A Monthly Indicator of Turnover using Business Activity Statement Data

This paper describes the key findings of recent work to develop a monthly indicator of turnover based on Business Activity Statement data


Oksana Honchar, Kayla McIntosh and Sabrina Zheng¹


The Australian Bureau of Statistics (ABS) is focused on maximising the value of existing ABS data and utilising alternative data sources including Single Touch Payroll (STP), Business Activity Statement (BAS) data, and aggregated de-identified banks transactions data to provide insights to inform decision making.

The ABS has been exploring potential uses of these datasets, including their suitability as data sources for more frequent and timely economic indicators.

This information paper summarises the key findings of recent work to develop a monthly indicator of turnover (‘the indicator’) by industry using BAS data. It describes the concepts, data sources, methodologies, and quality assessment of the indicator estimates.

This program of work also includes the development of monthly indicators of:

  • household spending using payments data from banks. The ABS has recently published a progress update on this project.
  • employee earnings using STP data.

Periodically the ABS will provide updates on the development of these indicators.

Feedback on the methodologies and the experimental estimates published in this paper is welcomed by email to national.accounts@abs.gov.au.


  1. The authors would like to acknowledge and thank Tom Davidson, James Farnell, Justin Farrow, Luke Hartigan, Irina Pribil, Joey Srinkapaibulaya, Jack Steel, Chris Thompson, and Vi Vu for their significant contribution to developing the indicator. The authors would also like to thank Justin Farrow, Grace Kim, Luisa Ryan and Michael Smedes for their comments which have improved this paper.


The development of the indicators focuses on testing: the suitability of BAS as the primary data source; the alignment with the national accounts concept of output; and the quality of the estimates produced. Data users were consulted in relation to their requirements, and to assess user satisfaction.

This information paper presents:

  • how the proposed measure of turnover fits within the existing measures of economic activity
  • the methodologies applied to transform raw BAS data into the indicator
  • a quality assessment of the indicator using the ABS Data Quality Framework
  • conclusions reached and intended next steps
  • a timeseries of the indicator for selected industries (in the associated datacubes).

Gross Domestic Product and Turnover

Gross Domestic Product (GDP) estimates are used as the key measure of economic activity in Australia. Data on businesses’ sales, or turnover, are a critical building block in the calculation of GDP, and hence there is a desire to ensure the indicator aligns with, and can support, the national accounts. This section explains the relationship between national accounts estimates and business turnover data.

The System of National Accounts (SNA) 2008 defines output as the value of total sales (or other uses of goods and services produced as outputs) plus the value of changes in the inventories of goods produced². There are three types of output in an economy³:

  • Market output is goods and services sold at economically significant prices or otherwise disposed of on the market, or output that is intended for sale or disposal on the market
  • Non-market output is goods and services supplied for free, or at prices that are not economically significant⁴
  • Output produced for own final use is goods and services retained for their own final use by the owners of the enterprises in which they are produced.  

Sales data from the Quarterly Business Indicator Survey (QBIS) and other data sources are used as an indicator of output for most industries in the quarterly national accounts. A more extensive approach, and a more complete set of data, are used for the annual national accounts⁵.

Conceptually, BAS turnover has reasonably good alignment with QBIS sales. They both measure the goods or services sold and supplied by businesses, the sale or lease of land and building, as well as the income from providing goods or services for sponsorship. Dividend income is excluded from both QBIS and BAS. Despite the similarities, there are scope differences. For example, QBIS sales exclude interest income and sale of business assets, whereas these are included in BAS turnover.

There are differences in the basis of QBIS and BAS reporting. QBIS is typically reported on a Profit and Loss (P&L) statement basis. In contrast BAS is reported on a Goods and Services Tax (GST) liabilities and credits basis. This means that BAS turnover relates to cash flows where the business is acting as an 'agent', with related income and expenses not appearing on the P&L statement. This difference between BAS turnover and QBIS sales can lead to significant discrepancies for industries that include a high proportion of commission-based activity. Examples include financial services, travel agents, advertising, and real estate services.

BAS turnover is better suited to capturing market output of private and public corporations. Therefore, industries with a large share of non-market output, namely, Public Administration and Safety, Education and Training, and Health Care and Social Assistance, are excluded from the indicator.

The similarities in characteristics of QBIS and BAS, and the use of QBIS in the quarterly national accounts provide opportunities for more timely insights into economic activity through the monthly indicator. However, output is only one aspect of a broader approach to measuring economic activity by industry as shown in figure 1, and there are known differences in scope between BAS and QBIS.

