EFSA Journal 2010; 8 (3): 1531
TECHNICAL REPORT OF EFSA
Collection and routine analysis of import surveillance data with a view to
identification of emerging risks1
European Food Safety Authority2, 3
European Food Safety Authority (EFSA), Parma, Italy
ABSTRACT
At the EU level, Eurostat‟s Comext database reports trade statistics of product mobility between
Member States of the European Union and between Member States and third countries. At an
international level, the United Nations, through the Comtrade database, reports trade data from its
member countries. This report describes the definitions and the methodology used in these two
international trade statistics, with a view to understanding the reasons for discrepancies. Five case
studies of trade data for selected food commodities (i.e. frozen shrimps and prawns, fresh or chilled
mackerel, cereals, live swine and intra-EU trade) were examined in order to assess the potential of
trade data for identifying emerging risks. The Eurostat Comext database has been found to be a tool
that could assist in the identification of emerging risks in combination with data coming from other
sources, including the Comtrade database. Expert judgment is pivotal for selecting the most
appropriate food commodities during a search, interpretation and evaluation of the weaknesses of
trade data, as well as their relevance to data from other sources. Recommendations are made for the
requirements of a system covering EFSA‟s mandate for automatic scanning of the Eurostat Comext
database. Such a system should provide alerts to the users, indicating for example i) significant
increase of the volume of a given product over time to a specific MS or EU in total ii) new trade
partners iii) new food or feed commodities entering the EU.
KEY WORDS
Eurostat Comext database, UN Comtrade database, international trade, emerging risk
1 On request of EFSA, Question No EFSA-Q-2009-00854, issued on 25th February 2010.
2 Correspondence: emrisk@efsa.europa.eu
3
Acknowledgement: EFSA wishes to thank the Joint Research Centre (JRC) at Ispra, the European Anti-Fraud Office
(OLAF) and the Food and Veterinary Office (FVO), and EFSA‟s staff members Tilemachos Goumperis and Tobin
Robinson for the support provided to this EFSA scientific output.
Suggested citation: European Food Safety Authority; Collection and routine analysis of import surveillance data with a view
to identification of emerging risks. EFSA Journal 2010; 8 (3): . [35 pp.]. doi:10.2903/j.efsa.2010.1531. Available online:
www.efsa.europa.eu
© European Food Safety Authority, 2010
Collection and routine analysis of import surveillance data
SUMMARY
There are many users of international trade statistics, including governments, commercial enterprises,
national and international organizations, researchers and the public at large. Users need different data,
depending on the intended use, ranging from data sets of varying detail by country and commodity to
aggregated figures.
At the EU level, Eurostat‟s Comext database reports trade statistics of product mobility between
Member States of the European Union and between Member States and third countries. At an
international level, the United Nations, through the Comtrade database, reports trade data from its
member countries.
This report describes the definitions and the methodology used in international trade statistics, with a
view to understanding the reasons for discrepancies that are revealed during bilateral comparisons of
trade data.
Trade data retrieved from Eurostat Comext and UN Comtrade databases were compiled in this report.
The Emerging Risks unit is using this information as one indicator of possible emerging risks. Five
case studies of trade data for selected food commodities (i.e. frozen shrimps and prawns, fresh or
chilled mackerel, cereals, live swine and intra-EU trade) have been examined in order to assess the
potential of trade data for identifying emerging risks.
Eurostat Comext database has been found to be a useful tool that could assist the identification of
emerging risks in combination with data coming from other sources, including the Comtrade database.
Searches in Comext and Comtrade databases revealed many discrepancies. These are probably due to
the following reasons: different data collection methodology among countries, false declaration of
product or country of origin, confidentiality, time delay, threshold and adjustment applications,
revisions of reported data, valuation and reporting in different commodity classifications
Expert judgment is pivotal for selecting the most appropriate food commodities during a search,
interpretation and evaluation of the weaknesses of trade data, as well as their relevance to data from
other sources. It follows that when estimating exposure, occurrence data should take into
consideration variability originating from trade.
Recommendations are made for the requirements of a system covering EFSA‟s mandate for automatic
scanning of the Eurostat Comext database. Such a system should provide alerts, indicating for
example i) high increase of the volume of a given product over time to a specific MS or EU in total ii)
new trade partners iii) new food or feed commodities entering the EU. This system should give to the
user the possibility of selecting commodity classification, countries, time period, and aggregates of
them. To develop such a system, firstly a copy of the Comext database would need to be downloaded
in EFSA. Updating this data every three or four months would be probably be sufficient, and should
include data of the last six to ten years for annual analysis and three to five years for monthly analysis.
Secondly, the algorithms for identifications of trends should be developed. This procedure will be a
continuous process, based on the experience and the needs over time.
It is noted that the conclusions derived must be treated with caution as the data sources used have
limitations and weaknesses. Further investigation using other potential sources of information is
required to verify the pertinence of such signals.
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Collection and routine analysis of import surveillance data
TABLE OF CONTENTS
Abstract ..................................................................................................................................................... 1
Summary ................................................................................................................................................... 2
Table of contents ....................................................................................................................................... 3
Background ............................................................................................................................................... 4
Terms of reference .................................................................................................................................... 4
Objectives ................................................................................................................................................. 6
Materials and Methods .............................................................................................................................. 7
1. General ............................................................................................................................................. 7
2. Eurostat‟s external trade statistics .................................................................................................... 7
3. Systems for measurement of trade ................................................................................................... 9
4. Commodity classifications ............................................................................................................... 9
4.1.
General .................................................................................................................................... 9
4.2.
Combined Nomenclature (CN) - Harmonised System (HS) ................................................. 10
4.3.
Standard International Trade Classification (SITC) .............................................................. 10
4.4.
Integrated Tariff of the European Communities (TARIC) .................................................... 11
4.5.
Broad Economic Categories (BEC) ....................................................................................... 12
5. Eurostat‟s metadata ........................................................................................................................ 12
5.1.
General .................................................................................................................................. 12
5.2.
Transmission deadlines.......................................................................................................... 12
5.3.
Publication deadlines ............................................................................................................. 12
5.4.
Statistical data ........................................................................................................................ 12
5.5.
Statistical thresholds .............................................................................................................. 12
5.6.
Adjustments for non-collected trade data .............................................................................. 13
5.7.
Partner country ...................................................................................................................... 13
5.8.
Valuation ............................................................................................................................... 13
5.9.
Confidentiality ....................................................................................................................... 13
5.10. Revisions ............................................................................................................................... 14
6. Statistical discrepancies and asymmetries ...................................................................................... 14
6.1.
General .................................................................................................................................. 14
6.2.
Intra-EU statistical discrepancies .......................................................................................... 14
6.3.
Extra-EU statistical discrepancies ......................................................................................... 15
6.4.
Discrepancies between EU and national figures ................................................................... 16
6.5.
Discrepancies between EU and international sources ........................................................... 16
6.6.
Quality assessment ................................................................................................................ 16
7. UN Comtrade ................................................................................................................................. 17
7.1.
Coverage ................................................................................................................................ 17
7.2.
Limitations............................................................................................................................. 17
Results and discussion ............................................................................................................................ 17
8. Case studies of food commodities: use of trade data for the identification of emerging risks ....... 17
8.1.
Frozen shrimps and prawns ................................................................................................... 18
8.2.
Fresh or chilled mackerel ...................................................................................................... 22
8.3.
Cereals ................................................................................................................................... 25
8.4.
Live swine ............................................................................................................................. 27
8.5.
Intra-EU trade ........................................................................................................................ 29
9. Exploitation of trade data: current work in other EU institutions and future development ........... 31
9.1.
The Joint Research Centre and OLAF: Automatic Monitoring Tool .................................... 31
9.2.
The Food and Veterinary Office system................................................................................ 31
9.3.
Requirements for a system for automatic scanning of Eurostat Comext ............................... 32
Conclusions ............................................................................................................................................. 33
References ............................................................................................................................................... 34
Glossary / Abbreviations......................................................................................................................... 35
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Collection and routine analysis of import surveillance data
BACKGROUND
The mission and tasks of the EFSA are described in Regulation (EC) No 178/2002 and include the
responsibility to set up a system for identifying emerging risks. “The Authority shall establish
monitoring procedures for systematically searching for, collecting, collating and analysing
information and data with a view to the identification of emerging risks in the field of its mission”
(art. 34.1).
Trade data have been identified by the EFSA Scientific Cooperation Working Group on Emerging
Risks as a potential useful parameter to detect and monitor emerging risks (EFSA, 2009). Trade data
are used to provide input for emerging risks identification.
Trade data for the EU Member States are available through the Eurostat website and specifically
under the Comext database. In December 2008, during the dioxin incident in Irish pork, exports of
relevant pork products from Ireland to the EU Member States were extracted by the Emerging Risks
Unit directly through Comext. The information retrieval demonstrated routes and trade volumes of
possible contaminated food commodities originated from pork (e.g. fresh, chilled and frozen meat,
bacon, ham, lard etc).
Moreover, Eurostat has set up a database including information related directly to food, i.e. „Food:
From farm to fork statistics‟. This database provides access to various sets of statistics related to food
products and data are collected from different areas within Eurostat. During the risk assessment for
the citrus black spot fungus from South Africa, the Plant Health Panel used, besides import data also
data on geographical areas with citrus trees and on citrus orchards irrigation4. The methodology for
data retrieval is almost the same for both databases (Comext and “farm to fork”).
