Publication - Research publication

Economic impacts for Scottish and UK seafood industries post-Brexit: report

Published: 5 Jun 2018
Directorate:
Marine Scotland Directorate
Part of:
Brexit, Marine and fisheries
ISBN:
9781788519755

The report presents findings from research examining the possible impacts of EU exit on Scottish and UK seafood industries.

101 page PDF

2.8 MB

101 page PDF

2.8 MB

Contents
Economic impacts for Scottish and UK seafood industries post-Brexit: report
3 Description of the Approach

101 page PDF

2.8 MB

3 Description of the Approach

3.1 Trade model

The modelling framework used is a standard partial equilibrium computable model of international trade - the Trade Analysis Partial Equilibrium Sussex ( TAPES) model created at the University of Sussex. The TAPES model is specifically designed to analyse the impact on trade (both imports and exports) and production of changes in trade policies, such as tariffs, quotas or non-tariff measures. The version of the model used is a slightly modified version of Francois et al. (1998), and is a perfectly competitive [9] multi-market model with an underlying Armington demand structure [10] : The advantage of a multi-market model is that trade – both exports and imports – can be simultaneously modelled between several partner countries. Hence, in the context of changes in policy arising from Brexit it allows both the change in UK imports from various sources, as well as changes in UK exports to different destinations, to be considered.

Partial equilibrium modelling involves treating each industry/goods/species separately (for the sake of brevity, subsequently referred to as ‘goods’), and does not take into account the interactions between goods. Hence, in this report the direct impact of changes in tariffs on trade flows are modelled for ten different fish species, and each of these is modelled independently. Any changes, therefore, in the price of haddock as a result of a change in tariffs, do not impact on the demand for a different species, such as cod. In a similar vein the model does not model factor markets, such as the labour market. Hence, as supply changes in response to a tariff change we do not model any changes in the labour market nor any changes therefore in wages. While to a degree this is clearly unrealistic it is important to note that the main impact of any change in tariffs would be the direct effect, and the interaction effects with other goods where they exist are typically very much second order and much smaller. Modelling the interaction effects between goods/markets is much more complex and much more demanding of the data, and all species or potential substitutes would also be affected by relatively similar shocks. Accounting for interactions between commodities would require a form of modelling known as computable general equilibrium ( CGE) modelling, which is only possible where there is a full set of input-output tables. As these are typically only available at a much more aggregated level it is not possible to run a CGE analysis on each individual species. The approach taken in this report therefore is to model the impact on different species at the most disaggregated level that was possible given the data constraints, and then to model the broader economic impacts which take into account inter-sectoral linkages separately. A substantial advantage of this approach is that it is possible to work at a much more detailed level of goods (fish species) than would otherwise be possible.

The TAPES model is specified as a non-linear system of equations and numerical methods are used for solving. [11] In order to achieve this, partial equilibrium modelling involves three stages. First the base (or equilibrium) data set needs assembling. For the scenarios ten fish species are modelled and so the data on the actual trade flows between countries for each of these species are needed, as well as information on domestic production (landings and processed output) for all countries, on the tariffs between the countries concerned, and then information on key parameters. The data are described in more detail in Section 3.2. Second, the model needs to be ‘calibrated’ to the base data. This entails ensuring that when the system of equations is solved, in the absence of any change in policy, the levels of trade and production which are generated replicate exactly the levels of trade and production in the base data set. The final stage is that of ‘simulation’. This involves changing a policy parameter – this could be a tariff, a quota, or a non-tariff measure, or some combination of the three – and then to run the model again. The change in policy impacts on prices, which in turn impacts on levels of trade and production which the model solves for. The comparison between the base equilibrium and the simulated equilibrium is therefore the simulated impact of the policy change. Appendix D provides a full description of the model and model equations.

3.1.1 Species

The modelling focusses on ten species (or species groups) (Table 3.1). A separate model was created for each of the species. These species (groups) were selected on the basis of their visibility in the Harmonised System ( HS) 6-digit trade codes and their importance in terms of:

  • Value of UK landings;
  • UK export values; and
  • Potential for quota redistribution under the principle of zonal attachment; and
  • Significance to the Scottish fleet.

