This bulletin presents estimates of the quantity bought (volume) and amount spent (value) in the retail industry for the period 30 June 2013 to 27 July 2013. Unless otherwise stated, the estimates in this release are seasonally adjusted.
Users are reminded that the figures contained within this release are estimates based on a monthly survey of 5,000 retailers, including all large retailers employing 100 people or more. The timeliness of these retail sales estimates, which are published just two weeks after the end of each month, makes them an important early economic indicator. The industry as a whole is used as an indicator of how the wider economy is performing and the strength of consumer spending.
|Most recent month on a year earlier||Most recent 3 months on a year earlier||Most recent month on previous month||Most recent 3 months on previous 3 months|
|Amount spent (Value)||4.9||3.8||1.4||2.1|
|Quantity bought (Volume)||3.0||2.2||1.1||1.8|
|Value excluding automotive fuel||4.9||4.0||1.2||2.3|
|Volume excluding automotive fuel||3.1||2.3||1.1||1.7|
In July 2013, the quantity of goods bought in the retail industry (volume) increased by 3.0% compared with July 2012. The amount spent (value) increased by 4.9% over the same period. Since July 2012, non-seasonally adjusted data show that the prices of goods sold in the retail industry (as measured by the implied price deflator) increased by 1.8%. More information on how the implied price deflator is calculated can be found in the background notes.
To enable a comparison of change, figure 1 shows the quantity of goods bought in the retail industry (all retailing sales volumes) and the amount spent (all retailing sales values) as indices referenced to 2010.
Both the volume and the value of retail sales grew steadily between January 2004 and January 2008. The volume of retail sales grew by 10.6%, while the value of retail sales increased by 13.8%. The difference between these two figures was due to price increases, with the Consumer Prices Index (CPI) increasing by 8.1% over the four years. Average weekly earnings (excluding bonuses) increased by 14.6% over the same period, implying that real household earnings were increasing.
Between January 2008 and July 2013 (the most recent month for which data are available), the volume of retail sales grew by just 4.0%, with the majority of the growth coming from the latest 6 months. At the same time, the value of retail sales rose by 17.2%, CPI increased by 19.2% and average weekly earnings (excluding bonuses) increased by 10.6% (although these data are only available up to June 2013). This highlights the extent to which prices have grown since the onset of the economic downturn as well as the squeeze felt on households’ real income.
The retail industry is divided into four retail sectors:
Predominantly food stores (supermarkets, specialist food stores, and sales of alcoholic drinks and tobacco);
Predominantly non-food stores (non-specialised stores, textiles, clothing & footwear, household goods, and other stores);
Non-store retailing (e.g. mail order, catalogues and market stalls);
Stores selling automotive fuel (petrol stations).
In July 2013, for every pound spent in the retail industry:
42 pence was spent in food stores;
41 pence in non-food stores;
6 pence in non-store retailing and
11 pence in stores selling automotive fuel.
Using these as weights, along with the year-on-year growth rates, we can calculate how each sector contributed to the total year-on-year growth in the quantity bought.
Figures 2 and 3 show the contribution of each sector to the quantity bought (volume) and amount spent (value) in retail between July 2012 and July 2013.
In July 2013, all the main sectors: food stores; non-food stores; non-store retailing and petrol stations, contributed to the increase in quantity bought (volume).
Sales within the food and non-store retailing sectors provided the largest contribution to the increase in quantity bought (volume) in the retail industry in July 2013.
In the non-food sector, sales within department stores increased by 3.1% in July 2013 compared with July 2012. Over the same period, sales within textiles, clothing & footwear increased by 1.4%.
In July 2013, all the main sectors: food stores; non-food stores; non-store retailing and petrol stations, contributed to the increase in amount spent (value). The main contribution came from the food sector.
In July 2013 the quantity bought in food stores increased by 2.1% compared with July 2012. This is the highest growth since April 2011. Over the same period the amount spent increased by 5.7%. This is the highest growth in the amount spent since September 2011. Even though the prices of goods sold in this sector increased by 3.4% compared with July 2012, feedback from supermarkets suggested that the sunny weather boosted sales across a range of products, including food, alcohol, clothing and outdoor items.
Figure 4 shows average weekly spending within the food sector over the last 6 years (seasonally adjusted). This indicates that average weekly spend within food stores (supermarkets) has increased steadily over the last 6 years, and has reached the highest value on record in July 2013.
In the July 2013 four week reporting period, the amount spent in the retail industry was £27.9 billion (non-seasonally adjusted). This compares with £34.2 billion in the June 2013 five week reporting period and £26.6 billion in the July 2012 four week reporting period.
This equates to an average weekly spend of £7.0 billion in July 2013, £6.8 billion in June 2013 and £6.7 billion in July 2012.
Average weekly spending online (internet sales values non-seasonally adjusted) in July 2013 was £586.6 million. This was an increase of 10.7% compared with July 2012.
The amount spent online accounted for 9.5% of all retail spending excluding automotive fuel.
As expected, more was spent online in the non-store retailing sector than any other sector. Spending online now accounts for 63.7% of total spending in this sector. In the food sector 3.2% of spending was online. This sector has the lowest proportion of online spending in relation to all spending.
