Introduction
1 The Apparent Consumption of Selected Foodstuffs, Australia, 2022–23 (cat. no. 4316.0) is an experimental series intended to provide information on trends in the food consumption and nutritional composition of products available from the food retail sector, a major component of the overall food supply in Australia. The data is complementary to the more detailed information available from national nutrition surveys, but with the advantage of being an ongoing (annual) collection that can be produced in a relatively timely and cost-effective manner. This helps to address a critical data gap relating to food and nutrient consumption required to inform policy makers, regulators, health researchers and the food industry.
2 The 2022–23 release of the Apparent Consumption of Selected Foodstuffs (ACSF) publication is the fourth release of ACSF since collection was resumed from 2018-19. The previous historic collection of ACSF ceased in 2000 (1998–99 reference year) due to data quality concerns around fitness for purpose of previous data sources used for estimating the availability and consumption of certain foodstuffs. A review at that time found the costs and complexity of addressing the data quality concerns to be too prohibitive to resolve within available funding and competing statistical priorities.
3 The current ACSF concept makes use of aggregate transactions from scanner data (SD) that were originally sourced to improve economic measures such as the quarterly Consumer Price Index (CPI). The development of the current ACSF collection was supported and funded by the Department of Health and Aged Care to help inform Australian food nutrition policy and research. The use of sales data to measure apparent consumption is a key difference from the previous ABS Apparent Consumption of Foodstuffs collection which used the conventional method based on national production plus imports minus exports.
4 The apparent consumption of selected foodstuff (ACSF) estimates in this publication comprise two components:
- Directly calculated measures of food quantities available for consumption from aggregated sales data provided by the major retailers using SD, and
- Indirectly estimated measures of the food quantities available for consumption that are not captured by the major retailers in the SD, which are based on household expenditure data.
Data sources
Scanner data
5 The primary data source for this publication is the aggregated scanner data (SD) provided to the ABS from major supermarkets. The major supermarkets provide aggregated data based on information compiled from barcode scanning at the point of sale. The aggregate information comprises the Stock Keeping Unit (SKU) of the sales item, a description of the item, the geographical region of sale and weekly aggregate amounts of the quantity and value of the sales. The major supermarkets that provide data to the ABS account for an estimated 82% of the food retail sector.
2015–16 Household Expenditure Survey
6 The Household Expenditure Survey (HES) was undertaken every six years until 2015-16 (the most recent HES). ACSF uses the expenditure patterns in the 2015–16 HES to estimate the ratio of expenditure in stores not represented by SD to expenditure from the supermarkets in scope of SD (for a given product category in a given geographical area). Although no comprehensive adjustment (by product type and geographic area) is made to the 2015–16 HES data to align it to the ACSF reference periods, the expenditure ratios for each reference period are calibrated to reflect the changes in the proportion of specialised food retail (from total food retail). The proportions (and adjustment factors) are based on annual estimates of retail turnover by industry sub-group from Retail Trade. Further information on the use of the HES for adjustment is provided in the 'Estimation' section below.
Scope
7 The scope for this publication is all food and non-alcoholic beverages purchased for consumption in Australia from the food retail sector. This includes all food and non-alcoholic products assigned to the food and non-alcoholic beverage expenditure classes (ECs) used to compile the CPI with the exception of Restaurant meals and Take-away and fast foods. For the complete list of ECs, see the Annual weight update of the CPI and Living Cost Indexes, Appendix 1.
8 The 2015–16 HES collects expenditure from households representing about 97% of people in Australia. In expenditure terms, the 2015–16 HES estimated an average weekly expenditure of $237 per household on Food and non-alcoholic beverages. Of this, 34% ($80) was estimated to be on Meals out and fast foods, leaving 66% ($157 in 2015–16) of total food expenditure as in scope in the ACSF.
9 All in scope foods are coded to the food classification system in the AUSNUT 2011–13 database which was developed for the 2011–13 Australian Health Survey. Assignment of AUSNUT codes provides the ACSF with an established food classification and enables SD and HES data to be assigned to useful major food groups and subgroups for analysis. In addition, AUSNUT 2011–13 contains comprehensive nutrient data for each food and non-alcoholic beverage product and links directly to other established metadata including information about the amounts (serves or grams) of Australian Dietary Guidelines (ADG) Food Groups. This alignment means that conceptually the data can be used for a similar high-level food and nutrient analyses (i.e. population averages) as may be done from a national nutrition survey. There are however important differences which should be considered due to the differing nature of foods as purchased (scanner data) and food as eaten (nutrition surveys).