This precluded the inclusion of some species, such as monkfish or anglers, which are grouped with a range of other families in the trade data at 6-digit level across all product types (fresh/chilled, frozen, fillets etc.) and therefore could not be differentiated for the purposes of the modelling. The 6-digit codes were used because data on world trade flows (imports and exports for all countries) were required for the model, and this global coverage is only available at the 6-digit level.

Table 3.1. Species modelled

Species (Group)

Species Included (Based on the Species Included in the Relevant Trade Codes)

1

Cod

Gadus morhua, Gadus ogac, Gadus macrocephalus

2

Crab

All crab species

3

Haddock

Melanogrammus aeglefinus

4

Hake

Merluccius spp., Urophycis spp.

5

Herring

Clupea harengus, Clupea pallasii

6

Mackerel

Scomber scombrus, Scomber australicus, Scomber japonicus

7

Nephrops

Nephrops norvegicus

8

Saithe

Pollachius virens

9

Salmon

Atlantic salmon ( Salmo salar), Danube salmon ( Hucho hucho) and Pacific salmon

10

Scallops

Genera Pecten, Chlamys or Placopecten

For each species, the trade codes that relate exclusively to each species (or species group) were selected for the model [12] . The list of codes used and their descriptions is provided in Appendix E. Because 2015 data are used, it is the HS2012 version of the trade codes that is used. The aim was to ensure that there is no overlap in the trade accounted for in the modelling exercise, but this means that the model may not capture all trade for a species. For example, for hake, the trade categories included (which relate exclusively to hake) are:

  • 030254: Fresh or chilled;
  • 030366: Frozen; and
  • 030474: Frozen fillets.

Hake may also be traded as fresh or chilled fillets; fresh or chilled other meat; dried, salted or in brine or smoked; prepared or preserved. However, for these categories, hake is mixed in a group with various other fish species, or in a general ‘other’ category. For these, it is impossible to know the proportion attributable to hake, and therefore they have been excluded from the analysis.

The ten species modelled account for 67% of the value of UK landings and 94% of the value of UK aquaculture production. The trade models account for 60% of the value of UK trade (imports and exports) in the 03 category, and 13% of UK trade in the 1604 and 1605 categories. The trade data used in this report (see Section 3.2.2) are taken from the United Nations’ ( UN) Comtrade database, which provides extremely detailed trade data up to the HS 6-digit level. These data were used as opposed to for example Office of National Statistics ( ONS) or Her Majesty’s Revenue and Customs ( HMRC) data because the modelling requires data on bilateral trade for all countries in the model, for example between Peru and Norway. This is only available from international data sources such as Comtrade. In principle, these data should be highly compatible with the UK official data, as it is supplied to the UN by the UK government, but in practice some differences between datasets do exist.

Of these ten species, mackerel, Nephrops, salmon and scallop dominate the value of UK exports, with cod, haddock and salmon dominating UK imports (Figure 3.1). UK imports of cod and haddock far outweigh exports of these species, whereas for Nephrops and scallops the opposite is true.

Figure 3.1. UK exports (left) and imports (right) of each species (annual average, 2013–2015), ($000)

Figure 3.1. UK exports (left) and imports (right) of each species (annual average, 2013–2015), ($000)

A large proportion of the exports of these species go to the EU27, although mackerel and salmon have the lowest proportions of exports to the EU. Imports of most species to the UK are dominated by non- EU countries, but over 80% of herring and mackerel imports are from EU27 countries (Figure 3.2).

Figure 3.2. Share of UK exports (left) and imports (right) of each species that are traded with the EU27 (2013–2015)

Figure 3.2. Share of UK exports (left) and imports (right) of each species that are traded with the EU27 (2013–2015)

3.1.2 Countries

The UK and EU27 are included in the models for every species. The other individual countries included in each species model account for at least 2.5% of the value of UK exports and of the value of UK imports of the HS2012 codes (on average over the period 2013–2015). The remaining countries are grouped in a ‘Rest of World’ (RoW) category. The EU27 was treated as a single entity in the model, because all its members have the same external trade policy (tariffs, border controls), which allowed more third countries to be included in the models.

