Orientering om Brexit rapport

university of copenhagen
Modeling the potential impacts of two BREXIT scenarios on the Danish agricultural
sectors
Yu, Wusheng; Elleby, Christian; Lind, Kim Martin Hjorth; Thomsen, Maria Nygård
Publication date:
2017
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Publisher's PDF, also known as Version of record
Citation for published version (APA):
Yu, W., Elleby, C., Lind, K. M. H., & Thomsen, M. N. (2017). Modeling the potential impacts of two BREXIT
scenarios on the Danish agricultural sectors. Department of Food and Resource Economics, University of
Copenhagen. (IFRO Report; No. 260).
Download date: 01. sep.. 2017
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Modeling the potential impacts of
two BREXIT scenarios on
the Danish agricultural sectors
Wusheng Yu
Christian Elleby
Kim Martin Lind
Maria Nygård Thomsen
260
IFRO Report 260
Modeling the potential impacts of two BREXIT scenarios on the Danish agricultural sectors
Authors: Wusheng Yu, Christian Elleby, Kim Martin Lind, Maria Nygård Thomsen
Scientific quality control: Per Svejstrup Hansen
Published June 2017
ISBN: 978-87-92591-81-4
This report is the result of a project commissioned by the Ministry of Environment and Food of
Denmark (MFVM).
The authors are responsible for the research findings contained in this report.
IFRO Report is a continuation of the series FOI Report, published by the former Institute of Food
and Resource Economics. The report series can be found here:
http://ifro.ku.dk/publikationer/ifro_serier/rapporter/
Find other IFRO Commissioned Work (mostly in Danish) here:
http://ifro.ku.dk/publikationer/ifro_serier/udredninger/
Department of Food and Resource Economics
University of Copenhagen
Rolighedsvej 25
DK 1958 Frederiksberg
www.ifro.ku.dk/english
2
Executive summary
This study provides a quantitative analysis of the potential medium-run impacts of two Brexit
scenarios on the Danish food and agricultural sectors. In the WTO scenario, the UK and EU are
assumed to treat each other on WTO MFN terms, implying rising bilateral tariffs to the MFN levels
of the EU and also rising non-tariff barriers; whereas in the FTA scenario, a normal free trade area
between the two sides are assumed, implying zero tariff but still rising NTBs. The UK is further
assumed to exit preferential trade agreements negotiated by the EU with third countries in both
scenarios, implying rising trade costs between the UK and these third countries.
The expected medium-run impacts of the two scenarios are evaluated in a common baseline in a
computable general equilibrium model. The baseline is constructed by projecting the world economy
to 2021, a year when the Brexit scenarios are assumed to be in effect. In the baseline, current
macroeconomic projections towards 2021 are targeted while current trade policies including the
membership of the UK in the EU common market are maintained. Additionally, a few important
preferential trade agreements currently negotiated or considered by the EU are also assumed in the
baseline. Simulation results suggest that bilateral exports from Denmark to the UK would shrink
significantly under the WTO scenario, particularly for key export products such as processed foods,
pork products, and dairy. Total Danish food and agricultural exports to the UK would fall by as much
as 79 percent under the WTO scenario and by about 48 percent under the FTA scenario. In addition
to the expected rise in tariff barriers, the assumed large increases in non-tariff barriers in the two
scenarios are also key driver behind these results. However, reductions of total Danish agri-food
exports would be quite limited in both scenarios, due to the possibilities for Danish exports to be
redirected within the EU and to countries that are partners to the various preferential trade agreements
of the EU and due to the fact that exports to UK are only a fraction of total Danish exports. While
total Danish exports are expected to drop slightly, prices of Danish exports would also be dampened
but only to even smaller extent. Reductions in domestic production of key export products would be
quite small as well. For processed foods, pork, and dairy, domestic outputs would be 2.5, 2.2, and 1.1
percent lower than the baseline levels in the WTO scenario. In connections with changes in domestic
outputs, Danish processed food, pork and dairy sectors’ labor employment would shrink by 1.1-2.6
percent under the WTO scenario and 0.2-1.5 percent under the FTA scenario.
At the macro level, nominal GDP for both Denmark and the UK are expected to decrease relative to
the baseline; however, losses to Danish GDP are expected to be much smaller at about 0.64 percent
3
under the WTO scenario and at 0.44 under the FTA scenario, as compared to the 4.8 and 3.4 percent
losses for the UK under the two scenarios respectively. Expected decreases in real GDP in the two
countries are much smaller in both scenarios. These relative differences in GDP losses are indications
of the asymmetric nature of the trade policy changes associated with the assumed Brexit scenarios as
the extent of rising trade barriers facing UK exports are far greater than those facing Danish exports.
Moreover, the EU single market and preferential accesses to the market of EU’s FTA partner
countries provide ample flexibilities to redirect trade flows for remaining EU member states such as
Denmark.
Based on these simulation results, this study suggests that the overall negative results arising from
rising trade costs due to Brexit are more pronounced for the UK. For Denmark, while bilateral exports
for key exportable sectors may be hit hard, overall Danish exports would be impacted relatively little.
Between the two scenarios considered, an FTA with the UK would enable both sides to avoid larger
losses to trade flows, domestic outputs, and employment. To create more flexibilities to fully
compensate the anticipated lost trade flows to the UK market, one option for the EU including
Denmark is to pursue further trade liberalization at multilateral and bilateral levels.
4
List of abbreviations
AVE Ad Valorem Equivalent
CAP Common Agricultural Policy
CGE Model Computable General Equilibrium Model
CIF Cost, Insurance, and Freight
EU European Union
GTAP Global Trade Analysis Project
GDP Gross Domestic Products
FTA Free Trade Area
MFN Most Favored Nation
NQT Model New Quantitative Trade Model
NTB Non-Tariff Barrier
PTA Preferential Trade Agreement
ROO Rules of Origin
WTO World Trade Organization
5
1 Introduction
The economic consequences of Brexit are a contested issue that continues to spur debate. Apart from
the inherent uncertainty generated by such a big change in international relations, the details of the
divorce settlement between the UK and the EU have important implications for the post-Brexit
trading relations and economic conditions for both the UK and the EU. Furthermore, the UK’s future
trading agreements with third countries can impact the UK, the EU as well as individual EU member
countries differently, depending upon e.g. specific stipulations for various commodities.
In this report we evaluate the impacts of Brexit1
. In light of the uncertainty regarding the future
relationship between the UK and the EU27, we limit our analytical attention to two possibilities: a
future Free Trade Agreement (FTA) between EU27 and the UK (“optimistic” scenario) and a scenario
where the UK and EU27 trade with each other on WTO’s Most Favored Nation (MFN) terms
(“pessimistic” scenario). Each of these scenarios is evaluated against a benchmark or “baseline”
where the UK is assumed to remain in the European Union.
Our modeling approach is fairly standard in that we use a global Computable General Equilibrium
(CGE) model2
to assess the impacts of Brexit. In our “optimistic” scenario where the UK enters into
an FTA with EU27 we find a nominal GDP impact of -0.4 percent for Denmark and -3.4 percent for
the UK. In the “pessimistic” scenario, where the UK and EU27 trade on MFN (rather than
preferential) terms, the Danish and UK GDP impacts become -0.6 and -4.8 percent, respectively.
Changes in real GDP in the two Brexit scenarios are smaller than changes in nominal GDP in both
countries, but the relative magnitudes of real GDP changes between Denmark and UK remain the
same. Our results therefore indicate that the Danish economy will be affected by Brexit but the impact
on the UK economy is one order of magnitude higher. Moreover, as expected, the WTO MFN
scenario leads to more negative impacts than the FTA scenario for both the UK and for Denmark.
These findings are also broadly in line with the literature. Emerson et al. (2017), for example, report
1
This report is the result of a project commissioned by the Ministry of Environment and Food of Denmark (MFVM) on
the potential impact of BREXIT on Danish agriculture sectors. Per Svejstrup Hansen from the Department of Food and
Resource Economics and the University of Copenhagen has acted as internal reviewer for this report and provided
useful comments, for which the authors are grateful.
2
We use the Global Trade Analysis Project (GTAP) model.
6
an average estimated UK GDP impact of -1.3 and -4.2 across all the “optimistic” and “pessimistic”
scenarios in the various published Brexit studies listed in Table 1 below.3
In this report the “optimistic” scenarios are those which lead to a small increase in trade barriers
whereas “pessimistic” scenarios are those which lead to larger trade costs increases.4
For example,
the “optimistic” scenario in Dhingra et al. (2016) refers to a situation where the UK remains in the
European single market and has a Free Trade Agreement (FTA) with EU27 (similar to Norway). In
their “pessimistic” scenario, on the other hand, the authors assume that the UK and EU27 cannot
agree on an FTA and the two subsequently trade on MFN terms. This is also how the “pessimistic”
scenario is defined in the other studies listed in Table 1. The “optimistic” scenarios in the other studies
in Table 1 all refer to an FTA between the UK and EU27 and, in some cases, the UK staying in the
single market.
Table 1. Estimated long term (2030) GDP impacts from BREXIT
Study Scenario UK impact EU27 impact
(% change in GDP relative to baseline)
Kierzenkowski et al. (2016) Optimistic -2.7 -
Central -5.1 -
Pessimistic -7.7 -0.8
Dhingra et al. (2016) Optimistic -1.3 -0.1
Pessimistic -2.6 -0.3
Aichele and Felbermayr (2015) Optimistic -0.6 -0.1
Pessimistic -2.3 -0.3
Rojas-Romagosa (2016) Optimistic -3.4 -0.6
Pessimistic -4.1 -0.8
Booth et al. (2015) Optimistic +1.5 -
Mid-range 1 +0.6 -
Mid-range 2 -0.8 -
Pessimistic -2.2 -0.3
Treasury (2016) Optimistic -3.8 -
Central -6.2 -
Pessimistic -7.5 -
Average Optimistic -1.3 -0.1
3
Differences in methods, model assumptions, scenarios and base years are the main reasons that results differ across
studies. For instance, as will be discussed below, the current study assumes a baseline of 2021 whereas studies surveyed
in Table 1 refer to a baseline of 2030.
4
“Optimistic” and “pessimistic” scenarios are also sometimes referred to as a “soft” or a “hard” Brexit, respectively.