Internet sales estimate how much was spent online through retailers across all store types in Great Britain. Figures are non-seasonally adjusted and the reference year is 2010=100. Table 2 shows the year-on-year growth rates for total internet sales by sector and the proportion of sales that each sector makes to total internet sales.
|Category||Year on year growth % (Value NSA)||Proportion of total sales made online|
|Textile, clothing & footwear stores||8.0||9.5|
|Household goods stores||-5.6||5.3|
|Percentage change over 12 months||Average weekly sales (£ billion)|
|Quantity bought (volume)||Amount spent (value)||Average store price|
|Predominantly food stores¹||2.1||5.7||3.4||2.6|
|Predominantly non-food stores²||2.0||2.3||0.4||2.8|
|Textiles, clothing & footwear stores||1.4||4.0||2.5||0.8|
|Household goods stores||-2.3||-2.8||-0.5||0.6|
Table 4 illustrates the mix of experiences among different sized retailers. It shows the distribution of reported change in sales values of businesses in the RSI sample, ranked by size of business (based on number of employees). This table shows, for example, that the largest retailers, with 100 or more employees, reported an average increase in sales values of 5.0% between July 2012 and July 2013. In contrast smaller retailers employing 10 to 39 employees reported an average increase in sales of 6.1%.
|Number of employees||Weights (%)||Growth since July 2012 (%)|
The reference table, Business Analysis (18.5 Kb Excel sheet) , shows the extent to which individual businesses reported actual changes in their sales between July 2012 and July 2013. The table contains information only from businesses that reported in July 2012 and July 2013. Cells with values less than 10 are suppressed for some classification categories; this is denoted by n.a. Note that ‘large’ businesses are defined as those with 100+ employees and 10–99 employees with annual turnover of more than £60 million, while ‘small and medium’ is defined as 0–99 employees.
Improvements to be introduced next month
The results from the annual seasonal adjustment review have been implemented in this month’s release.
Understanding the data
Interpreting the data
The Retail Sales Index (RSI) is derived from a monthly survey of 5,000 businesses in Great Britain. The sample represents the whole retail sector and includes the 900 largest retailers and a representative panel of smaller businesses. Collectively all of these businesses cover approximately 90 per cent of the retail industry in terms of turnover.
The RSI covers sales only from businesses classified as retailers according to the Standard Industrial Classification 2007 (SIC 2007), an internationally consistent classification of industries. The retail industry is division 47 of the SIC 2007 and retailing is defined as the sale of goods to the general public for household consumption. Consequently, the RSI includes all Internet businesses whose primary function is retailing and also covers Internet sales by other British retailers, such as online sales by supermarkets, department stores and catalogue companies. The RSI does not cover household spending on services bought from the retail industry as it is designed to only cover goods. Respondents are asked to separate out the non-goods elements of their sales, for example income from cafeterias. Consequently, online sales of services by retailers, such as car insurance, would also be excluded.
The monthly survey collects two figures from each sampled business: the total turnover for retail sales for the standard trading period, and a separate figure for sales made over the Internet. The total turnover will include Internet sales. The separation of the Internet sales figure allows an estimate relating to Internet sales to be calculated separately.
Definitions and explanations
The value or current price series records the growth since the base period (currently 2010) of the value of sales ‘through the till’ before any adjustment for the effects of price changes.
The volume or constant price series are constructed by removing the effect of price changes from the value series. The Consumer Prices Index (CPI) is the main source of the information required on price changes. In brief, a deflator for each type of store (5-digit SIC) is derived by weighting together the CPIs for the appropriate commodities, the weights being based on the pattern of sales in the base year. These deflators are then applied to the value data to produce volume series.
The implied deflator or the estimated price of goods is derived by dividing the non-seasonally adjusted value and volume data to leave a price relative. In general, this implied price deflator should be quite close to the retail component of the CPI. More information on the implied price deflator can be found in the Quick Guide to Retail Sales (116.9 Kb Pdf) .
Use of the data
The value and volume measures of retail sales estimates are widely used in private and public sector organisations both domestically and internationally. For example, private sector institutions such as investment banks, the retail industry itself and retail groups use the data to inform decisions on the current economic performance of the retail industry. These organisations are most interested in a long term view of the retail sector that can be obtained from year-on-year growth rates. Public sector institutions use the data to assist in informed decision and policy making and tend to be most interested in a snapshot view of the retail industry, which is taken from the month-on-month growth rates.
Information on retail sales methodology is available in Retail Sales Methodology and Articles.
1. Composition of the data
Estimates in this statistical bulletin are based on financial data collected through the monthly Retail Sales Inquiry. The response rates for the current month reflect the response rates at the time of publication. Late returns for the previous month’s data are included in the results each month. Response rates for historical periods are updated to reflect the current level of response at the time of this publication.
|Overall response rates (%)|
2. Seasonal adjustment
Seasonally adjusted estimates are derived by estimating and removing calendar effects (for example Easter moving between March and May) and seasonal effects (for example increased spending in December as a result of Christmas) from the non-seasonally adjusted (NSA) estimates. Seasonal adjustment is performed each month, and reviewed each year, using the standard, widely used software, X-12-ARIMA. Before adjusting for seasonality, prior adjustments are made for calendar effects (where statistically significant), such as returns that do not comply with the standard trading period (see section Methods, Calendar effects), bank holidays, Easter and the day of the week on which Christmas occurs.