The countries modelled for each species are listed in Table 3.2, and Appendix F lists the share of imports and exports for the top ten trading partners of the UK. By including these countries, over 95% of the UK’s trade in each species is accounted for in the model.

Table 3.2. Countries included in each species model

Cod

Crab

Haddock

Hake

Herring

China
EU27
Faroe Islands
Iceland
Nigeria
Norway
RoW
Russian Federation
United Kingdom

China
EU27
Indonesia
RoW
United Kingdom
Viet Nam
Thailand
China, Hong Kong SAR

Canada
China
EU27
Faroe Islands
Iceland
Norway
RoW
Russian Federation
United Arab Emirates ( UAE)
United Kingdom
USA

Argentina
EU27
RoW
South Africa
United States of America
United Kingdom

China
EU27
Nigeria
Norway
RoW
United Kingdom

Mackerel

Nephrops

Saithe

Salmon

Scallops

China
EU27
Nigeria
Norway
RoW
Russian Federation
Ukraine
United Kingdom

China
EU27
India
RoW
United Kingdom
Vietnam

China
EU27
Faroe Islands
Iceland
Norway
RoW
United Kingdom

Canada
China
EU27
RoW
United Kingdom
USA

Argentina
Canada
EU27
Japan
Peru
RoW
United Kingdom
USA

It is important to note that the trade data give us the value of recorded trade, for example between Norway and the UK. Additional complexities to do with boat ownership and rule of origin certifications (e.g. Norwegian-produced salmon that is exported to the UK via Sweden or other EU countries) etc. are implicitly captured in this data but are not explicitly identifiable.

3.2 Data sources

A range of different data types were brought together for the model:

  • Production data (value of production for each species, taking into account landings as well as production of processed fish and seafood);
  • Trade data (value of imports and exports for each species, between the UK and each of the countries in the model);
  • Tariffs for each of the countries in the model, both for the baseline and for each scenario;
  • Tariff-equivalents for non-tariff measures, both for the baseline and for each scenario; and
  • Other model parameters.

3.2.1 Production

The partial-equilibrium modelling requires data on production of each species modelled. The model for each species combines fresh and chilled fish, as well as processed fish (filleted, frozen, smoked etc.). Estimates of the value of production of each species, for each country, were therefore based on the volume of landings and aquaculture production (from FAO global production database – FAO, 2017) combined with reported data on the production of processed fish (from the FAO commodities and trade database – FAO, 2016b), taking into account conversion factors in processing (from EUMOFA). Full details are provided in Appendix G.1.

3.2.2 Trade

The data on trade in the model are derived from the UN Comtrade database. In order to ensure compatibility with the latest available production data, 2015 data are used. For each species, the bilateral trade flow is required, for example the level of UK imports from the EU and exports to the EU. These bilateral flows are needed for every pair of countries that are included for any given species. Further details are provided in Appendix G.2.

3.2.3 Tariffs

The data on tariffs derives from the UN Trade Analysis Information System ( TRAINS) database, which provides information on the 6-digit tariffs levied by each country on each importer. In the absence of a free trade agreement between countries the tariffs will be the Most Favoured Nation ( MFN) [13] applied tariffs; where there is a free trade agreement then the tariffs will be the preferential tariffs. As the model combines the trade for the different categories (fresh, frozen, fillets etc.) of a species together, a simple average tariff across the product categories for each species was used [14] . In the case of salmon, this was adjusted to take account of the differentiation between species, to avoid the average being weighted towards the lower tariffs of the fresh/chilled categories, where there is a greater level of species differentiation in the 6-digit trade codes. Further details are provided in Appendix G.3.

