Retail Trade, Australia methodology

1 This publication presents estimates of the value of turnover of "retail trade" for Australian businesses. The estimates are classified by industry, and by state and territory. For the purposes of this publication "retail trade" includes those industries as defined in paragraphs 5 and 6.

2 The estimates of turnover are compiled from the monthly Retail Business Survey. About 700 'large' businesses are included in the survey every month, while a sample of about 2,700 'smaller' businesses is selected. The 'large' business' contribution of approximately 69% of the total estimate ensures a highly reliable Australian total turnover estimate.

3 Monthly estimates are presented in current price terms. Quarterly chain volume measures at the state and industry levels are updated with the March, June, September and December publications.

4 Turnover includes:

• retail sales;
• online sales from both store-based and non-store based retailers (except non-employing and non-resident businesses)
• wholesale sales;
• takings from repairs, meals and hiring of goods (except for rent, leasing and hiring of land and buildings);
• commissions from agency activity (e.g. commissions received from collecting dry cleaning, selling lottery tickets, etc.); and
• from July 2000, the goods and services tax.

5 The industries included in the survey are as defined in the Australian and New Zealand Standard Industrial Classification (ANZSIC) (cat. no. 1292.0). Industry statistics in this publication are presented at two levels of detail:

• Industry group - the broadest industry level comprising 6 industry groups. This level is used to present monthly current price and quarterly chain volume measure estimates in this publication.
• Industry subgroup - the most detailed industry level comprising 15 industry subgroups. This level is used to present monthly current price estimates in time series spreadsheets. This data is now released in the 'Additional Information' release.

6 The following shows the level at which retail trade statistics are released. This defines each industry group and subgroup in terms of ANZSIC 2006 classes.

7 The scope of the Retail Business Survey is all employing retail trade businesses who predominantly sell to households. Like most Australian Bureau of Statistics (ABS) economic surveys, the frame used for the Survey is taken from the ABS Business Register which includes registrations to the Australian Taxation Office's (ATO) pay-as-you-go withholding (PAYGW) scheme. Each statistical unit included on the ABS Business Register is classified to the ANZSIC industry in which it mainly operates. The frame is supplemented with information about a small number of businesses which are classified to a non-retail trade industry but which have significant retail trade activity.

8 The frame is updated quarterly to take account of new businesses, businesses which have ceased employing, changes in industry and other general business changes. The estimates include an allowance for the time it takes a newly registered business to get on to the survey frame. Businesses which have ceased employing are identified when the ATO cancels their Australian Business Number (ABN) and/or PAYGW registration. In addition, businesses with less than 50 employees which do not remit under the PAYGW scheme in each of the previous five quarters are removed from the frame.

9 To improve coverage and the quality of the estimates and to reduce the cost to the business community of reporting information to the ABS, turnover for franchisees is collected directly from a number of franchise head offices. The franchisees included in this reporting are identified and removed from the frame.

10 The ABS uses an economic statistics units model based on the ABS Business Register. This describes the characteristics of businesses and the structural relationships between related businesses. Within large and diverse business groups, the units model is used to define reporting units that can provide data to the ABS. In mid 2002, the ABS began sourcing its register information from the Australian Business Register. At this time, the business register was changed to a two population model. The two populations used are the Profiled Population and the Non-Profiled Population. The main distinction between businesses in the two populations relates is the complexity of the business structure and the degree of intervention required to reflect the business structure for statistical purposes.

13 The Survey is conducted monthly primarily by telephone interview. A small number of questionnaires are mailed to businesses. The businesses included in the survey are selected by random sample from a frame stratified by state, industry and business size. The survey uses annualised turnover as the measure of business size. For the Non-Profiled Population, the annualised turnover is based on the ATO's Business Activity Statement item Total Sales. For the Profiled Population a modelled annualised turnover is used. For stratification purposes the annualised turnover allocated to each business is updated quarterly with the most recent Business Activity Statement (BAS) information.

14 Each quarter, some businesses in the sample are randomly replaced by other businesses so that the reporting load can be spread across smaller retailers. This sample replacement occurs in the first month of each quarter. This may increase the volatility of estimates between this month and the previous month, especially at the state by industry subgroup level.

15 Generalised regression estimation methodology is used for estimation. For estimation purposes, the annualised turnover allocated to each business is updated each quarter.

16 Most businesses can provide turnover on a calendar month basis. When businesses cannot provide turnover on a calendar month basis, the reported data and the period they relate to are used to estimate turnover for the calendar month.