10 It should be noted that estimates of 'apparent consumption' of food and non-alcoholic beverage products are derived using information related to sales (that is, food available for consumption from purchases made from the food retail sector). Therefore, they are at best a partial representation of the apparent consumption due to scope limitations (see ‘Out of scope’ below). Furthermore, ‘apparent consumption’ methodologies typically don’t adjust for waste (see below) or changes in household inventories i.e. all food and non-alcoholic beverages available for apparent consumption (purchased) in a particular year are assumed to have been consumed in that year.
Out of scope
11 While SD (when used with the HES) can represent the majority of the food available for consumption in Australia, there are clear gaps where either SD sales or 2015–16 HES do not adequately capture representative food and non-alcoholic beverage amounts. These are:
- Fast foods, cafe and restaurant meals
- Meals provided by institutions that source food from the non-retail sector
- Home grown or produced food
- Wild foods obtained by gathering/foraging, hunting or fishing.
12 The exclusions in the HES sample comprise of:
- Very Remote areas
- Residents of non-private dwellings (e.g. hotels, boarding schools, nursing homes and prisons)
- Households containing members of non-Australian defence forces or diplomatic personnel of overseas governments stationed in Australia.
The exclusions associated with the HES survey should have minimal impact on the indirect estimated measures compiled to represent the food quantities available for consumption from the non-major supermarkets not captured in the SD provided to the ABS.
Alcohol
13 Comprehensive alcohol data are not currently available from scanner data. Although the Apparent Consumption of Alcohol (ACA) collection provides national annual estimates for beer, wine and spirits, the ACA data are not incorporated in the ACSF because of differences in scope and coverage. The alcohol estimates from the ACA represented all alcohol available for consumption in Australia, while the ACSF is limited to just the foods available for consumption in the Food retail sector (less Restaurant meals and Take-away and fast foods). Therefore, including ACA estimates in the ACSF would present an over-estimate of the quantity of alcoholic beverages relative to foods and non-alcoholic beverages, and distort measurement of alcohol's relative contribution to the total dietary energy available for consumption.
Waste
14 The estimates presented in this publication are not adjusted for possible product wastage. The most recent estimate of total organic food wastage in Australia was around 3.1 million tonnes for 2018–19. This is the equivalent of around 340 grams per person per day (or 2.4 kg per week). While it may be expected that all perishable goods will be susceptible to a degree of waste, the waste estimates cannot provide further breakdowns, for instance how much is vegetables and fruits, and how much is baked goods, dairy produce, various meat products etc. Given most discretionary foods are packaged (and have a longer shelf life) they may be less likely to be disposed of than fresh produce. Such an imbalance in waste suggests greater care should be taken in interpreting the apparent consumption of non-discretionary foods and therefore the relative balance between discretionary and non-discretionary foods. In addition different fresh foods may have different edible proportions (e.g. grapes have a higher edible proportion than pineapple) and no adjustment has been made to subtract the inedible proportion which may lead to further over-estimation of the amount of certain fresh products and nutrients available for consumption.
Geographical coverage
15 The SD provides aggregate data representing all regions of Australia serviced by major supermarket chains. Estimates in this publication are presented at the national level. While state and sub-state estimates are feasible, they are not included in this release.
Reference period
16 The current release is the fourth edition in the new publication series, providing data for the five years from July 2018 to June 2023. The estimates referring to periods prior to July 2022 which were previously published have been revised to incorporate subsequent improvements in coding coverage and to ensure that all periods are on the same basis as the 2022-23 estimates.
Methods
AUSNUT coding
17 The process of AUSNUT coding involves assigning each food item SKU to an AUSNUT code and a size (weight or volume) based on the product description. AUSNUT coding has been performed on around 95% of the total value of SD foods in each year, equivalent to over 120,000 unique SKUs.
18 As noted above, the AUSNUT 2011–13 classification was designed to code foods as consumed, whereas many foods in the SD require preparation. Although the great variety and detail of AUSNUT focuses on foods in their prepared forms, AUSNUT also includes codes and nutrient data for the unprepared varieties (e.g. uncooked pasta, flour, jelly crystals, uncooked cuts of meat) and these were applied as appropriate. The general limitation of coding is the availability of specific and accurate codes to handle the variety of unprepared foods and the changing nature of the food supply. For example, where manufacturers have reformulated their products to reduce sugar or sodium content, the available AUSNUT code used may not reflect the most up-to-date nutrient profile.