Tracking of live animals and animal products which are entering the EU or transported between
Member States are reported into the TRACES system. These data have been used by the EFSA
Scientific Panel on Animal Health Welfare in opinions related to the risk assessments of Foot and
Mouth Disease introduction into the EU from developing countries5, crustacean6 , molluscan7 and fish
diseases8
The UN Comtrade database provides data for trade between third countries as well data that a third
country reports for trade with a European Union Member State. These data cannot be found in the
Eurostat Comext database. Thus, for worldwide trade data, the United Nations Comtrade database
could provide additional useful information. Since the scope and also the methodology and timeliness
of the data collection and reporting between the two databases are different, the accessibility and
pertinence of this additional source of information should be evaluated.
TERMS OF REFERENCE
According to the EFSA-M-2009-0079 self-mandate, the EMRISK unit is requested to carry out the
following tasks:
1. Set up a system for downloading and analysing the Eurostat Comext database data for the purpose
of detecting signals of emerging risks:
Set up a system for automated downloading and analysis of various data in the Comext
database;
4
http://www.efsa.europa.eu/EFSA/efsa_locale-1178620753812_1211902274417.htm
http://www.efsa.europa.eu/EFSA/efsa_locale-1178620753812_1178620774122.htm
6
http://www.efsa.europa.eu/EFSA/efsa_locale-1178620753812_1178672822550.htm
7
http://www.efsa.europa.eu/EFSA/efsa_locale-1178620753812_1178675503540.htm
8
http://www.efsa.europa.eu/EFSA/efsa_locale-1178620753812_1178661772108.htm
5
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Collection and routine analysis of import surveillance data
Report and assess the signals found in the Eurostat Comext database. This is done in first
instance in ad-hoc unit meetings and when appropriate in task force meetings (primary filter).
The task force consists of EFSA staff from the science directorates;
Disseminate the information to the appropriate Units and Panels when relevant signals are
detected;
Assess the efficiency of the Eurostat Comext database to detect and monitor signals of
emerging risks;
In urgent situations provide other Units and Panels with most recent available trade data.
2. Other potential databases
Characterise other databases (i.e. UN Comtrade, and those held by OLAF, TRACES),
describe their potential usefulness for the identification of emerging risks and establish
procedures for access by EFSA staff, as appropriate.
To achieve these goals it is essential for the Emerging Risks Unit to set up a close collaboration with
Eurostat, OLAF and DG SANCO (regarding the TRACES system)
Close collaboration is also needed with other units in the EFSA for selecting commodities that will be
downloaded and developing the procedures to analyse the data from these databases. A selection of
commodities could be done based on signals coming from the RASFF and the media monitoring.
Timelines and expected deliverables
From April 2009 onwards, monthly screening of the Eurostat Comext database for trade
volumes of selected food and feed commodities. Analysis and reporting of information to be
discussed in ad-hoc unit meetings and whenever signals are identified, discuss them with the
task force (primary filter).
Report relevant signals identified by the primary filter to appropriate units and
secondary filtering.
panels
for
By the 1st of May 2009 propose a draft handbook for the use of Eurostat Comext database,
which needs to be concise, specific and user friendly.
By 15th of August 2009 provide a technical report
o
with specific study cases of trade data for food commodities reported in the RASFF
or the media monitoring to assess the potential of trade data to identify emerging
risks. Data should be collected in first instance from Eurostat Comext database;
o
description of other databases (i.e. UN Comtrade, OLAF, TRACES), their potential
usefulness for the identification of emerging risks usefulness, and proposed
procedures for access by EFSA staff.
By the end of 2009
o
If the outcome of the assessment made on the use of the Eurostat Comext database as
a tool for monitoring emerging risks is positive, the system will be further developed
to automatically download and analyse various data.
Analysis and reporting of information from the Eurostat Comext and UN Comtrade databases for
trade volumes of selected food and feed commodities have started in June 2009.
The description and evaluation of OLAF‟s and TRACES databases are not part of this report. Access
to both is given to a restricted group of users. By the time of writing this report, the emerging risks
unit could not obtain such an access for either database. Moreover, as an initial trial step before setting
up the task force, analysis and reporting of signals has been carried out within the EMRISK unit.
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Collection and routine analysis of import surveillance data
OBJECTIVES
The objectives of this report are (i) to assess the potential of trade data for identifying emerging risks
through case studies of trade data collected from Eurostat Comext database and (ii) to describe the
UN Comtrade database and its potential usefulness for the identification of emerging risks.
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Collection and routine analysis of import surveillance data
MATERIALS AND METHODS
1.
General
As sources of information, Eurostat Comext, United Nations Comtrade and RASFF (Rapid Alert
System for Food and Feed) databases were used as well as media monitoring engines such as
MedISys (Medical Information System) and Google.
Comext is the Eurostat reference database for external trade. It provides access not only to both recent
and historical data from the EU Member States, but also to trade statistics from a significant number
of third countries. The database is accessible to the public through Eurostat‟s web page9. The
emerging risks unit has produced a handbook for accessing trade data from the Eurostat Comext
database.
The United Nations Commodity Trade Statistics Database (UN Comtrade) contains import and export
statistics reported by statistical authorities of close to 200 countries or areas. It concerns annual trade
data from 1962 to the most recent year. UN Comtrade is available to the general public via the
internet10.
RASFF is a tool to exchange information between Member States and the European Commission on
measures taken to ensure food safety. RASFF notifications are available on the RASFF archive
database11, from 1979 up to present time. Recently, EFSA developed a system for the routine analysis
of RASFF data to facilitate the identification of potentially relevant trends of emerging risks.
MedISys12 is an application of the Europe Media Monitor (EMM) developed by the Joint Research
Centre (JRC). To date, the EMM is a web-monitoring system that has a wide media and language
coverage, accessing an average of approximately 90,000 news articles from more than 2200 news
sites in 50 languages per day (figures of July 2009). MedISys displays only those articles with interest
to Public Health. It analyses the news and warns users with automatically generated alerts.
2.
Eurostat’s external trade statistics
There are many users of international trade statistics, including governments, commercial enterprises,
national and international organizations, researchers and the public at large. The different users need
different data, ranging from data sets of varying detail by country and commodity to aggregated
figures (United Nations, 1998).
Eurostat is the Statistical Office of the European Communities. Its mission is to provide the European
Union with statistical information. For that purpose, it gathers and analyses figures from the national
statistical offices across Europe and provides comparable and harmonised data for the European
Union to use in the definition, implementation and analysis of Community policies.
International trade statistics, as produced by Eurostat, report the value and quantity of goods traded
between Member States (MSs) of the European Union (Intrastat) and by MSs of the EU with third
countries (Extrastat). Community legislation in the field of international trade statistics ensures that
the statistics provided to Eurostat by the MSs are based on legal texts, directly applicable in the MSs,
and on definitions and procedures which, to a large extent, have been harmonised. Detailed and
aggregated data are published for the Euro area, the European Union and for each MS separately
(Eurostat, 2008).
9
http://epp.eurostat.ec.europa.eu/portal/page/portal/external_trade/data/database
http://comtrade.un.org/db/
11
http://ec.europa.eu/food/food/rapidnotification/archive_en.htm
12
http://medusa.jrc.it
10
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Collection and routine analysis of import surveillance data
The Intrastat system came into operation on 1 January 1993 when the Single Market was set up,
causing the disappearance of the borders and Customs formalities for imports and exports within the
EU. For intra-EU trade statistics, any natural and legal person registered for the value added tax
(VAT) in a MS and carrying out an intra-Community trade transaction is responsible for providing the
information. This condition excludes private individuals from reporting on their intra-Community
transactions. In addition, small and medium trade operators are mainly exempt. MSs have
implemented a threshold system which allows intra-Community traders not to report on their
transaction or provide less detailed information on condition that their total trade value does not
exceed a certain amount during the previous or present calendar year. However, MSs assure quality
standards when determining the national thresholds. The new Intrastat legislation was introduced on 1
January 2005 (the European Parliament and Council Regulation (EC) 638/2004, the Commission
Regulation (EC) 1982/2004 and the amending Commission Regulation (EC) 1915/2005).
Extra-EU trade statistics are collected on the basis of the statistical part of the single administrative
document (SAD) provided by the Customs authorities when transactions are above the extra-EU
transaction threshold (1000 EUR or 1000 kg in net mass). In addition to the data collected from SAD
and Intrastat declarations, MSs compile and provide Eurostat with adjustments in order to compensate
for the impact of the trade data not collected due to the threshold system. Therefore, the trade
coverage should be close to 100% (Eurostat, 2007a). The statistics are currently based on the Council
Regulation (EC) 1172/95, the Commission Regulation (EC) 1917/2000 and amending Commission
Regulations EC 1669/2001, 179/2005 and 1949/2005. Categories of goods explicitly excluded from
the compilation of statistics are listed in the above mentioned Regulations (Eurostat, 2008).
In addition to the EU legal requirements, there are a number of international recommendations and
conventions relevant to this topic, although they do not generally have direct legal force. Among
them, many recommendations are contained in the United Nations Statistics Division publication
International Merchandise Trade Statistics: Concepts and Definitions (Series M, No 52, Rev.2)
(IMTS); 199813, and the International Merchandise Trade Statistics: Compiler Manual; 200414, which
represents an international reference publication on this subject. A set of definitions concerning
Customs issues that are relevant for some data on trade statistics is given within the Kyoto
Convention (Eurostat, 2006).
