Retail sales, Great Britain: July 2016

The reporting period for this release covers a 4 week period from 3 July to 30 July 2016 and therefore includes data for the month following the EU referendum. The data are based on sales made in Great Britain and will include sales made to both Great Britain residents and non-residents.

In July 2016, the quantity bought (volume) of retail sales is estimated to have increased by 5.9% compared with July 2015; all sectors showed growth with the main contribution coming from non-food stores.

Compared with June 2016, the quantity bought increased by 1.4%; all sectors showed growth with the main contribution again coming from non-food stores.

Average store prices (including petrol stations) fell by 2.0% in July 2016 compared with July 2015. Compared with June 2016, there was a fall of 0.8%.

The amount spent (value) in the retail industry increased by 3.6% compared with July 2015 and 1.6% compared with June 2016.

The amount spent online increased by 16.7% compared with July 2015 and increased by 1.2% compared with June 2016.

This bulletin presents estimates of the quantity bought (volume) and amount spent (value) in the retail industry for the period 3 July 2016 to 30 July 2016. Unless otherwise stated, the estimates in this release are seasonally adjusted.

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 July 2016 was 60.2% of questionnaires, accounting for 87.5% 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 July 2016:

the quantity bought in the retail industry (volume):

• increased by 5.9% compared with July 2015; the 39th consecutive period of year-on-year growth
• increased by 1.4% compared with June 2016

the amount spent (value):

• increased by 3.6% compared with July 2015
• increased by 1.6% compared with June 2016

In the 4 week reporting period during July 2016, the amount spent in the retail industry was £29.6 billion (non-seasonally adjusted).

This compares with:

• £36.1 billion in the 5 week reporting period for June 2016
• £28.6 billion in the 4 week reporting period for July 2015

This equates to an average weekly spend of:

• £7.4 billion in July 2016
• £7.2 billion in June 2016 and
• £7.1 billion in July 2015

Main points

In July 2016:

• all store types showed increases in the quantity bought compared with July 2015 and June 2016

• non-seasonally adjusted data show that the prices of goods sold in the retail industry (as measured by the implied price deflator) decreased by 2.0% compared with July 2015; this was the 25th consecutive month of year-on-year price falls

• falls in average store prices were seen across all store types compared with July 2015, with the largest fall in petrol stations (4.1%); however, the fall of 2.0% in all retailing is the smallest fall we have seen in the last 18 months

• compared with June 2016 average store prices have fallen by 0.8%

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 1 shows the longer-term picture for the quantity bought in each of the main store types: food stores, non-food stores, non-store retailing and fuel.

The chart shows that between 1996 and 2008 there was sustained growth in the quantity bought within the retail industry. This growth was dominated by food and non-food while fuel and non-store retailing remained relatively constant throughout this period.

With the start of the economic downturn in 2008, growth stalled across all sectors. In late 2012, we started to see an increase in the quantity bought once more, however, the growth in the quantity bought at food stores was much slower than in the non-food and non-store retailing.

Figure 2 shows the longer-term picture for the quantity bought in each of the store types within the non-food sector: department stores, clothing and footwear stores, household goods stores, and other stores.

The chart shows that since 1996 the main contribution to the non-food sector has come from other stores. This includes stores that sell books, sporting goods, toys, jewellery, computers and cosmetics.

Up until the period of the economic downturn, household goods stores also performed well, however, sales have contracted since 2008 and have not reached the levels we saw prior to the downturn. Sales within clothing and footwear stores and department stores have showed sustained growth since the start of the period.

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 July 2016 was £963.8 million; this was an increase of 16.7% compared with July 2015
• the amount spent online accounted for 14.2% of all retail spending, excluding automotive fuel, compared with 12.6% in July 2015

Table 3 shows the year-on-year growth rates for total internet sales by sector and the proportion of sales made online in each retail sector.

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 3 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 July 2016 compared with July 2015, all 4 main retail sectors saw an increase in the quantity bought (volume) and amount spent (value). The largest contribution in the quantity bought and amount spent came from non-food stores. Average store prices have fallen across all store types in July 2016 compared with July 2015, resulting in consumers buying more goods.

