This bulletin presents estimates of the quantity bought (volume) and amount spent (value) in the retail industry for the period 29 December 2013 to 1 February 2014. 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)||4.4||4.5||-1.8||1.0|
|Quantity bought (Volume)||4.3||3.9||-1.5||1.1|
|Value excluding automotive fuel||5.2||5.4||-1.8||1.5|
|Volume excluding automotive fuel||4.8||4.5||-1.5||1.5|
In January 2014, the quantity bought in the retail industry (volume) increased by 4.3% compared with January 2013. The amount spent (value) increased by 4.4%. Since January 2013, non-seasonally adjusted data show that the prices of goods sold in the retail industry (as measured by the implied price deflator) increased by 0.2%. More information on how the implied price deflator is calculated can be found in section 3 of 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.
Prior to the 2008/09 downturn, both the quantity and the value of retail sales grew steadily. Between January 2005 and January 2008, the quantity of retail sales (including fuel) grew by 7.8%, while the value of retail sales increased by 11.7%. The difference was due to price increases, with the Consumer Prices Index (CPI) increasing by 7.0% over the three years. However, average weekly earnings (excluding bonuses) increased by 12.2% over the same period, implying that real household earnings were increasing.
Then from January 2008 to January 2013 (the area shaded in grey), as the value of retail sales continued to grow, increasing by 11.8%, the volume of retail sales was broadly flat, falling by 0.2%. This reflects the extent to which prices grew after the onset of the economic downturn, CPI increasing by 17.9%, as well as the squeeze felt on household real earnings.
Further analysis on the recent trend of real wages and potential causes, can be found in ONS’ “An Examination of Falling Real Wages, 2010 to 2013", published 31 January 2014.
However over the most recent year growth in volume terms has increased noticeably. In the 12 months to January 2014, the volume of retail sales increased by 4.2% - faster than the overall pattern experienced in the three years prior to 2008.
The retail industry is divided into four retail sectors:
Predominantly food stores (e.g. supermarkets, specialist food stores and sales of alcoholic drinks and tobacco);
Predominantly non-food stores (e.g. non-specialised stores, such as department stores, textiles, clothing & footwear, household goods and other stores);
Non-store retailing (e.g. mail order, catalogues and market stalls); and
Stores selling automotive fuel (petrol stations).
In January 2014, 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 January 2013 and January 2014.
In January 2014 three out of the four main retail sectors, non-food stores, non-store retailing and food stores saw an increase in the amount spent. As shown in figure 3 the same pattern can be seen in the quantity bought. Non-food stores provides the largest contribution to growth in both the amount spent and the quantity bought.
In January 2014 the quantity bought in non-food stores increased by 8.0% compared with January 2013. With the exception of clothing stores all store types saw an increase in the quantity bought with:
Non-specialised stores or department stores increasing by 8.0%;
Textile, clothing and footwear stores remaining unchanged;
Household goods stores increasing by 9.8%; and
Other stores increasing by 14.8%.
Figure 4 shows the index levels for each of these store types.
The two store types with the largest year-on-year increases were household goods stores (9.8%) and other stores (14.8%) but closer examination of figure 4 shows that in both cases this growth was in part a consequence of a weak January 2013 where heavy snowfall during the latter part of this month could have affected sales.
However, month-on-month there was a pick up in the quantity bought in January 2014 where sales in household goods stores increased by 5.3% and sales in other stores increased by 2.7%. In the household goods sector this was a result of an increase in the quantity bought in furniture stores, DIY stores and electrical appliance stores. Feedback from these retailers suggests that an increase in online sales helped to boost overall sales.
Table 2 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). It shows that businesses with 40-99 employees saw the largest growth in the amount spent comparing January 2013 with January 2014, while large stores experienced weaker but still moderate growth in the amount spent at 4.8%.
|Number of employees||Weights (%)||Growth since January 2013 (%)|
More information on the performance of the retail industry by store type and size can be found in the reference table, Business Analysis (25.5 Kb Excel sheet) , which shows the extent to which individual businesses reported actual changes in their sales between January 2013 and January 2014. The table contains information only from businesses that reported in January 2013 and January 2014. Cells with values less than 10 are suppressed for some classification categories; this is denoted by 'c'. 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.
In the January 2014 five week reporting period, the amount spent in the retail industry was £31.9 billion (non-seasonally adjusted). This compares with £44.1 billion in the five weeks of December 2013 and £24.3 billion in the four weeks of January 2013.
This equates to an average weekly spend of £6.3 billion in January 2014, £8.8 billion in December 2013 and £6.1 billion in January 2013.
The increase in the quantity bought in other stores of 14.8% and in the amount spent of 13.7%, are the highest year-on-year growth rates since April 2002 and the highest January on record.
The quantity bought in non-store retailing increased by 12.9% while the amount spent increased by 12.3%.
