This bulletin presents estimates of the quantity bought (volume) and amount spent (value) in the retail industry for the period 4 May 2014 to 31 May 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 three 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)||3.2||4.4||-0.5||0.8|
|Quantity bought (Volume)||3.9||4.9||-0.5||1.3|
|Value excluding automotive fuel||4.1||5.4||-0.6||0.9|
|Volume excluding automotive fuel||4.7||5.5||-0.5||1.1|
In May 2014, the quantity bought in the retail industry (volume) increased by 3.9% compared with May 2013. The amount spent (value) increased by 3.2%.
In May 2014, non-seasonally adjusted data show that the prices of goods sold in the retail industry (as measured by the implied price deflator) decreased by 0.7%.
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 show the quantity of goods bought (all retailing sales volumes) and the amount spent (all retailing sales values) in the retail industry, as indices referenced to 2010.
Prior to the 2008/2009 downturn, both the value and the volume of retail sales grew steadily. From January 2005 to January 2008, the increase in the value and volume of retail sales corresponded with increasing real household incomes. The consumer prices index (CPI),at 7.0%, increased at a slower rate than average weekly earnings (AWE),at 12.2%, suggesting growth in real household incomes. Over this period, the quantity of retail sales (including fuel) grew by 7.8%, whilst the value grew by 11.7%.
Between January 2008 and January 2013 (the area shaded in grey), the volume of sales remained roughly flat, falling by 0.2%, whilst the value of sales showed a significant increase of 11.9%. This reflects the price increases following the start of the 2008/2009 economic downturn. The CPI increased by 17.9% and in turn put pressure on real household 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”
However, since the turn of 2013 growth in volume terms has begun to increase sharply and has grown at a slightly higher rate than the value of sales in recent months, with volume increasing by 3.9% over the 12 months to May 2014 and value at 3.2% over the same period. At the same time, the CPI, excluding April’s figure, continued its downward trend and stood at 1.5% in May. This was its slowest rate since October 2009 and highlights the weakening of overall price pressure.
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 May 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 May 2013 and May 2014.
In May 2014, three out of the four main retail sectors (food stores, non-food stores and non-store retailing) saw an increase in the quantity bought (volume). The largest contribution came from the non-food stores sector.
In May 2014, three out of the four main sectors (food stores, non-food stores and non-store retailing) contributed to the increase in amount spent (value). The largest contribution came from the non-food stores sector.
The US Census Bureau released its advanced retail sales estimates for May 2014 on 12 June. The amount spent in the US retail industry, including motor vehicles & parts and food services, increased by 4.3% compared with May 2013 and by 0.3% compared with April 2014.
More information on international data can be found in the ONS article International Measures of Retail Sales published on 6 June 2014.
Compared with May 2013, the quantity bought in the other non-food stores sector increased by 9.1% in May 2014. This is the longest period of sustained year-on-year growth (14 months) in this sector since October 2008. Month-on-month the quantity bought increased by 2.2% compared with April 2014. Increases were reported by retailers in the following sectors:
Second-hand goods in stores increasing by 27.9%;
Sporting equipment, games & toys increasing by 3.9%;
Watches & jewellery increasing by 3.4%;
Other retail sales of new goods in specialised stores increasing by 2.1%; and
Computers & telecommunications increasing by 0.4%.
The largest contribution to the increase in the month-on-month growth in other stores in May 2014 came from second-hand goods. However, due to the nature of the businesses within this sector (including charity shops, antique shops and auction houses) this is a volatile series.
The second largest contribution comes from sporting goods and toy stores where we saw an upwards trend during 2012; boosted in particular by the effect of the Olympic Games. However, it is interesting to note that since January 2014 sales in this sector are showing continued growth with sporting equipment and clothes providing the largest contribution. May 2014 also saw the highest year-on-year growth of 28.9% since January 2009 (30.9%).