Coding weighting adjustment
19 Weighting has been applied to the coded data to account for the value and quantity of food items that were unable to be coded. The weights are based on the inverse of the proportion of coded amount from the total amount. The weights are calibrated and applied by the four dimensions of: EC, sales week, Greater Capital City Statistical Area (GCCSA) and supermarket chain level. This level of detail ensures the weighting is as targeted and reliable as possible with the available information. One implication of this weighting method is that each food item within an EC/sales week/GCCSA/retailer will have the same weight, even though at a finer product category level there may be differences in coding coverage.
20 Therefore, the principles and assumptions in this adjustment are:
- The adjustment is important to overcome the bias that would result without it (because coding ratios differ over time and vary between product groups and retailers)
- Adjustments are relatively minor because coding rates are relatively high within each product group (>95% of value) and therefore minimising the introduction of new bias through the adjustment
- The coded food products (numerator of coding rate) for each EC is representative of all the foods (denominator) within that EC
- The selection of products to code within an EC is systematic and conducted according to the ordered value of products (from higher to lower).
Estimation
21 As previously mentioned, there are two components to the estimates of apparent consumption of foodstuffs presented in this publication. The components are:
- Directly estimated component. This is simply the quantity of each item multiplied by the size (grams or mL) of the item in the SD.
- Indirectly estimated component. This uses 2015–16 HES data to derive an expenditure ratio of food items from the non-major supermarkets and other food retail outlets to the major supermarkets to estimate the food quantities available for consumption that are not captured in the SD. The expenditure ratios are then applied to a grams per dollar value to derive the estimated quantity (weight or volume) of that food or non-alcoholic beverage available for consumption from the non-major supermarkets and other food retail outlets.
22 The adjustment for the indirectly estimated component may be summarised as a five-step process:
- Map 2015–16 HES food and non-alcoholic beverage expenditure codes to AUSNUT 2011–13 codes.
- Estimate the Food retail sector expenditure ratio from non-major supermarkets and other food retail outlets to the major supermarkets by each food group by region from the data reported in the 2015–16 HES. This step provides the expenditure ratio for a defined food group for each GCCSA.
This is expressed below where i = a given food group in a given GCCSA geographical area, MS represents major supermarkets, NMS represents the food retailers from non-major supermarkets and other food retail outlets and Exp() represents the respective expenditure amounts.
\(\large{Coverage \ weight _{i}=\frac{\sum w_{i} \times E x p_{i}(N M S)}{\sum w_{i} \times E x p_{i}(M S)}}\)
3. The expenditure ratios for each ACSF year are then calibrated to the changes in the proportion of specialised food retail within total food retail since 2015-16. This calibration uses Retail Trade estimates of turnover published for industry sub-groups.
4. Derive amounts (grams or mL) per dollar for each product based on the known prices per product size in the SD.
5. Apply the amounts per dollar (from step 4) to the calibrated expenditure ratios (from step 3) and multiply by the observed sales values in the SD to derive the imputed amounts in grams or mL of foods and non-alcoholic beverages available for consumption from non-major supermarkets and other food retail outlets.
Limitations of method
23 Three main limitations have been identified which will impact the validity of the expenditure ratios. These are:
- Loss of fine product detail in mapping of HES expenditure codes (HEC) to AUSNUT codes
- Representation of identified stores in the subset of HES data available to estimate proportion of expenditure from the major and non-major supermarkets and other food retail outlets (and that it represents a period seven years prior to the current ACSF reference period)
- Estimation of the amount (grams or mL) per dollar of food represented in the SD.
Mapping of HES expenditure codes to AUSNUT codes
24 The aim of this step is to define common groups of products that represent broad types of food and non-alcoholic beverages groups in terms of their propensity to be bought from the major supermarkets or other retail outlets.
25 Alignment of 2015–16 HES food expenditures with the SD was performed by creating a correspondence which maps foods and non-alcoholic beverages from both the HES (124 relevant codes) and AUSNUT (5,740 codes) to common food groups. After collapsing a number of HES codes which were not similarly split in AUSNUT, each classification was mapped to a common set of 63 defined food groups.
Representation of identified stores in the subset of HES
26 The HES was used to provide estimates of the expenditure ratios (by food group and geography) of the in-scope supermarkets. However, the receipt information identifying the store at which food purchases were made in the 2015–16 HES was not an essential survey input data item, and only captured incidentally (where the respondent retained the receipt to assist in recording expenditures). Although a receipt identifying the store was provided for almost three-quarters (74%) of relevant food and non-alcoholic beverages expenditure, this subset is likely to over represent the transactions where the respondent purchased multiple grocery items. This led to a moderate correlation between a food item having a receipt and that food being purchased at one of the major supermarkets in scope of the SD. With no further adjustment made, this association may indicate some bias favouring expenditures from the major supermarkets. Such a bias may tend to underestimate the expenditure ratios for foods coming from specialty shops (e.g. butchers, bakeries, fresh food markets or seafood shops).
