ONS UK Trade Event

ONS UK Trade Event
16 April 2018
Email: ONS.economic.forum@ons.gov.uk
Twitter: #onsuktrade

Welcome
Jonathan Athow
Deputy National Statistician, Economic Statistics

Why trade?
• Referendum and UK’s future place in the
world
• Trade as key part of modern, globalised
economy
• Measurement challenges of cross-border
economic activity
• Economic ownership
• Value-added and complex supply chains
• Services
• Intellectual property

Agenda
Session 1 – New Trade Statistics Developments
10:10 – 10:40 New data and outputs (including Trade in Goods by commodity
and country) - Adrian Chesson, ONS
10:40 – 10:50 International Trade in Services by Partner Country Q4, 2017 -
Sami Hamroush, ONS
10:50 – 11:00 Regional Trade in Goods Statistics disaggregated by smaller
geographical areas – Rafael Mastrangelo, HMRC
11:00 – 11:25 How Department for International Trade uses ONS data - Tom
Knight, DiT
11:25 – 11:45 Q&A
11:45 – 12:00 Refreshment break

Agenda
Session 2 – Trade Asymmetries
12:00 – 12:20 Trade asymmetries ONS/HMRC analyses - Adrian
Chesson & Rafael Mastrangelo, HMRC
12:20 – 12:40 Reconciled Trade flow estimates - Richard Heys, ONS
12.40 – 13.00 Q&A
13:00 – 13:30 Lunch
Session 3 – Trade Analysis and Research
13:30 – 14.30 Value Added from Exports and Indicators of Global Value
Chain Involvement of key UK Manufacturing Industries -
Giordano Mion, ESCoE
14:30 – 15:15 Trade by Industry and Productivity - Philip Wales, ONS
15:15 – 15:30 Q&A
15:30 – 15:45 Round up and close - Jonathan Athow

Session 1 – New Trade
Statistics Developments

UK Trade Statistics
New Data and Outputs
Adrian Chesson
Office for National Statistics, UK
UK Trade Statistics Event
London
16/04/2018

Quality,
timeliness and
trust
Pace of
response
Granular
statistics and
analysis
Mode of supply
Price effects
Erratic trade
Trade in value
added
Trade
asymmetries
8
UK Trade Statistics – Post EU Referendum
User Priorities and Analysis

Trade in
goods
197
countries
(annual)
Trade in
services
197
countries
(annual)
12 service
types
UK Trade statistics
- where we were a year ago
SITC 1-digit
Commodities

Trade in
goods
197
countries
(annual)
235
countries x
125
commodities
Trade in
services
12 service
types
UK Trade statistics
- where we are today
SITC 1-digit
Commodities
Country by
service type
(ITIS
quarterly)
Asymmetries –
A UK
Perspective
(July 17)
Trade
Asymm-
etries
Deep dives
(US and
Ireland)
(Jan 18)
197
countries
(annual)

Trade in
goods
197
countries
(annual)
235
countries x
125
commodities
Industries
(Summer/
Autumn ‘18)
Trade in
services
197
countries
(annual)
12 service
types
Industries
(Second half
‘18)
UK Trade statistics
- where we are heading later in 2018
SITC 1-digit
Commodities
Further deep
dives
(Germany,
France,
Netherlands)
Country by
service type
(ITIS
quarterly)
Volume
Measures (£
million)
Volume
Measures (£
million)
Country by
service type –
all services
(quarterly)
Asymmetries –
A UK
Perspective
(July 17)
Trade
Asymm-
etries
Deep dives
(US and
Ireland)
(Jan 18)

Trade in
goods
197
countries
(annual)
235
countries x
125
commodities
Industries
(Summer/
Autumn ‘18)
Trade in
services
197
countries
(annual)
12 service
types
Industries
(Second half
‘18)
2.Consumption
abroad
4. Person(s)
present
1. Cross border
3. Commercial
presence
Mode of supply
UK Trade statistics
- future position
SITC 1-digit
Commodities
Country by
service type
(ITIS
quarterly)
Volume
Measures (£
million)
Volume
Measures (£
million)
Possible
reduction of
asymmetries
Further deep
dives
(Germany,
France,
Netherlands)
Country by
service type –
all services
(quarterly)
Asymmetries –
A UK
Perspective
(July 17)
Trade
Asymm-
etries
Deep dives
(US and
Ireland)
(Jan 18)

NEW TRADE OUTPUTS TODAY
• Trade in Goods by
Country and Commodity
on BoP basis

UK Trade: Already have Total Trade
Interactive Maps

What if we want to find out the value of cars we
imported from Germany in 2016?
We can see from our current bulletin that Germany is our largest import source, with value
£66 billion…

…and we can also see that the total value of imported cars in 2016 was £33
billion.

0.0 5.0 10.0 15.0 20.0
Germany
Belgium
Spain
Japan
France
Billions £
Countries
Top 5 Import Sources for Cars in 2016
With the new country by commodity data, we can find, for example, the countries from which
we import the most cars, and the commodities that we most import from Germany.
0.0 2.0 4.0 6.0 8.0 10.0 12.0 14.0 16.0
Cars
Medicinal & pharmaceutical products
Road vehicles other than cars
(intermediate)
Miscellaneous electrical goods
(intermediate)
General industrial machinery
(capital)
Billions £
Commodity
Top 5 Commodities Imported from Germany in 2016

We can also use
the online
visualisation tools
to explore the data;
this map shows our
top imports and
exports with a
selected country,
as well as country
rankings.

This tree map
can be used to
decompose
imports and
exports into
categories,
finding the top
trade partners
(shown here:
imports of
machinery and
transport
equipment in
2016)

International Trade in Services by
Partner Country
Sami Hamroush
Head of International Transactions, ONS
STATISITCS OFFICIAL – SENSITIVE until 09:30 on 16 APRIL 2018,
thereafter UNCLASSIFIED

Background
• Growing user demand for more timely and granular trade
in services statistics.
• Transformation programme commenced in August 2016 to
improve the quality, granularity and timeliness of data
collected from the International Trade in Services (ITIS)
survey, which would ultimately improve overall trade in
services statistics.
• Scope of this programme :
Improve the timeliness of granular trade in services
estimates (country and industry).
Improve the quality of annual estimates.