Upwards pressure to average store prices came from predominantly food stores and textiles, clothing & footwear stores. All of the other main store groupings saw average prices fall in comparison with January 2013.
|Percentage change over 12 months||Average weekly sales (£ billion)|
|Quantity bought (volume)||Amount spent (value)||Average store price|
|Predominantly food stores¹||0.1||1.8||1.8||2.7|
|Predominantly non-food stores²||8.0||7.6||-0.4||2.6|
|Textiles, clothing & footwear stores||0.0||1.4||1.5||0.7|
|Household goods stores||9.8||8.8||-1.0||0.6|
Note: more information on how average store prices are calculated can be found in the quick guide to retail sales (195 Kb Pdf) or in the background notes.
Seasonally adjusted Internet sales data are provided within this release. These seasonally adjusted estimates are published in the RSI tables (165 Kb Excel sheet) and include:
A seasonally adjusted value index; and
Year-on–year and month-on-month growth rates.
ONS will publish the seasonally adjusted proportion of sales made online in the February 2014 bulletin in March 2014. More information on the seasonal adjustment of these estimates can be found in section 4 of the background notes or in The quick guide to Internet sales (167.6 Kb Pdf) .
Internet sales are estimates of how much was spent online through retailers across all store types in Great Britain. The reference year is 2010=100.
Average weekly spending online in January 2014 was £650million. This was an increase of 8.9% compared with January 2013.
The amount spent online accounted for 10.7% of all retail spending excluding automotive fuel.
The online spend in textile, clothing & footwear stores was estimated at 12.9%
Table 4 shows the year-on-year growth rates for total Internet sales by sector and the proportion of sales that made online in each retail sector.
|Category||Year on year growth % (Value SA)||Proportion of total sales made online (NSA)|
|Textile, clothing & footwear stores||12.9||12.1|
|Household goods stores||5.2||5.6|
ONS is reviewing the seasonal adjustments for the proportion of sales made online. This will be implemented in March 2014 on February 2014 data.
The 2014 RSI workplan will be discussed at a seminar in London on 5 March 2014. Users are invited to reserve a place at this seminar through contacting firstname.lastname@example.org
This bulletin includes a new reference table for seasonally adjusted Internet pounds data (165 Kb Excel sheet) and separates the reference tables for retail sales (1.66 Mb Excel sheet) and Internet data.
Understanding the data
Statistical Special Events
Flooding and storms have produced localised damage and disruption in parts of the UK. Retail sales estimates for January are thought to be minimally affected by these conditions, which have not been designated as a statistical Special Event , although ONS continues to keep the situation under review.
Quick Guide to the Retail Sales Index
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 CPI components 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 can be found in the Quick Guide to Retail Sales (195 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.
The retail sales index feeds into estimates of gross domestic product in two ways. Firstly it feeds into the services sector when GDP is measured from the output approach. Secondly it is a data source used to measure Household Final Consumption Expenditure which feeds into GDP estimates when measured from the expenditure approach.
The data feeds into the first (or preliminary) estimate of GDP; the second estimate of GDP and the third estimate appears in the Quarterly National Accounts.
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 RSI 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 2014 was 29 December 2013 to 1 February 2014, compared with 30 December 2012 to 26 January 2013 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, 2008 and 2014 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
Standard errors of non-seasonally adjusted chained volume index movements have been developed for RSI to determine the spread of possible movements and a means of assessing the accuracy of the non-seasonally adjusted month-on-month and year-on-year estimates of all retail sales volumes. The lower the standard error, the more confident one can be that the estimate is close to the true value for the retail population.
The standard error for year-on-year growth in all retail sales (non-seasonally adjusted) volumes is 0.9%. This means that the year-on-year growth rate for all retail sales volumes (non-seasonally adjusted) falls within the range 4.9 ± 1.8 percentage points with a probability of 95%.
The standard error for month-on-month growth in all retail sales (non-seasonally adjusted) volumes is 0.5%. This means that the month-on-month growth rate for all retail sales volumes (non-seasonally adjusted) falls within the range -27.2 ± 1.0 percentage points with a probability of 95%.
The papers ‘ Measuring the accuracy of the Retail Sales Index’ (1.04 Mb Pdf) , Winton, J and Ralph, J (2011) and ‘ Updated accuracy measures for the Retail Sales Index (29.6 Kb Pdf) ’, Sanderson, R (2013) 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.
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.
|Growth in latest period (per cent)||Revisions between first publication and estimates twelve months later (percentage points)|
|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||0.5||-0.27||0.36|
|Latest month compared with previous month||2.5||-0.13||0.40|
A spreadsheet giving these estimates and the calculations behind the averages in the table is available on the ONS website.
Details of the policy governing the release of new data are available from the Media Relations Office. Also available is a list of the organisations given pre-publication access to the contents of this bulletin.
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.
Alternatively, for low-cost tailored data call 0845 601 3034 or email email@example.com
Next publication: Thursday 27 March 2014
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|Kate Davies||+44 (0)1633 455602||ONSemail@example.com|