17 Most retailers operate in a single state/territory. For this reason, estimates of turnover by state/territory are only collected from the larger retailers. These retailers are asked to provide turnover for sales from each state/territory in which the business operates. Turnover for the smaller businesses is allocated to the state of their mailing address as recorded on the ABS Business Register.

18 Stratified sampling is employed when, within a survey population, there are subpopulations which vary from the entire population. Stratification offers the advantage of sampling each stratum independently. The Retail Business Survey uses stratification to group the retail businesses to be surveyed into homogenous strata based on the annualised turnover allocated to each business. The annualised turnover variable is derived from BAS information from the taxation system and is used both as a sizing variable for stratification purposes and to form auxiliary information (estimation benchmarks) to support the regression estimation methodology used in the Retail Business Survey. The utilisation of BAS information enables the most efficient design for the survey, keeping sample sizes to a minimum while providing accurate results. From October 2013, the stratification benchmarks have been updated every quarter so as to improve the accuracy of level estimates derived from the survey as well as addressing the issue of aging stratification benchmarks which must otherwise be periodically updated.

19 The results of these statistics 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 tax data supplied by the ATO to the ABS under the Taxation Administration Act 1953. These require that such data is only used for the purpose of carrying out functions of the ABS. 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 is in the context of using the data for statistical purposes, and is not related to the ability of the data to support the ABR or ATO’s core operational requirements. Legislative requirements to ensure privacy and secrecy of this data have been followed. Only people authorised under the Australian Bureau of Statistics Act 1975 have been allowed to view data about any particular firm in conducting this survey. In accordance with the Census and Statistics Act 1905, results have been confidentialised to ensure that they are not likely to enable identification of a particular person or organisation.

20 Seasonally adjusted estimates are derived by estimating and removing systematic calendar related effects from the original series. In the Retail trade series, these calendar related effects are known as:

• seasonal e.g. annual patterns in sales, such as increased spending in December as a result of Christmas
• trading day influences arising from weekly patterns in sales and the varying length of each month and the varying number of Sundays, Mondays, Tuesdays, etc. in each month
• an Easter proximity effect, which is caused when Easter, a moveable holiday, falls late in March or early in April
• a Father's Day effect, which is caused when the first Sunday in September falls in the first few days of the month and Father's Day shopping occurs in August.
   

21 Each of these influences is estimated by separate factors which, when combined, are referred to as the combined adjustment factors. The combined adjustment factors are based on observed patterns in the historical data. It is possible that with the introduction of ANZSIC 2006 from July 2009 the historical patterns may not be as relevant to some series. For example Watch and jewellery retailing moved from the Other retailing n.e.c industry subgroup to the Footwear and other personal accessory retailing industry subgroup under ANZSIC 2006. The seasonal patterns for other businesses in the Footwear and other personal accessory retailing industry subgroup appear to differ from watch and jewellery retailers. The combined adjustment factors will evolve over time to reflect any new seasonal or trading day patterns, although in this example, an estimate for this impact (seasonal break) has been implemented in the combined adjustment factors.

22 The following Retail trade series are directly seasonally adjusted:

• Australian turnover
• each state total
• each Australian industry subgroup total
• each state by industry subgroup.
   

23 A "two-dimensional reconciliation" methodology is used on the seasonally adjusted time series to force additivity - that is, to force the sum of fine-level (state by industry subgroup) estimates to equal the Australian, state and industry subgroup totals. The industry group totals are derived from the lower level estimates.

24 Quarterly seasonally adjusted series used in the compilation of the chain volume measures are the sum of their applicable monthly series.

25 Autoregressive integrated moving average (ARIMA) modelling can improve the revision properties of the seasonally adjusted and trend estimates. ARIMA modelling relies on the characteristics of the series being analysed to project future period data. The projected values are temporary, intermediate values, that are only used internally to improve the estimation of the seasonal factors. The projected data do not affect the original estimates and are discarded at the end of the seasonal adjustment process. The retail collection uses an individual ARIMA model for each of the industry totals and state totals. The ARIMA model is assessed as part of the annual reanalysis.

26 In the seasonal adjustment process, both the seasonal and trading day factors evolve over time to reflect changes in spending and trading patterns. Examples of this evolution include the slow move in spending from December to January; and, increased trading activity on weekends and public holidays. The Retail series uses a concurrent seasonal adjustment methodology to derive the combined adjustment factors. This means that data from the current month are used in estimating seasonal and trading day factors for the current and previous months. For more information see Information paper: Introduction of Concurrent Seasonal Adjustment into the Retail Trade Series (cat. no. 8514.0).