Overview of the International Trade in Services (ITIS)
survey and methodological review of the sample design
• The ITIS survey is the main data source used to compile trade in
services statistics.
• The quarterly panel comprises the largest businesses engaged in
international trade, who account for over 70% of trade in services that is
captured by the ITIS surveys.
• The panel was previously optimised to ensure coverage at a product
group level.
• In order to produce quarterly geography breakdowns, the panel was
optimised to ensure coverage for 54 countries.
• This required the quarterly panel to double in size to approximately
2,200 businesses.

Quarterly country breakdowns
UK exports of services by partner country
Note that estimates exclude a number of industries including banking, travel and transport, are in current prices, and are non-seasonally adjusted.
£ Million Cumulative percentages of total services exports
Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4
United States 8,040 8,164 8,489 8,979 21.8 20.7 21.6 19.9
Ireland 2,203 2,274 2,718 3,267 27.8 26.5 28.5 27.1
Netherlands 1,994 2,462 2,698 2,931 33.2 32.7 35.4 33.6
Germany 2,552 2,507 2,742 2,837 40.1 39.1 42.3 39.9
Rest of World 22,082 24,031 22,670 25,283 100 100 100 100
World Total 36,871 39,438 39,317 43,297

Quarterly country breakdowns
UK imports of services by partner country
Note that estimates exclude a number of industries including banking, travel and transport, are in current prices, and are non-seasonally adjusted.
£ Million Cumulative percentages of total services exports
Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4
United States 4,364 4,899 4,586 4,551 23.5 25.6 22.5 22.7
Germany 1,441 1,404 1,649 1,594 31.3 33 30.6 30.6
Ireland 1,053 1,170 1,390 1,242 37 39.1 37.5 36.8
France 1,196 1,106 1,272 1,219 43.4 44.9 43.7 42.8
Rest Of World 10,495 10,524 11,462 11,480 100 100 100 100
World Total 18,549 19,103 20,359 20,086

Quarterly regional breakdowns
0 5,000 10,000 15,000 20,000
EU
North America
Asia
Non-EU Europe
Central and South America
Africa
Australasia, Oceania and Polar Regions
Q1 2017 Q2 2017 Q3 2017 Q4 2017
Exports
Estimates exclude a number of industries including banking, travel and transport, are in current prices, and are non-seasonally adjusted.

Quarterly regional breakdowns
0 5,000 10,000 15,000 20,000
EU
North America
Asia
Non-EU Europe
Central and South America
Africa
Australasia, Oceania and Polar Regions
Q1 2017 Q2 2017 Q3 2017 Q4 2017
Imports
Estimates exclude a number of industries including banking, travel and transport, are in current prices, and are non-seasonally adjusted.

Early annual estimates
£ MILLION
2016 2017 2016 2017
EU 53,267 62,362 30,879 32,660
NORTH AMERICA 34,822 35,634 17,441 19,132
ASIA 24,778 27,562 10,662 12,867
NON-EU EUROPE 16,818 19,176 5,831 7,598
CENTRAL AND SOUTH AMERICA 4,853 5,418 1,210 2,344
AFRICA 4,970 5,381 1,551 1,760
AUSTRALASIA, OCEANIA AND POLAR 2,261 2,252 903 1,207
WORLD TOTAL 142,657 158,922 68,718 78,097
1 - Provisional figures for 2017, estimated by summing quarterly data. These data are initial estimates and are therefore subject to revision.
2 - Estimates exclude a number of industries including banking, travel and transport, are in current prices, and are non-seasonally adjusted.

Looking ahead
• Quarterly country breakdowns currently limited to industries covered by
the ITIS survey.
• Currently reviewing systems and methods of other sources used for
overall UK trade in services. Once developed, the bulletin will switch to
publishing estimates for total UK trade in services.
Expected second half of 2018.
• Other work also ongoing aimed at producing estimates of trade in
services by industry, and for improving the quality of annual estimates.
Expected second half of 2018.
• Next publication scheduled for 16 July 2018
Q1 2017 to Q1 2018 estimates.

Regional Trade in Goods Statistics
dis-aggregated by smaller
geographical areas
April 2018
31|

Agenda
• Background – Our role…
• What is Regional Trade in Goods Statistics
• New release - Regional Trade in Goods Statistics
disaggregated by smaller geographical areas
32

Our role…
33
• collect, compile, quality assure and publish trade statistics on
goods physically entering and leaving the UK for trade
purposes.
• our data is provided to the Office for National Statistics (ONS)
as a component of the UK Trade Statistical Bulletin and
Balance of Payments
• represent UK within EU, United Nations and OECD forums
• engage with users of trade statistics to better understand
their needs

• Released quarterly, this publication provides a
breakdown of the flows of imports and exports between
regions of the UK and other countries – using the
Overseas Trade Statistics (OTS) data
• There are 12 UK regions, including;
• 9 English regions
• Wales, Scotland & Northern Ireland
• Includes:
• Value of exports/imports by region
• Count of exporters/importers by region
• Value of trade by SITC Section and partner country
Regional Trade in Goods Statistics (RTS)
34| Official | HMRC Regional Trade In Goods Statistics (RTS)

RTS dis-aggregated by smaller
geographical areas….the release
35
• Release on 28 March 2018 covering 2016 Imports and Exports
• Publication included the following for imports and exports
Table 1 NUTS1 (Summary Data)
Table 2 NUTS2 by EU/Non-EU
Table 3 NUTS3 by EU/Non-EU
Table 4 NUTS2 by EU/Non-EU and SITC Section
Table 5 NUTS2 by EU/Non-EU and pre-defined Partner Countries
• Possible future release in Autumn 2018 depending on
feedback (2017 data)

36
Total value (£ million) of EU Exports in 2016 (North East, Northumberland
and Tyne and Wear, Northumberland, Sunderland, and Tyneside)
0
1,000
2,000
3,000
4,000
5,000
6,000
7,000
8,000
North East Northumberland and Tyne
and Wear
Northumberland Sunderland Tyneside

37
Total business count of EU Exports in 2016 (North East, Northumberland
and Tyne and Wear, Northumberland, Sunderland, and Tyneside)
0
500
1,000
1,500
2,000
2,500
3,000
3,500
North East Northumberland and Tyne
and Wear
Northumberland Sunderland Tyneside

Links to releases and contact information
38
• All Regional Trade Statistics information can be found at
https://www.uktradeinfo.com/Statistics/RTS/Pages/default.aspx
• All our Trade Statistics publications can be found at
https://www.uktradeinfo.com/
• Any UK trade in goods statistics queries, contact
uktradeinfo@hmrc.gsi.gov.uk