The calibration process for each year’s weight (described in step 3 above) also has a number of limitations including:
- The estimates of specialised food retail are not broken down for type of products. In other words, this adjustment cannot account for different rates of change by different food specialisations (e.g. butcher shops, delicatessens and fruit and vegetable stores are all assumed to increase/decrease at the overall rate)
- Retail Trade estimates are based on sampled units which results in sampling error (i.e. the difference between results from the sample compared to if the entire population was surveyed). However, given the monthly food retailing estimates have relative standard errors of less than 2% and the ratios used for calibration are 12 month aggregations, the sampling error is expected to be a negligible component.
Deriving a gram or mL amount of food or beverage per dollar
27 Translating the expenditure ratios to amounts (grams or mL) of food per dollar was performed by:
- Deriving a median amount (grams or mL) per dollar at the AUSNUT code level, based on all contributing SKUs to each AUSNUT code
- Applying that gram or mL per dollar amount to the expenditure ratio to estimate the imputed amount of each food. In practice, foods being imputed are aggregates of AUSNUT coded foods, so the imputation uses weighted aggregates of the applicable AUSNUT code medians.
28 The major limitation of this method is the assumption that amounts per dollar from major supermarkets represent the amounts for other retailers (e.g. convenience stores and specialty stores). It is more likely that for many products the amounts of food or non-alcoholic beverage available for consumption per dollar expended are higher at the major supermarkets on equivalent items.
29 By using the median, it assumes the midpoint value is representative of the aggregate amount (grams or mL) per dollar across all products in the defined food group. Analysis of the SD shows some amounts (grams or mL) per dollar distributions are bi-modal, with product items concentrated around small sized products (lower grams or mL per dollar) and bulk products (higher grams or mL per dollar). Therefore, if the expenditure in non-major supermarkets and other food retail outlets includes less of the bulk packages then the imputed amount of food or non-alcoholic beverage (grams or mL) per dollar may be an overestimate.
Per capita estimates
30 Daily per capita estimates are derived by dividing the annual total by the number of days in the reference year and dividing by the Estimated Resident Population (ERP). Data representing each financial year uses the ERP for December. For example, the 2022-23 estimates use ERP for December 2022. No adjustments have been made to the ERP for foods and non-alcoholic beverages that are typically consumed by selected age groups only, such as baby food.
Measuring Australian Dietary Guidelines food groups
31 Because many food products are mixtures of food groups, measuring the total apparent consumption amounts of each of the food groups utilises an Australian Dietary Guidelines (ADG) database which measures the amounts of each of the ADG food groups in each AUSNUT food. The ADG database used was originally developed by Food Standards Australia New Zealand for the 2011–12 NNPAS. For more information, see Assessing the 2011–13 AHS against the Australian Dietary Guidelines - Classification System and Database Development Explanatory notes.
Revision of estimates
32 Estimates in this publication for years prior to 2021-22 have been revised from the previous releases. Any changes in the way sales items were treated have been implemented across all time periods to ensure comparability. The revisions may be categorised as:
- Revisions to AUSNUT coding
- Improvements in linkage between product information and the sales data
- Cleaning of input sales data to remove or adjust outliers.
33 The revisions to AUSNUT coding are enhancements to the database used to map food products to AUSNUT classification and include:
- changes to the weight/volume of products previously coded
- changes to the specific AUSNUT code assigned to products previously coded
- coding of additional food and beverage products that were not previously coded (but were sold previously)
- imputation of nutrient lines for particular foods to improve nutrient accuracy.
34 There were two important changes in the linkage of products to sales data in this edition which results in some changes from previously published estimates. These were:
- Inclusion of a number of high value SKUs which were not used in the previous edition due to difficulties identifying the product (i.e. previously underestimating).
- Removal of product codes which at different time through the 2018-19 to 2022-23 period were found to represent more than one unique product. Removing these codes was equivalent to removing around 0.2% of the sales value overall, but relatively higher (~0.4%) among the ECs of snacks and confectionary and soft drinks.
35 Scanner data can occasionally include systems abnormalities or even duplication in sales which may lead to unusually large sales figures. Where possible the ABS has applied standard statistical techniques such as windsorisation to identify and treat specific outliers.