Figure 1. Industry output and gross value added at basic prices

This diagram shows output is one of the components in measuring gross value added, and more broadly gross domestic product. Other components include intermediate consumption and taxes on products less subsidies on products.
This diagram shows output is one of the components in measuring gross value added, and more broadly gross domestic product. The output includes market output, non-market output and output for own final use. Gross domestic product is derived by output less intermediate consumption plus taxes on products less subsidies on products.


  1. https://unstats.un.org/unsd/nationalaccount/docs/sna2008.pdf, System of National Accounts 2008, United Nations
  2. https://www.ausstats.abs.gov.au/ausstats/subscriber.nsf/0/C5ACA29422243B56CA257F7D00177D09/$File/52160_2015_.pdf Australian System of National Accounts (ASNA) 2015, paragraph 9.4.
  3. ASNA 2015, paragraphs 9.5 & 9.11.
  4. ASNA 2015, Chapter 9.

Business Activity Statement Data

The Australian Taxation Office (ATO) collects BAS turnover data for the purposes of administering the GST, and provides it to the ABS for statistical purposes. Monthly BAS reporting covers businesses with GST annual turnover of $20 million or more and a proportion of smaller businesses that report BAS monthly on a voluntary basis.

The monthly BAS submission is due on the 21st day of the month following the end of the taxable period⁶. For example, a July monthly BAS is due on 21 August. If businesses want to fix a mistake in the BAS form or make adjustments, they can either fix the error(s) in the next BAS submission or revise the original via a revision lodgement.



Defining scope

The indicator includes all ‘alive’ employing and non-employing businesses in all types of economic activity with a few exceptions⁷. Both public corporations and private businesses that produce market output are in scope of the indicator.

ABS Business Register and frames

The ABS Business Register is a list of organisations which undertake economic activity in Australia⁸. The data on the ABS Business Register is primarily sourced from the Australian Business Register (ABR), the ATO and via ABS profiling of large, and/or complex businesses⁹. The ABS uses an Economic Units Model on the ABS Business Register to describe the characteristics of businesses, and the structural relationships between related businesses.

To ensure coherence across the various ABS collections, an extract from the ABS Business Register is taken each quarter. This extract, used as the sampling frame for ABS business surveys, is called the ‘Common Frame’¹⁰.

To produce the indicator a population of monthly BAS remitters is linked to the last available Common Frame. For example, the indicator estimates for April, May and June 2020 use the June 2020 Common Frame that is extracted from the ABS Business Register at the end of May. The July 2020 estimates use the September 2020 Common Frame, as the data is extracted around the same time the September frame is available in the ABS Business Register. The BAS data must be linked to the Common Frame in order to:

  • Limit the BAS population to the indicator scope
  • Enable the production of the indicator by industry
  • Assess and adjust for population under-coverage.

Statistical unit model

Statistical units are used to represent the individual businesses within the covered population. Statistical units on the ABS Business Register are based on the ABS Economic Units Model (EUM), which is used to describe the structure of Australian businesses and other organisations¹¹.

The EUM consists of the following:

  • Enterprise group (EG): an institutional unit that covers all operations within Australia's economic territory of legal entities under common control. Control is defined in Corporations legislation, and majority ownership is not required for control to be exercised.
  • Legal entity (LE): a unit that covers all operations in Australia of an entity which possesses some or all of the rights and obligations of individual persons or corporations, or which behaves as such in respect of those matters of concern for economic statistics. Examples of legal entities include companies, partnerships, trusts, sole (business) proprietorships, government departments and statutory authorities.
  • Type of activity unit (TAU): a producing unit comprising one or more legal entities, sub-entities or branches of a legal entity that can report productive and employment activities via a minimum set of data items. If accounts that are sufficient to approximate industry value added are available at the Australian and New Zealand Standard Industry Classification (ANZSIC) subdivision level, a TAU will be formed.
  • Location: a single, unbroken physical area, occupied by an organisation, at which or from which, the organisation is engaged in productive activity on a relatively permanent basis, or at which the organisation is undertaking capital expenditure with the intention of commencing productive activity on a relatively permanent basis at some time in the future.