The statistical value does not include taxes on export or import, such as customs duties, value added
tax, excise duty, levies, export refunds or other taxes with similar effect. It includes only incidental
expenses (freight, insurance) incurred, in the case of exports/dispatches, in the part of the journey
located on the territory of the reporting MS and, in the case of imports/arrivals, in the part of the
journey located outside the territory of the reporting MS. It is said to be a FOB value (Free On Board),
for exports/dispatches, and a CIF value (Cost, Insurance, Freight) for imports/arrivals (Eurostat,
2007a).
13
14
http://unstats.un.org/unsd/publication/SeriesM/SeriesM_52rev2E.pdf
http://unstats.un.org/unsd/publication/SeriesF/seriesf_87e.pdf
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Collection and routine analysis of import surveillance data
3.
Systems for measurement of trade
There are broadly two approaches, closely linked with customs procedures, used for the measurement
of international trade in goods. These are the general trade system and the special trade system.
The general trade system is the wider concept and under it the recorded aggregates include all goods
entering or leaving the economic territory of a country with the exception of simple transit trade. In
particular, all goods which are received into customs warehouses are recorded as imports at that stage
whether or not they subsequently go into free circulation in the MS of receipt. Similarly, outgoing
goods from customs warehouses are included in the general trade aggregates at the time they leave the
MS.
The special trade system, on the other hand, is a narrower concept. Goods from a foreign country,
which are received into customs warehouses, are not recorded in the special trade aggregates unless
they subsequently go into free circulation in the country of receipt (or are placed under the customs
procedures for inward processing or processing under customs control). Similarly, outgoing goods
from customs warehouses are not recorded as exports.
The differences between the two systems cause in particular a time lag when the movements are
recorded. Moreover, goods from country A, placed in a customs warehouse of country B and reexported from there to country C will appear in general trade statistics for country B (if such a system
is applied), but never in special trade statistics for that country.
Statistics on extra-EU trade are compiled on a special trade basis. Intra-EU trade statistics, however,
which are defined specifically in terms of the Intrastat system and do not have a direct link to customs
procedures, are not compiled on a general or special trade basis.
For their national figures of extra-trade, however, Denmark, Greece, Ireland and the United Kingdom
publish only according to the general trade system, but provide extra-EU trade data to Eurostat on a
special trade basis. Germany, Estonia, Cyprus and the Netherlands publish trade figures as well on a
general and a special trade basis.
Statistics do not cover goods in transit, which are goods that are merely passing across a MS, by any
means of transport, but are not stored there for any but transport reasons. Statistics do not generally
include illegal trade, although figures for Germany do include illegal trade where it has been
discovered (Eurostat, 2006).
4.
Commodity classifications
4.1.
General
The commodity structure of external trade flows of goods is analysed using various internationally
adopted commodity classifications. These have different levels of detail and are based on different
classification criteria. The basic reason for applying a goods nomenclature is to be able to identify
details of the commodities in order to satisfy a variety of purposes, including customs, statistical and
analytical purposes, particularly for the presentation of external trade statistics with the most detailed
commodity specifications. The complex nature of the basic customs and statistical needs makes it
necessary to have a rather detailed commodity classification.
The Harmonized Commodity Description and Coding System (Harmonized System, or HS), or
extended versions based on HS, such as the Combined Nomenclature used by the MSs of the
European Union provide such details. Classification using these nomenclatures is based on the nature
of the commodity. However, for analytical purposes, such a division of products is not the most
appropriate. Commodity categories more suitable for economic analysis are provided by the Standard
International Trade Classification, Revision 3 (SITC, Rev.3), which classifies commodities according
to their stage of production. The classification by Broad Economic Categories (BEC) is defined in
The EFSA Journal 2010; 8 (3): 1531
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Collection and routine analysis of import surveillance data
terms of SITC, Rev.3 and groups large economic classes of goods with reference to their end use
(United Nations, 2004).
4.2.
Combined Nomenclature (CN) - Harmonised System (HS)
For extra- and intra-EU trade purposes goods are classified according to the Combined Nomenclature
(CN). This classification is based on the Harmonised Commodity Description and Coding System
(HS) managed by the World Customs Organisation. The HS uses a six digit numerical code for the
coding of products and the Combined Nomenclature is further breaking down the coding into an
eighth digit level according to Community needs. The CN is extended with some alphanumeric codes
that are used to identify confidential or adjusted data and trade for which a breakdown of the results at
a detailed level of product classification is not possible (Eurostat, 2007a).
This is an example of the classification of a product in the Combined Nomenclature:
Chapter 10 of the HS: cereals;
Heading 10 06 of the HS: rice;
Sub-heading 10 06 20 of the HS: husked brown rice;
Sub-heading 10 06 20 11 of the CN: Parboiled round-grain rice, husked brown rice.
The HS and CN are in a sense multi-purpose classification for both customs and statistical
applications. It is therefore concerned heavily with the nature or material of the products. For
analytical purposes alternative classifications may be used (Eurostat, 2006).
4.3.
Standard International Trade Classification (SITC)
The Standard International Trade Classification (SITC) of the United Nations has a five-level
hierarchical structure with purely numerical coding. Eurostat‟s external trade statistics publish figures
according to the SITC Rev. 3.
The United Nations Statistics Division produced SITC, Rev.3, using the following considerations
(United Nations, 2004):
(a) The nature of the merchandise and the materials used in its production;
(b) The processing stage;
(c) Market practices and the uses of the product;
(d) The importance of the commodity in terms of world trade;
(e) Technological changes.
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Collection and routine analysis of import surveillance data
SITC, Rev.3 contains 10 sections, which are:
0 Food and live animals
1 Beverages and tobacco
2 Crude materials, inedible, except fuels
3 Mineral fuels, lubricants and related materials
4 Animal and vegetable oils, fats and waxes
5 Chemicals and related products, not elsewhere specified
6 Manufactured goods classified chiefly by material
7 Machinery and transport equipment
8 Miscellaneous manufactured articles
9 Commodities and transactions not classified elsewhere in SITC
The structure of the HS, CN and SITC classifications is illustrated in Table 1.
Nomenclature
Harmonised System (HS)
Combined Nomenclature
(CN)
SITC
Table 1:
4.4.
Level of breakdown
Code
Number of
categories
Chapter
Two digits
99
Heading
Four digits
1244
Sub -heading
Six digits
5224
Sub -heading
Eight digits
9842
Section
One digit
10
Division
Two digits
67
Group
Three digits
261
Sub -group
Four digits
1033
Sub -heading
Five digits
3118
Table 1: Architecture of the HS, CN and SITC classifications in 2006 (Eurostat, 2006).
Integrated Tariff of the European Communities (TARIC)
Results are also available at Eurostat under the TARIC sub-headings (Integrated Tariff of the
European Communities), although these data cannot be accessed by the general public. TARIC
applies only to imports (from third countries) and concerns Community measures such as quotas or
preferences. Each TARIC code comprises 10 digits (a sub-division of a CN eight-digit code)
(Eurostat, 2006).
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Collection and routine analysis of import surveillance data
4.5.
Broad Economic Categories (BEC)
The Broad Economic Categories (BEC) of the United Nations arranges external trade data into enduse categories that are meaningful within the framework of the System of National Accounts (SNA),
namely categories approximating the three basic classes of goods in the SNA: capital, intermediate
and consumer goods. The BEC includes nineteen basic categories. These are not further sub-divided
in the classification and are defined in terms of divisions, groups, subgroups and basic headings of the
SITC (Eurostat, 2007a).
5.
Eurostat’s metadata
5.1.
General
The main methodological issues of Eurostat are described bellow. In broad terms, outward flows from
a MS to a non-member country are called "exports" and outward flows from one MS to another are
called "dispatches". Inward flows from a non-member country are called "imports" and inward flows
from another MS are called "arrivals".
5.2.
Transmission deadlines
According to the EU legislation, Eurostat should be provided with (Eurostat, 2007b):
- extra and intra-EU aggregated statistics within 40 days after the reference month,
- extra-EU detailed statistics within 6 weeks after the reference month,
- intra-EU detailed statistics within 10 weeks after the reference month.
5.3.
Publication deadlines
First results (including estimates) on Euro area and EU trade balances are published on-line around 50
days after the reference month. Data are disseminated simultaneously to all interested parties through
a database update and on Eurostat's website. Data are revised frequently according to national needs
and practices. They become final from six months up to three years after the reference period
depending on the MS (Eurostat, 2007b).
5.4.
Statistical data
The main statistical data provided by Eurostat are:
- the reporting MS,
- the reference period (monthly or yearly),
- the trade flow (import or export),
- the product (as defined in different commodity classifications),
- the trading partner
- the trade value (in 1000 Euro),
- the trade quantity in 100 kg,
- the trade quantity in supplementary units (published for some products according to the Combined
Nomenclature, e.g. the number of live animals),
- the mode of transport.
5.5.
Statistical thresholds
As it has already been discussed, in order to limit the burden on businesses of providing information
on trade, while at the same time maintaining an acceptable quality of data, a system of thresholds is
operated for both intra-EU trade and extra-EU trade below which no information, or reduced
information, is collected.
For intra-EU trade, coverage above the threshold must be at least 97% of the total trade expressed in
value of the reporting MS. For extra-EU trade, legislation requires MSs to adjust their statistical data
The EFSA Journal 2010; 8 (3): 1531
12
Collection and routine analysis of import surveillance data
to incorporate trade below the threshold in their total results. The amount of trade below the
thresholds adopted by the MSs is generally below 1% for both imports and exports, but it may be
higher for some particular products (Eurostat, 2006).