7
Pessimistic -4.2 -0.5
Note: The EU27 impact in Kierzenkowski et al. (2016) refers to the medium term defined as year
2023. Source: Adapted from Emerson et al. (2017).
In order to implement these scenarios in a quantitative model, one has to make specific assumptions
about the changes in tariff and non-tariff barriers (NTBs) and the speed at which trade costs change
following Brexit. Dhingra et al. (2016), for example, assume that NTBs increase in both scenarios
but the increase in NTB trade costs in the “optimistic” scenario is only one third of the increase in the
“pessimistic” scenario. Moreover, in the “optimistic” scenario it is assumed that intra EU27 trade
costs fall 20 percent faster than in the rest of the world, while in the “pessimistic” scenario they fall
40 percent faster, as had been the case historically (see Méjean and Schwellnus, 2009). Although it
is not always stated clearly, any quantitative trade analysis necessarily involves a large number of
such choices and compromises. We will elaborate on the details of how we implement our own
scenarios as well as how we define the baseline in section 3.
As mentioned above, the Brexit impacts that we calculate in this report is based on a CGE model.
Two of the studies listed in
Table 1, namely Booth et al. (2015) and Rojas-Romagosa (2016), are also based on CGE model
simulations. The main advantage of a CGE model in general and the GTAP model in particular, is its
level of details. A CGE model is a stylized internally consistent representation of an entire economy,
made up of a number of sectors. This means that a shock to the price of a good in a given sector (e.g.
through changes to a tariff), for example, not only affects input demand and output supply in that
specific sector, it has ripple effects on all sectors of the economy as the economy moves towards a
new equilibrium. GTAP simulations thus provide very comprehensive and detailed impacts of policy
changes, including changes to global production patterns, trade flows, employment, wages etc., at a
sectoral as well as the aggregate level. The complexity of a CGE model is probably also its main
potential weakness. For example, the large number of behavioral parameters in the model means that
it is very difficult to evaluate how robust the results are to changes in assumption about relationships
between the variables in the model. Moreover, these parameter values are often not founded on
rigorous up-to-date empirical analysis as the exercises to calibrate models to new parameters are often
quite time-consuming. However, recent advances in parameter estimation and validations in CGE
models such as GTAP have led to increased confidence in modeling results and resulted in these
models’ popularities in trade policy analysis.
8
There are two main alternatives to CGE modelling of trade policy impacts. These are both based on
an econometric model where (some of the) key parameters are estimated prior to any impact
calculations. The first alternative is a micro based approach knows as the gravity model, which
quantifies the determinants of bilateral trade flows. These determinants can be grouped into three
main categories namely measures of economic size, geographic distance, and other factors affecting
trade costs such as common language, FTAs, etc. Estimates from a gravity model can be used to
predict the trade impact of a change in trade policy but they do not reflect welfare impacts or changes
in macro variable such as GDP or employment. Gravity estimates can, however, serve as inputs into
so called New Quantitative Trade (NQT) models. A NQT model is also a type of general equilibrium
model but with a much simpler structure than a CGE model. These can be used to calculate sectoral
and aggregate welfare impacts but not impacts on employment, wages and other economic variables
that are often of interest for policy makers. A main benefit of NQT models over CGE models is that
they are less complex, easier to comprehend and the data requirements are much less demanding.
Another advantage is that the key parameters are estimated prior to the impact calculations so there
is a stronger correspondence between the data and the results than in a CGE model. Among the studies
listed in Table 1, Dhingra et al. (2016) and Aichele and Felbermayr (2015) are based on a NQT model.
The second alternative to CGE models is a global macroeconometric model á la NiGEM.5
Unlike
CGE and gravity models that are both usually static, macroeconometric models are dynamic and
agents are forward looking. Key macroeconomic variables such as GDP, import and export are
determined within the model and can easily be forecasted. CGE models, on the other hand, allow for
a much more detailed analysis of the sectoral impacts and for a more thorough analysis of various
policy options. As for the NQT models, parameters are estimated prior to impact evaluation step but
a macroeconometric model allows for a forecast of the entire time path towards any given future date,
including the uncertainty involved, rather than a comparative static analysis of two situations where
the time dimension does not feature explicitly. The studies by Kierzenkowski et al. (2016) and
Treasury (2016) are both (partly) based on the NiGEM model.
Unlike the studies mentioned above, our focus in this report is on the agri-food sector impacts, namely
the agri-food sector in Denmark. Another novelty is that we analyze carefully the effects related to
some of EU’s Preferential Trade Agreements (PTAs) with third countries. For example, the EU has
a PTA with Korea, Norway, Turkey and many other countries. Moreover, EU has reached an
5
NiGEM stands for (the) National Institute Global Econometric Model.
9
agreement with Canada (CETA) and there are ongoing or prospective negotiations with Japan, Brazil
(as part of the negotiations with MERCOSUR), USA, Australia and New Zealand among others.6
We
evaluate how the impact of Brexit on the Danish economy in general and the agri-food sector in
particular, depends on the successful completion of these trade agreements. We assume that the UK
will not be part of any of these PTAs following Brexit.
At the sectoral level, our results suggest that the WTO scenario will lead to quite dramatic reductions
in bilateral exports of key agri-food products from Denmark to the UK, such as processed foods, pork
products and dairy; however, by taking into account the flexibilities to redirect trade flows within the
EU27 and to third countries, the overall Danish agri-food exports would fall very little.
The rest of the paper is organized as follows. Section 2 presents some basic facts on bilateral trade
patterns between Denmark and the UK and provides more details regarding the two Brexit scenarios
considered in the study. Section 3 is devoted to a discussion of methodologies, data and the
construction and implementation of the baseline and scenarios. Section 4 presents the results and the
analysis of the results. The final section concludes with the main findings and offers some
qualifications of such findings.
2 Trade patterns and post-Brexit options
2.1 Bilateral trade patterns between Denmark and the UK
The UK is an important export destination for Danish agricultural and food products, particularly for
the aggregated product categories of processed foods, pork and poultry (which is mainly pork based
products in the case of Danish exports), and milk and dairy products. During the period of 2011-2013,
Danish exports of processed foods, pork and poultry, and milk and dairy products were respectively
in the range of DKK3.9 to 4.5 billion, 4.9-5.7 billion, and 1.3 to 1.8 billion (See Appendix Table 1
for details; data sourced from the GTAP database), all representing significant shares of total Danish
exports in those categories. In total, Denmark’s exports of agri-food products to the UK amounted to
more than DKK 12 billion per year during the 2011-2013 period. In all, Danish exports of agricultural
and food products to the UK in this period were more than 20 percent of total Danish merchandise
6
Negotiations with Australia and New Zealand are not formally launched yet, although the scoping exercises have been
concluded. We therefore exclude the prospect FTAs with Australia and New Zealand from our baseline. Likewise,
future FTAs with the USA and Turkey (agriculture sectors) are not considered in the baseline, either. Current and future
FTAs that are assumed in the baseline are discussed in Section 3 and are listed in Tables 2 and 3.
10
exports to the UK.7
In contrast, total agricultural and food imports from the UK to Denmark were
much smaller, ranging from about DKK 2.6 billion in 2011 to 3.1 billion to 2013. This indicates a
rather large trade surplus in agricultural and food products for Denmark and points out to the potential
negative impacts of Brexit on the key agricultural and food sectors.
2.2 Post-Brexit options
When the UK leaves the European Union it needs to renegotiate its trade relationships with the
remaining members of the EU (EU27 hereafter) as well as with third countries with which the EU
has existing preferential trade agreements (PTAs) or is currently negotiating PTAs. Renegotiated
trade relationships may imply changes in import tariffs as well as regulation influencing trade flows
i.e. regulations acting as Non-Tariff Barriers (NTBs).
Options for EU27-UK bilateral trade arrangements
The literature on Brexit has so far revolved around five scenarios or models for the future EU27-UK
relationship, with different implications on trade costs and consequently trade flows (PwC, 2016,
Dhingra et al., 2016, Irwin, 2015, van Berkum et al., 2016, Kierzenkowski et al., 2016):
• The ”Norway model” where the UK joins the European Economic Area (EEA)
• The ”Switzerland model” where the UK negotiates a set of bilateral agreements with EU27
regarding trade and factor flows
• The ”Turkey model” where the UK enters into a customs union with EU27
• A Preferential Trade Agreement (PTA) scenario where tariffs on goods traded between the UK
and EU27 are partially removed or trade is fully liberalized in which case we refer to it as a Free
Trade Agreement (FTA)
• WTO scenario where the UK trades with EU27 (and all other WTO members) on MFN terms
The political process for Brexit just got started and clarities on the likely outcomes will not be known
in the near future. In the current study we therefore focus on two relevant benchmark scenarios:
• FTA scenario implying zero tariffs on trade between the UK and EU27 (“optimistic” scenario).
• WTO scenario where the UK trades with EU27 on MFN terms (“pessimistic” scenario)
7
Agricultural and food products are defined as the aggregate of product categories 1-12 and 14-22 in the aggregated
GTAP database used in this study. For details of these product categories, see Appendix Table 2a.
11
Both scenarios are evaluated against a baseline in which the UK is assumed to stay in the EU. There
are several reasons for focusing on these two particular scenarios rather than the other scenarios
mentioned above such as the “Switzerland” and “Norway” models. First, it is difficult to formally
model deep economic integration á la the “Switzerland” and “Norway” models due to their
complexity. Second, neither of the “Switzerland” and “Norway” agreements covers trade in
agricultural products which is the main focus of this study, and this is also the case for the custom
union between EU and Turkey. Third, the WTO scenario is the most extreme scenario in terms of
rising trade costs between the UK and EU, as well as between the UK and the EU’s FTA trade
partners; therefore it is expected that this arrangement would lead to large negative trade effects in a
“worst” case or “pessimistic” scenario. All other scenarios will lead to impacts that are somewhere
in between those resulted from the WTO scenario and the status quo (i.e. the UK remaining in the
EU single market). As the status quo is effectively ruled out by Brexit itself, the best hope for the UK
to maintain closer trade ties to the EU will have to be some kind of PTA as a “best” case or
“optimistic” scenario. We therefore assess the potential scope of such an arrangement in an FTA
scenario in which we assume that the two parties agree to remove all tariff barriers.
It is important to understand that, even if the UK manages to negotiate an FTA with EU27, such that
goods trade will not be subject to tariff barriers, this will presumably still lead to an increase in overall
trade costs. This is partly due to the introduction of border measures required to deal with country of
origin matters. Moreover, firms will face additional production costs on their exported goods due to
regulatory divergences over time. For example, future health and labelling standards imposed on
goods for domestic consumption by the UK government might be different from those applying to
goods consumed within EU27. Exactly how high the costs are associated with non-tariff barriers
(NTBs) is an empirical question which is subject to considerable debate.
