These research outputs are part of the faster indicators of UK economic activity project and are not official statistics. The indicators are still in development and not yet fully in production. We are making these data available at an early stage to invite feedback and comment on their further development.Back to table of contents
In today's publication, we introduce new monthly and weekly shipping indicators of unique ships and visits, including for cargo ships and tankers, covering the time period from April to October 2019; the weekly indicator dataset will be updated on a weekly basis and all new indicators are available for the same set of UK ports as the current indicators (an all-UK total is also available).
This release contains Value Added Tax (VAT) monthly diffusion indices for October 2019 in three industrial sectors and September 2019 in all other industries and for the total all-industries measure; new VAT reporters and record-type indices for October 2019; and data on shipping and road traffic by port in September 2019.
Initial monthly VAT indicators for October were around their historical averages in construction and agriculture.
Initial monthly VAT indicators for October in wholesale and retail were above their historical averages.
The monthly all-industry turnover diffusion index for September was above its historic average for the month-on-month turnover diffusion index and around its historical average for the month-on-year ago turnover diffusion index.
The average road traffic counts for major roads in England remained stable for the two longest vehicle categories, those over 11.66 metres (for example, larger coaches) and those between 6.6 metres and 11.66 metres (for example, buses), in September 2019.
The number of unique ships visiting important UK ports, the total number of visits to UK ports and time-in-port all increased in September 2019.
VAT heatmap and commentary
Figure 1: The balance of VAT indicators shows a broadly stable picture in October 2019
January 2008 to October 2019, UK
The monthly Value Added Tax (VAT) indicators show a broadly stable picture in October 2019. Figure 1 shows that most of the values were slightly above or around their 2008 to 2018 averages (light teal and light grey respectively), with no values considerably above (dark teal) or below (dark red) their long-run averages.
The month-on-month (MoM) turnover diffusion indices for both construction and agriculture were around their 2008 to 2018 averages (light grey) in October 2019, while for wholesale and retail trade the level was above its 2008 to 2018 average (light teal).
All of the VAT diffusion indices are in current prices. The levels of the month-on-year ago (MoY) turnover diffusion indices in October 2019, relative to their 2008 to 2018 averages, were the same as the MoM turnover diffusion indices for the three industrial sectors where data are available. Construction and agriculture were around their 2008 to 2018 averages (light grey), and wholesale and retail trade below its 2008 to 2018 average (light red) for October 2019.
Only three industries have a sufficient number of reporters to be able to compile monthly diffusion indices within a month of the reporting period, which is October 2019 in this release (see the Quality and methodology section for more information):
agriculture, forestry and fishing
wholesale and retail trade
Monthly diffusion indices for other industries and the all-industries measure, containing many more reporters, are available up to September 2019 in this release. For the retail industry, it should be noted that the official retail sales figures for October 2019 have already been released.
In September 2019, the all-industries MoM turnover diffusion index was above its 2008 to 2018 average (light teal) whilst the MoY turnover diffusion index was around its 2008 to 2018 average (light grey). This contrasts with the previous month where both the all-industries MoM turnover diffusion index and the all-industries MoY turnover diffusion index were slightly lower than their 2008 to 2018 averages (light red).
The heatmap is a useful visualisation tool to look across the indicators for a common signal. It can also help in identifying changes in particular indicators which are worth investigating in more detail, as in Figures 2 to 6.
Figure 2 shows the latest monthly diffusion indices for October 2019 for the three available industries. These are:
agriculture, forestry and fishing
wholesale and retail trade
The MoM seasonally adjusted turnover diffusion indices for the three industries shown in Figure 2 show a mixed picture in October 2019, with wholesale and retail trade, and agriculture being slightly positive, and construction being negative. This means that more firms in the wholesale and retail trade and agriculture industries reported an increase in turnover between September 2019 and October 2019 than reported a decrease in turnover between the two periods.