Figure 2. ABS Economic Units Model

This diagram shows the ABS Economic Units Model, which is used by the ABS to describe the structure of Australian businesses and other organisations.
This diagram shows the ABS Economic Units Model, which is used by the ABS to describe the structure of Australian businesses and other organisations. The model outlines the logical hierarchical relationships between enterprise group, legal entity, type of activity unit and their respective location.

Derivation of TAU level estimates

When the BAS dataset is linked to the Common Frame, BAS turnover for Australian Business Numbers (ABNs) in the profiled population is proportioned to create TAU level turnover data¹². The ABN level data is aggregated to enterprise group (EG) level and subsequently prorated across TAUs using modelled wages or turnover factors.

Monthly growth rates of the indicator are produced for a period of 10 years. Data are presented at the national level and the published industries are classified according to the Australian and New Zealand Standard Industrial Classification (ANZSIC), 2006.

Time of data extraction

As previously mentioned, the monthly BAS submission is due on the 21st day of the month following the end of the taxable period. In practice, businesses do not always report by the due date. Analysis of how the time of data extractions affects the response rate of the monthly remitters suggests the optimal day to commence analysis and compilation of the indicator is the 28th day of the month following the reference month. By this day, at least 75% of the monthly BAS reports have been submitted and response rates have stabilised.

Analysis also shows that while the accuracy of the data improves after the 28th day following the reference period, there isn’t a considerable change in total turnover.

Data editing

The ABS applies a number of micro-editing techniques to the raw BAS data to adjust for identified errors in the data. Macro-editing is also applied, which involves detecting anomalies through analysis of aggregated data and treating these anomalies. Both micro-editing and macro-editing are incorporated to improve the accuracy of the indicator.


Imputation is applied where there are gaps in BAS data due to delays in BAS submissions by monthly remitters. The missing turnover values are imputed using the last historical turnover value adjusted for turnover growth rate and the probability of the unit being alive:

\(\Large y_{t,i}^* = {y_{t - n}} \times \frac{{{{\bar y}_{t,d}}}}{{{{\bar y}_{t - n,d}}}} \times {f_c} \)

where yₜ₋ₙ is the last reported value for variable y for unit i. A growth factor is a ratio of average values of turnover in the current and previous months calculated at sub-industry level using common units across the two months. The factor has not been implemented in the current version of the indicator; however, it is intended to be implemented in a future version of the indicator estimates. The unit’s probability of being alive is calculated at ANZSIC sub-division level using historical data and depends on the individual business and the time lag since the last response. After 12 months since last reported response the probability of a business being alive is set to zero.

Coverage adjustments

The coverage of the indicator consists of the BAS monthly remitters. Under-coverage, such as the exclusion of quarterly and annual remitters, does not allow production of meaningful level estimates without an appropriate coverage adjustment. In terms of movements, under-coverage leads to distorted results if:

  • The movements in the population of quarterly and annual remitters are different to movements in the population of the monthly remitters. In this case the population of the monthly remitters does not represent movements in the whole population; or
  • The coverage of the population of monthly remitters changes over time. In this case movements in the indicator are partly explained by movements in the numbers of units rather than real economic processes.

Analysis shows that the population of monthly remitters adequately represents the turnover movement of the whole population. However, under-coverage adjustment should still be considered given that the population of monthly remitters does not grow at the same rate as the whole population and to also address any sudden changes in the coverage that distort movements in the indicator series.


  1. The business is considered to be alive if it is actively trading or is expected to be trading in the future. BAS is currently remitted or will be remitted to ATO in the future.
  2. https://www.abs.gov.au/ausstats/abs@.nsf/dossbytitle/AC79D33ED6045E88CA25706E0074E77A?OpenDocument, Australian Bureau of Statistics Business Register
  3. https://www.ato.gov.au/
  4. The Common Frame is scoped to businesses with a GST and/or Income tax withholding role.

  5. See Australian Bureau of Statistics Business Register and Standard Economic Sector Classifications of Australia (SESCA), 2008 (Version 1.1), Australian Bureau of Statistics
  6. Profiled population: The list of organisations on the ABS Business Register that are maintained and updated through direct contact with the organisation via a process called Profiling. Profiling is the process of determining the statistical structure of larger enterprise groups (including business structure, employment, ANZSIC etc.) and maintaining this information up to date. Source: Australian Bureau of Statistics Business Register.

Timeseries Analysis

Standard ABS seasonal adjustment procedures were applied to eliminate the impact of seasonal patterns on the series. All industries were found to be seasonal, with some having prominent seasonal patterns. Relevant adjustments for extreme values, trend breaks, and calendar effects have been applied as part of the seasonal adjustment process.