Options for UK-Third countries arrangements
The EU has a large number of PTAs with third countries. In fact, according to the WTO, the EU
currently trades on MFN terms with only 30 countries among which, however, are some of the world’s
largest agricultural exporters, such as Argentina, Australia, Brazil, Canada, New Zealand, Russia and
the United States. We assume that following Brexit, the UK will have to leave all PTAs it currently
is a party to as an EU member state. Moreover, the EU is a party to several ongoing trade negotiations
which UK will lose out on as well, should they materialize in the future. In particular, we assume that
12
the UK will not be able to reach a PTA with Canada, USA or any other country in the time horizon
under consideration.
In summary, this study assumes an exit of the UK from EU27’s PTAs under both the WTO and FTA
scenarios. The UK could, of course, choose to liberalize its trade policy by reducing its MFN tariffs
unilaterally. However, we will assume that WTO MFN tariffs will apply where applicable implying
that trade between the UK and relevant third countries will be subject to each of the respective
countries’ MFN tariffs.
3 Methodology, data and scenarios
To understand the potential impacts of different Brexit scenarios on the Danish economy, particularly
on the agricultural sectors including possible changes in bilateral and total trade flows, sectoral
production and employment effects, a quantitative economic model is needed. Such a model should
possess modeling structure and behavior to track the economy-wide and sector specific effects of
policy changes associated with the assumed Brexit scenarios, not only regarding the implied changes
on trade flows due to changing bilateral trade costs such as import tariffs and non-tariff barriers but
also on how changing trade flows influence domestic production and consumption at sectoral and
aggregated levels. These requirements point to the use of the trade-focused computable general
equilibrium (CGE) models. Typical CGE models are firmly based on microeconomic theory as they
assume utility-maximizing consumers and profit-maximizing (or cost minimizing) producers, allow
for inter-sectoral linkages through input-output linkages and competitions on the factors markets, and
observe resource constraints with regard to all factor markets. Among existing CGE models, trade
focused models have been used extensively in the trade policy literature, particularly for ex ante
evaluations of changes in trade policy due to formations of preferential trade agreements and of
options of trade negotiations involving multiple partner countries.
In this respect, the global CGE modelling framework and database nicknamed GTAP, developed in
Hertel and Tsigas (1997), is well suited for such purposes. The GTAP model is a widely used multi-
sector and multi-region computable general equilibrium model of the world economy. The standard
GTAP model assumes perfectly competitive markets and constant returns to scale technology. Nested
constant elasticity of substitution production functions are defined over intermediate inputs and
primary production factors such as land, capital, skilled and unskilled labors and natural resources.
On the demand side, private demand of a representative private household follows a constant
13
difference in elasticity demand function, which in turn enters into the aggregated demand function
together with government and saving demands. Countries and regions in the model are linked through
international trade linkages specified in the Armington structure and a global bank sector that
intermediates global savings and consumptions (for details see Hertel and Tsigas, 1997).
Typical ex ante modeling exercises with the GTAP model involves computing a new equilibrium
solution to the model due to “exogenous” changes to a set of policy variables from the levels
embodied in the benchmark equilibrium data set (which itself is an equilibrium solution to the model).
In the case of trade policy changes such as those assumed in the case of Brexit, the differences
between the new and benchmark equilibria can then be considered as the effects of the assumed policy
changes. Aside from the assumed changes in policy variables to be discussed in the next subsection,
another complication is to choose and construct a “business-as-usual” baseline from which the new
equilibrium solution is to be computed. In this case, the baseline has to be chosen in a year where the
Brexit scenarios are assumed to take effect. Given the difficulties in predicting when and what kind
of arrangements will be reached, this paper opts for a simple assumption that the analyzed Brexit
scenarios would take effect in the year 2021, under the assumptions that Britain would start the
negotiation process in 2017 and concluding the process within the pre-set 2-year period.
In the rest of this section, we proceed to the discussion on the baseline, the assumed scenarios, and
the data used to characterize the scenarios, particularly with respect to the assumed changes to import
tariffs and ad valorem equivalence of non-tariff barriers.
3.1 Descriptions of the Baseline and Scenarios
3.1.1 Database and baseline construction
The most recent and publically available GTAP database has base years in 2004, 2008, and 2011,
essentially providing three benchmark equilibrium datasets as solutions to the GTAP model. We
choose the 2011 data set for our purposes as it contains the most up-to-date data and is closest to the
assumed baseline year of 2021.8
The GTAP database contains data for 140 countries and 57 sectors. To limit the computation burden
and for ease of presentation of the results, an aggregated version along both the country and sector
8
As the analysis is built upon the baseline of 2021, a general equilibrium data set with more recent base year would be
more desirable for projecting the world economy to the baseline year of 2021. However, compiling such a data set is a
huge undertaking and generally occurs at a time lag of several years.
14
dimensions are used for this analysis. This version covers 28 sectors including 21 agricultural and
food sectors, 1 aggregated extraction sector, 3 aggregated manufacturing sectors and 3 service sectors,
and 39 countries and aggregated regions covering multiple countries. A detailed list of sectoral and
country aggregations is provided in Appendix Tables 2a and 2b. Within the EU, Denmark, France,
Germany, Ireland, Italy, Netherlands, Poland, Spain, and the UK are included as individual countries
and the rest of the EU is aggregated together. Additionally, another 30 countries/regions are included.
The selection of additional individual countries is based on the economic size and other
considerations such as whether these countries are part of a preferential trade agreement with the EU.
The construction of the 2021 baseline is essentially an extrapolation of the 2011 GTAP dataset to the
year 2021 by targeting current projections on GDP, labor force and population growth for all countries
and regions included in the model during the 2011-2021 period, while allowing capital and total factor
productivities to adjust to accommodate the above targets. The data on the targets are sourced from
Fouré et al. (2012).
In addition to the macro economic assumptions and adjustment above, in the baseline the following
assumptions are also adopted: that the UK remains a member of the EU implying no changes to the
bilateral trade relationship between the two; that existing preferential trade arrangements of the EU
are maintained, with the UK being a full member in these arrangements; and that several “likely”
FTAs of the EU are also fully implemented, with the UK being a full member in these new FTAs,
including those with Canada, Mexico, Mercosur, Japan, and several individual members of ASEAN
(Vietnam, Thailand, Indonesia, and the Philippines). This implies that the bilateral tariff barriers
within these arrangements are removed. Possible future FTAs with the US, Australia, New Zealand,
and Turkey (agriculture sectors) are excluded from the baseline, due to considerations of the current
negotiation status. This means that the status quo regarding bilateral trade relationships between the
EU and these countries is maintained in the baseline; similarly, no changes to the bilateral trade
relationship between the UK and these countries are assumed in the Brexit scenarios to be detailed
below.
3.1.2 Description of the scenarios
Following earlier discussions in this study, two core scenarios are considered in this study, namely
the FTA scenario under which the UK forms a free trade area with the EU, and the WTO scenario
where the UK and EU treat each other’s exports on the WTO MFN terms. In the first scenario, zero
15
tariffs are assumed between the UK and EU for all products. However, non-tariff barriers related to
standards and regulatory differences would rise, so would the cost related to the need to establish
rules of origin. These considerations effectively increase trade costs between the two sides. In the
case of the WTO scenario, the UK and EU are assumed to raise the bilateral import tariff rates to the
levels of the EU’s common external tariffs, as these also represent the UK’s MFN tariff in the WTO.
Moreover, non-tariff measures also rise in this case.
Therefore, the bilateral trade barriers in both scenarios rise for the UK and EU. Additionally, as
discussed earlier, we also assume the UK has to exit the various PTAs negotiated by the EU with
third countries. As such, bilateral tariffs between the UK and these third countries have to rise to their
respective WTO MFN levels in both scenarios.
3.2 Data on tariffs and non-tariff barriers
This section provides a more detailed account on the data underlying the two core scenarios, including
both import tariffs and ad valorem equivalence of non-tariff barriers.
3.2.1 MFN tariffs of the EU
Figure 1 illustrates EU’s current MFN tariffs. The numbers, which are based on the GTAP database,
are averages of the individual tariff lines belonging to each of the 28 product categories considered
in this study, weighted by the amount of import from the main EU MFN trade partners.
One major difficulty of such an aggregation exercise is that many of the underlying individual tariff
lines are specific tariffs or a mix of specific and ad valorem tariffs. To take an example, consider
“Fresh, chilled or frozen cuts of sheep with bone in” with the six digit HS12 number 020422. This
product category consists of 4 underlying (8 digit) tariff lines (referring to more specific cuts). EU’s
applied MFN tariffs on imports of these cuts of sheep consists of an ad valorem tariff of 12.8% + a
specific tariff ranging from 119.9 to 222.7 EUR/100 kg.9
In order to find the ad-valorem equivalent
(AVE) tariff of each of these mixed tariffs one needs to know the quantity and value of the affected
trade flows for determining a unit value as a base for finding the AVE.
Another complication arises from the fact that the trade values that are needed to calculate AVEs of
specific tariffs are affected by the tariffs themselves. This issue also makes it difficult to calculate
appropriate average AVEs for the aggregated product categories. On the one hand, it does not make
9
This information is obtained from the http://tariffdata.wto.org/Default.aspx on the WTO website.
16
sense to take a simple average of the AVE tariffs of the underlying disaggregated tariff lines since it
implies that all products are weighted equally. On the other hand, a weighted average where tariffs
applied to trade flows representing the most value are given larger weights will be biased towards
zero exactly because high tariffs reduce trade.
In light of these issues the EU MFN tariffs illustrated in Figure 1 are not calculated from the raw tariff
data available from the WTO. Instead we used the MAcMap-HS6 database of tariff protection which
is part of the GTAP database (see Guimbard et al., 2012), where all tariffs have been converted to
their AVEs (which solves the first challenge mentioned above). Specifically, we compiled a list of
countries trading with the EU on MFN terms. Then, we calculated the average AVE tariff for each of
the 28 aggregated product categories considered in this report, where each individual tariff line is
weighted by the corresponding value of EU’s import from all its MFN partners. Although this
procedure does not take care of the bias problem entirely, as a practical solution it generates more
sensible aggregated average tariffs compared to the bilateral trade shared-weighted tariffs, because
the trade weights chosen here likely to be less biased than trade weights associated with one particular
trade partner. In essence, the logic behind this method is similar to the reference group weighting
method used for compiling the MAcMap-HS6 database at HS-6 level.