Other Statistical releases
39
• Overseas Trade in Goods Statistics (OTS) – UK trade with over
230 partner countries by CN 8-digit commodity code (monthly)
• Trade in Goods by Business Characteristics
• The number of VAT registered importers and exporters
• Trade in Goods by Currency of Invoicing
• Trade in Goods Asymmetries report

UK Trade Statistics
Bilateral asymmetries analysis
41
Adrian Chesson
Office for National Statistics, UK
UK Trade Statistics Event
London
16/04/2018

What are trade asymmetries?
• The difference between trade estimates by
bilateral partner countries

How big are the UK’s Trade Asymmetries?
Eurostat Trade in Services Analysis
BE
BG
CZ
DK
DE
EE
IE
GR
ES
FR
HR
IT
CY LV
LT
LU
HU
MT
NL
AT
PL
PT
RO
SI
SK
FI
SE
GB
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
Asymmetry
Inconsistency

What data are available for analysis?
Source OTS, Balance of
Payments or Both
Goods / Services or
both
UK or mirror data or
both
HMRC OTS OTS Goods UK
ONS Pink Book Both Both UK
UN Comtrade OTS for goods, BOP
for services
Both Both
Eurostat Comext
database
Both (aggregate trade
across all partner
countries)
Both Both
Other national
statistics institute
(NSI) or national
central bank (NCB)
mirror data
BOP Both Mirror

45
Largest UK asymmetries, 2014
Source: UN comtrade Credits (Exports) Debits (Imports)
UK
export
data
Mirror
(Import)
data
Absolute
Asymmetry
UK
import
data
Mirror
(Export)
data
Absolute
Asymmetry
Total
Asymmetry
Trade in Goods $ bn $ bn $ bn $ bn $ bn $ bn $ bn
USA 64.2 55.3 8.9 58.6 53.8 4.8 13.7
France 32.5 26.0 6.5 43.5 40.2 3.3 9.8
Netherlands 36.7 33.4 3.3 53.6 48.5 5.0 8.3
Ireland 29.3 22.9 6.4 19.4 17.8 1.6 8.0
Belgium 20.8 22.1 1.4 34.0 39.4 5.3 6.7
Germany 52.0 50.6 1.5 100.3 104.8 4.5 6.0
Trade in Services
USA 83.4 47.9 35.5 38.6 61.2 22.6 58.1
Luxembourg 5.1 12.3 7.2 2.5 14.5 11.9 19.2
Ireland 15.6 15.1 0.5 8.4 26.8 18.4 18.9
France 19.3 25.0 5.7 19.8 30.5 10.7 16.4
Netherlands 18.2 15.8 2.4 7.1 20.5 13.4 15.8
Germany 19.9 25.7 5.7 16.1 25.1 9.0 14.7
Data downloaded 17 March 2017

Trade In Services
Between UK and US
UK Exports
UK Reporter: $83.4
Mirror Reporter: $47.9
UK Imports
UK Reporter: $38.6
Mirror Reporter: $61.1
Balance:
UK Bi-lat Balance: $44.8
US Bi-lat Balance: $13.3
In billions ($)
Source: UN Comtrade (March 2017)

How big are the UK’s Trade Asymmetries?
USA
Lux
Irl
Fra
NL
Ger
Bel
Hong Kong
Rus
Swe
-20
-10
0
10
20
30
40
50
60
-20 -10 0 10 20 30 40 50 60
Othercountries'viewoftradeinservicesbalance
($bn)
UK view of trade in services balance ($bn)
Figure 2: UK and other countries' view of UK trade in services surplus/deficit, 2014, $bn
Source: UN Comtrade

• Prioritised two countries initially: US and Ireland.
• Germany, China, France, Netherlands, Belgium and
Luxembourg also identified as priorities for future
work.
• So far we have focussed on services as the
asymmetries are larger, services are harder to
measure and the data sources are not as strong as
for goods.
48
Deep Dive Approach (ONS)

• ONS identified US as priority country
• Initial BEA/ONS discussions held
• Started regular BEA/ONS audio conferences (every 2-3 weeks) in October
2017
• Shared publically available data by lowest possible service type
• Shared documents about data sources and methodologies
• Calculated asymmetries by service type
• Identified various definitional and methodological differences between the BEA
and ONS data
• Estimated the size of differences where possible.
• Supported each other in publishing separate BEA and ONS articles.
• Discussions continue
49
Deep Dive: UK/US

50
Initial Results – Asymmetry between
US exports and UK imports

51
Initial Results – Asymmetry between
US imports and UK exports

Definitional differences
Difference US exports minus UK
imports, 2016 ($bn)
US imports minus UK
exports, 2016 ($bn)
FISIM included in services by
ONS, implicitly in income by BEA
-1.3 -2.6
Net spread earnings included by
ONS, not by BEA
n/a -3.9
Manufacturing services included
by ONS, in goods by BEA
-0.1 -0.2
Construction imports related to
work done in US included by
ONS, not by BEA
-0.1 n/a
Outright sales/purchases of
franchises and trademarks
included in services by BEA, in
capital account by ONS
0.1 +0.0
Total currently identified -1.4 -6.6
Total asymmetry 25.5 -18.9

• Methodological and definitional differences identified so
far explain some of the asymmetry
• Remaining unexplained asymmetry likely to be largely due
to source data differences
• Bilateral engagement and collaboration are key to making
progress
53
UK/US conclusions so far

• BEA will continue work to enhance its trade in services
statistics.
o Reclassifying certain transactions related to intellectual property
o Introducing a personal, cultural, and recreational services category
o Exploring methods to estimate manufacturing services and FISIM
• Continue to analyse and quantify definitional and
methodological differences
• Review methodologies and information about source data
to understand statistical differences
• Continue and expand engagement with other priority
countries (France, Germany, The Netherlands and others)
• Next update article later this year
Lead contact for work: adrian.chesson@ons.gov.uk
54
Next steps

Trade in Goods Asymmetries
April 2018

Agenda
• Background
• Concepts on measuring asymmetries
• Highlights from 2017 asymmetries report
• Possible reasons for asymmetries
• Next steps and useful links

Background – why we conduct asymmetries analysis
• Quality Assuring Trade Statistics
• Annual process since 2010
• Trade data is finalised prior to analysis, by autumn the
following year. Eg. 2016 analysis due out late 2017.
• Eurostat co-ordinate a reconciliation round of “Intra-EU
Asymmetries”, every 2 to 3 years.
• Opportunity for Member States to collaborate together to solve
the largest asymmetries.