The opposite trend was seen in the construction industry, with more firms reporting a decrease in turnover between September 2019 and October 2019 than reporting an increase. The MoY turnover diffusion index was positive for the wholesale and retail industry, with more firms in these industries reporting turnover increasing between October 2018 and October 2019 than reporting turnover decreasing between these periods. In construction and agriculture, the MoY turnover diffusion index was negative, indicating that more firms reported a decrease in turnover between October 2018 and October 2019 than reported an increase between these periods.
The MoM seasonally adjusted expenditure diffusion index for wholesale and retail trade was positive 0.05 in October 2019. This means more wholesale and retail trade firms reported an increase in expenditure than reported a decrease in October 2019 compared with September 2019. VAT expenditure data include intermediate consumption, investment in capital assets and inventories.
Figure 3 shows the latest monthly diffusion indices for July to September 2019 for all industries combined. The MoM seasonally adjusted turnover diffusion index for all industries combined was positive in September 2019 after being negative in August and flat in July. At positive 0.02 in September 2019, this means that slightly more firms reported an increase in turnover between August 2019 and September 2019 than reported a decrease in turnover between the two periods.
Analysis published in Faster indicators of UK economic activity: Value Added Tax returns showed that monthly reporters are more likely to be firms making repayment claims, which are often from certain industries. While firms from all industries can contribute towards these indices, these biases mean that the industry distribution of firms in these "all industries" indices is not equal to that in the economy.
Despite this, the all-industry MoM turnover diffusion index in September 2019 has 37,860 firms contributing towards it in comparison with the individual industry MoM turnover diffusion indices for October 2019, where:
120 firms contribute towards the agriculture, forestry and fishing index
60 firms contribute towards the construction index
90 firms contribute towards the wholesale and retail trade index
Monthly data to the end of September 2019 are available for other industries in the Economic activity, faster indicators, UK dataset. Each firm contributing to the indices has the same weight regardless of industry, turnover and size.
Figure 4 shows the volatility in the time series for the MoM seasonally adjusted turnover diffusion indices.
The October 2019 value for the MoM turnover diffusion index for the wholesale and retail trade industry was positive 0.05, its highest level since April 2019.
The MoM turnover diffusion index for the construction industry was negative 0.01 in October 2019. The MoM turnover diffusion index for the agriculture, forestry and fishing industry in October 2019 was positive 0.01.
Figure 5 shows the latest values of the MoM seasonally adjusted expenditure diffusion indices in context.
The MoM expenditure diffusion index for construction was negative 0.11 in October 2019, its lowest level since April 2019. The MoM expenditure diffusion index for wholesale and retail trade was flat in October 2019 while it was positive 0.05 for agriculture.
Figure 6 shows the number of new VAT reference numbers appearing in the VAT returns data, seasonally adjusted. In October 2019, the number of new reporters ticked up after falling to its lowest level in two years in September 2019.
Road traffic commentary
In September 2019, the average traffic counts for major roads in England remained stable for the two longest vehicle categories, those over 11.66 metres and those between 6.6 metres and 11.66 metres. The number of vehicles over 11.66 metres in length increased by 0.5% and the number of vehicles between 6.6 metres and 11.66 metres in length increased by 0.4%. Meanwhile, the average road traffic counts for other vehicle categories decreased in September 2019.
We expect larger vehicles (over 6.6 metres in length, such as lorries) to be more closely related to the movement of goods than smaller vehicles (such as cars), and this is what was found in Faster indicators of UK economic activity: road traffic in England.
The new shipping indicators
In today's publication we are introducing a new set of shipping indicators in order to improve the timeliness and relevance of the indicators. Full details and discussion of these new indicators can be found in the article Faster indicators of UK economic activity: more timely and relevant shipping indicators.