Figure 1. EU MFN tariffs. Source: Own calculations based on the GTAP database. Note: labels refer
to product categories according to the GTAP classification
As can be seen from Figure 1 above, the EU sectors with the highest levels of protection are sugar,
bovine meat, pork and poultry, and milk and dairy. It should be noted that pork and poultry, and milk
and dairy are among key exports from Denmark to the UK.
8 11
3 5
0
5
0 2
21
53
15
7
3
57
25
4
1 0
45
0
8
0
8
2 1 0 0 0
0
10
20
30
40
50
60
Paddy
rice
Wheat
Other
grains
Veg_fruits
Oil
seeds
Sugar
cane/beets
Plant
fibers
Other
crops
Processed
rice
Sugar
Processed
food
Beverage
tobacco
Bovine
animal
Bovine
meats
Pork&poultry
Other
animal
products
Vegetable
oils
Raw
milk
Milk&dairy
Wool
Fisheries
Extraction
TextWapp
LightMnfc
HeavyMnfc
Util_Cons
TransComm
OthServices
Percent
17
3.2.2 MFN tariffs of the third countries
As discussed earlier, another complication arising from BREXIT is that the UK may need to exit all
PTAs negotiated by the EU, as assumed in both of our scenarios. Therefore, we need to find the
aggregated MFN tariffs that would be imposed by these countries on the UK exports. We follow the
same procedure outlined in the previous section to generate these aggregated average MFN tariffs.
Table 2 presents the aggregated average MFN tariffs on the 28 product categories for 7 important
countries with an existing EU PTA. The numbers are calculated the same way as those in Figure 1
i.e. they represent averages of individual disaggregated tariffs weighted by each of the countries’
imports from its major trade partners. It is worth noting that these countries have very high MFN
tariffs on several food products but there are quite a lot of variations across countries and products.
We are assuming that the UK will be facing these tariffs when exporting to these countries following
Brexit. On the other hand, these countries will be facing the current EU MFN tariffs shown in Figure
1 when exporting to the UK. Trade between EU27 and the countries listed in Table 1 will be subject
to the existing preferential import tariffs.
Similarly, we assume in both scenarios that the UK would not be part of the PTAs that are currently
under negotiations. As these PTAs are assumed to be implemented by 2021, the MFN tariffs of the
partner countries to these future PTAs would also prevail for exports originated from the UK.
Table 2. Third-country MFN tariffs on food import by partner countries in existing EU PTAs (%)
Korea Switzerland Norway Turkey Ukraine South Africa Egypt
Paddy rice 5.0 0.4 17.5 32.0 4.3 0.0 1.7
Wheat 1.7 37.7 125.2 65.0 1.4 0.0 0.0
Other grains 426.3 10.5 90.2 42.4 0.4 0.7 0.0
Veg_fruits 46.5 8.1 7.6 25.0 2.5 7.2 2.4
Oilseeds 420.1 11.8 17.9 4.2 4.4 8.6 0.2
Sugar cane/beets 0.3 0.0 0.0 0.0 0.0 0.0 0.0
Plant fibers 0.0 0.0 0.0 0.0 0.0 7.8 0.0
Other crops 34.7 5.0 12.3 22.9 1.6 51.2 9.0
Processed rice 5.0 1.7 11.9 28.6 4.9 0.0 2.0
Sugar 9.4 3.8 55.8 31.2 49.4 0.0 2.5
Processed food 40.7 9.2 19.3 15.7 5.3 7.2 43.2
Beverage tobacco 55.0 16.4 9.5 9.3 9.7 6.6 526.7
Bovine animal 8.6 35.6 1.7 18.6 0.0 0.0 0.0
Bovine meats 34.7 47.3 178.7 2.0 13.8 16.4 1.0
Pork&poultry 24.3 75.6 17.3 10.4 10.0 14.2 29.4
Other animal products 6.2 1.4 1.1 3.1 6.9 1.0 2.1
18
Vegetable oils 7.3 37.7 41.8 16.4 0.4 8.2 2.2
Raw milk 0.0 0.0 0.0 0.0 0.0 0.0 0.0
Milk&dairy 58.3 58.0 32.3 166.3 9.4 12.7 4.8
Wool 3.0 0.0 0.0 0.0 0.0 0.0 0.0
Fisheries 17.0 0.1 0.2 32.1 7.1 1.5 4.8
Source: Own calculations based on the GTAP database
Table 3. Third-country MFN tariffs on food import by realistic future EU27 PTA partners (%)
Canada Vietnam Japan Brazil Argentia Thailand IdnPhl Mexico
Paddy rice 0.0 39.8 410.6 7.7 3.0 30.0 50.0 0.0
Wheat 0.0 1.7 22.1 4.9 4.8 27.0 4.1 0.1
Other grains 0.0 13.4 8.0 2.8 0.5 20.7 5.3 6.0
Veg_fruits 0.1 20.8 12.3 10.7 8.7 26.7 6.6 32.1
Oilseeds 0.0 0.1 1.2 4.1 4.0 13.2 2.5 0.4
Sugar cane/beets 0.0 0.0 0.0 0.0 0.0 0.0 0.1 0.0
Plant fibers 0.0 0.0 0.0 8.7 5.6 0.0 0.0 0.0
Other crops 0.6 22.6 0.3 3.7 2.4 36.0 5.1 2.3
Processed rice 0.0 27.8 241.2 8.5 2.6 23.0 9.0 0.3
Sugar 0.1 12.3 14.5 11.3 8.1 36.6 9.6 41.2
Processed food 12.7 12.8 10.9 11.7 12.6 8.3 10.1 14.8
Beverage tobacco 2.9 45.5 6.8 19.9 18.0 26.7 10.7 2.1
Bovine animal 0.0 0.2 10.4 0.5 0.4 5.2 4.7 0.0
Bovine meats 0.5 12.1 33.8 7.7 4.4 31.0 7.2 11.2
Pork&poultry 56.4 15.3 62.4 10.4 7.8 3.2 24.9 11.9
Other animal products 15.6 1.0 4.8 5.7 3.5 7.0 4.5 2.6
Vegetable oils 6.0 0.6 2.0 10.0 11.5 15.5 1.6 1.2
Raw milk 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
Milk&dairy 201.4 3.6 61.8 15.3 17.6 11.6 3.7 26.7
Wool 0.0 0.2 23.6 0.0 7.6 0.1 0.3 1.0
Fisheries 0.0 4.1 4.6 4.8 1.2 5.5 4.6 10.4
Source: Own calculations based on the GTAP database. Note: Brazil, Argentina and Mexico are
currently negotiating an FTA with the EU as members of the Mercosur FTA
Table 3 presents the MFN tariffs for eight countries that are currently negotiating PTAs with the EU.
As in Table 2, there are a lot of variations in the tariffs applied to imports of different products within
and across these countries. We are assuming that the UK exports to these countries will be subject to
the tariffs in Table 2 following Brexit; moreover, tariffs applying to trade between the countries listed
in Table 2 and the EU27 will be removed completely in the baseline and remains zero in the two
19
scenarios. That is, we are assuming that the countries listed in Table 2 manage to agree on an FTA
with EU27 covering all products. Finally,Table 4 shows the MFN tariffs of 3 possible future EU27
PTA partners that will be imposed by these partners on exports from the UK. Note that these countries
have relatively low tariffs on all products.
Table 4. Third-country MFN tariffs on food import. Possible future EU27 PTA partners (%)
Australia New Zealand USA
Paddy rice 0.0 0.0 1.3
Wheat 0.0 0.0 1.6
Other grains 0.0 0.0 0.1
Veg_fruits 0.3 0.0 1.0
Oilseeds 0.0 0.0 1.2
Sugar cane/beets 0.0 0.0 0.1
Plant fibers 0.0 0.0 0.0
Other crops 0.1 0.2 1.2
Processed rice 0.0 0.0 3.7
Sugar 0.0 0.0 12.3
Processed food 2.0 2.8 3.2
Beverage tobacco 2.9 2.1 1.1
Bovine animal 0.0 0.0 2.5
Bovine meats 0.0 0.2 2.9
Pork&poultry 0.1 2.6 1.1
Other animal products 0.2 0.0 0.5
Vegetable oils 0.1 0.2 1.6
Raw milk 0.0 0.0 0.0
Milk&dairy 4.6 3.8 10.5
Wool 0.0 0.0 0.4
Fisheries 0.9 0.0 0.2
Source: Own calculations based on the GTAP database.
In terms of implementation of the baseline and scenarios, the baseline represents the situation where
the UK would be part of these existing and future PTAs, therefore exports from the UK would be
subject to the preferential tariffs. In the WTO and FTA scenarios, however, exports from the UK
would be met with the MFN tariffs on the markets of the third countries, implying that tariffs would
rise to the relevant MFN levels presented in tables 2-5.
3.2.3 NTBs in the scenarios
Figure 2 illustrates our assumptions regarding administrative trade costs in ad valorem equivalents
(AVEs) applying to trade flows between FTA partners as compared with EU’s common market. That
20
is, the numbers indicate how much higher the UK-EU27 trade costs will be if the UK manages to
negotiate an FTA with EU27 following Brexit.
Figure 2. NTBs in the FTA scenario. Source: Own calculations based on Egger et al. (2015)
The numbers are based on those in Egger et al. (2015) but adapted to the aggregation scheme used in
this report. We distinguish between two types of administrative trade costs, namely Rules of Origin
(ROO) costs and regulatory barriers (NTBs).
ROO costs are incurred when officials must spend resources determining the extent to which an
imported product is produced in a PTA partner country rather than a third-country without preferential
access. In line with the literature we assume that ROO costs amount to 4 percent of the CIF (Cost,
Insurance and Freight) price in most cases (e.g. Carrère and De Melo, 2006). NTBs (Non-Tariff
Barriers) are costs associated with regulatory differences across countries such as labelling
requirements, health standards, control procedures etc. Although UK’s regulation is currently based
on the EU one, it is assumed that the two will diverge over time following Brexit. When a product
must satisfy different standards in different markets producers must devote resources to comply with
these different rules which increase costs. We therefore assume that CIF prices on goods traded
between EU27 and the UK will increase by these amounts in FTA scenario. It should be noted that
these numbers are subject to considerable uncertainty.