5.6.
Adjustments for non-collected trade data
As Intrastat data collection does not cover 100 % of MS trade with other EU MS, in order to have
complete trade coverage in trade statistics, the deficit caused by the exemption threshold must be
compensated with adjustments. Similarly, the loss of coverage due to late-response or non-response
must be offset by means of adjustments.
The problem of non-response for extra-EU trade should theoretically not exist since extra-EU trade
statistics are based on customs declarations. Nevertheless, adjustments for "late" response may be
necessary, as well as adjustments for trade below the thresholds when an exemption threshold is
applied (Eurostat, 2008).
5.7.
Partner country
For exports and dispatches, the trading partner is in principle the country (or MS) of final destination
of the goods, as it is known at the time of export/dispatch. This practice is also applied by all MSs in
their national figures.
For imports (extra-EU trade), the trading partner is the country of origin of the goods. Goods
obtained entirely from a given country are regarded as originating in that country; goods produced in
two or more countries are deemed to originate in the last country where a substantial processing took
place. In certain well defined cases (returned goods, goods which have been processed in a third
country, works of art), the partner country required for imports is the country of consignment.
For arrivals (intra-EU trade), the trading partner is the MS of consignment of the goods. This is the
MS from which the goods were dispatched without some halt or legal formality in another country
apart from any for transport reasons. Conversely, if there was such an operation in another country,
that country becomes the MS of consignment. The method of trade allocation to a partner country is
one major reason for problems that arise with the comparison of national and community figures
(Eurostat, 2006).
5.8.
Valuation
The statistical value, which is used for the trade data, is the value calculated at national frontiers. It is
an FOB value (Free On Board), for exports and dispatches, or CIF (Cost, Insurance, Freight) for
imports and arrivals. Values are collected in the national currency. In the Eurostat publications, they
are expressed in multiples of Euros. The currency conversion is based on the monthly average of the
conversion rates or for recent figures the fixed conversion rates from national currencies to Euros
(Eurostat, 2006).
5.9.
Confidentiality
As a general definition, data used by the MSs and Community authorities for the production of
Community statistics are considered confidential when they allow statistical units to be identified,
either directly or indirectly, so disclosing individual information. The precise operational criteria
determining which statistical data are considered confidential are fixed by each MS in the light of
national legislation or practice. Data can be classed as confidential for all types of trade flows
(imports, exports, arrivals or dispatches) (Eurostat, 2006).
There are three types of confidentiality:
-Partner Confidentiality: in order to conceal the destination or the source or origin of a product, the
code of the partner country is replaced by a „secret country code‟, different for intra- and extra-EU
trade.
The EFSA Journal 2010; 8 (3): 1531
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Collection and routine analysis of import surveillance data
-Product confidentiality: in order to suppress the nature of the commodity involved, all or part of the
trade is allocated to a confidential product code. Information about a product may be regarded as
commercially sensitive either for the value, the quantity or their ratio, since it would give an
indication of the price of the product.
-Product and Partner Confidentiality: the two preceding types are applied at the same time; therefore
both the partner and the product are hidden.
5.10.
Revisions
Early versions of data sent to Eurostat by MS are inevitably subject to revision for a number of
reasons. MSs must inform Eurostat of the revisions to be made for each past month. Several MSs
regularly make such corrections and some transmit revisions only once a year to Eurostat for an entire
12-month period. Corrections, when received, are entered in the databases. They can entail many,
often major, modifications to the published results.
Original data and revisions are entered onto the database as soon as practicable. The users of the online database have the benefit of the latest data available, although the lack of known timetables for
updating can lead to the possibility of confusion (Eurostat, 2006).
6.
Statistical discrepancies and asymmetries
6.1.
General
Users interested in the flow of trade from country A to country B may examine exports from A to B
(as reported by A) or imports into B from A (as reported by B) or both. They may use national
figures, Eurostat data or those of other international organisations. Each source is likely to give, to
some extent, different data. This causes uncertainty and difficulties for the user (Eurostat, 2006).
Eurostat advises that in bilateral comparisons, users have to ensure that the comparisons are possible
in the sense that data are legitimately comparable. In particular, it is difficult to make comparisons of
flows that do not follow from the same basic concept (for example external trade and balance of
payments).
Problems can also arise concerning aggregated data for the "European Union". The exports of the EU
to the rest of the world are clearly not the same as the sum of the total exports/dispatches of each MS
since the latter includes intra- EU trade.
6.2.
Intra-EU statistical discrepancies
In theory, intra-EU statistics of MSs are easily compared (Eurostat, 2006), in particular if the Comext
database is used rather than national figures, since:
-The data to be compared are drawn up on the basis of a broadly common methodology and common
definitions;
-The problem of the FOB and CIF valuations generally plays a smaller role in view of the
geographical context and the structure of intra-EU trade;
-Given the rules for determining reference periods, time delays should not have such a large impact, at
least on annual results;
-The trading partner for arrivals is always the MS of consignment, not the country of origin of the
goods.
However, bilateral comparisons have revealed major and persistent discrepancies in the various MSs
intra-EU trade statistics. The main reasons are:
The EFSA Journal 2010; 8 (3): 1531
14
Collection and routine analysis of import surveillance data
-Intrastat is based on a system of thresholds which makes it possible to exempt small and mediumsized enterprises from statistical formalities. For a given transaction, therefore a company might be
required to provide statistical information in one MS, whereas its supplier or customer in another MS
is exempted. Since January 2005, the principle of full coverage is in force which implies that MSs
should estimate undeclared trade (including trade below threshold) at least at chapter level and by
partner country;
-The phenomenon of late or non-response by certain companies is a serious weakness in the Intrastat
system. The majority of MSs try to offset the loss of coverage by means of adjustments (ranging from
less than 1% to 14% of the trade value);
-Confidentiality;
-Time lag between the date of registration of a transaction as a dispatch in one MS and the date on
which the same transaction is recorded as an arrival in another;
-Wrong classification of products;
-Triangular trade: in the intra-EU context triangular trade can exist in the case of a company in MS A
which sells goods to a company in MS B, which in turn sells them to a company in MS C, although
the goods are "physically" forwarded only once, from A to C. In cases such as this, intra-Community
trade statistics should record a dispatch from A bound for C, and an arrival in C of goods from A.
There is, however, a considerable risk that A or C will regard MS B as its trading partner.
An example illustrating another problem linked to indirect movements, in particular when combined
with the special treatment of transit trade adopted by some MSs through major ports, such as
Rotterdam is given below. The phenomenon described is known as the "Rotterdam effect”,
alternatively known as the “Antwerp Effect”.
Japanese goods are imported into Europe; they are released for free circulation in the Netherlands, and
then dispatched to France (MS of consumption). For such an operation, the various recordings will be
as follows.
For Netherlands national statistics, no trade is recorded, as the import from Japan and dispatch to
France is regarded as transit trade. For French national statistics, goods originating from Japan are
entered as imports. France records Japan as the country of origin, as indicated on the Intrastat
declaration. This information is considered statistically more relevant at national level.
For Community statistics, however, three operations are recorded:
-import of goods originating from Japan (with the Netherlands as the declaring MS, since the customs
declaration is made there);
-dispatch (intra) from the Netherlands to France;
-arrival (intra) in France.
Principally, the Rotterdam Effect causes imports and exports to be attributed to the country of transit
as opposed to the „real‟ partner country. The Rotterdam Effect is not confined to trade between EU
MSs, but can affect trade between any pair of countries where the goods are transported through one
or more additional countries (HM Revenue & Customs, 2005).
6.3.
Extra-EU statistical discrepancies
A comparison of the statistics on extra-EU trade with the figures published by non-member states for
the same trade flows inevitably shows some discrepancies (Eurostat, 2006). These exist whether
national or Community sources are used.
The EFSA Journal 2010; 8 (3): 1531
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Collection and routine analysis of import surveillance data
Many of these differences can be largely explained by the following factors:
-Methodological differences: trade coverage, definition of partner country, definition of statistical
territory, different valuations in theory or practice particularly the difference between FOB and CIF
valuations;
-Time lag: the same operation can be recorded under a different reference period because of transport
times or also because of processing delays;
-Statistical confidentiality: the same operation cannot be recorded in the trade of one of the two
partners because of statistical confidentiality (or the procedures used to avoid disclosure may differ);
-Different practices in the treatment of revisions;
-Problems of currency conversion.
6.4.
Discrepancies between EU and national figures
Differences exist between external trade statistics published by Eurostat and those published by MSs
due to the methodology applied (Eurostat, 2007a). The main sources of conceptual differences
between national and EU figures are:
-Different treatment of goods in transit;
-Certain MSs use a general trade system completely for their national figures while providing data on
a special trade basis to Eurostat;
-Partner country for imports: one MS provides data for their imports to Eurostat on a country of origin
basis, but publishes them at national level on a country of consignment basis;
-Partner country for arrivals: certain MSs provide data to Eurostat on a country of consignment basis,
but they use the country of origin as criterion for their national figures.
6.5.