For more information on ABS seasonal adjustment methodology, refer to the Time Series Analysis: The Basics.

Quality Assessment

The indicator has been assessed against the different dimensions of quality as defined by the ABS Data Quality Framework. This ensures the fitness for purpose of the indicator, but also describes limitations of the indicator to ensure users are well informed when using this data for their specific purposes. 

Institutional Environment

The indicator estimates are derived using monthly BAS turnover data provided to the ATO by businesses. An extract of the BAS turnover data is provided to the ABS under a Memorandum of Understanding (MOU) between the ABS and the ATO.

Appropriate confidentiality rules have been applied to the BAS turnover data to protect the information of the businesses. All information is handled in accordance with the Australian Privacy Principles contained in the Privacy Act 1988. Detailed information on the confidentiality rules can be found in 1160.0 - ABS Confidentiality Series, Aug 2017.

The successful production of the indicator relies on the stability of BAS data maintenance processes and arrangements of BAS reporting. Administrative changes, such as changes in due dates for BAS reporting, changes in a turnover threshold for monthly reporting and changes in the ATO systems, might impact on the indicator series.

For information on the institutional environment of the ABS, including the legislative obligations of the ABS, financing and governance arrangements, and mechanisms for scrutiny of ABS operations, please see ABS Institutional Environment.


The indicator has a reasonably comprehensive representation of the current domestic economic environment. The indicator scope encompasses all alive employing and non-employing businesses in all types of economic activity with a few small exceptions.

The indicator estimates provide indicative monthly insights into industry output. Data is presented at the national level and the published industries are classified according to the Australian and New Zealand Standard Industrial Classification (ANZSIC), 2006 (cat. No. 1292.0). The indicator is presented in current price terms only.

Monthly growth rates are produced for a period of 10 years and they are presented as original and seasonally adjusted series at the industry level. This provides a coherent and consistent basis to compare with other relevant data such as QBIS, and meaningful conclusions can be drawn from the movements.


The timeliness of the BAS data can be affected by several factors:

  • Despite the specified due date for BAS submission, the timing of business reporting can change depending on circumstances. In the case of a natural disaster or events like the COVID-19 pandemic, businesses can negotiate a different due date, which may subsequently impact on the data availability.
  • Businesses can make changes to their BAS lodgement, either to make a correction or an adjustment. The changes can be incorporated into their next BAS submission or the original BAS submission through revisions. This can impact on both the timing and accuracy of the BAS data.


BAS data is an administrative collection and is not designed for statistical purposes. Therefore, the ABS has limited control on data quality. Unlike most ABS collections, BAS is not a sample survey and is therefore not prone to sampling errors, although it can potentially be impacted by the non-sampling errors outlined below.

Coverage error

The scope of the indicator is broad and includes all public and private businesses, although the coverage is much smaller as it only includes businesses that lodge their BAS monthly. As the table below indicates, the number of monthly remitters and their shares of turnover vary considerably by industry.

Table 1. Proportion of remitters and turnover by industry and reporting frequency, 2019-2020 financial year
ANZSIC DivisionNumber of BAS records, %Turnover, %
Agriculture, Forestry and Fishing10.
Electricity, Gas, Water and Waste Services27.371.
Wholesale Trade31.767.01.283.416.50.1
Retail Trade26.372.21.573.626.20.3
Accommodation and Food Services14.
Transport, Postal and Warehousing10.188.41.677.921.70.4
Information Media and Telecommunications13.483.92.786.912.90.2
Financial and Insurance Services11.681.96.576.023.30.7
Rental, Hiring and Real Estate Services11.586.02.553.545.01.6
Professional, Scientific and Technical Services10.187.52.463.535.80.7
Administrative and Support Services13.084.72.359.639.80.5
Public Administration and Safety32.166.31.692.57.40.1
Education and Training36.861.61.777.422.40.2
Health Care and Social Assistance13.385.01.754.944.40.6
Arts and Recreation Services11.486.32.365.134.30.6
Other Services14.384.01.748.350.90.8

Percentages may not add to 100% due to rounding.