27 The seasonal and trading day factors are reviewed annually at a more detailed level than possible in the monthly processing cycle. The annual reanalysis can result in relatively higher revisions to the seasonally adjusted series than during normal monthly processing.

28 The seasonally adjusted estimates still reflect the sampling and non-sampling errors to which the original estimates are subject. This is why it is recommended that trend series be used with the seasonally adjusted series to analyse underlying month-to-month movements.

29 The trend estimates are derived by applying a 13-term Henderson moving average to the seasonally adjusted monthly series and a 7-term Henderson moving average to the seasonally adjusted quarterly series. The Henderson moving average is symmetric, but as the end of a time series is approached, asymmetric forms of the moving average have to be applied. The asymmetric moving averages have been tailored to suit the particular characteristics of individual series and enable trend estimates for recent periods to be produced. An end-weight parameter 2.0 of the asymmetric moving average is used to produce trend estimates for the Australia, State and Australian industry group totals. For the other series a standard end-weight parameter 3.5 of the asymmetric moving average is used. Estimates of the trend will be improved at the current end of the time series as additional observations become available. This improvement is due to the application of different asymmetric moving averages for the most recent six months for monthly series and three quarters for quarterly series. As a result of the improvement, most revisions to the trend estimates will be observed in the most recent six months or three quarters.

30 Trend estimates are used to analyse the underlying behaviour of the series over time. Trend estimates from March 2020 to June 2022 are not available due to the degree of disruption and volatility caused by COVID-19. Trend estimates throughout the pandemic period are likely to be unhelpful and potentially misleading for users in interpreting underlying trend in retail activity. For further information on seasonally adjusted and trend estimates, see:

• Feature article: Use of ARIMA modelling to reduce revisions in the October 2004 issue of Australian Economic Indicators (cat. no. 1350.0)
• Information Paper: Introduction of Concurrent Seasonal Adjustment into the Retail Trade Series (cat. no. 8514.0)
• Information Paper: A Guide to Interpreting Time Series - Monitoring Trends (cat. no. 1349.0)

31 Monthly current price estimates presented in this publication reflect both price and volume changes. However, the quarterly chain volume estimates measure changes in value after the direct effects of price changes have been eliminated and hence only reflect volume changes. The chain volume measures of retail turnover appearing in this publication are annually reweighted chain Laspeyres indexes referenced to current price values in a chosen reference year. The reference year is advanced each September issue and is currently 2022-23. Each year's data in the Retail chain volume series are based on the prices of the previous year, except for the quarters of the 2024-25 financial year which will initially be based upon price data for the 2022-23 financial year. Comparability with previous years is achieved by linking (or chaining) the series together to form a continuous time series. Further information on the nature and concepts of chain volume measures is contained in the ABS publication Information Paper: Introduction of Chain Volume Measures in the Australian National Accounts (cat. no. 5248.0).

32 There are two types of error possible in estimates of retail turnover:

• Sampling error which occurs because a sample, rather than the entire population, is surveyed. One measure of the likely difference resulting from not including all establishments in the survey is given by the standard error. Sampling error may be influenced by the sample replacement that occurs in the first month of each quarter. This may increase the volatility of estimates between this month and the previous month especially at the state by industry subgroup level.
• Non sampling error which arises from inaccuracies in collecting, recording and processing the data. The most significant of these errors are: misreporting of data items; deficiencies in coverage; non-response; and processing errors. Every effort is made to minimise reporting error by the careful design of questionnaires, intensive training and supervision of interviewers, and efficient data processing procedures.

33 Seasonally adjusted and trend estimates and chain volume measures are also subject to sampling variability. For seasonally adjusted estimates, the standard errors are approximately the same as for the original estimates. For trend estimates, the standard errors are likely to be smaller. For quarterly chain volume measures, the standard errors may be up to 10% higher than those for the corresponding current price estimates because of the sampling variability contained in the prices data used to deflate the current price estimates.