3.2.4 Non-tariff measures

Reliable estimates of the size of non-tariff barriers are not available, especially at extremely detailed levels of trade codes. A review of the literature (see Appendix G.4) indicates a range of AVEs for different types of NTM, of the order of –3% to +20%. We therefore assume a base level of NTMs of 5% for trade with the EU27 and 15% for trade with non- EU countries, and adjust this to reflect the NTMs in play in each scenario. Further details are provided in Appendix G.4. In choosing these levels of NTMs, if anything the assumptions made are conservative, and these probably represent a lower bound. NTM estimates do not account for the cost of ‘doing business’ in other countries.

3.2.5 Other model parameters

The model requires information or assumptions about: (a) the elasticity of demand for each species; (b) the elasticity of substitution between countries for any given species; and (c) the elasticity of supply.

The elasticity of demand represents how much demand changes with a change in price. It therefore captures the extent to which a change in price is then reflected in a change in demand for a given fish species. Information on the elasticity of demand is obtained from the detailed econometric estimation undertaken by Kee et al. (2008). Their work provides estimates at the HS1988 6-digit level for a range of countries, and for a range of seafood products. As the partial equilibrium model requires a single elasticity which is then applied to all countries, for each of the relevant 6-digit codes for each species the median elasticity as estimated by Kee et al. (2008) is used to take the simple average across the 6-digit codes.

The elasticity of supply represents how responsive supply is to a change in demand or price, i.e. how readily supply can be increased or decreased. The elasticity of substitution represents whether a product can be easily substituted by a similar product from a different source (country), for example whether cod from Norway is very similar to cod from Iceland. There are no estimates either of the elasticity of substitution nor of the elasticity of supply for the fish species modelled, so common practice requires some assumption: In the base simulations the elasticity of substitution is assumed to be 5 for fish species and 2.5 for shellfish species (due to fresh product being particularly important for UKEU trade); and the elasticity of supply 1 [15] (see Appendix D.2). Sensitivity analysis on these parameter values was carried out in order to assess the extent to which this impacts on the results ( Appendix J) [16] Nevertheless it is important to note that there is considerable uncertainty as to the actual value of all of these elasticities and the best that any model can do is to use best-guess estimates and undertake sensitivity analysis. In turn this means that the results should not be viewed as predictions or forecasts, but more as indicators of the possible orders of magnitude that could occur, while holding all other factors that might impact on trade constant.

3.2.6 Corrections to the data

Landings to foreign ports

UK landings to foreign ports for herring and mackerel have been shown to be lacking from the trade statistics for UK exports (Seafish, 2017). A correction to the trade data was therefore made for herring and mackerel landed by UK vessels in non- UK ports, to account for unrecorded exports to Norway and the EU27 (Table 3.3).

Table 3.3. Correction for unrecorded exports landed directly to foreign ports (2015 data)

Norway

EU27

Non- EU

Herring

Value of UK landings to foreign ports ($000)

10,521

18,817

0

Value of UK exports (fresh/chilled) ($000)

-

7,806

8,754

Unrecorded exports ($000)

10,521

11,011

n/a

Mackerel

Value of landings to foreign ports ($000)

106,758

42,083

6

Value of UK exports (fresh/chilled) ($000)

3,495

12,479

15,974

Unrecorded exports ($000)

103,264

29,604

n/a

Faroese salmon trade flows

Sources indicate that Faroese salmon passes through the UK in transit to other countries, but it is not processed or consumed in the UK. Therefore, imports of salmon to the UK from the Faroe Islands, and a corresponding value of UK salmon exports to the EU27, were removed from the trade data, as agreed with the Project Steering Group.

Value of production and exports

In the trade model, the value of production must be greater than the value of exports. In some cases, exports exceeded production, as a result of the combination of different datasets, and imported raw material being used as inputs to the processing industry, and the product subsequently exported. In these cases, the value of production was adjusted to allow for the value of exports and an estimate of domestic consumption. Domestic consumption was calculated based on: FAO per capita fish and seafood supply [17] , population size, and an estimate of the significance of the species in question for local diets based on consumer preferences; the average ratio of exports to production from the other countries in the model; or the intra-regional trade flows.