0
5
10
15
20
25
30
Paddy
rice
Wheat
Other
grains
Veg_fruits
Oilseeds
Sugar
cane/beets
Plant
fibers
Other
crops
Processed
rice
Sugar
Processed
food
Beverage
tobacco
Bovine
animal
Bovine
meats
Pork&poultry
Other
animal
products
Vegetable
oils
Raw
milk
Milk&dairy
Wool
Fisheries
Extraction
TextWapp
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AVE
(percent)
NTBs ROO
21
Figure 3 illustrates the NTB costs in ad valorem equivalents that we assume apply to trade flows
between MFN trade partners. EU’s current MFN tariffs from Figure 1 are also shown for comparison.
The source of the NTB numbers is the same as above. In general, current literature finds that the EU’s
NTBs for agriculture imports from non-EU member states are much higher than the corresponding
MFN tariffs, indicating that the NTBs may be a more important trade barrier for the UK and EU27
to access each other’s markets following Brexit.
Figure 3. NTBs in the WTO scenario. Source: Own calculations based on Egger et al. (2015)
As also found in the literature (e.g. Egger et al., 2015), NTB costs are higher when trade partners are
not part of a PTA and therefore trade on WTO MFN basis. In the context of the EU and UK, the
reason that NTB costs are higher in the WTO scenario than in the FTA scenario is due to additional
regulatory divergences between EU27 and the UK in the latter scenario. Based on the estimates of
Egger et al. (2015) and following the application of their estimates in Rojas-Romagosa (2016), we
assume that the NTBs in the WTO scenario are twice as high as in the FTA scenario which makes
them much higher than the tariffs for most of the product categories, as can be seen in Figure 3 (also
note that we assume zero ROO costs in the WTO scenario). For instance, the AVEs of the NTBs in
the crop sectors are above 20 percent and are much higher than the corresponding MFN tariff rates;
in the case of processed food, beverage and tobacco, and meat and dairy products, the AVEs of the
associated NTBs exceed 40 percent. Therefore, it is expected that much of the trade-reducing effects
0,0
10,0
20,0
30,0
40,0
50,0
60,0
Paddy
rice
Wheat
Other
grains
Veg_fruits
Oilseeds
Sugar
cane/beets
Plant
fibers
Other
crops
Processed
rice
Sugar
Processed
food
Beverage
tobacco
Bovine
animal
Bovine
meats
Pork&poultry
Other
animal
products
Vegetable
oils
Raw
milk
Milk&dairy
Wool
Fisheries
Extraction
TextWapp
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Util_Cons
TransComm
OthServices
Tariffs NTBs
22
of Brexit under the WTO scenario will be driven by these assumptions. Owing to the fact that current
literature only contains relatively few estimates on the NTBs (such as the widely referenced estimates
from Egger et al. (2015)), caution should be exercised when perusing the results based on these
estimates.
4 Results
This section first reports the main simulation results at sectoral levels, including changes in bilateral
trade flows from Denmark to the UK with focus on key agricultural sectors, and changes in sectoral
outputs and employment. Following that, key aggregated results are also reported, mainly on GDP.
In addition to reporting the total effects for the two scenarios, a decomposition analysis is also offered
regarding how changes in trade barriers by different parties (i.e. UK, EU27, and third countries) and
different types of trade costs (i.e. NTBs vs. import tariffs) contribute to the total effects. In relation
to the latter point and for clarity of presentation, an extra simulation is conducted in the WTO scenario
where the MFN tariffs are assumed but not the NTBs. This extra scenario is named WTO-MFN only
and the complete WTO scenario is named WTO MFN+NTB. It should also be noted that the results
reported here are based on a comparative static framework whereby the assumed time horizon is of
the “medium run” nature. In addition, several likely EU FTAs with third countries are assumed in the
baseline; therefore, the results reported here reflect these assumptions as well.
4.1 Bilateral and total export flows from Denmark
With rising bilateral trade costs, Danish exports of key agri-food products to the UK would
drop from the baseline levels in both the WTO and FTA scenarios. The magnitude of changes
from the baseline depends on the assumed rising trade costs. For instance, as shown in
Table 5, exports of processed food products, pork and poultry, and milk and dairy products are
predicted to decrease by between 71 and 94 percent under the WTO MFN+NTB scenario, due to the
assumed increase in both MFN tariffs and NTBs, whereas in the case of the FTA scenario, decreases
in bilateral exports to the UK are in the order of 44 (processed food) to 56 percent (for milk and
dairy). Under the WTO MFN+NTB scenario, the assumed increases in NTBs appear to be more
damaging (than the MFN tariffs) to many of Denmark’s exports to the UK, as can be seen from the
first column in Table 5 where the effects from raising the MFN tariffs only (i.e. the WTO MFN only
scenario) are reported. In that case, reductions in bilateral exports to the UK for other food, pork and
poultry, and milk and dairy would be about 26, 47, and 74 percent (as compared to 71, 94 and 93
percent) respectively.
23
Overall, simulation results suggest that total Danish exports to the UK would be reduced from the
baseline by nearly 51 percent under the WTO MFN+NTB scenario and 36 percent under the FTA
scenario (see Table 6). Within the WTO scenario, if the assumed NTBs were absent, total Danish
exports to the UK would only drop by 10 percent. For agri-food products, Danish exports to the UK
would decrease more in both the WTO and FTA scenarios (79 and nearly 48 percent, respectively),
as compared to the aforementioned results for total bilateral exports. This reflects the higher trade
costs that would be imposed on agri-food exports under the two Brexit scenarios. Similarly, when the
assumed NTBs were absent under the WTO scenario, Danish agri-food exports would be reduced by
about 39 percent, about half of the simulated percentage changes when rising trade costs associated
with the assumed NTB are considered.
Table 5. Changes in bilateral exports from Denmark to UK, % from baseline
WTO (MFN only) WTO (MFN+NTB) FTA
Vegetable and Fruits -7.4 -38.5 -24.7
Oilseeds 0.1 -59 -48.9
processed foods -25.9 -70.7 -44.3
beverage_tabacco -8.5 -41.8 -26.6
Bovinemeats -93.6 -97.7 -70.5
Porkpoultry -46.7 -94.3 -51
other animal products 10.4 -14 -7.2
vegetable oils -1.7 -85.7 -68.5
Milkdairy -73.8 -92.6 -56.6
Note: WTO (MFN only) refers to the WTO scenario with increasing MFN tariff only; WTO
(MFN+NTB) refers to the WTO scenario with increasing MFN tariffs and rising NTBs; FTA refers
to the FTA scenario. Source: simulation results by authors.
Table 6. Bilateral exports from Denmark to UK (million USD)
2011 2021
baseline
WTO
(MFN
only)
WTO
(MFN+NTB)
FTA
Vegetable and Fruits 3.8 4.1 3.8 2.5 3.1
Oilseeds 0.4 0.5 0.5 0.2 0.3
Processed food
products
739 708 524 207 393
Beverage&tabacco 44 45 41 26 33
Bovinemeats 34 27 2 1 8
Pork&poultry 1068 565 301 32 276
Other animal products 7 8 9 7 7
Vegetable oil 14 14 14 2 5
Milk&dairy 342 280 73 21 121
24
Total 11,296 11,024 9,894 5,432 7,08
5
Total agrifood 2,368 1,803 1,103 372 945
% from base2021,
agrifood products
-38.8 -79.3 -47.6
% from base2021, all
products
-10.3 -50.7 -35.7
Source: simulation results by authors.
While the simulation results for the two Brexit scenarios point to very large reductions in Danish
exports to the UK, particularly for processed foods, pork products, and milk and dairy, total Danish
exports may not drop as much even though the UK is an important destination for Danish exports.
This is because the remaining EU single market which is still much larger than the UK market would
allow for potentials to redirect significant exports within the single market in the event of rising trade
costs on the UK market. Additionally, trade costs for exporting to third countries’ markets are either
assumed to be unchanged or lowered (due to the additional PTAs of the EU) in the baseline, relatively
speaking these markets become more attractive for Danish exports as compared to the UK market.
Therefore, these third country markets provide another channel for re-directing Danish exports.
Table 7. Changes in total exports from Denmark, % from baseline
WTO (MFN only) WTO (MFN+NTB) FTA
Vegetable and Fruits 0.8 1.9 1
Oilseeds 0.8 3.1 1.8
Processed foods -1.3 -4.1 -2.4
Beverage_tobacco 0.4 0.5 0.4
Bovinemeats -1.4 1.3 1.1
Porkpoultry -1.6 -2.7 -1.3
Other animal products 0.6 1.6 1
Vegetable oils 0.3 -1.7 -1.6
Milk&dairy -3.3 -3.3 -1.3
Source: simulation results by authors.
Indeed, simulation results reported in Table 7 suggest very modest reductions in total Danish agri-
food exports under both the WTO and FTA scenarios. For processed foods, pork and poultry, and
milk and dairy products, total exports would drop by 4.1, 2.7, and 3.3 percent under the WTO
MFN+NTB scenario and by 2.4, 1.3 and 1.3 percent under the FTA scenario. Note that these
simulated percentage reductions of total exports are far smaller than the percentage changes reported
for Danish exports destined to the UK markets, even after those percentage changes are scaled down
by UK’s shares of total Danish exports. This is indeed consistent with additional simulation results
(as reported in Appendix Table 3 for the case of pork products) that Danish exports to other markets
25
rise, particularly for products that would be affected the most by Brexit. In the case of pork products,
simulation results from the WTO scenario reported in Appendix Table 3 suggest notable increases in
exports to non-EU markets such as China and Japan, among others. Within the EU, pork exports are
expected to rise for Germany, Poland, Ireland, Italy and France. These increases would lead to a
smaller overall reduction in pork export volume from Denmark as well as an even smaller percentage
reduction.10
Overall, total Danish exports would drop by 0.43 and 0.29 percent in the WTO and FTA scenarios
(as reported in Appendix Table 4), as compared to the much larger decreases in overall exports from
the UK at respectively 9.33 and 7.23 percent in the same two scenarios. These aggregated results
again demonstrate the asymmetric nature of the negative trade effects of Brexit, with the negative
burden being placed disproportionately larger on the UK. By the same measure, most other EU
member states would also suffer from Brexit, notably Ireland, the Netherlands, Poland, France, Italy
and Germany. On the other hand, marginal increases in total exports are expected from countries such
as Vietnam, Thailand, Switzerland, and Turkey. These countries – which either have already
completed PTAs with the EU or are assumed to have concluded PTAs with the EU in the baseline –
are expected to increase their trade flows with the EU27, while trade flows between the UK and EU27
are expected to shrink due to Brexit.
