Retail sales, Great Britain: November 2016

In November 2016, the quantity of goods bought (volume) in the retail industry was estimated to have increased by 5.9% compared with November 2015; all store types showed growth with the largest contribution coming from non-store retailing.

Compared with October 2016, the quantity bought was estimated to have increased by 0.2%; there was a mixed picture across store types with strong growth reported in some sectors. In particular, within non-food stores, feedback from household goods stores stated that “Black Friday” events had boosted sales in November.

The underlying pattern in the retail industry continues to show strong growth with the 3 month on 3 month movement in the quantity bought increasing by 2.1%; this is the 35th consecutive period of 3 month on 3 month growth.

Average store prices (including petrol stations) increased by 0.1% in November 2016 compared with November 2015; this was the first year-on-year increase since June 2014. The largest contribution to the increase came from petrol stations. This is consistent with the latest Consumer Prices Index (CPI) data, which showed an increase in petrol prices.

The amount spent (value) in the retail industry increased by 5.9% compared with November 2015 and increased by 0.5% compared with October 2016.

The amount spent online increased by 24.9% compared with November 2015 and by 3.0% compared with October 2016.

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This bulletin presents estimates of the quantity bought (volume) and amount spent (value) in the retail industry for the period 30 October 2016 to 26 November 2016. Unless otherwise stated, the estimates in this release are seasonally adjusted. Estimates for November 2016 included “Black Friday”, however, “Cyber Monday” is not covered in this reporting period and will be included in the estimates for December 2016 published on 20 January 2017.

The estimates are based on a monthly survey of 5,000 retailers, including all large retailers employing 100 people or more and those with annual turnover of greater than £60 million who employ 10 to 99 people. It is estimated that this survey covers approximately 95% of all known retail turnover in Great Britain.

The quality of the estimate of retail sales

Retail sales estimates are produced from the Monthly Business Survey – Retail Sales Inquiry (RSI). The timeliness of these retail sales estimates, which are published just 3 weeks after the end of each trading period, 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. Current price non-seasonally adjusted data are revised for the previous 13 published periods. More information about the data content for this release can be found in the background notes.

Revisions are an inevitable consequence of the trade-off between timeliness and accuracy. The response rate in November 2016 was 60.2% of questionnaires, accounting for 91.9% of registered turnover in the retail industry. Therefore, the estimate is subject to revisions as more data become available.

All estimates, by definition, are subject to statistical uncertainty and for the retail sales index we publish the standard error associated with the non-seasonally adjusted estimates of year-on-year and month-on-month growth in the quantity bought as a measure of accuracy. More information on these standard errors can be found in the background notes and in the quality tables of this release.

We are continually working on methodological changes to improve the accuracy of the retail sales estimates; progress on these can be found on the continuous improvement page.

The datasets offer different ways to access the data, they include:

• non-seasonally adjusted and seasonally adjusted volume and value indexes by industry
• year-on-year and month-on-month growth rates by industry

At a glance

In November 2016:

the quantity bought in the retail industry (volume):

• increased by 5.9% compared with November 2015; the 43rd consecutive period of year-on-year growth
• increased by 0.2% compared with October 2016

the amount spent (value):

• increased by 5.9% compared with November 2015
• increased by 0.5% compared with October 2016

In the 4 week reporting period during November 2016, the amount spent in the retail industry was £34.2 billion (non-seasonally adjusted).

This compares with:

• £30.7 billion in the 4 week reporting period for October 2016
• £32.1 billion in the 4 week reporting period for November 2015

This equates to an average weekly spend of:

• £8.5 billion in November 2016
• £7.7 billion in October 2016 and
• £8.0 billion in November 2015

The retail industry is divided into 4 retail sectors:

• predominantly food stores (for example, supermarkets, specialist food stores and sales of alcoholic drinks and tobacco)

• predominantly non-food stores (for example, non-specialised stores such as department stores, textiles, clothing and footwear, household goods and other stores)

• non-store retailing (for example, mail order, catalogues and market stalls)

• stores selling automotive fuel (petrol stations)

Figure 1 shows that for every pound spent in the retail industry:

• 40 pence was spent in food stores

• 43 pence in non-food stores

• 8 pence in non-store retailing

• 9 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.

In November 2016 compared with November 2015, all main retail sectors, except petrol stations saw an increase in the quantity bought (volume) while all sectors saw an increase in the amount spent (value). The largest contribution in the quantity bought and amount spent came from non-food stores.

In November 2016 compared with October 2016, all main retail sectors except food stores and petrol stations saw an increase in the quantity bought (volume) and amount spent (value). The largest contribution in the quantity bought came from non-food stores and non-store retailing while the largest contribution in the amount spent came from non-food stores.