Zero trade flows

In the model, trade flows cannot be equal to zero. Therefore, where the trade data showed there was no trade in a species between two countries, this was set to a very low level ($0.1) in order to allow the model to run.

Lack of trade data

In some cases, such as for the Faroe Islands, no trade data were available. In these instances, the mirror flows were used (i.e. as reported by the partner country importing from or exporting to the Faroe Islands).

3.3 Applying the scenarios

3.3.1 Tariffs and NTMs

The TAPES model is set up to be able to work with both ad valorem tariffs, and specific tariffs, as well as quotas and non-tariff barriers. Effectively applied tariffs ( AHS) are used for the baseline. A change to tariffs was applied by inputting the new tariff levels in the model.

The treatment of non-tariff measures ( NTMs) in the model is stylised and these are modelled as ad valorem equivalent tariffs (but without generating any government revenue). For example, suppose that there is an NTM related to standards. For this to be included in the model, and for the model to be able to simulate changes in the non-tariff barrier, then the model needs information on the extent to which this NTM restricts trade if it were equivalent to a tariff. For example, a 10% NTM, assumes that the NTM restricts trade to the same degree as would a 10% tariff. The NTM levels modelled are based on available evidence on existing NTMs.

The level of NTMs in each scenario was based on available literature (see Appendix G.4) and adjusted to reflect the different types of NTM in each scenario. A change to NTMs was applied by adding the AVE NTM level to the new tariff levels and inputting them to the model. The range of NTM AVEs used reflect reasonable levels of NTMs where trade takes place under normal conditions (which is appropriate for the simulated equilibrium of the model, which does not incorporate short-term transitional impacts). It is possible that NTM levels could be much higher in the short-term and for some shipments if there are severe disruptions to trade flows such as significant border delays or trade defence measures imposed.

3.3.2 Zonal attachment

The new level of production for each species was calculated based on the additional UK landings anticipated from a change of TAC or quota distribution to the zonal attachment principle, by stock. The zonal attachment percentage estimate for each stock was taken from University of Aberdeen & SFF (2017), which provides one interpretation of the zonal attachment principle, applied to the 2015 TAC. The change in landings was calculated as the difference between the calculated UK zonal attachment quota estimate for each species, compared to relevant UK landings for 2015. Zonal attachment for Nephrops was estimated based on ICES stock advice for the individual Functional Units ( FU) and the proportion of each FU in UK waters. It was assumed that UK production expands to fulfil the quota allocation. The exception was North Sea and West of Scotland Nephrops, where 2015 landings were significantly below the UK’s initial quota allocation. However, the increase in Nephrops landings modelled (1,244 tonnes) is comparable to the overall potential increase in quota under the Zonal Attachment principle (1,421 tonnes).

The change in the value of production was calculated based on the anticipated change in landings, and the average value of production per tonne of live weight landings, for each country. The average value of production takes into account the split between fresh and processed categories and the different values per tonne of those categories, and therefore represents potential production value taking into account processing in line with current activity. Full details are provided in Appendix H.

A change in production as a result of a change in the distribution of TACs and quotas between the UK and EU on the basis of the zonal attachment principle was applied by increasing the UK’s production value and decreasing the EU’s production value by the amounts/percentages indicated by the zonal attachment calculations.

No a priori change in production was modelled for the non-quota species crab and scallop, nor for salmon which is produced by aquaculture, although production of these species may change in response to the trade regime.

3.4 Wider economic impacts

Wider economic impacts on Scotland’s economy were considered only in relation to the direct, indirect and induced impacts of Scenarios 1 and 4. In order to take the trade modelling outputs (for the UK) and apply multipliers and Input-Output (I-O) analysis, the results for each species were disaggregated to the primary (fishing and aquaculture) and processing sectors, and further disaggregated between Scotland and the UK. Changes in the direct, indirect and induced impacts on output, GVA and employment for Scotland were calculated, and the upstream economic sectors affected were identified.