- classify ships as "cargo ships or tankers" or "others" so that ships not associated with the movement of goods, such as passenger ships and pleasure craft, can be excluded from the data
- create new weekly indicators for "cargo ships and tankers", for the number of visits to each port and the number of unique ships visiting each port
- reduce the lag between the reference period and publication from around eight weeks to four weeks
- update the weekly indicators on a weekly basis
In this release, the new indicators are available from April to October 2019. We anticipate that the weekly series will be updated incrementally by one week on a weekly basis for a time period that will be subject to user feedback and review. The weekly release will be paused for one week for New Year. Dates for the releases and the time periods covered can be found in the Release calendar. Note that the weekly updates will be published with the monthly release where the dates coincide.
One of the goals for the shipping indicators is to provide an early indication of large changes in the movement of goods into and out of the UK. In these new indicators, we have been able to identify those ships classified as cargo ships or tankers, which are clearly related to the movement of goods. We also exclude all other types of ships such as fishing boats or yachts.
We note that the AIS Shiptype classification that we have used is different from that used in the Department for Transport's UK Port Freight Statistics. In particular, roll-on/roll-off (Ro-Ro) passenger ships are identified in our classification as passenger ships, although they may also carry road freight.
Passenger ships are not currently included in our new indicators, although we will explore additional indicators for passenger ships in the future. This has a particular relevance for Dover, which has by far the largest Ro-Ro vessel arrivals of any UK port. This issue is discussed in more detail in the article Faster indicators of UK economic activity: more timely and relevant shipping indicators where a full description of the data, quality and methodology, and a comparison of the indicators with official trade statistics can also be found.
The dataset for the new indicators can be found at Research Output: Weekly Shipping Indicators - Economics Activity Faster Indicators UK. The new indicators are provided for 13 UK ports, and an all-UK total constructed from the sum of the counts for those ports.
This section discusses the shipping indicators based on counts of all vessel types, as published since April 2019.
The shipping indicators are non-seasonally adjusted, making any monthly changes harder to interpret. The shipping indicators are available from August 2016 in the Economic activity, faster indicators, UK dataset, but a change in the data provider and methodology means only the data since October 2018 are comparable with the latest data. See Shipping indicators in Section 5 for more details.
As discussed in Faster indicators of UK economic activity: shipping, we expect the shipping indicators to be related to the import and export of goods. The relationship with imports and exports, and caveats, are presented in more detail in that release.
The number of unique ships visiting important UK ports in September 2019 increased by 4.5% compared with August 2019.
We previously introduced the number of visits to UK ports as an additional measure of traffic in UK ports. This is distinct from the number of unique ships visiting UK ports. The number of visits indicator is expected to be higher than the number of unique ships indicator, as the former will capture visits by ships that make multiple trips to a port in a given time period.
This is particularly relevant for ports such as Dover where there is a regular passage of ferries and cross-channel ships. It is also expected that the number of visits indicator will contain more noise because of the presence of small craft. More details on this new indicator can be found in the Quality and methodology section.
The number of visits to important UK ports increased by 2.7% in September 2019 compared with August 2019, with a marked increase in the number of visits to Dover. In contrast, there was a large fall in the number of visits to Southampton in September 2019.
The total time ships spent in important UK ports increased by 15.1% in September 2019 compared with the previous month, and reached the highest level since December 2018.Back to table of contents
This release is part of the Faster indicators of UK economic activity project, led by the Data Science Campus. The project is delivering new, faster indicators of economic activity constructed from novel data sources. These indicators are available up to one month in advance of official estimates of gross domestic product (GDP). The release includes indicators constructed from three datasets.