Main points

In November 2016:

• all store types showed increases in the quantity bought and amount spent compared with November 2015

• the largest increases in both the quantity bought and amount spent came from non-store retailing

• non-seasonally adjusted data show that the prices of goods sold in the retail industry (as measured by the implied price deflator) increased by 0.1% compared with November 2015; this was the first year-on-year price increase since June 2014

• compared with October 2016 average store prices have increased by 0.5%, with the largest increase seen in petrol stations (1.4%)

• compared with November 2015 there were falls in average store prices across all store types, except textile, clothing and footwear stores and petrol stations

More information on how the implied price deflator and other estimates in this release are calculated can be found in section 2 part iii of the background notes.

Figure 4 shows the longer-term picture for the quantity bought, amount spent and average store price in household goods stores. There was sustained growth in both the quantity bought and amount spent from late 2013 to late 2015. Since late 2015, the series has been very volatile with periods of growth and contraction, with no real growth in the underlying trend. The pattern in average store price over the same period has been one of price falls.

In November 2016 compared with November 2015:

• the quantity bought increased by 6.7%

• the amount spent increased by 6.1%

• average store price decreased by 0.6%

Compared with October 2016:

• the quantity bought increased by 6.0%

• the amount spent increased by 5.0%

• average store price decreased by 0.8%

Table 3 shows the store types within household goods and their year-on-year and month-on-month growth in November 2016.

All main sub-sectors showed year-on-year growth in the quantity bought, while 3 of the 4 sub-sectors showed year-on-year growth in the amount spent. The largest increase in both the quantity bought and amount spent was reported by hardware, paints and glass.

On the month, all sub-sectors showed increases in both the quantity bought and amount spent. The largest increase was reported by electrical household appliances, where the quantity bought increased by 19.1% and the amount spent increased by 20.4%. Feedback from retailers suggested that sales of electrical goods in particular had been boosted due to “Black Friday” sales in the month.

Seasonally adjusted internet sales data are published in the Retail Sales Inquiry (RSI) internet tables and include:

• a seasonally adjusted value index
• 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 2013=100.

Main points:

• average weekly spending online in November 2016 was £1.1 billion; this was an increase of 24.9% compared with November 2015

• the amount spent online accounted for 15.8% of all retail spending, excluding automotive fuel, compared with 13.3% in November 2015

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.

Table 5 shows how sales varied among different-sized retailers. It shows the distribution of reported change in sales values of businesses (from the RSI sample), ranked by size of business (based on number of employees).

Businesses with 40 to 99 employees saw the largest growth in the amount spent in November 2016 compared with November 2015 (25.0%). Businesses with 100 and over employees showed an increase of 3.6%.

More information on the performance of the retail industry by store type and size can be found in the Business Analysis dataset.

Figure 5 compares a rolling 3 month period with the same period in the previous year and highlights that the volume of retail sales started to grow strongly from mid-2013. The latest data show retail sales growth of 5.6% in the 3 months to November 2016, a slight decrease of 0.2 percentage points from the 3 months to October 2016. This slight fall in growth was the first slowdown since April 2016, highlighting the strength of retail sales in the last few months. The rolling 3 month on 3 month a year ago growth in retail sales has averaged 4.6% since the start of 2016, which is the same as the 2015 calendar year average.

Three distinct periods emerge from Figure 5. Between November 2006 and August 2008, retail sales volumes were experiencing continuous growth, although to a different degree. Growth in inflation (Consumer Prices Index CPI) was lower than average weekly earnings over most of this period, which resulted in rising real earnings, an indicator of the purchasing power of consumers. Moreover, between November 2006 and August 2008, consumer credit increased by 8.9%, which may have been a factor driving retail sales growth.

However, between September 2008 and July 2013, the volume of retail sales fluctuated between periods of contraction and expansion, which may be partly explained by the economic climate over this period; it also coincided with a reduction in consumer credit of 24.2%. Moreover, growth in average weekly earnings was lower than inflation over most of the period, which implies that earnings fell in real terms.

The most recent data show a notable pickup in underlying sales volumes. Between August 2013 and November 2016, the price level (shown by the implied deflator) fell by 5.1%, coinciding with 16.6% growth in the volume of retail sales over this period. In addition, this upturn in spending has been accompanied by a decline in the saving ratio, from an average of 8.6% over the period 2008 to 2012, to an average of 6.4% between Quarter 1 (Jan to Mar) 2013 and Quarter 2 (Apr to June) 2016. Moreover, since August 2013, consumer credit has followed a broadly upward trend, growing by 20.2% between August 2013 and November 2016. However, prices have started to rise again. In the year to November 2016, growth in the implied retail sales deflator rose to 0.1% from negative 3.3% in November 2015.

Figure 6 breaks down the growth of volumes in total retail sales into the contribution made by food and non-food stores (which includes department stores, other stores, clothing and household goods), non-store retailing (that is, mail orders) and petrol, between November 2006 and November 2016. In the 10 year period, non-store retailing has, on average, made the largest contribution to growth and is the only component to have made a consistently positive contribution to retail sales growth. In the most recent period, all 4 components have made positive contributions to growth, with predominately food and non-food stores contributing the most and petrol making sizeable contributions since the end of 2014 (a time of historically low oil prices).