Discrepancies between EU and international sources
Data management problems are regarded as major contributory factors to the differences between EU
figures and other international sources (Eurostat, 2007a). These problems usually arise from the
following issues:
-MSs send their trade statistics to the UN, the Organisation for Economic Co-operation and
Development (OECD) or the International Monetary Fund (IMF). The differences that exist between
data published by Eurostat and those published by MSs will therefore exist between Eurostat data and
that published by these other international organisations;
-The revisions issue: The national practices in revising data to correct past estimates are complex and
vary between MSs as does their practice in providing revisions to Eurostat and other international
organisations;
-Conversion Methods: The frequency to convert national data into a common currency - euro for EU
figures, dollars for other sources - may be different (monthly, quarterly, annually conversion).
6.6.
Quality assessment
A quality report on foreign trade statistics is available from the web site of Eurostat. The purpose of
this quality report is to provide the users of the European Union foreign trade statistics with a tool for
assessing the quality of these statistics. It provides a summary of the main quality indicators which
are: relevance, accuracy, timeliness, accessibility, clarity, comparability, coherence and completeness
(Eurostat, 2008).
The EFSA Journal 2010; 8 (3): 1531
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Collection and routine analysis of import surveillance data
7.
UN Comtrade
7.1.
Coverage
The United Nations Commodity Trade Statistics Database (UN Comtrade) is continuously updated.
Whenever trade data are received from the national authorities, they are standardized by the UN
Statistics Division and then added to the UN Comtrade (United Nations, 2009).
All commodity values are converted from national currency into US dollars using exchange rates
supplied by the reporter countries or derived from monthly market rates. Quantities, when provided
with the reporter country data, and when possible, are converted into metric units. Commodities are
reported in the current classification and revision (HS2002 in most cases) and are converted to the
earliest classification SITC revision.
7.2.
Limitations
UN Comtrade administrators recognise that certain limitations exist for the provided data and suggest
that users take into consideration the following points before extracting and using the data (United
Nations, 2009):
-The values of the reported detailed commodity data do not necessarily sum up to the total trade value
for a given country dataset. Due to confidentiality, countries may not report some of its detailed
trade. This trade will, however, be included at the higher commodity level and in the total trade value.
For instance, trade data not reported for a specific 6-digit HS code will be included in the total trade
and may be included in the 2-digit HS chapter. Similar situations could occur for other commodity
classifications.
-Countries (or areas) do not necessarily report their trade statistics for each and every year. This
means that aggregations of data into groups of countries may involve countries with no reported data
for a specific year. UN Comtrade does not contain estimates for missing data. Therefore, trade of a
country group could be understated due to unavailability of some country data.
-Data are made available in several commodity classifications, but not all countries necessarily report
in the most recent commodity classification. Again, UN Comtrade does not contain estimates for
data of countries which do not report in the most recent classification.
-Imports reported by one country do not coincide with exports reported by its trading partner.
Differences are due to various factors including valuation (imports CIF, exports FOB), differences in
inclusions/exclusions of particular commodities, timing etc.
-Almost all countries report as partner country for imports the country of origin, which is
determined by the rules of origin established by each country. Hence, the term „partner country‟ in the
case of imports does not necessarily imply any direct trading relationship.
RESULTS AND DISCUSSION
8.
Case studies of food commodities: use of trade data for the identification of emerging
risks
The results section is constituted by five case studies, each one including:
-The reason for selecting the food commodities i.e. the signals coming from the RASFF, the media
monitoring, the scientific literature or from other EFSA outputs;
-A description of the commodity codes and the time period selected;
-The relevant trade data from Eurostat Comext and in some cases from UN Comtrade databases;
The EFSA Journal 2010; 8 (3): 1531
17
Collection and routine analysis of import surveillance data
-Short discussion of the results.
8.1.
Frozen shrimps and prawns
Background
The use of veterinary drugs in aquaculture has contributed to the detection of chloramphenicol and
nitrofuran antimicrobial residues in shrimps imported from South East Asian countries and China into
the EU in 2001. These findings have led the EU in the past to impose temporary controls for the
presence of antimicrobial residues on all shrimps imported from those countries.
An exceptional increased number of nitrofuran metabolite notifications for shrimps originating from
India, Bangladesh and Sri Lanka to the RASFF are noted during the first five months of 2009,
compared to previous annual figures. Searches in the media have shown that similar findings of
residues in aquatic products of Chinese origin imported into the USA forced the authorities to apply
border controls in 200715, 16, 17 .
To investigate these signals, searches in the Eurostat Comext and UN Comtrade databases were
performed for the commodity “03611- Shrimps and prawns, frozen” of the Standard International
Trade Classification (SITC) and for the period 2003 to 2008 (2008 being the last year for which a
complete dataset was available).
Trade data
The relevant findings in the Eurostat Comext database were:
The EU is importing frozen shrimps and prawns from many trade partners around the world.
In 2008, the EU imports accounted for 466,188 tonnes and Ecuador, India and Greenland
were the main trade partners. Detailed trade figures for the period 2003-2008 are provided in
Table 2. However, it is not possible to distinguish the quantities coming from wild capture
and aquaculture;
The EU imports of frozen shrimps and prawns from South-East Asia (India, Bangladesh,
Thailand, Vietnam, Indonesia, Malaysia) and China are illustrated in Figure 1 and have been
increasing from 2003 onwards; from 106,135 tonnes in 2003 to 181,699 tonnes in 2008
(Table 2). This is a disproportionate increase of 71% compared to an increase of 14% of the
total EU imports from all over the world during the same period;
India was the main exporter to the EU of the South-East Asia region for the period 2003-2008
and its trade volume has been increasing from 40,210 tonnes in 2003 to 52,400 tonnes in
2008. China was the second most important exporter from this region to the EU after 2005
(lift of the EU‟s ban on Chinese shrimps in 2004; Decision 2004/621/EC). Imports accounted
for 34,812 tonnes in 2008, having a peak of 38,211 tonnes in 2006. Bangladesh exports
accounted for 27,902 tonnes in 2008, showing an increase from 2004 onwards. Vietnam and
Thailand have been expanding their exports to the EU after the revocation of control
measures for the presence of antimicrobial substances in 2002 and 2003 respectively
(Decisions 2002/770/EC and 2003/477/EC). In 2008, imports from Vietnam 21,005 tonnes
and from Thailand accounted for 22,967 tonnes;
15
http://www.cidrap.umn.edu/cidrap/content/fs/food-disease/news/jun2807china.html
http://www.medicinenet.com/script/main/art.asp?articlekey=82214
17
http://www.fda.gov/NewsEvents/Newsroom/PressAnnouncements/2007/ucm108941.htm
16
The EFSA Journal 2010; 8 (3): 1531
18
Collection and routine analysis of import surveillance data
In contrast, imports from Indonesia have been decreasing over the last years, from 21,211
tonnes in 2003 to 18,645 tonnes in 2008. Malaysian exports to the EU have been decreasing
as well; from 15,758 tonnes in 2003 to 3,969 tonnes in 2008;
Small volumes from Sri Lanka have been imported to the EU over the same period; 424
tonnes in 2003, 183 tonnes in 2004, 256 tonnes in 2005, 89 tonnes in 2006, 146 tonnes in
2007 and 209 tonnes in 2008. Sri Lanka is a minor trade partner of the EU for this food
commodity, but it has been notified 8 times for nitrofurans in the RASFF in April and in May
2009.
Exports of South-East Asian countries and China for the period 2003-2008 to all trade partners as
reported in the United Nations‟ Comtrade database are illustrated in Figure 2.
The relevant findings in the Comtrade database were:
Exports of frozen shrimps and prawns from Vietnam and Bangladesh have increased by 42%
and 113% respectively from 2003 to 2007 (figures for 2008 were not available for both
countries), to 213,828 tonnes and 70,110 tonnes respectively in 2007. The same trend can be
observed for Thailand and Malaysia; exports have increased by 68% and 80% respectively
from 2003 to 2008, accounted for 190,204 tonnes and 56,172 tonnes respectively in 2008.
Indonesia‟s exports in 2008 were at the same level compared to 2003 after a peak in 2006;
In contrast, exports from India and China have decreased over the same period by 28% and
44% respectively.
Discussion
According to the trade figures of the United Nations and Eurostat, a significant increase of exports of
frozen shrimps and prawns from some South East Asian countries to world trade partners and to the
EU respectively from 2003 onwards can be noticed. It has been suggested that increasing trade (and
hence, most probably, increasing production) is an important factor contributing to an increased
disease pressure in aquaculture, due to intensification of aquaculture activity (Achterbosch, 2007;
Kleter et al. 2009). Therefore the probability of a disease outbreak in the associated aquaculture of
those counties could be increased. The latter might lead once more to the use of antimicrobial
substances in aquaculture, such as nitrofurans, the use of which, and occurrence in foodstuffs, are
prohibited in the EU, resulting in food safety problems of two kinds: increased frequency of antibiotic
resistant bacterial strains in the environment and residues of veterinary pharmaceuticals in the shrimps
for human consumption.
United Nations‟ Comtrade database is providing trade data reported by countries all around the world
and not only from EU MSs, as Eurostat does. However, statistics from these two organisations are not
directly comparable as methodological inconsistencies may exist among different countries during
collection, analysis and reporting of information.
In the context of this example, the data from UN Comtrade is used to indicate a possible
intensification of aquaculture through increasing exports to all countries The data from Eurostat,
indicates the main exporters to the EU and trends in the level of imports over time, and is compared to
the number of notifications in the RASFF as an indication of a potential emerging risk originating
from importation.