Indicators from HM Revenue and Customs (HMRC) Value Added Tax (VAT) returns:
monthly diffusion indicators from turnover reported on VAT returns, January 2008 to October 2019
quarterly diffusion indicators from turnover reported on VAT returns, January 2008 to September 2019
monthly diffusion indicators from expenditure reported on VAT returns, January 2013 to October 2019
quarterly diffusion indicators from expenditure reported on VAT returns, January 2013 to September 2019
VAT reporting types and new VAT reporters, January 2007 to October 2019
Road traffic sensor data for England from Highways England:
monthly average road traffic counts for major roads in England and English port areas, January 2007 to September 2019
monthly average road speeds for major roads in England and English port areas, January 2007 to September 2019
Shipping indicators from Automated Identification Systems (AIS):
a monthly count of the time spent in UK ports from August 2016 to September 2019
a monthly count of unique ships in UK ports from August 2016 to September 2019
a monthly count of visits to UK ports from October 2018 to September 2019
The indicators new to this release are:
a monthly count of unique ships classified as cargo ships or tankers in UK ports from April to October 2019
a monthly count of visits by cargo ships or tankers to UK ports from April to October 2019
a weekly count of unique ships classified as cargo ships or tankers in UK ports from April to October 2019
a weekly count of visits by cargo ships or tankers to UK ports from April to October 2019
a weekly count of unique ships for all ship types in UK ports from April to October 2019
a weekly count of visits for all ship types to UK ports from April to October 2019
It is important to note that we are not attempting to forecast or predict GDP or other headline economic statistics here, and the indicators should not be interpreted in this way. Rather, by exploring big, closer-to-real-time datasets of activity likely to have an impact on the economy, we provide an early picture of a range of activities that supplement official economic statistics and may aid economic and monetary policymakers and analysts in interpreting the economic situation.
Although some of the indicators we have developed track GDP and other economic statistics relatively well over some periods, there is sufficient difference that none should be used to predict GDP on their own. Rather, they should be considered early warning indicators providing timely insight into real activities in the economy, and their potential impact on headline GDP should be carefully interpreted. It may be that these indicators have the power to improve the performance of nowcasting or forecasting models, as components of these models.
A full description of the data, methodology and economic analysis, describing the time series, can be found in Faster indicators of UK economic activity and associated articles.Back to table of contents
The Value Added Tax (VAT) indicators are constructed from the VAT returns reported to HM Revenue and Customs (HMRC) by all VAT-registered firms. Details on who reports, the timing of reporting, and differences between the approach used for these indicators and the use of VAT returns in official statistics can be found in Faster indicators of UK economic activity: Value Added Tax returns.
Constructing the VAT diffusion indices
To construct the VAT diffusion index, all the firms that are in both the time period of interest (time, t) and the comparison period, for example, the previous month for month-on-month indices, are selected. Firms with zero values in both periods are excluded. The index for each time period (t) is then constructed using the following formula:
Note that each firm is given equal weight. We do not adjust for the size of firms' activity.
The formula ensures the indices fall in the interval [negative one to one], inclusive. If all firms report an increase in the latest period relative to the base period, the index would be one. If all firms report a decline, the index would be negative one. If an equal number of firms grow and decline, the index would be zero.
There are four main quality considerations for the VAT indicators.
Although the number of firms included in the indicator is over 250,000 on average for the all-industry quarterly diffusion indicators, the monthly diffusion indicators contain fewer than 100 firms in some periods. The number of firms contributing to each indicator are included in the dataset.
Monthly reporters, used in the monthly diffusion indices, are not representative of the balance of firms across the economy, particularly those reporting in month one (within a month). The agriculture, forestry and fishing, construction, and wholesale and retail trade industries dominate the monthly returns in month one. More generally, it is possible that early-reporting firms may have different characteristics from firms reporting later, even in the same industry.
Changes to tax and collection policies and the data checks performed by HMRC may have an impact on the indices that are not related to the underlying economic climate.
The expenditure measure captures all expenditure that must be reported to HMRC for VAT purposes. This means that it is the sum of intermediate consumption, investment in capital assets, and inventories. Care should be taken in interpreting which of these elements any changes should be attributed to.
Avoiding the identification of individual firms
Splitting the data by industry occasionally results in only a small number of firms left in the indices. In cases where fewer than 15 firms have reported in a particular component or industry, we suppress the entire series. In the event where only a single series is removed, we also remove the next smallest to prevent any derivation of the suppressed series from the total.