In the latest month, growth continues to be driven by a broad increase in all 4 categories, with all but petrol making significant contributions in November. Petrol provided its lowest contribution to growth since November 2014, which could reflect rising petrol prices. The highest contribution to growth was non-food stores, contributing 2.1 percentage points to growth. This large increase in non-food volumes could be a result of consumers taking advantage of "Black Friday" deals on a number of non-food products.

Non-store retailing also performed very strongly in November, contributing 1.9 percentage points to growth. Meanwhile, food sales made a lower contribution to growth in November than recent months. These trends were also emphasised by the November British Retail Consortium/KPMG survey, which suggested "Black Friday" boosted non-food sales in November with shoppers taking advantage of the variety of deals available.

The latest estimates for retail trade were published by the US Census Bureau on 15 November 2016 in its advanced retail sales estimates for November 2016. They include the amount spent in the US retail industry, including motor vehicles and parts, and food services.

The latest estimates of the volume of retail trade across the European Union, published by Eurostat on 5 December 2016 for October 2016, show the seasonally adjusted volume of retail trade in both the euro area (EA19) and EU28 when compared with September 2016. Note that an accurate comparison cannot be made as Eurostat data are calculated on a 2010 = 100 basis, while data for Great Britain are calculated on a 2013 = 100 basis.

The Retail sales Quality and Methodology Information document contains important information on:

• the strengths and limitations of the data and how it compares with related data
• users and uses of the data
• how the output was created
• the quality of the output including the accuracy of the data

1. Quality

i. Basic quality information

The standard reporting periods can change over time due to the movement of the calendar. Every 5 or 6 years the standard reporting periods are brought back into line by adding an extra week. For example, January is typically a 4 week standard period but January 1986, 1991, 1996, 2002, 2008 and 2014 were all 5 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, you should focus on the seasonally adjusted estimates as these have the systematic calendar-related component removed. Due to the volatility of the monthly data, growth rates should be calculated using an average of the latest 3 months of the seasonally adjusted estimates.

When interpreting the data, the relative weighted contributions of the sectors in the “all retailing” series should be considered. Based on SIC 2007 data, total retail sales consists of: predominantly food stores 40.4%, predominantly non-food stores 42.6%, non-store retailing 7.6% and automotive fuel 9.4%.

ii. Standard error

Standard errors determine the spread of possible movements and are 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 we can be that the estimate is close to the true value for the retail population.

The median standard error of 12-month movement for “All Retailing” in November 2016 is 0.8%. The same median is seen for “All Retailing” in November 2015. In the previous 12 months, median standard errors have remained close to this level with other fluctuations occurring in July 2016 to September 2016, and December 2015 to April 2016 at 0.9%.

Table 6 shows the year-on-year movement for the non-seasonally adjusted chained volume measure alongside the standard error across the published sector breakdowns for November 2015 and November 2016. The differences between November 2015 and November 2016 highlight that the standard error has only increased for “Household goods stores” and “Textile, clothing and footwear stores” whilst it has decreased or remained stable for other sector breakdowns. The greatest decreases are seen for “automotive fuel”, “non-specialised stores”, and “other stores”.

More information on standard errors can be found in the Retail sales quality tables datasets, which are part of this release.

iii. Revisions triangles

Revisions to data provide one indication of the reliability of main indicators. Table 7 shows summary information on the size and direction of the revisions made to the volume data covering a 5-year period. Note that changes in definition and classification mean that the revisions analysis is not conceptually the same over time.

The data section of this bulletin provides these estimates and the calculations behind the averages in the table.

2. Methods

An overview of the Retail Sales Index and a number of methodological articles are also available.

i. Composition of the data

Retail sales estimates are based on financial data collected through the monthly Retail Sales Inquiry. Response rates at the time of publication are included for the current month and the 3 months prior. The response rates for those historical periods are updated to reflect the current level of response, incorporating data from late returns. There are 2 response rates included with a percentage for the amount of turnover returned and another percentage for the amount of questionnaire forms. Historical response rates are available in the quality information dataset.

ii. 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 January 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-13-ARIMA-SEATS. 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 (there is more information in the Methods, Calendar effects section), bank holidays, Easter and the day of the week on which Christmas falls.

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 3 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 is the peak in January and the fall in February)

• “irregular” or “noise”, for example, deeper falls within the non-seasonally adjusted series due to bad 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. It leaves a seasonally adjusted time series made up of the trend and irregular components.

In the non-seasonally adjusted RSI we see large rises in January each year and a fall in the following February, but these are not evident in the seasonally adjusted index. This peak in January is larger than the subsequent fall, but the trend and irregular components in both months are likely to be similar. This means that the movements in the unadjusted series are almost completely a result of the seasonal pattern.