Absolute asymmetry:
Absolute Difference between the declarations (e.g. the absolute value of ‘Reporting
country (i.e. UK) imports from Partner’ minus ‘Partner (i.e. Spain) exports to UK’)
Relative asymmetry:
Difference between declarations as a percentage of the mean of the declarations
The closer the relative asymmetry is to ±200%, the worse the asymmetry.
UK exports to Spain = £178.0m
Spain imports from UK = £212.3m
Relative asymmetry = (-£34.3m ÷ £195.2m) Χ 100 = -18%
Difference between declarations
Mean of the summed declarations( ) x 100
Example:
Key concepts on measuring asymmetries
No real data was used. This is a theoretical example

Highlights from 2017 Asymmetries report
• Time series analysis of asymmetries, covering 2014 to 2016
data.
• Each Member State (MS) asymmetries analysed at a total
level. Able to assess how UK compared against other MS.
• Explored the relationship between absolute and relative
asymmetries.
• A need to conduct the same research for Non EU trade.

0
10
20
30
40
50
60
70
80
90
0
10
20
30
40
50
60
70
80
90
RelativeAsymmetry%
Billions€
UK arrivals from the EU and relative asymmetry 2016
UK arrivals from EU (€m) Relative Asymmetry (%)

0
10
20
30
40
50
60
70
80
0
5
10
15
20
25
30
35
40
45
RelativeAsymmetry%
Billions
UK dispatches to the EU and absolute relative asymmetry 2016
UK dispatches to EU (€m) Relative Asymmetry (%)

Possible reasons for asymmetries
• Threshold differences amid countries
• Exchange rate differences
• Specific movements
• Timing differences
• Leased goods and repairs
• Fraud
• Confidentiality and suppressions
• Customs warehousing
• Missing declarations
• Triangular trade
• Transit trade
• Misclassification (product or
country)

PT Disp: €106 m
HS8: 8703 3219 Diesel Motor Cars
UK Arr: €197m
PT Disp: €122 m
HS8: 8703 2319 Petrol Motor Cars
UK Arr: €37m
Asymmetry: + €91m
Asymmetry: - €86m
Balanced
Product misclassification
Working example from Eurostat Reconciliation Round 2013

Triangular trade
Netherlands place
order for Poland to
export
No real data was used. This is a theoretical example
Poland export
£10m to UK
UK places order of
£10m with
Netherlands

Next Steps and useful links
• Non EU asymmetries analysis will be published in May 2018
• Aim to update the asymmetries overview paper which explains
reasons for asymmetries.
• Planning to add information on why different data sources can
lead to discrepancies. Eg COMEXT vs COMTRADE
• All our Trade Statistics asymmetries reports can be found at
https://www.uktradeinfo.com/Statistics/OverseasTradeStatistics/AboutOv
erseastradeStatistics/Pages/Asymmetries.aspx
• Any UK trade in goods statistics queries, contact
uktradeinfo@hmrc.gsi.gov.uk

Reconciled Trade in Good Flow
estimates
Richard Heys
Deputy Chief Economist - ONS
16th April
This presentation is a summary of externally commissioned research. This presentation does not represent the views of
the ONS, or the speaker. Research funded by the ONS into potential areas of improvements in Economic Statistics is
designed to inform debate, not set a firm direction of travel. The undertaking of research does not commit the ONS to
changing Economic Statistics. All Economic Statistics are only revised or updated in line with the UKSA Code of Practice
(2018) and the consultation processes outline within. All questions on the paper this presentation should be directed to
Dr. Thomas Baranga at baranga@fas.harvard.edu

Asymmetries are a global phenomenon…
67
Average absolute asymmetry in mirror reports – Comtrade – trade in
goods data

…which is reflected in UK mirror statistics
68
Comtrade – trade in goods
data

This is partly driven by gaps in the reports…
Comtrade – trade in goods
data

…but these are mostly in the partner countries’
data, rather than the UK’s.
70

Recent gaps in the Comtrade data for UK trade are
often driven by changes in the nature of states
Year Country $(US)
asymmetry
– UK
imports
$(US)
asymmetry
– UK
exports
1992 Slovenia 141,120,832 73,737,232
1992 Croatia 56,240,392 64,503,648
1998 Kiribati 10,195
2000 Palestine 355,955
2005 Serbia 84,017,467 129,091,521
2005 Wallis & Futuna
Islands
219,876
2006 Wallis & Futuna
Islands
230,846
Initial break-up of
Yugoslavia
Break-up of Serbia
and Montenegro

ONS commissioned research
• Baranga (2017) delivers a model to reconcile trade asymmetries using the UN’s
Comtrade database, where all countries trade is converted in current price US
dollars.
• Taking the difference between mirror reports removes the (unobserved) true /
common trade flow, and allows estimation of the average biases on a
country-by-country basis. These estimated biases can then be used to adjust
reports in the reconciliation process.
• Use an FGLS estimator to control for heteroskedasticity in country reporting.
• The analysis then weights reports, net of their biases, based on the estimated
precision of countries’ reporting. The “optimal” weights minimize the variance in
the error between the reconciled estimate and the true underlying trade flow.
• Also reflect any uncertainty introduced by estimation of the biases and FOB-CIF
margin.

Baranga model: Export report
Model of trade reporting in levels
Xi
ij = Bi
X Xij Ui
ij Ei
ij (1)
Xi
ij Exporter i’s report of exports from i to j
Bi
X Bias in i’s average report of their exports
Xij Exports from i to j
Ui
ij Binary decision to report (1 if report, 0 if not)
Ei
ij Mean reporting error

Baranga model: Mirror Import report
Model of trade reporting in levels
Xj
ij = Bj
MCij Xij Uj
ij Ej
ij (2)
Xj
ij Importer j’s report of imports from i to j
Bj
M Bias in j’s average report of their imports
Cij CIF (Cost, insurance and freight) adjustment
Xij Exports from i to j
Uj
ij Binary decision to report (1 if report, 0 if not)
Ej
ij Mean reporting error

