full enumeration would have given a figure in the range of 53.4% and 55.4%, and nineteen chances in twenty that it would be in the range of 52.4% to 56.4%.

In this publication, indications of sampling variability are measured by relative standard errors (RSEs). The relative standard error is a useful measure in that it provides an immediate indication of the percentage errors likely to have occurred due to sampling, and thus avoids the need to refer to the size of the estimate. Relative standard errors are shown in the Relative Standard Error table in this section. RSEs for all data included in this release (including data cube content) are available upon request.

To annotate proportion estimates, a value of 50% has been used in the calculation of RSE rather than the estimated proportion from the survey data. This avoids inconsistencies between the way very low and very high proportions are annotated. Relative standard errors for estimates in this publication have been calculated using the actual standard error and the survey estimate (referred to as x) in the following manner: RSE%(x) = (SE(x)*100)/50.

Using the previous example, the standard error for the estimated proportion of businesses with a web presence was 1.0%. Multiplied by 100 and then divided by 50 gives an RSE calculated on this basis of 2.0%. It is these figures that appear in the table appended to this chapter.

Estimates may have corresponding RSE range values annotated. Depending on the level of RSE, data should be used with caution. Estimates with RSEs between 10% and 25% are subject to sampling variability too high for some purposes. Estimates with RSEs between 25% and 50% are subject to sampling variability too high for most practical purposes and estimates with an RSE greater than 50% indicate that the sampling variability causes the estimates to be considered too unreliable for general use.

Estimates with an annotated RSE of between 10% and 25% should be used with caution as the estimate from full enumeration could lie more than a decile away. For example a proportion estimate of 30% with this RSE annotation, means the full enumeration value could lie beyond the range 20% to 40%. Estimates with an annotated RSE of between 25% and 50% could lie more than a quartile away and is subject to sampling variability too high for most practical purposes. A proportion estimate of 30% annotated with this RSE annotation, means the full enumeration value could lie beyond the range 5% to 55%. Proportion estimates annotated with RSE greater than 50% have a sampling error that causes the estimates to be considered too unreliable for general use.