|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)||-||0.6||-0.4||-0.6|
|Quantity bought (Volume)||-0.6||0.1||-0.6||-0.8|
|Value excluding automotive fuel||1.2||1.7||-0.1||-0.2|
|Volume excluding automotive fuel||0.2||0.9||-0.5||-0.6|
This bulletin presents estimates of the amount spent and quantity bought in the retail sector. The statistics are based on a monthly survey of 5,000 retailers, including all large retailers employing 100 people or more. The timeliness of these retail sales statistics, which are published just two weeks after the end of each month, makes them an important early economic indicator. The sector as a whole is used as an indicator of how the wider economy is fairing and the strength of consumer spending.
In January 2013, the quantity of goods bought in the retail sector (volume) decreased by 0.6% compared with January 2012. The amount spent (value) was unchanged over the same period. Since January 2012, the prices of goods sold in the retail sector (as measured by the implied price deflator) increased by 0.8%.
To enable a comparison of change, Figure 1 shows the average weekly amount spent in the retail sector (all retailing sales values) in pounds million. Through the time period shown, the amount spent has grown steadily, however, in more recent months growth has slowed and in January 2013 is unchanged when compared with January 2012.
By removing the effects of price changes from these data, estimates of the quantity bought in the retail sector as shown in Figure 2 can be calculated. The growth in the quantity bought increases steadily towards the end of 2008 where the underlying pattern, although one of growth, slows. In September 2012, this underlying pattern changes to one of contraction and in January 2013, the quantity of goods bought was 0.6% lower compared with January 2012.
The retail industry is divided into four retail sectors; predominantly food stores, predominantly non-food stores, non-store retailing and stores selling automotive fuel. In January 2013, for every pound spent in the retail sector 41 pence was spent in food stores, 42 pence in non-food stores, 5 pence in non-store retailing and 12 pence was spent on 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 3 and 4, show the contribution that each of these sectors has had to the amount spent and quantity bought in retail over the period January 2012 to January 2013.
Sales at petrol stations provided the main source of downwards pressure to the amount spent in the retail sector, there were also falls in the household goods and other stores sub sector of non-food stores. When sales at petrol stations are excluded the amount spent increased by 1.2%.
The main contributor to the fall in the quantity bought in January 2013 compared with January 2012 was the food sector, where the quantity bought was estimated to have fallen by 2.6%. Figure 5, shows the quantity bought in the food sector in £millions.
Looking now at non-seasonally adjusted data and splitting the food sector into small and large retailers, where large is defined as those stores with 100 or more employees, small stores saw a fall in the quantity bought of 14.9% but large stores saw an increase of 0.3%.
This sector covers supermarkets, specialist food stores such as butchers, bakers and fishmongers, off licenses and tobacconists. Feedback from small food retailers suggest that sales were affected by the heavy snow fall across most of the country during the last week of this trading period. In comparison feedback from large food stores, that is supermarkets, suggests that sales were boosted by consumers using the internet to purchase groceries. This is supported by the proportion of sales made online by supermarkets in January 2013, which rose by 27.1% compared with January 2012 to stand at 3.7% of all food sector sales.
In the January 2013 four week reporting period, the amount spent in the retail sector was £24 billion (non-seasonally adjusted). This compares with £43 billion in the December 2012 five week reporting period and £24 billion in the January 2012 four week reporting period.
This equates to an average weekly spend of £6.1 billion in January 2013, £8.5 billion in December 2012 and £6.1 billion in January 2012.
The average weekly spend online (Internet sales values non-seasonally adjusted) in January 2013 was estimated at £546.5 million. This is an increase of 8.7% compared with January 2012.
The amount spent online accounted for 10.1% 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 68.1% of total spending in this sector, up from 67.2% in January 2012. In the food sector 3.7% of spending was online, up from 3.0% a year earlier. This sector has the lowest proportion of online spend in relation to all spending but stands at record levels.
Internet sales measure how much was spent online through retailers 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 contribution that each sector makes to total Internet sales.
|Category||Weight||Year on year growth %||Contribution to year on year growth % points|
|Textile, clothing and footwear stores||11.7||8.7||1.2|
|Household goods stores||8.2||-23.5||-1.7|
Predominantly food stores in January 2013, saw an increase in the amount spent (0.9%), compared with January 2012. The quantity bought decreased by 2.6% over the same period as prices of goods sold increased by 3.7%. However, even though overall sales for this sector were down, non-seasonally adjusted data show that online sales in these stores were up 27.1% in January 2013 compared with January 2012.
In January 2013, average weekly sales in this sector were £2.6 billion, which was more than any other sector. Also in this sector, 3.7% of sales, £96.2 million, were made via the Internet. This was the largest proportion of online sales made in the food sector on record.
Predominantly non-food stores in January 2013, saw an increase in the quantity of goods bought (1.7%) and in the amount spent (0.8%) when compared with January 2012. The prices of goods sold decreased by 0.9% in the year to January 2013.