Coverage errors can occur in various scenarios as follows:

  • The population of the monthly remitters may represent the turnover movement of the whole population quite well, however if the population of the quarterly and annual remitters have different growth rates to the monthly remitters this could result in errors in the level estimates and movements.
  • The coverage of the population could change over time. For example, the coverage might change monotonically, meaning the level estimates and growth rates are partly driven by changes in population number rather than purely by economic events. In addition, coverage might change unexpectedly. Examples include legislation or rule changes to reporting frequency of BAS data, unusual events such as natural disasters or the COVID-19 pandemic. In these situations, the resulting levels and movements could be distorted by changes in coverage.
  • Stability of under-coverage over time. The share of monthly remitters has remained relatively stable over time, as have the shares of quarterly and annual remitters. However, the level estimates would be under-estimated because of under-coverage.

Frame error

When the BAS dataset is linked to the ABS Business Register it is exposed to potential frame errors as follows:

  • Incorrect classification of industry and sector on the ABS Business Register.
  • Differences between selection unit and frame unit. The ATO collects and stores BAS data at the ABN level while the ABS Business Register is a list of statistical units which are a combination of ABNs and TAUs. Errors can emerge when converting ABN units to TAU units.
  • The ABS Business Register is updated regularly to capture the most up-to-date information of the business units. The indicator uses the latest available extract at the time of compilation. However, the extract is updated quarterly rather than monthly.
  • Businesses can change their reporting frequency from quarterly to monthly. The under-coverage adjustment method proposed for the indicator would apply historical ratios to the latest monthly data using previous quarter’s remitters. As the ABS Business Register Extract may not incorporate this change due to its quarterly frequency, it could lead to an over-estimation of the indicator estimates.

Non-response error

Businesses may not submit their BAS reports on time. Additionally, some businesses might be classified incorrectly and thus treated as non-responses. This again would lead to reduced accuracy of the estimates. Examples of unit misclassifications are:

  • 'Dead' business – due to delay with the ABS Business Register extract update¹³.
  • Businesses that changed BAS reporting frequency from monthly to quarterly – in such case there would be a period of time when they would be classified as non-responses. These factors would result in non-response for the reference month and subsequently bias and volatility in the indicator estimates.

When the new monthly BAS data extract becomes available, it includes updates for the previous months’ data due to BAS resubmissions and corrections. This means that when accessing historical data, it is not possible to separate actual real-time non-responses out as their values have already been overwritten with the latest data. Non-response rates are an indication of bias risk from a statistical perspective, and the actual bias depends on the average difference between responding and non-responding businesses with respect to BAS turnover.

Table 2 shows the non-response rates for the monthly remitters by industry for the March 2021 month. The non-response rates are calculated using all alive businesses and based on data extracted on the 28th day of April. Any BAS submissions or re-submissions after this date are not used in the calculation. 

Table 2. Non-response rates by industry, March 2021
ANZSIC DivisionNon-response rate, %
Agriculture, Forestry and Fishing32.4
Electricity, Gas, Water and Waste Services23.1
Wholesale Trade22.3
Retail Trade27.9
Accommodation and Food Services41.1
Transport, Postal and Warehousing51.0
Information Media and Telecommunications40.7
Financial and Insurance Services36.5
Rental, Hiring and Real Estate Services40.7
Professional, Scientific and Technical Services45.0
Administrative and Support Services43.5
Public Administration and Safety36.0
Education and Training27.0
Health Care and Social Assistance33.6
Arts and Recreation Services41.5
Other Services37.2

Non-response error (bias) is the difference between the statistics computed from the collected data and those that would be computed if there were no missing values. Failure to address non-response error may lead to under-estimated level estimates and growth rates. An imputation method based on historical turnover data adjusted for turnover growth rate and probability of a unit being alive has been developed to address this issue.

The non-response bias varies by industry as well as by calendar month. The table below shows the non-response bias by industry for February 2021, with the largest effect on Public Administration and Safety.

Table 3. Non-response bias by industry, February 2021*
ANZSIC DivisionNon-response bias as % of reported monthly turnover
Agriculture, Forestry and Fishing0.0
Electricity, Gas, Water and Waste Services0.3
Wholesale Trade0.0
Retail Trade0.0
Accommodation and Food Services0.0
Transport, Postal and Warehousing0.6
Information Media and Telecommunications0.1
Financial and Insurance Services0.0
Rental, Hiring and Real Estate Services0.0
Professional, Scientific and Technical Services0.0
Administrative and Support Services-0.5
Public Administration and Safety2.1
Education and Training-0.1
Health Care and Social Assistance0.0
Arts and Recreation Services0.6
Other Services0.2

* Non-response bias has been calculated for businesses that were recorded as a non-response in the February 2021 reference month, based on the data available on the 28th of March but who reported actual values by the 28th of April.