The EFSA Journal 2010; 8 (3): 1531
19
Collection and routine analysis of import surveillance data
EU imports of frozen shrimps & prawns by SITC
Extracted on
FLOW
PRODUCT
REPORTER
2009/05/28 14:12:46
import
03611 - Shrimps and prawns, frozen
EU27
Quantities in tonnes
PARTNER/PERIOD
Total EU27
2003
407663
2004
399807
2005
430266
2006
487027
2007
492374
2008
466188
Ecuador
India
Greenland
Argentina
China
Bangladesh
Thailand
Vietnam
Indonesia
Colombia
Canada
Madagascar
Brazil
Venezuela
Honduras
Nicaragua
Mozambique
Morocco
Guatemala
Panama
Malaysia
Nigeria
Senegal
19195
40210
56680
39558
1143
22090
857
4866
21211
10647
16209
12168
37132
3974
5061
1766
6891
5746
5006
1358
15758
5944
5732
30638
35942
57712
27462
3461
20524
2333
6462
25666
9145
16067
11219
43000
8134
6572
2747
6170
4628
4172
2087
15076
6208
4861
43083
38581
58258
6582
34211
23725
4865
13062
20233
11947
20593
10073
40089
7728
6856
3451
7809
5590
3660
3079
10346
6347
5373
55292
46946
61914
31871
38211
26412
8332
14063
18954
11779
22090
10503
32503
9733
9650
4335
7502
4978
6197
3671
10319
5937
4499
62950
49673
56064
45230
37312
26986
16543
14879
18656
12280
27539
9848
18158
8620
10927
7376
8395
5892
3747
4727
8343
4761
4616
73643
52400
51507
38564
34812
27902
22967
21005
18645
12914
11925
10174
9592
8745
7261
7000
6402
5736
4445
4273
3969
3780
3424
Total SE Asia & China
106135
109463
145022
163237
172391
181699
Table 2: Volume of the EU imports of frozen shrimps and prawns. Source: Eurostat Comext database.
Note: only trade partners with exports above 3000 tones in 2008 are included in the Table and are sorted
in the first column according to the trade figures of 2008.
The EFSA Journal 2010; 8 (3): 1531
20
Collection and routine analysis of import surveillance data
60000
50000
Quantity in tonnes
India
40000
China
Bangladesh
30000
Thailand
Vietnam
20000
Indonesia
Malaysia
10000
0
2003
2004
2005
2006
2007
2008
Figure 1: Volume of EU imports of frozen shrimps & prawns from South-East Asia and China. Source:
Eurostat Comext database. Note: lift of EU ban on Chinese shrimps in 2004.
220000
200000
180000
Quantity in tonnes
160000
140000
120000
100000
80000
60000
40000
20000
0
2003
Viet Nam
Bangladesh
2004
2005
Thailand
China
2006
India
Malaysia
2007
2008
Indonesia
Figure 2: Volume of total exports of frozen shrimps & prawns from South-East Asia and China. Source:
United Nations Comtrade database. Note: Vietnam and Bangladesh figures of 2008 were not available.
The EFSA Journal 2010; 8 (3): 1531
21
Collection and routine analysis of import surveillance data
8.2.
Fresh or chilled mackerel
Background
More than 140 notifications regarding the parasite Anisakis in fish can be found in RASFF since 2004.
The main reporting country is Italy. This country has made 20 notifications to the RASFF concerning
specifically fresh mackerel contaminated with Anisakis sp. originating from Norway or from Norway
via Denmark during the period 2004-2009.
To investigate this issue, searches have been conducted in Eurostat Comext database for the
commodity “03417 - mackerel (scombrids) fresh or chilled (excluding livers and roes)” of the SITC
and for the period 2002 to 2008.
Trade data
The EU imports of fresh or chilled mackerel have increased from 2680 tonnes in 2004 to 7132 tonnes
in 2008, having a peak in 2006 of 10486 tonnes, according to trade data reported by the Eurostat
(Figure 3). A parallel increase of the UK imports from extra-EU countries can be observed for the
same period; from 2 tonnes in 2004 to 5910 tonnes in 2008, with a peak of 9184 tonnes in 2006. The
UK imports from EU MSs decreased, mainly due to the decrease of imports from Ireland. The
contribution of the extra-EU imports to the total imports to the UK of fresh or chilled mackerel has
been changing every year, accounting for 30% in 2005 and 2007, 51% in 2006 and 39% in 2008
(Table 3). The predominant extra-EU partners of the UK in the period 2005-2008 were Norway and
the Faroe Islands (Figure 4).
According to the Eurostat data, Italy imported fresh or chilled mackerel mainly from the EU MSs
during the period 2002-2008 (Table 4). The main trade partners were Spain, France and Denmark and
the total intra-EU imports accounted for 6163 tonnes in 2008. Imports from extra-EU countries were
limited, having a maximum of 35 tonnes in 2007.
12000
Quantity in tonnes
10000
8000
6000
4000
2000
0
2002
2003
2004
EU27 imports
2005
2006
2007
2008
UK imports
Figure 3: Volume of EU and UK imports of fresh or chilled mackerel from extra-EU partners. Source:
Eurostat Comext database.
The EFSA Journal 2010; 8 (3): 1531
22
Collection and routine analysis of import surveillance data
UK imports of fresh or chilled mackerel by SITC
Extracted on
2009/04/09 15:51:20
FLOW
import
PRODUCT
03417- Mackerel (scombrids), fresh or chilled (excluding livers
and roes)
REPORTER
United Kingdom
Quantities in tonnes
PARTNER/PERIOD
2002
2003
2004
2005
2006
2007
2008
EU27_EXTRA
26
0
2
4630
9184
4954
5910
Norway
4
1
8594
3339
4627
Faroe Islands
22
4629
499
1561
1124
EU27_INTRA
19832
12294
9983
10576
8849
11356
9112
Ireland
19522
12096
9484
9782
4663
4844
4533
Denmark
15
27
201
139
3468
3921
2635
Germany
1
0
0
0
0
987
0
Netherlands
0
1
0
51
146
555
991
229
385
Sweden
France
38
71
87
146
280
420
281
Spain
257
27
123
199
104
204
218
258
175
194
69
Belgium
0
Italy
71
87
2
7
2
12294
9985
15206
18033
16310
Total imports
19858
15022
Table 3: Volume of UK imports of fresh and chilled mackerel from extra-EU and intra-EU partners.
Source: Eurostat Comext database. Note: some minor imports are not included.
10000
9000
Quantity in tonnes
8000
7000
6000
5000
4000
3000
2000
1000
0
2002
imports from Norway
2003
2004
2005
imports from Faroe Islands
2006
2007
2008
imports from other partners
Figure 4: Volume of UK imports of fresh and chilled mackerel from extra-EU partners. Source: Eurostat
Comext database.
The EFSA Journal 2010; 8 (3): 1531
23
Collection and routine analysis of import surveillance data
Italy's imports of fresh or chilled mackerel by SITC
Extracted on
FLOW
PRODUCT
REPORTER
PARTNER/PERIOD
2009/06/14 00:17:12
import
03417- Mackerel (scombrids), fresh or chilled (excluding livers
and roes)
Italy
Quantities in tonnes
2002
2003
2004
2005
2006
2007
2008
EU27_EXTRA
3
15
18
9
35
9
Croatia
3
15
18
9
35
8
EU27_INTRA
Spain
5237
3321
4217
1641
6032
3374
6308
3924
9659
7563
6409
4199
6163
3833
France
Denmark
1022
760
1573
727
1481
841
1557
601
1335
558
1662
453
1481
765
Sweden
Slovenia
Netherlands
Germany
Greece
Portugal
81
75
27
1
1
1
22
177
1
21
1
4
59
74
1
18
110
4
14
0
13
21
59
14
7
0
2
0
48
10
10
9
4
0
Belgium
Austria
Ireland
0
1
0
9
25
7
0
17
Total imports
5237
4220
6047
6326
9668
6444
41
4
19
53
6172
Table 4: Volume of Italy’s imports of fresh and chilled mackerel from extra-EU and intra-EU partners.
Source: Eurostat Comext database. Note: some minor imports are not included.
Discussion
No imports from Norway or the Faroe Islands have been reported (Table 4) by Italy even though
relevant notifications can be found in the RASFF during the same period. This discrepancy may be
due to underreporting, false declaration of product commodity (e.g. wrong fish species) or of country
of origin (e.g. Denmark instead of Norway, due to trade with a MS being the intermediate partner).
Despite the fact that UK imports of mackerel from extra-EU partners have increased significantly
from 2005 onwards and accounted for 79-88% of the total EU imports for the same period, there were
no notifications in the RASFF for Anisakis sp. from the UK authorities. Considering that during the
same period Italy has reported Anisakis many times in mackerel from the same extra-EU partners, and
declares a much smaller volume of imports, the data implies that there may be a recent and significant
increase in human exposure to Anisakis that is underreported.
The EFSA Journal 2010; 8 (3): 1531
24
Collection and routine analysis of import surveillance data
8.3.
Cereals
Background
Many notifications related to cereals can be found in the RASFF and they are of significant interest
because of their importance in the human (and farm animal) diet. Therefore, searches in Eurostat
were conducted for the commodities “044 – maize”, “041 – wheat” and “042 – rice” of the SITC and
for the period 2005 to 2008.
Trade data
Trade data for maize are given in Table 5. The increase of EU imports from 3,721 thousand tonnes in
2006 to 10,827 thousand tonnes in 2007 was due to the increase of imports from Brazil and Argentina.