3.4.1 Applying the model outputs to the primary and processing sectors

The trade model outputs aggregate the impacts on the fishing, aquaculture and processing sectors for each species, combining production of unprocessed and minimally processed fresh/chilled product, as well as processed products such as fillets, smoked, canned and prepared/preserved products. The percentage change in output from the trade modelling (which captures a proportion of output and trade in each species) was applied to the UK output of fishing, aquaculture and processing sectors for each species. This assumes that the percentage changes from the trade modelling will apply to the rest of the sector.

The I-O tables include sectors that relate to the two primary industries of fishing and aquaculture production, as well as the secondary industry of fish processing. To identify the impact on turnover (output), GVA and employment, the change in output from the trade modelling was disaggregated into impact on the primary industries (fishing or aquaculture production) and impact on the processing industry, based on the location of landings and production, and the structure of the supply chain in each case.

The apportionment was based on the output of the fishing sector (value of landings by UK vessels to UK and non- UK ports), the output of the aquaculture sector (value of production), and a calculation of the output of the processing sector for each species (Table 3.4). Further details are provided in Appendix I.

Table 3.4. Disaggregation of change in output to primary and processing industries

Species

Fishing

Aquaculture

Processing

Cod

37%

0%

63%

Crab

16%

0%

84%

Haddock

22%

0%

78%

Hake

27%

0%

73%

Herring

18%

0%

82%

Mackerel

31%

0%

69%

Nephrops

29%

0%

71%

Saithe

11%

0%

89%

Salmon

0%

34%

66%

Scallop

17%

0%

83%

Note: Change in GVA and employment were disaggregated to the primary and processing sectors according to specific percentages based on the processing GVA or employment attributed to each species as a proportion of the total GVA or employment (from processing and fishing/aquaculture for each species).

3.4.2 Identifying impacts on the Scottish economy

The outputs of the trade modelling are for the UK as a whole. In order to determine the proportion of these impacts that might occur in Scotland, the UK-level impacts were apportioned to Scotland for each species, based on the relative geographic footprint of the catching or aquaculture sectors and of the processing sector (Table 3.5). Further details are provided in Appendix I.

Table 3.5. Apportionment of fishing, aquaculture and processing output between Scotland and Rest of UK

Species

Fishing

Aquaculture

Processing

Scotland

Rest of UK

Scotland

Rest of UK

Scotland

Rest of UK

Cod

46%

54%

-

-

47%

53%

Crab

37%

63%

-

-

29%

71%

Haddock

83%

17%

-

-

47%

53%

Hake

54%

46%

-

-

47%

53%

Herring

65%

35%

-

-

59%

41%

Mackerel

82%

18%

-

-

59%

41%

Nephrops

74%

26%

-

-

29%

71%

Saithe

62%

38%

-

-

47%

53%

Salmon

-

-

99%

1%

77%

23%

Scallop

59%

41%

-

-

29%

71%

Note: For GVA and employment, modified percentages were used for apportioning between Scotland and the UK, based on Scottish processing turnover, GVA and employment as a proportion of UK (data from Seafish).

3.4.3 GVA and employment

The results of the trade modelling, in terms of the changes in output, were used to calculate the direct, indirect and induced changes in output, GVA and employment. For the direct effects, GVA to output ratios were taken from the Scottish Input-Output (I-O) tables [18] . Employment data were obtained from industry-specific sources (Cefas, 2015; SG, 2016a,b; Seafish, 2016; MMO, 2017; HIE, 2017). For the indirect and induced effects, Type I and Type II multipliers for fishing, aquaculture and processing from the Scottish I-O tables were used. Because GVA will be affected by both output and price, the change in GVA in the scenarios was calculated from the model results by combining the price and output effects. The apportionment of output, GVA and employment between Scotland and the UK for the processing sector was calculated separately for each species group, based on 2014 Seafood Processing Industry Survey data provided by Seafish.