Measurement error

Measurement errors are those that occur during data collection and cause the recorded values of variables to be different from the true ones. Because BAS data are collected by the ATO for administrative purposes, it is not possible to query businesses on a specific movement, or to resolve erroneous values in real time. Micro-editing and macro-editing techniques have been developed to minimise any potential bias and variances presented in the data. The amount of micro-edits applied to the BAS data is very small and does not significantly impact indicator estimates.

Processing error

Different data processing steps, including data entry, data editing, coding, and imputation, occur between data collection and statistical analysis. Errors introduced in these stages are processing errors, and they can cause bias and variances in the resulting statistics.

Processing errors also occur in BAS reporting. Businesses could submit their BAS reports several months after the reference months. An imputation method has been developed to address this issue. The table below shows the percentage of imputed turnover for March 2021.

Table 4. Percentage of imputed turnover by industry, March 2021
ANZSIC DivisionImputed turnover, %
Agriculture, Forestry and Fishing4.3
Electricity, Gas, Water and Waste Services4.0
Wholesale Trade1.1
Retail Trade1.4
Accommodation and Food Services2.2
Transport, Postal and Warehousing3.4
Information Media and Telecommunications0.9
Financial and Insurance Services0.4
Rental, Hiring and Real Estate Services3.2
Professional, Scientific and Technical Services3.2
Administrative and Support Services1.4
Public Administration and Safety2.2
Education and Training11.0
Health Care and Social Assistance1.7
Arts and Recreation Services5.1
Other Services1.7

Linkage and TAU proportioning error

When BAS turnover data are linked to the ABS Business Register, ABN is used as the unit identifier. However, some units in BAS data are outside the scope of the ABS Common Frame, including where the ABN does not have a classification such as the ANZSIC division. This leads to a record linkage error.

In addition, there is a potential risk of incorrect estimation of industry proportions when ABN level turnover is proportioned across TAUs. For example, a TAU’s turnover may contribute 30% to ABN’s turnover in one month but decreases to 10% in the following month. Given that TAU proportioning uses annual information, it would over-estimate this TAU’s turnover for the following month.


Revisions occur when businesses make changes to their BAS lodgement, either to make a correction or an adjustment. The ABS considers the latest version of the reported BAS data as final at the time of publication. For most industries, revisions due to BAS resubmissions are relatively small, but for a few industries such as Education and Training, Retail Trade, Professional, Scientific and Technical Services, Financial and Insurance Services, and Other Services, revisions are much larger.

Further revisions may occur as the methodologies for the indicator are refined.


The indicator uses the same data source, classifications and consistent methodology for all industries and sectors, and therefore guarantees coherence across these dimensions.

BAS turnover data has reasonably good conceptual alignment with QBIS sales. Both estimates measure the goods or services sold/supplied by businesses, the sale or lease of land and building, as well as the income from providing goods or services for sponsorship. Dividend income is excluded from both statistics. Despite the similarities, differences remain.

The indicator estimates are produced over a period of 10 years, which is long enough to compare with similar statistical products both in the ABS and in other statistical agencies. The response pattern among the monthly remitters is assumed to be consistent from month to month, and any occasional delays in response would not significantly impact the monthly growth rates.


The indicator is provided in original and seasonally adjusted terms. The seasonally adjusted series removes the seasonal effects but does not smooth out the volatility in the data. The original series neither removes the seasonal effects nor smooths out the volatility in the data.

Seasonally adjusted estimates, by their nature, are subject to revisions over time. In most instances, the larger revisions will be to the previous month and the same month a year ago. For more information, see Time Series Analysis Frequently Asked Questions (cat. no. 1346.0.55.002).


  1. The business is considered to be dead if it has ceased generating income and incurring expenses and has no intention to operate in the future. No BAS will be remitted by this business to ATO in the future. The ABS Business Register will classify businesses as long-term non-remitters until the business is cancelled and formally leaves the economy.

Comparison with Quarterly Business Indicator Survey

The indicator is aggregated into a quarterly series to compare with QBIS sales for alignment assessment. To ensure consistency, industries used for comparison have been limited to industries with small (less than 10%) public corporation contribution (figure 3) and are similar in scope to QBIS. This means that for the purposes of comparing to QBIS Agriculture, Forestry and Fishing, Electricity, Gas, Water and Waste Services, Transport, Postal and Warehousing, Financial and Insurance Services, Public Administration and Safety, Education and Training, and Health Care and Social Assistance industries have been excluded.