Figures are also rounded, to prevent possible inference of exact values. The diffusion index and percentage of new reporters are rounded to two decimal places, and the number of firms for any measure is rounded to the nearest 10.
It should be noted that for some indicators, although they meet these disclosure thresholds, the number of firms contributing can still be low, for instance below 100 firms. As such, caution is needed in interpreting the data.
Road traffic indicators
Average counts and average speed data for traffic on English motorways and major A-roads were obtained from Highways England's TRIS dataset, which lists the roads covered. Traffic flow is measured by induction loop and radar sensors. The data can be split by four categories of vehicle length as follows:
less than 5.2 metres (for example, cars and motorcycles)
5.2 metres to 6.6 metres (for example, panel vans and minibuses)
6.6 metres to 11.66 metres (for example, rigid lorries and buses)
greater than 11.66 metres (for example, larger rigid lorries, coaches and articulated lorries)
Constructing the road traffic indicators
To construct the road traffic indicators for ports in England included in the dataset, we first take the geographic location of each port using the address and visual inspection. Then we find all sensors and road sections that start or end within a 10-kilometre radius of this point. Since the data often have gaps in the sensor outputs, we use all sensors or road sections within 10 kilometres of each port in constructing the indicators.
Further details can be found in Faster indicators of UK economic activity: road traffic. The data for each port are available in the accompanying dataset.
For the road traffic indicators, there are three main issues that need to be considered when interpreting the data.
Individual sensors can drop out unexpectedly, for example, because of road works or faults. The missing data can cause gaps in the time series and affect the average values. For example, if sensors drop out in an area of high traffic counts, the overall average will fall, making it difficult to interpret the time series. The total number of counts for each area is included in the dataset.
There was a change to the data collection methodology in 2015, which causes a step change and a gap in the time series. From January 2007 to December 2014, traffic counts and average speed were monitored for road sections (that is, between two junctions), at 15-minute intervals. From April 2015 onwards, traffic counts and average speeds were collected for individual sensors, also for 15-minute intervals.
There may be biases in the positioning of the sensors, which could be preferentially deployed to areas of heavy traffic, and in recent years, to road sections requiring active traffic management.
The shipping indicators are computed from Automated Identification Systems (AIS) data, which are available from various data providers.
For the period of July 2016 to August 2018, we have used a dataset provided by the Maritime and Coastguard Agency (MCA). Since October 2018, we have used data provided by ORBCOMM. The ORBCOMM data uses satellites to track the position and movement of ships. The new dataset allows us to update the indicators more quickly and gives us access to global shipping information.
The change to the data source has resulted in a step change between the end of the MCA time series and the beginning of the ORBCOMM time series. As we currently have no overlapping period for the two data sources, we cannot carry out a full comparison between the two datasets.
The early indications are that different data collection methodology (satellite compared with terrestrial) results in different distributions for the captured message types and subsequently a discrepancy in the datasets. Therefore, the time series representing the August 2016 to July 2018 period and the time series since October 2018 should not be compared.
For the indicators for cargo ships and tankers, in the time period from April to October 2019, we use data from exactEarth, which has been enriched with the AIS Shiptype number to identify the type of ship. For these indicators, we select ships in the "cargo ships" and "tankers" categories. More information on Shiptypes and a full list of the classifications can be found in the article What is the significance of the AIS Shiptype number? We also make available port traffic and number of visits indicators for all ships for this data source.
More than 99% of the ships in the exactEarth dataset are classified in this way. The classification of the remaining ships is modelled, using ship behaviour, geolocation and time spent at rest. The full details of this methodology, further information on the quality of these indicators, and economic analysis can be found in Faster indicators of UK economic activity: more timely and relevant shipping indicators.