4.2 Changes in domestic outputs
Results from section 4.1 suggest very large reductions in agri-food exports from Denmark to the UK
but quite modest reductions in total agri-food exports from Denmark. Therefore, it is expected that
the domestic market effects of Brexit for the Danish agricultural sectors are to be mainly influenced
by the simulated total export effect rather than the effect on bilateral exports to the UK. Table 8
provides the simulated results due to the WTO (including both the WTO MFN and WTO MFN+NTB
scenarios) and the FTA scenarios.
For the three key agri-food sectors (i.e. processed foods, pork and poultry, and milk and dairy), the
main WTO scenario (i.e. WTO MFN+NTB) would lead to reductions in domestic output similar in
magnitude to those of total Danish exports, ranging from 2.5 percent for processed foods, 2.2 percent
for pork and poultry , to 1.1 percent for milk and dairy. Without considering rising NTBs, as in the
10
Given the contrast between the very large impacts on bilateral exports from Denmark to the UK caused by reductions
in tariffs and NTBs and the much smaller impacts on overall Danish exports, the uncertainties associated with the
underlying assumptions on the NTBs are unlikely to have substantial impact on the size of the latter effect.
26
WTO MFN scenario, decreases in domestic outputs would be mostly smaller, with domestic outputs
for processed foods, pork and poultry, and milk and dairy dropping by 0.8, 1.2 and 1.1 percent
respectively. Under the FTA scenario, reductions in domestic outputs are also of smaller magnitude
for these three key products (by 1.4, 1, and 0.3 percent respectively).
Table 8. Changes in domestic production, % from baseline
WTO (MFN only) WTO (MFN+NTB) FTA
Vegetable and Fruits 0.46 0.99 0.55
oilseeds 0.58 2.22 1.26
other crops 0.24 -0.15 -0.38
processed foods -0.76 -2.51 -1.44
beverage_tabacco 0.12 0.24 0.16
bovinemeats -0.54 1.31 1.05
Porkpoultry -1.22 -2.17 -1.01
other animal products -0.63 -1.02 -0.4
vegetable oils 0.17 -1.34 -1.19
Milkdairy -1.09 -1.08 -0.26
Fishery -0.06 -0.01 -0.02
Source: simulation results by authors.
Another simulation result deserving some attention is the slight increase of total exports of several
other agrifood products such as vegetable and fruits, oilseeds, beverage and tobacco, bovine meats
and other animal products by between 0.5 to 3.1 percent (see Table 7). These products are not
currently heavily exported by Denmark to the UK and therefore are products that would be directly
impacted relatively little by the assumed Brexit scenarios. However, as a result of resource
reallocations associated with decreasing total exports and outputs in some key agrifood sectors, these
sectors that would be relatively unaffected by Brexit would attract economic resources such as labor
and land away from those negatively impacted sectors in a general equilibrium setting, particularly
in the medium and longer run. In fact, even though Danish exports of these products to the UK are
currently very small, total Danish exports of these products to the world are not negligible
(particularly for beverage and tobacco and bovine meats). This explains their rising outputs and
exports.
4.3 Employment effects
Underlying the simulated changes in domestic outputs reported in section 4.2 are reallocations of
economic resources such as primary production factors (i.e. land, labor, and capital), as well as
changing demand for intermediate inputs. Of particular societal concern is the possible employment
effect arising from the assumed Brexit scenarios. While the GTAP model used for the current study
27
assumes full employment of all primary factors including skilled and unskilled labor and the
simulation results are of the “medium run” nature (i.e. all markets including factor markets are in
equilibrium), the simulated changes in sectoral employment may be considered an indication of
sectoral unemployment, particularly in the short run where the labor market is adjusting to
accommodate the reallocated workers in other sectors.
Table 9. Changes in sectoral employment, % from baseline
WTO (MFN only) MFN (MFN+NTB) FTA
Unskilled
labor
Skilled
labor
Unskilled
labor
Skilled
labor
Unskilled
labor
Skilled
labor
Vegetable and
Fruits
0.4 0.4 0.95 0.91 0.56 0.53
Oilseeds 0.53 0.52 2.24 2.21 1.3 1.28
Processed
food products
-0.74 -0.77 -2.4 -2.6 -1.4 -1.5
Beverage&tab
acco
0.14 0.11 0.3 0.2 0.2 0.1
Bovine animal -0.39 -0.39 1.0 1.0 0.8 0.8
Bovinemeats -0.52 -0.55 1.4 1.3 1.1 1.0
Pork&poultry -1.2 -1.23 -2.1 -2.2 -1.0 -1.0
other animal
products
-0.76 -0.77 -1.2 -1.2 -0.5 -0.5
vegetable oil 0.19 0.16 -1.3 -1.4 -1.1 -1.2
Raw milk -1.11 -1.12 -1.3 -1.3 -0.4 -0.4
milk&dairy -1.07 -1.1 -1.0 -1.1 -0.2 -0.3
Source: simulation results by authors.
Table 9 therefore reports simulated changes in sectoral employment for both skilled and unskilled
workers in the Danish agricultural sectors. Under the WTO MFN+NTB scenario, Danish processed
foods, pork and poultry, and milk and dairy sectors are expected to experience reduced employment
of unskilled workers by respectively 2.4, 2.1, and 1 percent and employment of skilled workers by
similar magnitudes. Without the assumed increase in NTBs (i.e. WTO MFN only scenario), simulated
reduction of sectoral employment in processed food sector would be about one-third of that under the
WTO MFN+NTB scenario; for pork and poultry, employment would be reduced by about 1.2 percent;
for the milk and dairy sector, the employment effect would be similar between the WTO MFN and
WTO MFN+NTB scenarios (however, labor force in producing raw milk would shrink more in the
latter scenario). Under the FTA scenario, smaller simulated reductions of sectoral employment are
observed, as compared to the WTO MFN+NTB scenario, with unskilled (skilled) employment for
processed foods, pork and poultry, and milk and dairy dropping by 1.4 (1.5), 1.1 (1), and 0.2 (0.3)
percent, respectively.
28
4.4 Macroeconomic effects
To measure the macroeconomic impacts of the two Brexit scenarios, changes of gross national
products (GDP) from the baseline are presented in Table 10. Other macroeconomic effects such as
changes in price levels and economic welfare can also be potentially interesting11
; however, as the
main focus of the report is on Danish industry interests, GDP appears to be a more appropriate
measure for discussion. To develop some comparative perspectives, in what follows we discuss
percentage changes of nominal GDP for both Denmark and UK from the baseline levels. Finally, to
better understand the GDP effects in connection with the trade policy shocks assumed in the two
scenarios, contributions of individual policy shocks to the combined GDP effects are computed
separately for each of the two scenarios.
In the WTO scenario with both increasing MFN tariff and NTBs, Denmark’s GDP would decrease
by less than two-third of a percentage point from the baseline, as compared to the much larger loss of
GDP for the UK at 4.8 percent. As reported in Table 10 the much smaller GDP reduction effect for
Denmark can be decomposed into four components:
• 1.1 percent reduction to Denmark’s GDP, due to rising trade costs by the UK (i.e. MFN tariffs
and NTBs) against exports from Denmark, as these extra trade costs reduce Denmark’s exports;
• 0.35 percent increase in Denmark’s GDP, due to rising trade costs by the EU (including Denmark)
on UK exports as these trade costs reduce UK’s exports to Denmark and lead to higher domestic
outputs in Denmark;
• 0.06 percent increase in Denmark’s GDP, due to rising trade costs by UK on exports originated
from the EU’s FTA partner countries, as these trade costs reduce the partner countries’ exports to
the UK, thereby increasing the UK’s domestic production and its imports from elsewhere
(including those from EU member states);
• and similarly 0.01 percent increase in Denmark’s GDP, due to rising trade costs by the EU’s FTA
partner countries against exports from the UK, as these trade costs lowers exports from the UK
to the FTA partner countries, thereby indirectly increasing exports from EU member states to
their FTA partner countries.
11
It is worth noting that with rising bilateral trade barriers/costs, aggregated export price indices for exports from both
the EU (including Denmark) and the UK would decrease under both the WTO and the FTA scenarios. For instance,
Denmark’s export price indices by products would decrease by between 0.3 and 0.6 percent under the FTA scenario,
and by between 0.4-0.9 percent under the WTO scenario.
29
Overall, the last two effects appear to be rather insignificant,12
while the negative effects due to rising
UK trade costs are expected to be partially offset by the positive GDP effect from rising EU trade
costs against UK exports.
Table 10. Changes in GDP from baseline, % from baseline
Effects due to
Total
effects
Rising UK
trade costs on
EU exports
Rising EU
trade costs on
UK exports
Rising UK trade costs
on exports from EU
FTA countries
Rising trade costs by
EU FTA countries on
UK exports
WTO (MFN+NTB)
DK -0.64
(-0.15)
-1.09 0.35 0.06 0.01
UK -4.82
(-1.08)
1.24 -6.05 0.43 -0.54
FTA
DK -0.44
(-0.09)
-0.76 0.22 0.08 0.01
UK -3.41
(-0.61)
1.04 -4.25 0.33 -0.52
Note: except numbers in parentheses which are defined in the GTAP model as percentage changes
in quantity of GDP, all other numbers are percentage changes in the values of GDP.
Source: own simulation results with the GTAP model and database.
Similarly, the much larger negative GDP effect on the UK can be decomposed into contributions
from the same four policy shocks, as reported in Table 10. In particular, rising trade costs by the EU
alone would reduce UK GDP by over 6 percent, which is expected to be partially offset by a 1.2
percent increase due to rising trade costs by the UK itself. Additionally, rising trade costs by the UK
against exports from EU FTA partner countries also increase UK GDP by 0.43 percent; however, a
slightly large decrease in the UK GDP is expected from rising trade costs by the EU FTA partner
countries against exports from the UK.
Under the FTA scenario, reductions of GDP in Denmark and the UK are expected to be at 0.44 and
3.4 percent respectively, smaller than those simulated under the WTO scenario. In the case of
Denmark, this aggregated GDP effect can again be decomposed by the four different policy shocks,
12
If instead the UK is included in the FTAs negotiated by the EU with third countries, these small positive GDP effects
(at about 0.07 percent) would disappear, resulting in slightly smaller GDP gains for Denmark.