Baranga model: Estimating these equations
Model of trade reporting in logs
xi
ij = βi
X + xij + εi
ij, if Ui
ij = 1 (3)
xj
ij = βj
M + γcij + xij + εj
ij, if Uj
ij = 1 (4)
Scaling of asymmetries with global trade suggests asymmetries are
stable in proportions, not levels
Log linearisation – percentage deviations from reference values
Heteroscedasticity issues arise with log linearisation
- address this with feasible generalised least squares (FGLS)

Baranga model
• Identifying reporting biases and CIF-FOB measurement corrections:
estimate β0 by OLS on
xi
ij – xj
ij = βi
X – βj
M - γCij + eij
eij = ei
ij – ej
ij
γCij CIF costs that go above FOB costs
• Fit êij = xi
ij - xj
ij - βi
X0 - βj
M - γCij
• Estimate var(êij) by non-linear GMM
• Construct FGLS weights wij = 1/√var(êij)
• Estimate β FGLS by OLS on wij (xi
ij – xj
ij ) = wij (βi
X – βj
M - γdij + eij )
Allows us to draw conclusions about relative reporting biases under an
identification constraint – either benchmarking the average bias as zero,
or for a group of countries – Eurostat or OECD.

Distribution of estimated export biases assuming Eurostat
countries are collectively accurate
77
Figure 35

Distribution of Import Biases
78
Figure 37

So how do we measure how well the
UK produces export statistics?
‘The UK consistently
has one of the lowest
estimated reporting
variances.’
‘While the quality of
the top quarter of
countries has
remained very stable
over time, there has
been a tendency for
the range to spread
up, with the third
quartile's precision
worsening over time.’
Figure 45: Variance = Var(eij)

And imports?
‘The UK appears
historically to have had
more noise in its import
measurements than in its
export measurements.’
‘Historically imports
appear to be reported
more accurately than
exports…, which is
consistent with countries
having stronger
incentives to monitor
imports more closely in
order to assess customs
duties.’
Figure 48: Variance = Var(eij)

So are imports more accurately
reported internationally than exports?
Import estimate dominates Export estimate dominates
Analysis suggests
that in recent years,
export estimates
are, internationally,
(slightly) more
accurately
measured than
imports estimates,
in line with the UK.
Figure 54

UK Aggregate Exports & Imports,
1962-2014
82
Exports Imports
Figures 57 & 58

UK’s reconciled trade balance
Figures 59

Summary
• Asymmetries in trade in goods reports have grown steadily as more countries report
data.
• This research delivers a proposed method to reconcile mirror trade reports based on
their estimated precision.
• The UK is recognised, in line with most developed countries, as having relatively
accurate statistics.
• The UK is estimated to have a small over-estimation of exports and small under-
estimation of imports, assuming the Eurostat countries are collectively assumed to be
accurate in aggregate.
• However globally export statistics appear to be more reliably estimated
• Further research taking method into Trade in Services.
84

Session 3
Trade Analysis and
Research

Value-added from trade for key industries
Monique Ebell, ESCoE, NIESR
Giordano Mion, ESCoE, Sussex
Jack Pilkington, Bank of England
Jeremy Rowe, ESCoE, ONS
Sylaja Srinivasan, ESCoE, NIESR
Disclaimer: Any views expressed are solely those of the authors and so cannot be taken to represent those
of the Bank of England or the Office for National Statistics or to state Bank of England policy. The estimates
presented should be viewed as preliminary and may be revised in future work. This work contains statistical
data which is Crown Copyright; it has been made available by the Office for National Statistics (ONS)
through the Secure Data Service (SDS) and has been used by permission. Neither the ONS nor SDS bear any
responsibility for the analysis or interpretation of the data reported here. The work uses research datasets
which may not exactly reproduce National Statistics aggregates.

Disclaimer
“HM Revenue & Customs (HMRC) agrees that the figures and descriptions of results
in the attached document may be published. This does not imply HMRC's
acceptance of the validity of the methods used to obtain these figures, or of any
analysis of the results.
Please note that all statistical results remain Crown Copyright, and should be
acknowledged either as such and/or as "Source: Calculations based on HMRC
administrative datasets". Copyright of the statistical results may not be assigned.
Written work intended for publication should include a note to the effect that: "This
work contains statistical data from HMRC which is Crown Copyright. The research
datasets used may not exactly reproduce HMRC aggregates. The use of HMRC
statistical data in this work does not imply the endorsement of HMRC in relation to
the interpretation or analysis of the information.”

How important are exports to the domestic economy?
• Gross export flows are not an exhaustive measure
• Need to net off inputs and particularly imported inputs

Value-added from exports
0 50 100 150 200 250
Manufacturing (D)
Real estate, renting and business activities (K)
Financial intermediation (J)
Wholesale and retail trade; repairs (G)
Mining and quarrying (C)
Transport, storage and communications (I)
Other community, social and personal services (O)
Direct domestic value-added
Indirect and reimported domestic value-added
Foreign value-added
£bn
Sources of value-added within UK gross exports in 2011?*
* ISIC Rev 3 sectors. The sum of gross exports in all other sectors is under £15bn. See article (Chart 1) for other relevant footnotes.
Source: OECD-WTO TiVA dataset

Newer, more granular data!
0 50 100 150 200 250
Manufacturing (D)
Real estate, renting and business activities (K)
Financial intermediation (J)
Wholesale and retail trade; repairs (G)
Mining and quarrying (C)
Transport, storage and communications (I)
Other community, social and personal services (O)
Direct domestic value-added
Indirect and reimported domestic value-added
Foreign value-added
£bn
Sources of value-added within UK gross exports in 2011?*
Legal and accounting services
Banks and building societies
(monetary financial institutions, MFIs)
Over more recent periods, we estimate
the component of these blue bars from:
Source: OECD-WTO TiVA dataset
* ISIC Rev 3 sectors. The sum of gross exports in all other sectors is under £15bn. See article (Chart 1) for other relevant footnotes.
Motor vehicles

Key questions
• How are exports related to UK domestic economic activity?
• GVA ≈ Wages + Profits
• What share of exports can be directly related to UK
wages and profits?
• Direct domestic value-added (DDVA); one key component
of GVA
• How important are exports in UK domestic economic
activity?
• What share of GVA in each industry is currently reliant on
EU and non-EU exports (demand) and imports (supply)?
• How exposed are these detailed industries to potential
losses in market access and suppliers?