3.4.4 Sectors affected

I--O analysis was used to consider the distribution of indirect impacts across the economy, to identify which sectors are likely to be most affected by changes in output and GVA.

3.5 Key assumptions and limitations

For any modelling study, what is not included can be just as important as what is included. The specific assumptions of the model (beyond those outlined above) and issues excluded from the model, are as follows:

Production sector

  • UK-flagged vessels are treated as UK vessels. Issues surrounding beneficial ownership and potential changes to the economic link criteria are not taken into account.
  • It is assumed that the UK fleet will be able to catch the increased quota allocations based on the zonal attachment principle. In reality it may be that the industry might not utilise all the additional quota implied by a switch to zonal attachment. This could be due to mixed fishery and landing obligation considerations, as well as industry interest and specialisation for certain target species.

Agreements and quota allocations

  • Reciprocal access and landing rights in the UK and EU do not change. The potential change to landings from a change in quota distribution based on the zonal attachment principle is assessed; the potential for changes to landings as a result of restriction of access to fishing grounds is not assessed.
  • The EU-Norway agreement is important for Scotland, but how this may change is unclear. Changes to bilateral agreements with other countries in the North East Atlantic region are excluded from the scope and we assume an arrangement is achieved between the UK and Norway that continues the status quo position in relation to fisheries access and quotas.
  • Any changes to the distribution of quota allocations between the EU and UK will not change on day 1 of Brexit, but are more likely take place over a longer time period. A partial equilibrium model is typically a static model that does not take into account any adjustment period and does not have an explicit time horizon. The impact of changes in policy which are modelled therefore give the simulated outcomes of each scenario once a new equilibrium has been reached.
  • Quota allocation under the zonal attachment principle has been calculated based on University of Aberdeen & SFF (2017). Zonal attachment percentages for individual stocks may differ from the percentages used from this study as a result of: the incorporation of time spent in each jurisdiction, different life history stages, and taking account of the mismatch between survey data coverage and stock assessment units.
  • The trade codes included in the models for each species are those which are specifically attributable to the species in question at HS 6-digit level. Not all trade in a species is therefore captured, as trade codes that group the species together with other species in a more generic category are excluded from the analysis.
  • The focus of the approach on the ten species means that trade of fish in more generic species categories in the trade codes – including frozen block fillets which are important as inputs for the processing sector – is not captured. This means that the potential impact on these trade flows for the processing sector is not fully captured.

Tariffs, NTMs and tariff rate quotas

  • Tariff rate quotas are not explicitly modelled.
  • Changes to NTMs have been modelled based on the literature review of tariff equivalent costs of additional border checks, testing and certification requirements, rules of origin and catch certificates. The percentages used do not reflect the complete break-down of trading arrangements and implementation of trade defence measures. In the event of these circumstances arising, potential impacts could be significantly greater than those modelled.
  • The changes in the NTMs have been assumed to be the same across all species - in reality this may not be the case.

Macroeconomic and other factors

  • The percentage changes in output from the trade modelling are assumed to apply to the wider UK output of fishing, aquaculture and processing sectors for each species. This may overestimate the impact on the processing sector for the trade codes modelled.
  • The data used are from 2015, after the Russian trade embargo on European food production (including fish) products was introduced. The trade analysis is therefore based on trade flows with the trade embargo in place. In the event that the UK has a different trading arrangement with Russia, potential trade with Russia could increase. In particular, Russia has the potential to be an important market for mackerel (prior to the embargo, up to 20% of mackerel processed in Scotland went directly to Russia [19] ).
  • The data used are from 2015, prior to the Referendum vote for the UK to leave the EU, and therefore prior to the change in the Sterling-Euro and Sterling-Dollar exchange rates.
  • The potential for a breakdown in negotiations with the EU and UK unilaterally setting their quota levels (which could result in the aggregate quotas being set above the level of scientifically-advised TACs, overfishing occurring and landings subsequently declining, together with the potential implementation of trade sanctions) is beyond the scope of the study.

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