Over the same period, imports from Serbia have been decreasing to one third; from 1,059 thousands
tonnes to 355 thousands tonnes. Russia appears to be a new trade partner; no imports have been
reported for 2005 and 2006, in contrast with one thousand tonnes in 2007 and 50 thousand tonnes in
2008.
The volume of the EU imports of wheat have varied over the last years; from 7102 thousand tonnes in
2005, to 5613, 6402 and 6847 thousands tonnes in 2006, 2007 and 2008 respectively (Table 6).
Imports from Ukraine had significantly decreased from 1924 thousand tonnes in 2005 to 714 and 212
thousand tonnes in 2006 and 2007 respectively and increased to 2759 thousand tonnes in 2008. Spain
was the predominant importing MS of Ukrainian wheat in 2008 by 69% of the total imported quantity
into the EU. No imports from Mexico have been reported for 2005, while 151, 182 and 234 thousand
tonnes have been imported in 2006, 2007 and 2008 respectively and only by two MSs i.e. Italy and
Spain.
The EU imports of rice have been increasing by 10% every year during the period 2005 to 2008
(Table 7). Imports from Thailand and Pakistan doubled during this period, while imports from the
USA decreased by 56%. Imports from Uruguay have been increasing significantly from ten thousand
tonnes in 2005 to 125,000 tonnes in 2008; the main importers in 2008 were Germany and the UK. The
Dominican Republic appears to be a new trade partner of this commodity in 2008.
Discussion
Trade data for these three cereal commodities have shown that significant changes in trade pathways
have occurred. When the import profile of a MS or the EU in total changes, the exposure to hazards
may also alter. As an example, if one country is highly notified in the RASFF for ochratoxin A in rice
and imports from this country increase over time, incidentally EU rice consumers would be exposed
to this hazard to a greater extent.
Imports from new or traditional trade partners also constitute a potential vehicle for the transmission
of new plant diseases to European cultivation. For example, for their pest risk assessment on
Guignardia citricarpa Kiely, citrus black spot fungus, the Panel on Plant Health used Eurostat import
data of citrus fruits from South Africa. The Panel concluded that G. citricarpa is able to survive
transport and storage and that the importation of citrus fruit from infested areas of South Africa is a
possible pathway for the introduction of this pest into the EU cultivations (EFSA, 2008).
The EFSA Journal 2010; 8 (3): 1531
25
Collection and routine analysis of import surveillance data
Trade partner
2005
2006
2007
2008
Total extra-EU
2615
3721
10827
9734
Brazil
117
847
6975
4152
Argentina
1525
1065
2801
3731
Ukraine
340
378
82
1177
Paraguay
0
103
512
265
Serbia
319
1059
355
129
Croatia
55
168
3
85
Russia
0
0
1
50
USA
46
32
35
47
India
0
0
0
32
Table 5: Volume of the EU imports of maize (code 044 by SITC). Quantities in thousand tonnes. Source:
Eurostat Comext database. Note: Some minor trade partners are not included in the table.
Trade partner
2005
2006
2007
2008
Total extra-EU
7102
5613
6402
6847
Ukraine
1924
714
212
2759
Canada
1657
1760
1852
1304
USA
1744
875
2001
1151
Russia
786
778
1026
724
Kazakhstan
252
240
412
534
Mexico
0
151
182
234
Australia
394
376
7
55
Table 6: Volume of the EU imports of wheat (code 041 by SITC). Quantities in thousand tonnes. Source:
Eurostat Comext database. Note: Some minor trade partners are not included in the table.
The EFSA Journal 2010; 8 (3): 1531
26
Collection and routine analysis of import surveillance data
Trade partner
2005
2006
2007
2008
Total extra-EU
1193
1320
1455
1638
Thailand
256
306
437
507
India
235
306
371
306
Pakistan
99
111
125
203
USA
290
203
44
127
Uruguay
10
50
145
125
Guyana
106
91
134
120
Egypt
135
170
128
62
Vietnam
11
7
10
45
Brazil
0
14
8
37
Dominican Republic
0
0
0
29
Table 7: Volume of the EU imports of rice (code 042 by SITC). Quantities in thousand tonnes. Source:
Eurostat Comext database. Note: Some minor trade partners are not included in the table.
8.4.
Live swine
Background
During the risk assessment of foot and mouth disease introduction into the EU from developing
countries, the Panel of Animal Health and Welfare used data from Eurostat to estimate quantities of
animals and animal products introduced legally into the EU (EFSA, 2006). Data for international
trade were provided by the FAO. The Panel mentioned that there were statistically significant
differences between the import data from the Comext database and other data sources.
To investigate these issues, searches in the Eurostat Comext and UN Comtrade databases were
performed for live swine and for the period 2006 to 2008. In Comext, commodity codes of the
Combined Nomenclature (CN) related to live swine were selected; i.e. the codes 0103100018,
0103911019, 0103919020, 0103921121, 0103921922 and 0103929023 were combined as an aggregate of
“live swine by CN” (Table 8). Only in this commodity classification it was possible to retrieve data in
animal units rather than in Kg. In Comtrade, the commodity “0013-live swine” of the Standard
International Trade Classification (SITC) was selected, as CN is not used by Comtrade.
Trade data
According to the Eurostat trade data, during the last three years, the EU has imported live swine
mainly from the Russian Federation, Canada, Norway and the USA followed by smaller lots coming
from Albania, Switzerland and Belarus. In some cases the country of origin is not specified. The total
imports in the EU accounted for 5953, 1793, 1708 animals in 2006, 2007 and 2008 respectively
(Table 8).
18
Pure-bred breeding swine
Domestic swine, weighing < 50 kg (excl. pure-bred for breeding)
20
Live non-domestic swine, weighing < 50 kg
21
Live domestic sows, having farrowed at least once, weighing >= 160 kg (excl. pure-bred for breeding)
22
Live domestic swine, weighing >= 50 kg (excl. sows having farrowed at least once and weighing >= 160
23
Live non-domestic swine, weighing >= 50 kg
19
The EFSA Journal 2010; 8 (3): 1531
27
Collection and routine analysis of import surveillance data
Table 8: Imports as reported in EUROSTAT Comext database by CN8
Extracted on
INDICATORS
PRODUCT
PERIOD
Jan.-Dec. 2006
Jan.-Dec. 2007
Jan.-Dec. 2008
2009/07/22 09:47:23
SUPPLEMENTARY_QUANTITY (animal units)
live swine by CN
PARTNER/REPORTER
EU27
AT
EU27_EXTRA
NORWAY
CANADA
ALBANIA
5953
4884
306
218
COUNTRIES AND TERRITORIES
NOT SPECIFIED
213
UNITED STATES
RUSSIAN FEDERATION
SWITZERLAND
165
150
20
EU27_EXTRA
NORWAY
CANADA
RUSSIAN FEDERATION
UNITED STATES
SWITZERLAND
ALBANIA
BELARUS
1793
838
623
135
87
48
42
20
EU27_EXTRA
RUSSIAN FEDERATION
CANADA
NORWAY
UNITED STATES
1708
792
517
250
126
COUNTRIES AND TERRITORIES
NOT SPECIFIED
23
The EFSA Journal 2010; 8 (3): 1531
BE
CZ
DE
DK
EE
FI
FR
GB
5
82
35
16
16
140
140
32
221
1
58
8
32
95
GR
HU
IE
2
2
IT
LT
PL
SE
SI
10
256
4726
4726
428
6
106
218
3
12
27
210
126
150
4
12
4
178
49
130
49
29
29
249
249
132
42
45
267
135
267
166
14
132
546
546
135
87
48
42
20
124
23
124
231
20
62
20
62
190
41
6
233
6
148
85
23
28
544
544
343
248
49
46
122
122
0
Collection and routine analysis of import surveillance data
Table 9 shows the export of live swine that the Russian Federation has reported to United Nations
Statistic Division.
Period
Partner
Trade Quantity
(animal units)
Mongolia
55
Kazakhstan
39
Georgia
470
Kazakhstan
62
Kazakhstan
90
2006
2007
2008
Table 9: Number of live swine exports as reported by the Russian Federation. Source: United Nations
Comtrade database
Discussion
Comparing Eurostat data (Table 8) and UN figures (Table 9), a discrepancy can be noted for the
reported imports into Poland in 2006 and 2008, and into Lithuania in 2007 and 2008 from the Russian
Federation. The latter has reported exports only to Asian countries for the same period i.e. to
Mongolia, Kazakhstan and Georgia. In such cases, the users may collect trade data from all the
potential sources and evaluate their reliability before use.
Even if EU legislation allows imports of live animals and animal products from a restricted group of
third countries, imports of such commodities may introduce emerging diseases to MSs livestock
species and intra-EU transport may assist the transmission of such hazards among MSs. Trade
pathways are changing over time and such consideration are useful in the anticipation of the
introduction of diseases or when, for example, an outbreak is observed in the country of origin or in
the same broad geographical area.
8.5.
Intra-EU trade
Background
When figures of bilateral trade between two MSs are compared using the Eurostat database, ideally no
significant discrepancies should be expected. The methodology of collecting, collating and reporting
of those data to Eurostat, to a large extent, has been harmonised. However, as described in chapter 5.2,
persistent discrepancies in intra-EU trade statistics exist and imports reported by one MS do not
coincide identically with exports reported by its trading intra-EU partner.
To investigate this issue, a list of random selected food commodities that have been traded between
Germany and Italy is given in Table 10 together with the reported trade figures for 2007 and 2008.