In January 2013, average weekly sales were £2.5 billion. In this sector 8.3% of sales were made via the Internet, resulting in £203.3 million of online spending.
Non-specialised stores, or department stores, provided an upwards contribution to the quantity bought in all retailing with an increase of 8.8% in January 2013 compared with January 2012. The amount spent increased by 6.5% in January 2013 compared with January 2012. The prices of goods sold in this sector continued to decrease, falling 2.2% in the year to January 2013.
In January 2013, average weekly sales were £0.5 billion. In this sector, 9.6% of sales (£46.3 million) were made via the Internet.
The Textile, clothing and footwear stores sector also provided an upwards contribution to the quantity bought in all retailing with an increase of 1.2% in January 2013 compared with January 2012. The amount spent increased year-on-year by 1.9% and the prices of goods increased by 0.7% in the year to January 2013.
In January 2013, average weekly sales were £0.7 billion. In this sector, 10.6% of sales (£75.4 million) were made via the Internet.
Household goods stores in January 2013, saw a decrease in the quantity bought (0.8%) and the amount spent (0.5%) compared with January 2012. The prices of goods sold increased by 0.1% in the year to January 2013.
In January 2013 average weekly sales were £0.6 billion. In this sector 5.2% of sales (£29.6 million) were made via the Internet.
Other stores in January 2013, saw a decrease in the quantity bought (0.7%) and in the amount spent (2.9%) compared with January 2012. The prices of goods sold in this sector continued to decrease, falling 2.5% in the year to January 2013.
In January 2013 average weekly sales were £0.7 billion. In this sector 7.5% of sales (£52.0 million) were made via the Internet.
The Non-store retailing sector in January 2013 saw a rise in the quantity bought, (8.6%), and in the amount spent,(7.4%) compared with January 2012. The prices of goods sold fell by 1.1% in the year to January 2013.
In January 2013 average weekly sales were £0.4 billion. In this sector 68.1% of sales, £246.9 million, were made via the Internet.
The non-store retailing sector comprises of stalls and markets, mail order and those retailers that sell mainly online.
Predominantly automotive fuel stores saw a year-on-year fall in both the quantity bought, (8.0%), and the amount spent, (8.8%). The prices of goods sold in this sector decreased by 0.2% in the year to January 2013.
In January 2013 average weekly sales were £0.7 billion.
Table 3 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 1.6% between January 2012 and January 2013. In contrast smaller retailers employing 0 to 9 employees reported an average decrease in sales of -19.6%.
|Number of employees||Weights (%)||Growth since January 2012 (%)|
The reference table,
Business Analysis (30.5 Kb Excel sheet)
shows the extent to which individual businesses reported actual changes in their sales between January 2012 and January 2013. The table contains information only from businesses that reported in January 2012 and January 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
Not applicable this month.
Following consultation with users, ONS are no longer producing the Retail Sales summary statistics table. Records of any significance will be highlighted in the main text of the statistical bulletin.
Understanding the data
1. Quick Guide to the Retail Sales Index (195 Kb Pdf)
2. 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 sector in terms of turnover.
The RSI covers sales only from businesses registered as retailers according to the Standard Industrial Classification (SIC), an internationally agreed convention for classifying industries. The retail sector 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 sector 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.
3. Definitions and explanations
The value or current price series records the growth since the base period (currently 2009) 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 comparing the value and volume data non-seasonally adjusted. In general, this implied price deflator should be quite close to the retail component of the CPI.
4. Use of the data
The value and volume measures of retail sales estimates are widely used in private and public sector organisations. For example, private sector institutions such as investment banks, the retail sector itself and retail groups use the data to inform decisions on the current economic performance of the retail sector, 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 sector, 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 April) 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 is the amount of money taken through the tills of retailers; these are non-seasonally adjusted data. These data consists 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 January 2013 was 30 December 2012 to 29 January 2013, compared with 1 January 2012 to 28 January 2012 the previous year. Table 5 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 41.3 per cent, predominantly non-food stores 41.6 per cent, non-store retailing 5.3 per cent and automotive fuel 11.8 per cent.
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 per cent confidence interval for the index movement is roughly twice the standard error. The paper ‘Measuring the accuracy of the Retail Sales Index’, 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 per cent. This means that the year-on-year growth rate for all retail sales volumes falls within the range 0.3 ± 1.4 per cent with a probability of 95 per cent.
The standard error for month-on-month growth in all retail sales volumes is 0.4 per cent. 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 per cent.
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. Revision 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 which have been 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.
|Volume seasonally adjusted|
|Revisions between first publication and estimates twelve months later (percentage points)|
|Growth in latest period (%)||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||-0.8||-0.25||0.36|
|Latest month compared with previous month||-0.6||-0.11||0.45|
Methodological changes were introduced in the April 2009 and January 2010 releases. For more detail see:
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|Kate Davies||+44 (0)1633 455602||ONSfirstname.lastname@example.org|