Data and Methodology
Construct direct domestic value-added (DDVA) from firm-level data:
• Manufacture of motor vehicles, trailers and semi-trailers:
HMRC VAT, export and import data
• Legal and accountancy services:
ONS Annual Business Survey
• Banks and building societies (MFIs):
Bank of England firm-level data underlying ONS Pink Book trade statistics
Key assumption:
Symmetry between production for export and domestic markets at the firm-
level

Contribution to existing data and studies
• We build on existing firm-level data
• Our analyses allow measuring very precisely DDVA at a fine
industry level with minimum time lags required so providing
timely and valuable insights into the importance of foreign
demand for the output of specific industries while further
breaking this down into EU28 and extra-EU28 demand
• Our analysis is also easily replicable and is based on existing
datasets that have been extensively used and cross-checked
by both the data producers and various researchers.

From now onwards we focus on the analysis
done for the Motor vehicles industry that has
been subsequently extended to all 4-digit
industries belonging to Manufacturing

Methodology
Table 2 - Individual firms DDVA in exports
1 2 1 -2 3 4 3-4
Total
Output
Total
Intermediate
consumption
Total
GVA Exports
Intermediate
consumption
associated
with exports
DDVA in
exports
Firm 1 100 40 60 20 8 12
Firm 2 100 20 80 60 12 48
Firm 3 100 75 25 0 0 0
Sum across
Firms 300 135 165 80 20 60

Data
• HMRC VAT panel dataset
• The data is available for the period 2011-2015 on an annual
basis (fiscal year) and in a typical year there are about 2.3
million businesses.
• The data basically covers the entire UK non-financial
population of businesses with a turnover of about £80k and
above.
• The VAT panel dataset provides information on output and
intermediates as well as the main industry (5-digit SIC) of the
business.

Data
• Exports and Imports panel dataset
• provides detailed micro data on trade in goods: exports and
imports by firm, product (CN 8-digit nomenclature), country,
and calendar month.
• For the purpose of this study we have aggregated the data at
the calendar year level and focused on 2012-2015.
• We have also aggregated the product and country dimensions,
while distinguishing between EU28 and non-EU28 countries,
and got rid of classified trade transactions as well as
transactions not involving a transfer of ownership.

Data
We finally aggregate business-level variables at the 4-digit SIC
level to obtain a dataset allowing to measure, for each 4-digit
industry in a given year, the following variables:
• Number of businesses
• Output
• Intermediates
• GVA= Output-Intermediates
• Exports, Exports to EU28, Exports to non-EU28
• Imports, Imports from EU28, Imports from non-EU28
• DDVA= GVA* Exports/ Output

Basic Numbers
4-digit Industry Year N firms Output Intermediates GVA
Motor vehicles 2012 462 52,210.10 45,423.70 6,786.40
Motor vehicles 2013 493 58,112.40 50,176.50 7,936.00
Motor vehicles 2014 511 61,986.40 53,053.00 8,933.40
Motor vehicles 2015 548 64,988.90 55,593.20 9,395.70
car bodies, trailers and semi-trailers 2012 632 2,254.60 1,822.40 432.2
car bodies, trailers and semi-trailers 2015 693 4,891.90 2,442.80 2,449.10
electrical and electronic equipment 2012 196 1,119.40 950.7 168.7
electrical and electronic equipment 2013 199 1,118.50 938.7 179.8
electrical and electronic equipment 2014 210 1,209.30 1,023.50 185.7
electrical and electronic equipment 2015 221 1,269.90 1,049.40 220.5
other parts and accessories 2012 1,100 11,390.90 9,530.50 1,860.50

DDVA and reliance on Exports
4-digit Industry Year
Output
GVA
DDVA
Export
Intensit
y
% EU
exports
% non-EU
exports
Motor vehicles 2012 52,210.1 6,786.4 1208.5 17.8% 62.8% 37.2%
Motor vehicles 2013 58,112.4 7,936.0 2476.7 31.2% 38.5% 61.5%
Motor vehicles 2014 61,986.4 8,933.4 3269.6 36.6% 36.8% 63.2%
Motor vehicles 2015 64,988.9 9,395.7 3306.4 35.2% 37.8% 62.2%
car bodies, trailers and semi-trailers 2012 2,254.6 432.2 26 6.0% 54.2% 45.8%
car bodies, trailers and semi-trailers 2013 2,256.5 484.2 28.1 5.8% 60.1% 39.9%
car bodies, trailers and semi-trailers 2015 4,891.9 2,449.1 77.2 3.2% 58.8% 41.2%
electrical and electronic equipment 2012 1,119.4 168.7 71.5 42.4% 76.0% 24.0%
other parts and accessories 2012 11,390.9 1,860.5 337.7 18.1% 77.9% 22.1%
other parts and accessories 2014 11,996.1 2,152.9 362 16.8% 78.4% 21.6%

DDVA and reliance on Imports
4-digit Industry Year
Inputs
GVA
DDVA
Import
Intensit
y
% EU
imports
% non-EU
imports
Motor vehicles 2012 45,423.7 6,786.4 1208.5 39.2% 75.0% 25.0%
Motor vehicles 2013 50,176.5 7,936.0 2476.7 38.5% 75.4% 24.6%
Motor vehicles 2014 53,053.0 8,933.4 3269.6 39.8% 78.5% 21.5%
Motor vehicles 2015 55,593.2 9,395.7 3306.4 37.9% 77.3% 22.7%
car bodies, trailers and semi-trailers 2013 1,772.3 484.2 28.1 13.6% 91.2% 8.8%
car bodies, trailers and semi-trailers 2015 2,442.8 2,449.1 77.2 11.8% 90.8% 9.2%
electrical and electronic equipment 2012 950.7 168.7 71.5 40.7% 74.2% 25.8%
electrical and electronic equipment 2013 938.7 179.8 78.7 50.9% 80.0% 20.0%

Top 5 industries in terms of total DDVA
• SIC codes 3030 (“Manufacture of air and spacecraft and related
machinery”) with £6.95bn
• SIC code 2910 (“Manufacture of motor vehicles”) with £3.31bn
• SIC code 2120 (“Manufacture of pharmaceutical preparations”) with
£1.65bn
• SIC code 2110 (“Manufacture of basic pharmaceutical products”) with
£1.39bn
• SIC code 2651 (“Manufacture of instruments and appliances for
measuring, testing and navigation”) with £1.08bn.
Interestingly, all of these industries rely on a large amount of imports, and
very often EU28 imports, to source intermediate goods needed for
production.