The EFSA Journal 2010; 8 (3): 1531
29
Collection and routine analysis of import surveillance data
Trade data
Even if it is difficult to define an acceptable variation, in certain cases, the reported quantities are not
varying significantly. This is the fact for honey in 2008; Germany has reported imports of 2,091,300
Kg and exports of 848,200 Kg, while Italy has reported exports of 1,937,800 Kg and imports of
861,500 Kg, revealing differences of 8%24 and 2% respectively. However, for the same product in
2007, variances of reported data are higher; i.e. 24% in both trade directions.
Moving to another example, Germany has reported rice imports of 107,727,800 Kg and exports of
777,900Kg, while Italy has reported exports of 121,125,500 Kg and imports of 198,100 Kg. The
reported data for the most important direction in terms of quantity (from Italy to Germany), show
difference of 12%, while to the other direction (from Germany to Italy) differ almost 400%. For 2008,
the differences are 22% and up to 1000% respectively. Similar findings can be noted for the other food
commodities listed on Table 11; i.e. ice-cream, oysters and unmilled durum wheat.
Reporter Germany /
Reporter Italy /
Partner Italy
Partner Germany
Product
period
import
export
import
export
honey
2007
29568
7209
8923
22874
honey
2008
20913
8482
8615
19378
rice
2007
1077278
7779
1981
1211255
rice
2008
1053502
5755
60098
1357843
ice cream
2007
87105
135466
100047
93041
ice cream
2008
63739
130389
119984
101006
oysters
2007
164
34
242
37
oysters
2008
55
33
210
50
2007
1470701
0
105812
106234
2008
578957
2617
36038
115465
durum wheat
unmilled
durum wheat
unmilled
Table 10: Trade between Germany and Italy. Quantities in 100Kg. Source Eurostat Comext database by
SITC.
24
The imports reported by Germany are compared to the exports reported by Italy; by dividing 2,091,300Kg
with 1,937,800Kg the variation is approximately 8%.
The EFSA Journal 2010; 8 (3): 1531
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Collection and routine analysis of import surveillance data
Discussion
Major asymmetries have been revealed in most of the examined cases of bilateral intra-EU trade. The
reasons for their existence are explained in detail in the Methods. The user has the possibility to
retrieve trade data as they are reported by both trade partners, compare them and evaluate their
reliability.
9.
Exploitation of trade data: current work in other EU institutions and future
development
9.1.
The Joint Research Centre and OLAF: Automatic Monitoring Tool
The Joint Research Centre (JRC) has designed and is maintaining the Automated Monitoring Tool
(AMT) on external trade and in particular on Comext database. This tool is in use by OLAF and
partners of OLAF in the Member States for the protection of Community‟s budget, agriculture and
customs.
The AMT comprises two parts:
ARIADNE, a set of SAS macros, driven by graphical user interfaces to produce the signals of
interest (spikes of trade quantities and price outliers of traded goods);
THESEUS, a website where results (tables and graphs) are published. The website has various
features to facilitate user navigation and data export.
JRC has developed a set of alarms and the application shows products which at least have one active
alarm.
At the current stage, THESEUS has some limitations for helping EMRISK to identify trade trends of
interest, which are:
It is using only the CN trade classification;
The tool is not an interactive one, so the user cannot make aggregates of code commodities,
countries or time periods;
THESEUS applies fixed thresholds.
The data for the AMT are from Comext, obtained through OLAF. These data are fed to ARIADNE.
The latter is an application running in client-server architecture. Its development into a web
application is being explored by the JRC.
9.2.
The Food and Veterinary Office system
The Food and Veterinary Office (FVO) of DG-SANCO is in the process of developing a system called
Food Safety Data Management System (FSDMS). The system is a data warehouse solution using
Business Objects. The original idea was to combine 11 different existing data sources, but at the
present stage the work is concentrated on a set of three databases i.e. Comext, RASFF and TRACES.
The main task is to create a platform with a single access interface for them. Subsequently, the
different sources should be combined using certain common fields, e.g. ISO country code, product etc.
Country code based on ISO classification is possible, but commodity names, products or animals are
defined in a different ways in every database, and thus is difficult to compare data directly.
Until now, the system can only provide reports derived from a single database, and no statistical
method is applied on the obtained results. Expert evaluation is essential for the resulting report before
dissemination. The development of this tool should be followed, as it could be used for compilation of
data coming from those data sources.
The EFSA Journal 2010; 8 (3): 1531
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Collection and routine analysis of import surveillance data
9.3.
Requirements for a system for automatic scanning of Eurostat Comext
In order to monitor trade data, an automated or semi-automated system is required by the EFSA. Such
a system covering EFSA‟s mandate for automatic scanning of Eurostat Comext database should
provide “alerts” (or signals) to the users, indicating for example i) high increase of the volume of a
given product over time to a specific MS or EU in total ii) new trade partners iii) new food or feed
commodities entering the EU.
This system should be easy to use and interactive. It should give to the user the possibility of selecting
commodity classification, countries, time period, and aggregates of them. The outcome of the
scanning should be similar to the JRC/OLAF product i.e. a table indicating the changes and alarms,
followed by graphs for fast interpretation.
To develop such a system, firstly a copy of the Comext would need to be downloaded in EFSA, which
following discussion with the Eurostat, seems possible. Updating this data every three or four months
would be probably be sufficient, and should include data of the last six to ten years for annual analysis
and three to five years for monthly analysis.
Secondly, the algorithms for identifications of trends should be developed. This procedure will be a
continuous process, based on the experience and the needs over time. JRC and OLAF have already
spent time on developing alert algorithms and the EFSA could possibly cooperate with them. A second
scenario for EFSA is to develop the algorithms in house with support of internal and external
recourses.
The EFSA Journal 2010; 8 (3): 1531
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Collection and routine analysis of import surveillance data
CONCLUSIONS
According to the results of this study the following conclusions can be drawn:
Eurostat Comext database is an important source of information for intra- and extra-EU trade
trends. In particular, Comext data can give information on the alteration of food and feed trade
volume over time, new trade partners, new products entering the EU and trade pathways
capable of introducing or distributing hazards in the EU;
Comext database is a potential tool for the identification of emerging risks to be used in
combination with data coming from other sources;
UN Comtrade database can be used to complement data from Comext, as it provides trade
data reported by countries all around the world and not only from EU MSs. However,
statistics from these two databases are not directly comparable as methodological
inconsistencies may exist among different countries during collection, analysis and reporting
of information.
Searches in Comext and Comtrade databases revealed many discrepancies. These are probably
due to the following reasons: different data collection methodology among countries, false
declaration of product or country of origin, confidentiality, time delay, threshold and
adjustment applications, revisions of reported data, valuation and reporting in different
commodity classifications;
Scientific expert judgment is pivotal for selecting the most appropriate food commodities
during a search, interpretation of trade data and evaluation of their weaknesses as well as their
relevance to data from other sources;
When estimating exposure, occurrence data should take into consideration variability
originating from trade.
The EFSA Journal 2010; 8 (3): 1531
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Collection and routine analysis of import surveillance data
REFERENCES
EFSA, 2006. Scientific Opinion of the Panel on Animal Health and Welfare on assessing the risk of
foot and mouth disease introduction into the EU from developing countries. The EFSA Journal,
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<http://www.efsa.europa.eu/cs/BlobServer/Scientific_Opinion/plh_op_ej925_cbs_en.pdf?ssbinary=tru
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EFSA, 2009. Technical Report of Scientific Cooperation Working Group on Emerging Risks. The
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<http://epp.eurostat.ec.europa.eu/cache/ITY_OFFPUB/KS-BM-06-001/EN/KS-BM-06-001-EN.PDF>
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Eurostat, 2008. Quality report on international trade statistics.
<http://epp.eurostat.ec.europa.eu/cache/ITY_OFFPUB/KS-RA-08-008/EN/KS-RA-08-008-EN.PDF>
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statistics with particular regard to the impact of the Rotterdam and Antwerp effects.
<https://www.uktradeinfo.com/pagecontent/documents/edicom_rotterdam_effect_2005.pdf
Kleter G.A., Groot M.J., Poelman M. , Kok E.J., Marvin H.J.P., 2009. Timely awareness and
prevention of emerging chemical and biochemical risks in foods: Proposal for a strategy based on
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transformation and shrimp aquaculture in Thailand and Vietnam: pathways to ecological, social,
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<http://unstats.un.org/unsd/publication/SeriesM/SeriesM_52rev2E.pdf>
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Collection and routine analysis of import surveillance data
GLOSSARY / ABBREVIATIONS
BEC
CIF
Broad Economic Categories
Cost, Insurance, Freight
CN
Combined Nomenclature
Comext
EU foreign trade statistics database
Comtrade
United Nations Commodity Trade Statistics Database
DG SANCO
EC
Directorate General for Health and Consumers
European Commission
EU
European Union
EU-27
FAO
European Union of 27 Member States from 1 January 2007
Food & Agricultural Organisation of the United Nations
FDA
Food and Drug Administration of the USA
FOB
Free On Board
FVO
Food and Veterinary Office
HS
Harmonised System
IMF
International Monetary Fund
JRC
Joint Research Centre
MS
Member State
OECD
Organisation for Economic Co-operation and Development
OLAF
RASFF
European Anti-Fraud Office
Rapid Alert System for Food and Feed
SITC
Single Administrative Document
Standard International Trade Classification
SNA
System of National Accounts
TARIC
Integrated Tariff of the European Communities
UN
United Nations
USA
United States of America
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