30
with rising trade costs by the UK reducing Denmark’s GDP by about three-fourth of a percentage
point and other policy shocks provide offsetting GDP effects. For the UK, rising trade costs by the
EU and the EU FTA partner countries would reduce UK’s GDP by nearly 4.8 percent, whereas rising
trade costs by the UK against the EU and its FTA partner countries provide smaller positive effects.
In summary, as measured by changes in GDP values from the baseline, under the WTO scenario it
appears that Denmark would suffer a loss of GDP at around two-third of a percentage point whereas
the loss of GDP for the UK to be much larger at nearly 5 percent. The major driving forces behind
the negative GDP effects for Denmark (UK) are the rising trade barriers by the UK (Denmark). For
each of the two countries, its own rising trade barriers would moderate but would not be enough to
offset these GDP losses. Additionally, while rising trade barriers between the UK and the EU’s FTA
partner countries would increase Denmark’s GDP marginally, rising bilateral trade barriers by the
UK and by the EU’s FTA partner countries would generate opposite GDP effects for the UK. In the
FTA scenario, similar patterns are observed, albeit with smaller scale.13
It is worth noting that the above reported percentage changes in nominal GDP reflect both changes
in the levels of gross domestic outputs and in the levels of the associated prices. When the price levels
are taken out, real GDP would decrease at a much smaller scale for both countries under the two
scenarios. For instance, under the WTO scenario, real GDP in Denmark would only shrink by 0.15
percent and that in the UK would decrease by 1.08 percent (see numbers in parentheses in Table 10).
A simple explanation for the much smaller real GDP effect is that rising trade costs due to Brexit
drives up domestic prices of imported goods.
5. Conclusion and discussions
With the UK referendum paving the way for the UK to exit the EU, there has been pressing demand
on understanding its potential impacts. In the case of Denmark, UK has been a very important trade
partner in general and a particularly important export destination for a number of agricultural and
food products. This study therefore offers a set of simulation results obtained from a CGE modeling
exercise under two specific Brexit scenarios to meet this need. The scenarios considered include a
“normal” FTA between the UK and EU and a WTO scenario in which the two sides have to treat each
other on ordinary WTO MFN terms. What differentiates the current study from existing studies rests
13
As the baseline assumes several FTAs likely to be agreed or implemented in 2021, there are some further flexibilities
for remaining EU members including Denmark to redirect trade away from UK. Therefore, in the absence of these
assumed FTAs, it is possible that the negative trade and GDP effects arising from Brexit would be larger.
31
on three points. First, the current study has an explicit focus on the impact on Danish agricultural
sectors, particularly in relation to processed foods, pork products, and milk and dairy products. This
is a key difference from most of the existing studies that are focused on the macroeconomic effects
of Brexit. Second, we consider not only the rising tariff barriers but also rising NTBs across different
sectors, particularly in the WTO scenario where the latter is likely to rise substantially. And indeed
these NTBs have very significant effects on the results. Third, the two scenarios examined in the
current study not only consider rising total trade costs between the EU (including Denmark) and the
UK, they also take into consideration potential complications arising from the need for the UK to
reconfigure its trade arrangement with the EU’s FTA partner countries. Towards this end, we assume
that the UK will have to exit these FTA arrangements. Such an assumption has important implications
for both the UK and EU (including Denmark) in connections with their ability to redirect bilateral
trade flows.
Simulation results obtained from the modeling exercises of the current study are of the “medium-run”
nature, as all domestic and international markets are assumed to be in equilibrium following the
assumed trade policy shocks. As compared to a baseline for the year 2021, simulation results suggest
that bilateral exports from Denmark to the UK would shrink significantly under the WTO scenario,
particularly for key export products such as processed foods, pork products, and dairy. Total Danish
food and agricultural exports to the UK would fall by as much as 80 percent under the WTO scenario
and by about 48 percent under the FTA scenario, under the assumed increases in bilateral tariffs and
NTBs between the EU and UK as well as rising tariffs between the UK and third countries with which
the EU has FTA arrangements. On the flip side, the UK’s agri-food exports are also expected to drop
significantly.
Despite the very large simulated decrease in bilateral exports from Denmark to the UK, results from
model simulations also suggest that reductions of total Danish agri-food exports would be quite
limited. For pork, dairy, processed foods, the simulated reductions in exports are between 2.7 and 4.1
percent only. This seemingly surprising result – given the UK’s significant share in total Danish
exports in these sectors – is due to the possibilities for Danish exports to be redirected within the EU
and to third countries particularly those countries which are partners to the various preferential trade
agreements of the EU assumed in the baseline. It should however be noted that redirected Danish
exports to other markets would be accompanied by slightly reduced export prices.
32
As total agri-food exports from Denmark only suffer small losses due to the assumed Brexit scenarios,
simulation results from the current study also suggest that reductions in domestic production of key
export products in Denmark would be quite small. For instance, for processed foods, pork, and dairy,
outputs would be 2.5, 2.2, and 1.1 percent lower than their respectively baseline levels. In connections
with changes in domestic outputs, sectoral reallocations of production factors such as labor and capital
are also expected in the “medium run” horizon assumed in the model. As an indication of possible
short run sectoral unemployment, the processed food, pork and dairy sectors’ employment of skilled
and unskilled labor would shrink by 1-2.6 percent under the WTO scenario; under the FTA scenario,
simulated negative employment effects are smaller for the three sectors, in the range of 0.2-1.5
percent.
At the macro level, nominal GDP for both Denmark and the UK are expected to decrease relative to
the baseline; however, losses to Danish GDP are expected to be much smaller at about 0.64 percent
under the WTO scenario and at 0.44 under the FTA scenario, as compared to 4.8 and 3.4 percent
losses for the UK under the two scenarios respectively. Losses of real GDP are smaller for both
Denmark and the UK, with the percentage losses for Denmark being smaller than that for the UK.
These relative differences in GDP losses are indications of the asymmetric nature of the trade policy
implications of Brexit: while the assumed increases in trade costs are the same for both the UK and
EU, in effect the UK would have to face rising trade costs from all 27 remaining member states of
the EU, a much larger export destination accounting for a large portion of its exports, as well as rising
trade barriers from the EU’s FTA partner countries; for Denmark and other EU member states, rising
trade costs only extend to the UK markets. Therefore, losses to GDP associated with reduced trade
volumes would be understandably small for the remaining EU member states such as Denmark, not
least because the EU single market and preferential accesses to the market of EU’s FTA partner
countries provide ample flexibilities to redirect trade flows.
Taken together, the results reported in this study are in line with the majority of existing studies
regarding the harmful effects of Brexit to bilateral trade flows with the UK as well as the negative
GDP effects for the UK and its trading partners within the EU. As an important destination market
for Denmark, losing the UK as part of the EU single market would damage the export prospects of
several key agri-food products originated from Denmark; however, such negative effects at the
bilateral level can be partially avoided by redirecting trade within the EU market and with other
countries. Following this logic and the numerical results, it is possible that further trade liberalization
33
either through the multilateral process or through the EU’s various bilateral initiatives aiming at
further reducing trade barriers elsewhere will bring new trade opportunities for Danish products. With
regard to the bilateral trade-relationship between Denmark and UK in the future, it is quite apparent
that a WTO MFN relationship is not desirable for either side, and that an FTA between the UK and
EU, while representing several steps back from status quo, would be much preferable to the WTO
MFN option.
Several caveats of the study also deserve some discussion. First, the simulation results are dictated
by both the modeling structure and the assumptions regarding the Brexit scenarios including the
magnitude of rising import tariffs and NTBs. In the latter case, uncertainties associated with the
magnitude of estimated NTBs adopted from the limited current academic literature may be a cause
of concern. Therefore, cautions need to be exercised when scrutinizing the simulation results resting
on such assumptions. Second, out of the many possible Brexit scenarios, the current study – as in the
case of a few other existing studies – only considers some indicative benchmarking scenarios (i.e. the
WTO and FTA scenarios in the current study). The actual arrangements to be agreed by the UK and
EU and possibly with third countries are of course not known at this time. Therefore, the presented
results in the current study should not be treated as “predictions” and should only be understood
within the specific assumptions and context of the two scenarios. Third, the scope of the current study
is to model only the impacts of two alternative Brexit trade arrangement scenarios and the results are
solely driven by the assumed changes in trade policies. Therefore, other considerations such as those
surveyed in Kierzenkowski et al. (2016) are not taken into account. In relation to this point, for the
agricultural sectors, a notable omission is about the implications of Brexit on the Common
Agricultural Policy (CAP) of the EU and how possible changes in the CAP due to Brexit may
influence agri-food trade. As a net contributor to the EU budget and the CAP budget, Matthews
(2016) points out several channels through the Brexit can influence the CAP and agri-food trade,
including future market orientations of the CAP, the size of the EU budget, regulatory environment
in the EU, and EU’s trade relationship with third countries. A quantitative study by Boulanger and
Philippidis (2015) finds that the UK can actually gain from withdrawal from the EU budget but
administrative, procedural and trade facilitation costs due to exit from the EU’s single market would
lead to overall welfare losses for the UK. Moreover, a full withdrawal from the EU would lead to
welfare losses for both the EU and for Britain. It is however not clear from existing literature how the
CAP dimension would impact Denmark. This therefore can be an interesting issue to investigate in a
further study.
34
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35
Appendix Table 1. Bilateral exports/imports of selected products from/to Denmark in 2011-2013
DK exports in mn DKK to
2011 2012 2013
UK EU World UK EU World UK EU World
Processed Food 3,968 7,859 34,107 3,897 8,119 35,223 4,545 8,993 38,652
BeverageTabacco 224 1,330 5,740 297 1,188 6,493 516 1,384 7,230
Bovinemeats 181 527 5,105 121 530 5,040 122 616 5,075
PorkPoultry 5,746 3,640 28,305 4,916 3,846 28,486 5,050 3,673 28,208
OtherAnimalProd 39 1,197 9,036 83 1,351 10,659 43 1,248 11,920
VegeOil 74 1,415 3,091 141 1,315 3,125 71 1,293 2,980
MilkDairy 1,832 3,295 14,802 1,767 3,428 14,751 1,292 3,163 14,991
All 53,878 123,631 520,206 52,825 124,664 527,205 46,811 123,084 528,735
All AgriFood 12,709 22,554 114,599 12,152 22,990 117,685 12,948 23,538 123,577
DK imports in mn DKK from
2011 2012 2013
UK EU World UK EU World UK EU World
Processed Food 995 5,023 26,036 869 5,253 26,690 1,299 5,952 28,325
BeverageTabacco 551 940 6,880 586 914 6,591 860 1,222 7,522
Bovinemeats 204 261 3,906 100 290 3,972 169 275 4,347
PorkPoultry 207 699 6,501 181 704 7,836 206 730 7,650
OtherAnimalProd 71 1,156 2,695 74 917 2,885 54 1,547 3,636
VegeOil 58 442 6,347 32 581 7,427 7 630 8,191
MilkDairy 152 1,061 4,368 150 968 3,750 142 1,060 4,645
All 27,225 119,921 488,942 22,991 123,229 492,111 25,772 122,238 494,527
All AgriFood 2,595 12,269 71,804 2,512 12,472 75,250 3,108 13,841 80,493
Source: compiled from the GTAP database version 9. Trade data from the GTAP database are originally sourced from the UN
COMTRADE database. Product classifications listed in column 1 are based on the GTAP classification (see www.gtap.org) Official annual
exchange rates are used for converting the GTAP data in USD into DKK.