Top 5 industries in terms of import dependence
Overall imports
• SIC code 3316 (“Repair and maintenance of aircraft and spacecraft”)
• SIC code 2720 (“Manufacture of batteries and accumulators”)
• SIC code 2830 (“Manufacture of agricultural and forestry machinery”)
• SIC code 2020 (“Manufacture of pesticides and other agrochemical products”)
• SIC code 2444 (“Copper production”).
EU imports
• SIC code 2830 (“Manufacture of agricultural and forestry machinery”)
• SIC code 2020 (“Manufacture of pesticides and other agrochemical products”)
• SIC code 2444 (“Copper production”)
• SIC code 2931 (“Manufacture of electrical and electronic equipment for
motor vehicles”)
• SIC code 2732 (“Manufacture of other electronic and electric wires and
cables”)

Top 5 industries in terms of export dependence
EU exports
• SIC code 2931 (“Manufacture of electrical and electronic equipment for
motor vehicles”)
• SIC code 2053 (“Manufacture of essential oils”)
• SIC code 2815 (“Manufacture of bearings, gears, gearing and driving
elements”)
• SIC code 2012 (“Manufacture of dyes and pigments”)
• SIC code 2442 (“Manufacture of ceramic sanitary fixtures”).

What’s next?
• The analysis now covers all manufacturing 4-digit industries
• Very detailed
• Lots of information
• Can soon be updated to 2016
• Full report will be soon available via ONS as well as on my
website:
https://sites.google.com/site/giordanomionhp/workshops

Productivity and Trade in Goods: Survey and
Administrative Data Linking
106
Philip Wales
Head of Productivity, Office for National Statistics
16 April 2018

Outline
1. Motivation
2. Literature
3. Data & Methodology
4. Publication plans
107

Motivation
• The UK’s recent productivity performance has been
strikingly weak – known as the ‘Productivity Puzzle’…
108

Motivation
109
75
80
85
90
95
100
105
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
Output per hour Output per worker
The UK’s ‘Productivity Puzzle’: Q4 2007=100

Motivation
110
UK output per hour growth, rolling 10-year compound
average annual growth rate, 1770-2017
-2
-1
0
1
2
3
4
5
1770 1790 1810 1830 1850 1870 1890 1910 1930 1950 1970 1990 2010
Bank of England ONS
%
Source: ONS Productivity Bulletin, January 2018

Motivation
• … but differences across firms – even within the
same industry – are equally striking…
111

Motivation
112
0.0%
0.5%
1.0%
1.5%
2.0%
2.5%
-10 0 10 20 30 40 50 60 70 80 90 100
Distribution of output per worker across firms, £000s per
worker per year
2015 2009 2007
Source: Understanding firms in the bottom 10% of the labour productivity distribution in Great Britain

Motivation
• …and the UK economy is quite open in comparison
to the US – where a lot of the trade literature is
focussed…
113

Motivation
114
0
10
20
30
40
50
60
70
80
90
Germany Canada Spain France United
Kingdom
Italy Japan United
States
Total Imports + Exports % of GDP

Motivation
• …and the UK economy is quite open in comparison
to the US – where a lot of the trade literature is
focussed…
• …and these issues have a clear cut across to policy
115

Aims
A large literature (mostly based on data from other
countries) relates trade to productivity. We want to:
a) Understand whether the findings of that literature
apply in the UK context
b) Identify how changes in trading patterns have
influenced UK productivity growth in recent times
This work feeds into broader ONS ambitions, to publish
granular data on trade by product, destination and
industry
116

Literature
117
1. Labour productivity tends to be higher among
businesses which export/import
2. The number of markets served to and the range of
products traded rises with productivity
Although the evidence is divided on whether this is a causal
channel – self selection or learning through trade?

Literature
118
1. Labour productivity tends to be higher among
businesses which export/import
2. The number of markets served to and the range of
products traded rises with productivity
Although the evidence is divided on whether this is a causal
channel – self selection or learning through trade?
3. Trade and productivity are closely related to FDI status
4. Firms which trade tend to also have greater profitability,
pay higher earnings and are more likely to survive

Data:
119
IDBR
HMRC transaction level
dataset
ABS

HMRC Data
1. Exports to the EU (Intrastat)
2. Exports to the non-EU (CHIEF)
3. Export coverage adjustments for incomplete
returns
4. Imports from the EU (Intrastat)
5. Imports from the non-EU (CHIEF)
6. Import coverage adjustments for incomplete
returns
120

HMRC Data
• Each observation is a
specific flow (export/import)
of a specific product
by a specific firm
from a specific port
to a specific geography
at a specific point in time.
• >> Consequently a firm x product x destination x time
vector may appear more than once in any given period
121

• Totals checked against BOP and HMRC
published OTS by commodity and destination
• Data closely matches ONS headline
measures
122
HMRC Data

Alignment with official sources
123
0
5
10
15
20
25
30
35
40
45
Q1 2008 Q1 2010 Q1 2012 Q1 2014 Q1 2016
UK Exports: EU and non-EU, ONS and HMRC, SITCs 0-8, NSA
ONS EU Exports
HMRC EU Exports
ONS Non-EU Exports
HMRC Non-EU Exports
£bn/quarter

124
0
10
20
30
40
50
60
70
Q1 2008 Q1 2010 Q1 2012 Q1 2014 Q1 2016
UK Imports: EU and non-EU, ONS and HMRC, SITCs 0-8, NSA
ONS EU Imports
HMRC EU Imports
ONS Non-EU Imports
HMRC Non-EU Imports
£bn/quarter

125
0 20 40 60 80 100 120
0 Food & live animals
1 Beverages & tobacco
2 Crude materials
3 Fuels
4 Animal & vegetable oils & fats
5 Chemicals
6 Material manufactures
7 Machinery & transport equipment
8 Miscellaneous manufactures
UK Exports, ONS and HMRC, £bn, 2015
Exports HMRC
Exports ONS

126
0.0 20.0 40.0 60.0 80.0 100.0 120.0 140.0 160.0 180.0
0 Food & live animals
1 Beverages & tobacco
2 Crude materials
3 Fuels
4 Animal & vegetable oils & fats
5 Chemicals
6 Material manufactures
7 Machinery & transport equipment
8 Miscellaneous manufactures
UK Imports, ONS and HMRC, £bn, 2015
Imports HMRC
Imports ONS

Methods
• The HMRC data to which we have access is
for VAT units…
127

IDBR Structure
128
Enterprise
VAT
Unit
PAYE
Unit
Reporting
Unit
Local
Unit
Enterprise Group