The data collected from the retail sales survey estimate the amount of money taken through the tills of retailers; these are non-seasonally adjusted data. These data consist of three components:
trend which describes long-term or underlying movements within the data;
seasonal which describes regular variation around the trend, that is peaks and troughs within the time series, the most obvious in this case being the peak in December and the fall in January;
irregular or ‘noise’, for example deeper falls within the non-seasonally adjusted series due to harsh weather impacting on retail sales.
To ease interpretation of the underlying movements in the data, the seasonal adjustment process estimates and removes the seasonal component to leave a seasonally adjusted time series consisting of the trend and irregular components.
In the non-seasonally adjusted retail sales index we see large rises in December each year and a fall in the following January, but these are not evident in the seasonally adjusted index. This peak in December is larger than the subsequent fall but the trend and irregular components in both months are likely to be similar, meaning that the movements in the unadjusted series are almost completely as a result of the seasonal pattern.
3. Calendar effects
The calculation of the RSI has an adjustment to compensate for calendar effects that arise from the differences in the reporting periods. The reporting period for July 2013 was 30 June 2013 to 27 July 2013, compared with 1 July 2012 to 28 July 2012 the previous year. Table 6 shows the differences between the calendar and seasonally adjusted estimates.
|Year on year percentage change|
1. Basic quality information
The standard reporting periods can change over time due to the movement of the calendar. Every five or six years the standard reporting periods are brought back into line by adding an extra week. For example, January is typically a four-week standard period but January 1986, 1991, 1996, 2002 and 2008 were all five-week standard periods. The non-seasonally adjusted estimates will still contain calendar effects. If the non-seasonally adjusted estimates are used for analysis this can lead to a distortion depending on the timing of the standard reporting period in relation to the calendar, previous reporting periods and how trading activity changes over time.
The non-seasonally adjusted series contain elements relating to the impact of the standard reporting period, moving seasonality and trading day activity. When making comparisons it is recommended that users focus on the seasonally adjusted estimates as these have the systematic calendar related component removed. Due to the volatility of the monthly data, it is recommended that growth rates are calculated using an average of the latest three months of the seasonally adjusted estimates.
When interpreting the data, consideration should be given to the relative weighted contributions of the sectors within the all retailing series. Based on SIC 2007 data, total retail sales consists of: predominantly food stores 41.5%, predominantly non-food stores 41.3%, non-store retailing 5.7% and automotive fuel 11.5%.
2. Standard errors
The standard error of an index movement is a measure of the spread of possible estimates of that movement likely to be obtained when taking a range of different samples of retail companies of the same size. This provides a means of assessing the accuracy of the estimate: the lower the standard error, the more confident one can be that the estimate is close to the true value for the retail population. An approximate 95% confidence interval for the index movement is roughly twice the standard error. The paper ‘Measuring the accuracy of the Retail Sales Index’ (1.04 Mb Pdf) , written by Winton, J and Ralph, J (2011) reports on the calculation of standard errors for month-on-month and year-on-year growth rates in the RSI as well as providing an overview of standard errors and how they can be interpreted.
The standard error for year-on-year growth in all retail sales volumes is 0.7%. This means that the year-on-year growth rate for all retail sales volumes falls within the range 0.3 ± 1.4% with a probability of 95%.
The standard error for month-on-month growth in all retail sales volumes is 0.4%. This means that the month-on-month growth rate for all retail sales volumes falls within the confidence interval -0.1 ± 0.8 with a probability of 95%.
3. Summary quality report
A Summary Quality Report (114 Kb Pdf) for the RSI.
This report describes, in detail the intended uses of the statistics presented in this publication, their general quality and the methods used to produce them.
4. Revisions triangles
Revisions to data provide one indication of the reliability of key indicators. The table below shows summary information on the size and direction of the revisions made to the volume data covering a five-year period. Note that changes in definition and classification mean that the revision analysis is not conceptually the same over time. A statistical test has been applied which has shown that the average revision in month-to-month statistics are not statistically different from zero.
A spreadsheet giving these estimates and the calculations behind the averages in the table is available on the ONS website.
|Revisions between first publication and estimates twelve months later (percentage points)|
|Growth in latest period (per cent)||Average over the last five years (mean revision)||Average over the last five years without regard to sign (average absolute revision)|
|Latest three months compared with previous three months||1.8||-0.27||0.36|
|Latest month compared with previous month||1.1||-0.13||0.40|
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The complete run of data in the tables of this statistical bulletin is available to view and download in electronic format using the ONS Time Series Data service. Users can download the complete bulletin in a choice of zipped formats, or view and download their own sections of individual series. The Time Series Data are available.
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Next publication: Thursday 19 September 2013
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