In July 2016 compared with June 2016, all of the 4 main retail sectors saw an increase in the quantity bought (volume), while 3 of the 4 main sectors (non-store retailing, non-food stores and food stores) saw an increase in the amount spent. The largest upwards contribution for both quantity bought and amount spent came from non-food stores. Compared with June 2016, there were falls in average store prices apart from fuel where there was an increase of 0.7%.

Table 4 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 July 2016 compared with July 2015 (17.9%). 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.

Annual growth in retail sales strengthened in July 2016, continuing the trend seen in the last few years. This was mainly driven by department stores but also clothing, which may have been influenced by unusually warm weather in July.

Figure 6 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 an increase in retail sales growth, from 4.9% in the 3 months to June 2016, to 5.2% in the 3 months to July 2016, continuing its upward trend since the start of the year. The rolling 3 month on 3 month a year ago growth in retail sales has averaged 4.3% since the start of 2016, which is lower than the 2015 average of 4.5%.

Three distinct periods emerge from Figure 6. Between July 2007 and July 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 July 2007 and July 2008, consumer credit increased by 8.6%, which may have been a factor driving retail sales growth.

However, between August 2008 and May 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 and coincided with a reduction in consumer credit of 24.8%. Moreover, growth in average weekly earnings was lower than inflation over most of the period, which implies that earnings fell in real terms.

The third period shown in Figure 6 started in June 2013, when growth in volume terms began to increase notably, despite average weekly earnings growing at a slower rate than CPI until September 2014. Moreover, since June 2013, consumer credit has followed a broadly upward trend, growing by 18.8% between June 2013 and June 2016. Between June 2013 and July 2016, the price level (shown by the implied deflator) fell by 5.1%, coinciding with 13.9% growth in the volume of retail sales over this period. In addition, this upturn in spending has been accompanied by a decline in the savings ratio, from an average of 8.6% over the period 2008 to 2012, to an average of 6.5% from Quarter 1 (Jan to Mar) 2013 to Quarter 1 2016.

Figure 7 breaks down the growth in total retail sales into the contributions made by food and non-food stores (which includes department stores, other stores, clothing, and household goods), non-store retailing, and petrol, between July 2007 and July 2016. In the 9 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 predominantly food and non-food stores contributing the most and petrol making sizeable contributions since the end of 2014 at a time of historically low oil prices.

In the latest month, growth continues to be driven by a broad-based increase in all 4 categories, but more notably in food and non-food stores, contributing 1.5 percentage points and 1.8 percentage points respectively to the 5.2% growth in retail sales in July. Growth in non-food stores accounts for almost all of the increase in retail sales growth from the 3 months to June to the 3 months to July and has continuously increased in its contribution to overall retail sales growth since the 3 months to April 2016.

This upward trend in retail sales since the start of 2016 is supported with the latest survey by the British Retail Consortium and KPMG suggesting retail sales grew robustly in July at its highest rate since the start of the year.

The only international estimate of retail sales available for July 2016 was published by the US Census Bureau on 12 August 2016 in its advanced retail sales estimates for July 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 3 August 2016 for June 2016, show the seasonally adjusted volume of retail trade in both the euro area (EA19) and EU28 when compared with May 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

Quality

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%.

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 standard error of year-on-year movement for “all retailing” is 0.8%. This represents a drop from 0.9% in July 2015. Between this and the current period, the year-on-year movements mostly remained at 0.9% with the only other fluctuations occurring in May 2016, October 2015 and November 2015, where there also was a standard error of 0.8%.

Table 5 shows the year-on-year movement for the non-seasonally adjusted chained volume measure alongside the standard error across the published sector breakdowns for July 2015 and July 2016. The differences between July 2015 and July 2016 highlight that the standard error has only increased for “household goods stores” while it has decreased or remained stable for other sector breakdowns. The greatest decreases are seen for “non-specialised stores”.

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

Revisions triangles

Revisions to data provide one indication of the reliability of main indicators. Table 6 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.

Methods

Information on retail sales methodology is available on our website.

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.

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.