34 Estimates, in original terms, are available from the Data downloads section of this issue on the ABS website. Estimates that have an estimated relative standard error (RSE) between 10% and 25% are annotated with the symbol '^'. These estimates should be used with caution as they are subject to sampling variability too high for some purposes. Estimates with a RSE between 25% and 50% are annotated with the symbol '*', indicating that the estimates should be used with caution as they are subject to sampling variability too high for most practical purposes. Estimates with a RSE greater than 50% are annotated with the symbol '**' indicating that the sampling variability causes the estimates to be considered too unreliable for general use.

35 To further assist users in assessing the reliability of estimates, key data series have been given a grading of A to B. Where:

• A represents a relative standard error on level of less than 2%. The published estimates are highly reliable for movement analysis.
• B represents a relative standard error on level between 2% and 5%, meaning the estimates are reliable for movement analysis purposes.
   

36 The tables below provide an indicator of reliability for the estimates in original terms. The reliability indicator is based on an average RSE derived over four years.

37 Standard errors for the Australian estimates (original data) for October 2024 contained in this publication are:

38 The trending process dampens the volatility in the original and seasonally adjusted estimates. However, trend estimates are subject to revisions as future observations become available.

39 The estimates of Retail turnover in this publication will differ from sales of goods and services by the Retail trade industry in Business Indicators, Australia (cat. no. 5676.0). This publication presents monthly estimates of the value of turnover of retail businesses, is sourced from the Retail Business Survey, includes the Goods and Services Tax and includes some retail trade businesses classified to a non-retail trade industry but which have significant retail trade activity. Estimates for sales of goods and services in Business Indicators, Australia are sourced from the economy wide Quarterly Business Indicators Survey and exclude the Goods and Services Tax. In addition, the Retail Business Survey 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 use of different samples in the two surveys also contributes to differences.

40 Quarterly Retail trade chain volume estimates contribute to the quarterly national accounts in two main areas. First, they are an indicator of Household Final Consumption Expenditure (HFCE) in the expenditure side of Gross domestic product. Historically, Retail Trade estimates have contributed as much as 55-60% of Household Final Consumption Expenditure. This relative contribution has reduced over time and can vary from quarter to quarter as household expenditure shifts between retail trade and areas like personal services, travel and leisure activities which are outside the scope of retail trade. Second, Retail trade estimates, along with estimates from Business Indicators, Australia, contribute to estimates for the Retail trade Division in the production side of Gross domestic product.

41 The contribution of Retail Trade estimates to Household Final Consumption Expenditure will reduce further ahead of the cessation of the Retail Business Survey and Retail Trade publication in 2025. In the December quarter 2023, Australian National Accounts (ANA) release, the first wave of transitioning to utilising new data sources was implemented with data being sourced from the same bank transaction data that is used for the Monthly Household Spending Indicator. The average Retail Trade contribution to Household Final Consumption Expenditure over the year to December 2023 was approximately 24%. For more information on the transition away from Retail Business Survey data to new data sources, please refer to Retail Business Survey Replacement: Wave 1.

42 The estimates of retail turnover per capita are compiled from the monthly Retail Business Survey and the quarterly Estimated Resident Population (ERP) published within National, state and territory population statistics (Cat. no. 3101.0). Retail turnover per capita estimates are the ratios of total quarterly retail turnover to the quarterly ERP. The methods used in deriving Retail turnover per capita estimates are consistent with those used for the derivation of GDP per capita. As quarterly ERP estimates currently lag quarterly retail trade estimates by approximately six months, the two most recent quarters of Retail per capita estimates use ERP projections based on current trend. 

43 Retail turnover per capita trend estimates are derived by applying a 7-term Henderson moving average to the seasonally adjusted per capita series. 

44 The scope, coverage and methodology for the Retail Business Survey and ERP estimates are included in the explanatory notes of the corresponding publications. Detailed discussion around the derivation methodology, ERP projection and interpretation of retail turnover per capita estimates are available as an Appendix within the Explanatory notes tab to the June 2014 release of this publication.

45 Current price estimates and chain volume measures, in original, seasonally adjusted and trend terms are available from the data downloads section of this issue on the ABS website. Revisions to the retail turnover per capita series will occur with every future revision of quarterly ERP estimates and also following any revisions to Retail Trade estimates.

46 Users may also wish to refer to the following publications:

• Australian National Accounts: National Income, Expenditure and Product (cat. no. 5206.0)
• Australian Industry (cat. no. 8155.0)
• Business Indicators, Australia (cat. no. 5676.0).
   

47 As well as the statistics included in this and related publications, the ABS may have other relevant data available. Enquiries should be made to the Customer Assistance Service via the ABS website Contact Us page.