To determine alignment between the indicator and QBIS sales, a simple observation test was developed with alignment based on the relative frequency that growth is either positive for both the indicator and QBIS sales, or both negative, in the same reference period. The higher the percentage, the more aligned the two series. Industries are classified into one of four categories:

  • Good alignment – alignment equal to, or greater than, 90%
  • Reasonably good alignment – alignment equal to, and between, 80% and 89.9%
  • Poor alignment – alignment equal to, and between, 70% and 79.9%
  • No alignment – alignment less than, or equal to, 69.9%.

Overall, the indicator and QBIS sales align well, though it varies by industry. Ten industries – Mining, Manufacturing, Construction, Wholesale Trade, Retail Trade, Accommodation and Food Services, Information Media and Telecommunications, Professional, Scientific and Technical Services, Administrative and Support Services, and Other Services – have good or reasonably good alignment with QBIS sales. Rental, Hiring and Real Estate Services, and Arts and Recreation Services have poor alignment. This alignment could improve with refinement of the methodologies used to produce the indicator.

When a series has a strong seasonal pattern, alignment between the two series in original terms can be misleading. Excluding the seasonal pattern from the indicator series has resulted in poorer alignment with QBIS.

The following graphs demonstrate the different results of alignment between the indicator, aggregated to a quarterly frequency, and QBIS sales, current price, in original and seasonally adjusted terms¹⁴.




Wholesale Trade

Retail Trade

Accommodation and Food Services

Information Media and Telecommunications

Rental, Hiring and Real Estate Services

Professional, Scientific and Technical Services

Administrative and Support Services

Arts and Recreation Services

Other Services

Contribution of gross value added by industry

The table below shows industry shares of gross value added for the 2018-19 financial year¹⁵. Mining contributes 10.6% to total value added, whereas Construction contributes 7.9%. The table outlines the contribution by industry for the 2018-19 financial year. While this gives an indication of the economic significance of each industry for this point in time, the table doesn’t capture the dynamics of the industries on a monthly basis, nor is it representative of the compositional change in industry share over time.

Table 5. Industry shares of gross value added at basic prices, 2018-19 financial year¹⁶
ANZSIC DivisionPercentage points of industry share of gross value added*
Agriculture, Forestry and Fishing2.3
Electricity, Gas, Water and Waste Services2.6
Wholesale Trade3.9
Retail Trade4.4
Accommodation and Food Services2.4
Transport, Postal and Warehousing4.9
Information Media and Telecommunications2.4
Financial and Insurance Services9.0
Rental, Hiring and Real Estate Services3.1
Professional, Scientific and Technical Services7.3
Administrative and Support Services3.7
Public Administration and Safety5.6
Education and Training5.0
Health Care and Social Assistance7.6
Arts and Recreation Services0.9
Other Services1.9
Ownership of Dwellings8.7

* The percentages are calculated based on the total of all ANZSIC industries. Percentages may not add to 100% due to rounding.


  1. The QBIS sales data are sourced from Business Indicators, Australia, March 2021 quarter
  2. The most recent balanced year.
  3. https://www.abs.gov.au/statistics/economy/national-accounts/australian-system-national-accounts/2019-20 Australian System of National Accounts, 2019-20 financial year

Comparison with Retail Trade Survey

The Retail Trade industry results for the indicator is compared with the monthly Retail Trade Survey (RTS) turnover¹⁷. Cafes, restaurants and takeaway food services has been excluded in RTS to better align the scope to Retail Trade industry in the indicator. There is still some residual misalignment in scope as RTS only includes some of the Retail Trade sub-industries, while the indicator includes all retail trade sub-industries.

The RTS does not include all classes in the ANZSIC Retail Trade division but includes Cafes, restaurants and takeaway food services from the Accommodation and Food Services division. The inclusion of GST and retail businesses that are classified to a non-retail industry also contribute to the differences.

Monthly RTS used excludes cafes, restaurants, and takeaway services, so that the same industry scope is used for this comparison.

Monthly RTS used excludes cafes, restaurants, and takeaway services, so that the same industry scope is used for this comparison.