IDBR Structure
129
Enterprise
VAT
Unit
PAYE
Unit
Reporting
Unit
Local
Unit
Enterprise Group WOWREF
ENTREF
RUREF
LUREF
VATREF PAYEREF

IDBR Structure
130
Enterprise
VAT
Unit
PAYE
Unit
Reporting
Unit
Local
Unit
ENTREF
RUREF
LUREF
VATREF PAYEREF
HMRC TIG transaction data

Methods
• The transactions data to which we have
access is for VAT units…
• …However, the estimates of productivity that
we hold are on a Reporting Unit basis…
131

IDBR Structure
132
Enterprise
VAT
Unit
PAYE
Unit
Reporting
Unit
Local
Unit
ENTREF
RUREF
LUREF
VATREF PAYEREF
HMRC TIG transaction data
Productivity data

Methods
• The transactions data to which we have
access is for VAT units…
• …However, the estimates of productivity that
we hold are on a Reporting Unit basis…
• …which means that our work has proceeded
in two stages:
a) Linking VATREF TIG data to Enterprises
b) Apportioning Enterprise trade to Reporting units
133

Methods
Linking TIG data on a VATREF basis to the IDBR is fairly
straightforward…

135
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
2011 2012 2013 2014 2015 2016 2011 2012 2013 2014 2015 2016
Total trade in goods matched to IDBR Unregistered or no VAT reference
Foreign VAT reference Administrative entry
Other match failure Exclusion of non-monetary gold
Step 1: TIG >> IDBR
Exports Imports

Methods
straightforward…
… but apportioning trade from Enterprises to reporting units
is less so…

Methods
straightforward…
… but apportioning trade from Enterprises to reporting units
is less so…
…because businesses can have quite complex reporting
arrangements…

Matching: Simple Enterprises
138
ENT A
VAT 1 RU
LU LU
ENTGRP 1
VAT 2

139
ENT A
VAT 1 RU
LU LU
ENTGRP 1
VAT 2

140
ENT A
VAT 1 RU
LU LU
ENTGRP 1
VAT 2

141
ENT A
VAT 1 RU
LU LU
ENTGRP 1
VAT 2
• We know where to
assign the data
• Link between
RUREF and
VATREF is unique
• (So can have
multiple VATREFs
but still be simple)

142
ENT A
VAT PAYE RU
LU LULU
RU
ENTGRP 1
Complex Enterprises

Complex Enterprises
143
ENT A
VAT PAYE RU
LU LU
ENT B
VAT PAYE RU
LU LULU
RU
ENTGRP 1

Matching and Apportionment
144
ENT A
VAT PAYE RU
LU LU
ENT B
VAT PAYE RU
LU LULU
RU
ENTGRP 1

Apportionment methods
1. By employment
Divide Enterprise trade among the multiple reporting units of a business in
proportion to employment
145

146
Suppose an Enterprise A is observed importing £100 of product [x].
How do we split the £100 among the Enterprise’s three Reporting Units?
Apportionment by employment: Enterprise A
Employment Est. Trade
RU 1 15 15/50*100=£30
RU 2 10 10/50*100=£20
RU 3 25 25/50*100=£50
Total 50 £100

147
Suppose an Enterprise A is observed importing £100 of specialist roofing materials.
Employment Industry Est. Trade
RU 1 15 41 15/50*100=£30
RU 2 10 42 10/50*100=£20
RU 3 25 65 25/50*100=£50
Total 50 £100

148
This methods ends up apportioning some of the imports of roofing materials to the
finance firm. Does this ‘feel’ right?
Employment Industry Est. Trade
RU 1 15 41 15/50*100=£30
RU 2 10 42 10/50*100=£20
RU 3 25 65 25/50*100=£50
Total 50 £100

1. By employment
Divide Enterprise trade among the multiple reporting units of a business in
proportion to employment
2. By Export/Import intensity of reporting unit industries
Divide Enterprise trade among the multiple reporting unit of a business
based on industry-product weights matrix.
149

150
ENT A
VAT 1 RU
LU LU
ENTGRP 1
VAT 2
• Use the ‘simple’ enterprises
(where we know the industry
of the reporting unit) to
develop a matrix of weights:
• Imports per head of
employment on a product x
industry basis
• Exports per head of
employment on a product x
industry basis

151
Employment Industry
RU 1 15 41
RU 2 10 42
RU 3 25 65
Total 50

152
Employment Industry IPH
RU 1 15 41 55
RU 2 10 42 75
RU 3 25 65 10
Total 50

153
Employment Industry IPH Weighted Emp.
RU 1 15 41 55 =(15*55)=825
RU 2 10 42 75 =(10*75)=750
RU 3 25 65 10 =(25*10)=250
Total 50 1825

154
Employment Industry IPH Weighted Emp. Est. Trade
RU 1 15 41 55 =(15*55)=825 825/1825*100=£45
RU 2 10 42 75 =(10*75)=750 750/1825*100=£41
RU 3 25 65 10 =(25*10)=250 250/1825*100=£14
Total 50 1825 £100

155
Employment Industry Employment IPH
RU 1 15 41 £30 £45
RU 2 10 42 £20 £41
RU 3 25 65 £50 £14
Total 50 £100 £100
Apportionment Methods: Enterprise A

Next steps
• Analysis of the linked dataset, combining both data from
the ABS and HMRC, is ongoing.
• Work to triangulate our methods with other sources is
also ongoing – making changes to improve the match
quality.
• Sensitivity work – to test the dependence of our results
on our methods – is also ongoing.
• We plan to publish our first results alongside our next
productivity release on 6th July

New trade by industry outputs - plans
• Trade in goods – Q3 2018 publication
• Commodity (SITC or CPA) by industry (SIC) by country
• Level of granularity will depend on risk of disclosure based on HMRC
reporters
• Annual current price data, annual publication consistent with
Pink/Blue book aggregate estimates
• First version will be based on industry of VATREF – will then extend
and improve using methods to link to RUREF (outlined in earlier
slides)
• Trade in Services – Q4 2018
• Service type (EBOPS or CPA) by industry (SIC) by country
• Level of granularity will depend on risk of disclosure based on ITIS
reporters and availability of other data sources
• Welcome feedback from users on detail required (product or
industry), and uses for this data: trade@ons.gov.uk

ONS UK Trade Event

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