Constructing the shipping indicators
After initial filtering (which removes the messages from ships that do not move more than a predefined threshold distance over a rolling period of six months), the rectangular geo masks, defined in Faster indicators of UK economic activity: shipping, are used to mark the messages as originating from a list of UK ports. Then, through appropriate grouping and aggregation operations, the values of the "time-in-port", "number of unique ships" and "number of visits" indicators are computed for each port.
In particular, the "time-in-port" indicator is computed for each specific port by summing all the periods between messages originating from within the port. The "number of unique ships" indicator is computed by counting the number of unique Maritime Mobile Service Identity (MMSI) observations that have originated within the port area in the particular period.
For the "number of visits" indicator, a visit to a specific port area is defined by a ship entering the boundary of a port after being outside of this area for at least one hour and staying for at least 15 minutes within the port boundary.
Because of the inherent noise in the location signal, there is a probability that ships close to the port boundary suddenly cross into a port when in reality this is just noise in their position data. This is why the 15-minute threshold is enforced upon any visit. We apply similar logic to the one-hour threshold, assuming that a ship to have concluded a meaningful journey, it has to be away from the port for at least an hour before it returns.
The thresholds of 15 minutes and one hour are defined using heuristics and aim to capture a broad spectrum of visit purposes. More data-driven approaches to estimating these thresholds can be researched in further work. Once a marker for a visit is created in the data using the aforementioned conditions, these are then counted for a given time period and constitute the "number of visits" indicator.
The UK's 10 largest ports by cargo in 2017, as reported by the Department for Transport in Port freight annual statistics: 2017 final figures, are included throughout the dataset. These 10 ports cover around 70% of total UK port freight (2017).
The indicators from October 2018 also contain three further ports: Holyhead, Warrenpoint and Larne. Although these three ports are a small fraction of the total for the shipping indicators, this will also contribute to discrepancies between the pre-August 2018 time series, and that from October 2018. The data for each port are available in the accompanying dataset.
For each indicator the value for all ports is the sum of the values for each individual port.
A large number of corrupted messages have to be removed from the raw AIS data. Additionally, a high proportion of the MMSI identifiers report single or inconsistent messages. These must be removed before any sensible aggregations are possible. Also, some ships, like pilot vessels, spend most of their time in port.
Removing all of these messages is based on the presumption that active ships must travel a certain distance over a certain period of time. The specific filter rule that is used in computation of the indicators is that ships must move by more than 0.5 degrees in a combination of latitude and longitude over a period of six months.
Different AIS data providers use different methods for AIS data collection. This inconsistency results in different properties of the data distributions and noise patterns in the datasets, which makes them incompatible. For this reason, the time series representing the August 2016 to July 2018 period and the time series since October 2018 should not be compared.
Gaps in the data represent a significant problem for accurate aggregations. As the number of received messages should be relatively constant, monitoring the number of incoming messages in each period is used to detect and identify gaps in the data.
Avoiding the identification of individual ships
In order to avoid disclosure, we have suppressed any counts of five or fewer and have suppressed further cells to avoid secondary disclosure where necessary. This mostly affects the weekly indicators.
Seasonal adjustment for the VAT indicators was performed using the software X-13ARIMA-SEATS. The method of seasonal adjustment used is the X-11 algorithm. The parameters used in the March 2019 publication were fixed for this release.
The monthly road traffic series were seasonally adjusted using the standard JDemetra+ seasonal adjustment package, with default settings. In JDemetra+, missing values are treated as outliers while X-13ARIMA-SEATS does not handle missing observations.
This methodology and the new data have led to small changes in the seasonally adjusted series relative to the previous publication.
Full details of the data, quality, methodology and economic analyses can be found in Faster indicators of UK economic activity and associated articles.Back to table of contents
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You might also be interested in:
- Faster indicators of UK economic activity
- Faster indicators of UK economic activity: Value Added Tax returns
- Faster indicators of UK economic activity: shipping
- Faster indicators of UK economic activity: road traffic data for England
- Faster indicators of UK economic activity: more timely and relevant shipping indicators