  1. The monthly Retail Trade Survey estimates are sourced from Retail Trade Australia, March 2021.

Conclusion and Where to From Here

The monthly indicator based on BAS turnover has been demonstrated to be a high quality, high frequency economic indicator for selected industries. Following the application of statistical methods to transform the BAS data to an indicator of turnover, the ABS conducted a quality assessment in line with the ABS Data Quality Framework. This assessment focused on the robustness of the estimates in respect to the dimensions of quality, with a particular emphasis on coherence with current lower frequency estimates. This indicator can provide insights to changes in economic conditions in the Australian economy, noting there are some trade-offs in terms of accuracy. Table 6 summarises the results of the assessment of the indicator in respect to various quality dimensions and provides insights into the economic significance of each industry. 

Table 6. Summary of indicator quality and alignment by industry
ANZSIC DivisionAppropriateness of turnover for measuring output (a)Proportion of total turnover reported by monthly remitters, 2019-20 financial year (%) (b)Percentage of imputed turnover, March 2021 (%) (c)Non-response bias as percentage of reported monthly turnover, February 2021 (%) (d)Percentage points of industry share of gross value added, 2018-19 (%) (e)Coherence with QBIS sales, (current price, original) (f)
Agriculture, Forestry and FishingNo34. (g)
MiningYes96. good
ManufacturingPartial (excludes inventories) good
Electricity, Gas, Water and Waste ServicesYes94. (g)
Wholesale TradePartial (measures total turnover rather than margin) good
Retail TradePartial (measures total turnover rather than margin)
Accommodation and Food ServicesYes33. good
Transport, Postal and WarehousingYes77. (g)
Information Media and TelecommunicationsYes86. good
Financial and Insurance ServicesNo76. (g)
Rental, Hiring and Real Estate ServicesYes53.
Professional, Scientific and Technical ServicesYes63. good
Administrative and Support ServicesYes59.61.4-0.53.7Reasonably good
Public Administration and SafetyNo92. (g)
Education and TrainingNo77.411.0-0.15.0N/A (g)
Health Care and Social AssistanceNo54. (g)
Arts and Recreation ServicesYes65.
Other ServicesYes48. good

(a) Refer to section “Gross Domestic Product and Turnover”
(b) Refer to table 1
(c) Refer to table 4
(d) Refer to table 3
(e) The percentages are calculated based on the total of all ANZSIC industries. Ownership of dwellings is separated from Rental Hiring and Real Estate Services and presented separately in the National Accounts. Ownership of dwellings contributes 8.7% to industry share of gross value added.
(f) Refer to section “Comparison with Quarterly Business Indicator Survey”
(g) Industry not included in the QBIS alignment test due to proportion of public sector greater than 10%, and/or appropriateness of QBIS as a data source

This paper has outlined the methodological approach taken by the ABS in developing the indicator, and the key findings from this exploratory work. This provides a firm basis for future work, including updated methods and the experimental indicator of turnover estimates.

Refinement of methodology

The ABS will put this indicator into production and review the methodological approaches. Alternative imputation and coverage adjustment techniques will be tested before publishing the indicator estimates later this year.

Seeking feedback

The ABS will be seeking the feedback from a range of external stakeholders as work proceeds. The feedback will be considered when producing future versions of the indicator. Feedback on this paper is welcome. To provide feedback, or for further enquiries, please email national.accounts@abs.gov.au.

Data Download

Table 1. Monthly indicator of turnover by industry, current price, percentage change from previous month


The results of this analysis are based, in part, on ABR data supplied by the Registrar to the ABS under A New Tax System (Australian Business Number) Act 1999 and on tax data supplied by the ATO to the ABS under the Taxation Administration Act 1953, which requires that such data is only used for the purpose of administering the Census and Statistics Act 1905. No individual information collected under the Census and Statistics Act 1905 is provided back to the Registrar or ATO for administrative or regulatory purposes. Any discussion of data limitations or weaknesses described in this paper is in the context of using the data for statistical purposes and is not related to the ability of the data to support the ATO's core operational requirements.

Legislative requirements to ensure privacy and secrecy of this data have been adhered to. Only those authorised under the Australian Bureau of Statistics 1975 have been allowed to view data about any particular firm in conducting these analyses. In accordance with the Census and Statistics Act 1905, results have been confidentialised so no person or organisation can be identified.


The ABS would like to acknowledge the support from the Australian Taxation Office (ATO) in enabling the ABS to produce these statistics.
The ABS would also like to thank the Reserve Bank of Australia (RBA) for their contribution in the development of the indicator.

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