36
Appendix Table 2a. List of sectors
Aggregated sectors Disaggregated GTAP sectors included
1 Pdr Paddy rice
2 Wht Wheat
3 Gro Cereal grains nec
4 v_f Vegetables, fruit, nuts
5 Osd Oil seeds
6 c_b Sugar cane, sugar beet
7 Pfb Plant-based fibers
8 Ocr Crops nec
9 bovineanimal Bovine cattle, sheep and goats, horses
10 Oap Animal products nec
11 rawmilk Raw milk
12 Wool Wool, silk-worm cocoons
13 Extraction Forestry, Coal, Oil, Gas, Minerals nec
14 Fish Fishing
15 bovinemeats Bovine meat products
16 porkpoultry Meat products nec
17 Vol Vegetable oils and fats
18 milkdairy Dairy products
19 Pcr Processed rice
20 Sugar Sugar
21 Ofd Food products nec
22 b_t Beverages and tobacco products
23 Textwapp Textiles, Wearing apparel
24 LightMnfc Leather products, Wood products, Paper
products, publishing, Metal products, Motor
vehicles and parts, Transport equipment nec,
Manufactures nec
25 HeavyMnfc Petroleum, coal products, Chemical, rubber,
plastic products, Mineral products nec, Ferrous
metals, Metals nec, Electronic equipment,
Machinery and equipment nec
26 Util_Cons Electricity, Gas manufacture, distribution,
Water, Construction
27 TransComm Trade, Transport nec, Water transport, Air
transport, Communication
28 OthServices Financial services nec, Insurance, Business
services nec, Recreational and other services,
Public Administration (Defense, Education,
Health), Dwellings
Source: own aggregation of sectors in the GTAP database.
37
Appendix Table 2b. List of aggregated countries/regions
Aggregation of region Countries/regions included
Australia Australia
NZL New Zealand
RestofWorld Rest of Oceania
China China, Hong Kong
Japan Japan
Korea South Korea
RoEAsia Mongolia, Taiwan, Rest of East Asia
SEAsia Brunei Darussalam, Cambodia, Laos, Malaysia,
Singapore, Rest of Southeast Asia
IdnPhl Indonesia, Philippines
Thailand Thailand
Vietnam Vietnam
RoSAsia Bangladesh, Nepal, Pakistan, Sri Lanka, Rest of
South Asia
India India
Canada Canada
USA USA
Mexico Mexico
RofNAme Rest of North America
Argentina Argentina
LatinAmer Bolivia, Chile, Colombia, Ecuador, Paraguay,
Peru, Uruguay, Venezuela, Rest of South
America, Costa Rica, Guatemala, Honduras,
Nicaragua, Panama, El Salvador, Rest of
Central America, Dominican Republic, Jamaica,
Puerto Rico, Trinidad and Tobago, Caribbean
Brazil Brazil
RofEU Austria, Belgium, Cyprus, Czech Republic,
Estonia, Finland, Greece, Hungary, Latvia,
Lithuania, Luxembourg, Malta, Portugal,
Slovakia, Slovenia, Sweden, Bulgaria, Croatia,
Romania
Denmark Denmark
France France
Germany Germany
Ireland Ireland
Italy Italy
Netherlands Netherlands
Poland Poland
Spain Spain
UK Great Britain
Switzerland Switzerland
Norway Norway
38
RestofWorld Rest of EFTA, Albania, Belarus, Rest of Eastern
Europe, Rest of Europe, Kazakhstan,
Kyrgyzstan, Rest of Former Soviet Union,
Armenia, Azerbaijan, Georgia, Rest of the
World
Russia Russia
Ukraine Ukraine
MENA Bahrain, Iran, Israel, Jordan, Kuwait, Oman,
Qatar, Saudi Arabia, United Arab Emirates,
Rest of Western Asia, Morocco, Tunisia, Rest
of North Africa
Turkey Turkey
Egypt Egypt
SSA Benin, Burkina Faso, Cameroon, Cote dIvoire,
Ghana, Guinea, Nigeria, Senegal, Togo, Rest of
Western Africa, Central Africa, South Central
Africa, Ethiopia, Kenya, Madagascar, Malawi,
Mauritius, Mozambique, Rwanda, Tanzania,
Uganda, Zambia, Zimbabwe, Rest of Eastern
Africa, Botswana, Namibia, Rest of South
African Customs Union
SAfrica South Africa
Source: own aggregation of countries/regions in the GTAP database.
39
Appendix Table 3. Changes in bilateral pork exports from Denmark, from baseline levels
% changes in bilateral exports changes in bilateral exports (mn USD)
WTO
(MFN
only)
WTO
(MFN+NTB)
FTA WTO
(MFN
only)
WTO
(MFN+NTB)
FTA
Australia 2.1 7.4 5.3 -5.1 1.8 -0.9
NZL 2.9 6 5.1 -5.6 -5.4 -5.5
China 1.7 7.8 4.8 -0.2 15.0 7.5
Japan 0.6 1.8 1 3.0 50.6 13.8
Korea 1.5 3.6 2.3 -1.1 2.7 0.2
RoEAsia 2.2 8.5 5.7 0.0 0.0 0.0
Vietnam 3.3 8 5.5 0.1 0.4 0.3
Thailand 0.4 3.8 1.3 -0.1 0.3 0.0
IdnPhl 3.5 8 5.7 0.4 1.7 1.0
SEAsia 0.4 7 3.3 -0.2 1.3 0.4
11 India 1.3 3.6 1.4 0.0 0.1 0.0
RoSAsia 2.6 6.1 3.2 0.0 0.0 0.0
Canada 1.8 8 5.3 -0.1 0.3 0.1
USA 1.8 7.6 5.1 -8.2 -2.2 -4.8
Mexico 2 8.1 5.6 -0.1 0.2 0.1
RofNAme 1.3 4.4 2.8 -0.1 0.4 0.1
Argentia 0.3 7.7 3.5 0.1 0.8 0.4
Brazil 2.8 6.4 3.8 0.0 0.0 0.0
LatinAmer 1.9 8.7 5.1 0.0 1.3 0.6
Denmark 3.1 3.9 2.9 0.0 0.0 0.0
France 2.4 3.3 2 2.3 2.5 2.0
Germany 2 3.4 1.9 22.1 27.2 20.6
Ireland 28 37.2 27.9 7.5 9.8 7.4
Italy 1.1 2.3 1.1 2.3 3.6 2.0
Netherlands 0.2 4.2 1.5 0.0 0.5 0.2
Poland 5.2 5.9 4.5 15.1 15.9 12.0
Spain 2 3.4 1.8 0.8 1.0 0.7
RofEU 1.3 1.4 0.6 9.3 8.0 5.2
UK -46.7 -94.3 -51 -254.9 -523.5 -280.0
Switzerland 1.2 4.9 2.6 0.0 0.1 0.0
Norway 1.6 4.2 2.4 0.3 2.1 0.8
Russia 1.3 6.2 3.8 -0.6 8.3 3.9
Ukraine 2 5.3 3.1 0.1 0.2 0.1
Turkey 1.2 6.2 3.6 0.0 0.0 0.0
Egypt -0.6 6.7 2.6 0.0 0.3 0.1
MENA 0.6 6.6 3.4 0.2 1.6 0.8
SAfrica 5.2 11.3 7.9 0.6 1.3 0.9
SSA 1.4 6.3 3.6 0.0 0.5 0.2
RestofWorld 1.8 6.3 3.9 0.0 2.1 0.9
total -1.6 -2.7 -1.3 -212.4 -369.7 -208.6
Source: simulation results.
40
Appendix Table 4. Changes in total exports by exporting countries, % from baseline
WTO (MFN only) WTO (MFN+NTB) FTA
Australia -0.07 -0.48 -0.34
NZL -0.1 -0.06 -0.11
China -0.06 -0.32 -0.23
Japan -0.12 -0.64 -0.45
Korea -0.07 -0.07 -0.07
RoEAsia 0 0.07 0.06
Vietnam 0.08 0.05 0.09
Thailand -0.06 0.08 0.09
IdnPhl -0.08 -0.25 -0.19
SEAsia 0.05 0.27 0.21
India 0.05 0.29 0.25
RoSAsia -0.01 -0.08 -0.04
Canada -0.08 -0.11 -0.09
USA -0.1 -0.5 -0.36
Mexico -0.04 -0.19 -0.14
RofNAme 0.08 0.34 0.28
Argentia -0.11 -0.77 -0.46
Brazil -0.08 -1.03 -0.61
LatinAmer -0.01 -0.12 -0.09
Denmark -0.06 -0.43 -0.29
France -0.13 -0.57 -0.45
Germany -0.07 -0.48 -0.36
Ireland -0.44 -1.77 -1.26
Italy -0.08 -0.49 -0.35
Netherlands -0.09 -0.76 -0.56
Poland -0.11 -0.5 -0.38
Spain -0.07 -0.22 -0.19
RofEU -0.03 -0.22 -0.16
UK -3.12 -9.33 -7.23
Switzerland 0.01 0.1 0.1
Norway 0.02 0.14 0.1
Russia -0.03 -0.2 -0.14
Ukraine -0.03 0.01 0.03
Turkey -0.01 0.14 0.13
Egypt -0.11 -0.42 -0.3
MENA 0 -0.02 -0.02
SAfrica -0.37 -0.32 -0.32
SSA 0.03 0.03 0.01
RestofWorld 0.01 0 0
Source: simulation results. Changes exceeding +/-0.25% are highlighted.