Feedback from retailers in these stores has suggested that the increase in sales in May 2014 is due to the build-up of the FIFA World Cup. A better picture of the impact of the World Cup on retail sales statistics will be available when June data are released on 24 July 2014.
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 0-9 employees saw the largest growth in the amount spent of 16.9% comparing May 2014 with May 2013, while stores with 40-99 employees experienced a fall of 19.2% in the amount spent.
|Number of employees||Weights (%)||Growth since May 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 May 2013 and May 2014. The table contains information only from businesses that reported in May 2013 and May 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 May 2014 four week reporting period, the amount spent in the retail industry was £27.8 billion (non-seasonally adjusted). This compares with £27.9 billion in the four week reporting period for April 2014 and £27.0 billion in the four week reporting period for May 2013.
This equates to an average weekly spend of £7.0 billion in May 2014, £7.0 billion in April 2014 and £6.8 billion in May 2013.
All sectors except fuel stores showed increases in the quantity bought year-on-year. The decrease of 3.2% in the quantity bought in fuel stores was the largest decrease since March 2013 (10.1%);
All sectors, except household goods stores and fuel stores, showed increases in the amount spent compared with May 2014;
Predominantly food stores was the only sector to see average prices rise in May 2014, all other sectors saw average prices fall compared with May 2013.
|Percentage change over 12 months||Average weekly sales (£ billion)|
|Quantity bought (volume)||Amount spent (value)||Average store price|
|Predominantly food stores¹||0.7||1.1||0.4||2.9|
|Predominantly non-food stores²||6.0||5.2||-0.8||2.8|
|Textiles, clothing & footwear stores||4.2||4.2||-0.1||0.8|
|Household goods stores||0.4||-0.3||-0.5||0.6|
Note: More information on how average store prices are calculated can be found in the quick guide to retail sales 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 (195 Kb Excel sheet) and include:
A seasonally adjusted value index; and
Year-on-year and month-on-month growth rates.
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 May 2014 was £727.5million. This was an increase of 15.1% compared with May 2013;
The amount spent online accounted for 11.4% of all retail spending excluding automotive fuel, compared to 10.3% in May 2013; and
The online spend in department stores increased by 26.5% year-on-year but decreased by 15.7% in other stores.
Table 4 shows the year-on-year growth rates for total Internet sales by sector and the proportion of sales made online in each retail sector.
|Category||Year on year growth (%)||Proportion of total sales made online (%)|
|Textile, clothing & footwear stores||17.6||11.3|
|Household goods stores||3.5||5.9|
ONS is currently gathering views from users on how the retail sales data are used. Please e-mail comments to firstname.lastname@example.org.
A new imputation method has been introduced in June 2014, this has affected data from May 2013. The impact on data is minimal in all months except December 2013 and the changes are unbiased with both upwards and downwards data movements. More information can be found in the ONS article ‘ Change to Imputation Method used for the Turnover (74.4 Kb Pdf) Question in Monthly Business Surveys’ published today.
After consultation we have published new reference tables (1.73 Mb Excel sheet) which have replaced reference tables 1-4. Pdf versions of tables 1-4 are still available.
Understanding the data
1. Quick Guide to the Retail Sales Index
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 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.
3. Definitions and explanations
The value or current price series records the growth 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; and
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.
4. 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 industries 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.
Information on retail sales methodology is available.
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 May 2014 was 4 May 2014 to 31 May 2014, compared with 28 April 2013 to 25 May 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; and
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 3.7 ± 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 -0.1 ± 1.0 percentage points with a probability of 95%.
The papers ‘Measuring the accuracy of the Retail Sales Index’, Winton, J and Ralph, J (2011) and ‘Updated accuracy measures for the Retail Sales Index’ Sanderson, R (2013) report 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 is available.
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 (%)||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||1.3||-0.27||0.36|
|Latest month compared with previous month||-0.5||-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 can be accessed.
Alternatively, for low-cost tailored data call 0845 601 3034 or email email@example.com
Next publication: Thursday 24 July 2014
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