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The continental divide? Economic exposure to Brexit in regions and countries on both sides of The Channel

2017; Elsevier BV; Volume: 97; Issue: 1 Linguagem: Inglês

10.1111/pirs.12334

1435-5957

Chen Wen, Bart Los, Philip McCann, Raquel Ortega‐Argilés, Mark Thissen, Frank van Oort,

Globalization, Economics, and Policies

In this paper we employ an extension of the World Input-Output Database (WIOD) with regional detail for EU countries to study the degree to which EU regions and countries are exposed to negative trade-related consequences of Brexit. We develop an index of this exposure, which incorporates all effects due to geographically fragmented production processes within the UK, the EU and beyond. Our findings demonstrate that UK regions are far more exposed than regions in other countries. Only regions in the Republic of Ireland face exposure levels similar to some UK regions, while the next most affected regions are in Germany, The Netherlands, Belgium and France. This imbalance may influence the outcomes of the negotiations between the UK and the EU. En este artículo se emplea una extensión de la Base de Datos Mundial de Input-Output (WIOD, por sus siglas en inglés) con detalles regionales para los países de la UE, con el fin de estudiar en qué medida están expuestas las regiones y los países de la UE a las consecuencias negativas de Brexit relacionadas con el comercio. Se desarrolla un índice de esta exposición, que incorpora todos los efectos debidos a los procesos de producción fragmentados geográficamente dentro del Reino Unido, la UE y el resto del mundo. Los hallazgos demuestran que las regiones del Reino Unido están mucho más expuestas que las regiones de otros países. Tan solo las regiones de la República de Irlanda se enfrentan a niveles de exposición similares a algunas de las regiones del Reino Unido, mientras que las siguientes regiones más afectadas se encuentran en Alemania, los Países Bajos, Bélgica y Francia. Este desequilibrio puede influir en los resultados de las negociaciones entre el Reino Unido y la UE. 本稿では、EU加盟国の地域の詳細情報を加えた、拡張版World Input-Output Database (WIOD) を使用して、EU内の地域およびEU加盟国が、貿易に関してブレグジッド(Brexit:イギリスのEU離脱)の悪影響をどの程度受けているかを検討する。本稿では、イギリス国内、EU内、さらに広範囲に地理的に分断されている生産プロセスによるあらゆる効果を組み込んだ、ブレグジッドの影響の程度を示す指標を作成する。結果から、イギリスの地域は他の国の地域よりも悪影響をはるかに大きく受けていることが示される。アイルランド共和国の地域のみが、イギリスの一部の地域に近いレベルで悪影響を受けており、ドイツ、オランダ、ベルギー、フランス、以上の各国の地域がこれに続く。この不均衡はイギリスとEU間の交渉の結果に影響する可能性がある。 Since the UK decided in 2016 by referendum to leave the European Union there has been a large and growing body of material explaining the reasons for the decision in both the academic arena as well as in the popular press. In contrast, there has been much less post-referendum material emerging regarding the likely long-term impacts of this decision, and there are probably two main reasons for this. First, there were various forecasts produced by different organizations prior the referendum which predicted a rapid UK recession immediately following the decision to leave, and this has simply not transpired. Although there is now emerging evidence of the effects of a devalued Sterling on UK inflation and living standards (Financial Times, 2017a), as well as a fragile trade balance even under currency depreciation, the fact that no UK recession has so far materialized (The Economist, 2017) probably makes many forecasters rather loathe to speculate any further. Second, and more importantly, Brexit has not yet actually happened, and it is very difficult to speculate in detail about the likely long-run impacts of Brexit until the specific outlines of the new UK–EU trading relationships become clear. What we aim to analyse here is the exposure of regions in the UK and the EU to Brexit, via an analysis of the nature and scale of their trade linkages. Other potential advantages and disadvantages, for example as a consequence of the relocation of UK subsidiaries of multinational firms or the redistribution of subsidies to regions are not considered. 1 In this study, we intend to quantify the shares of regional and national GDP and labour income (in the UK and the EU) that are at risk due to Brexit. The extent to which these risks will actually materialize, for example via tariffs and non-tariff barriers to trade between the EU and the UK, will depend on the final agreements reached (if any). 2 Simple measures of gross exports and imports tell us very little about the potential impacts of Brexit on a nation or region, because both the back-and-forth trade in raw materials, parts and components and business services (often within the boundaries of multinational enterprises) typical of global value-chains obscures the links between local value-added and trade (Baldwin, 2016). In order to overcome these problems, we develop a measure of regional exposure to Brexit building upon a flourishing strand of literature using global input-output tables to link trade to value added (see, e.g., Johnson & Noguera, 2012; Koopman, Wang, & Wei, 2014; Timmer, Los, Stehrer, & de Vries, 2013). The measure gives the share of regional GDP or labour income contained in trade flows between EU exporters and UK importers, and vice versa. We apply the novel measure by employing a version of the World Input-Output Database (WIOD), in which the larger EU countries are geographically disaggregated into regions at the NUTS 2 level. 3 Our results demonstrate that almost all UK regions are systematically more vulnerable to Brexit than regions in any other country. Due to their longstanding trade integration with the UK, Irish regions have levels of Brexit exposure, which are similar to those of the UK regions with the lowest levels of exposure, namely London and northern parts of Scotland. Meanwhile, the other most risk-exposed EU regions are all in southern Germany, with levels of risk which are typically half that of any UK or Irish region, and one third of that displayed by many UK regions. There is also a very noticeable economic geography logic to the levels of exposure with north-western European regions typically being the most exposed to Brexit, while regions in southern and eastern Europe are barely affected at all by Brexit, at least in terms of the trade linkages. Gravity thus plays its well-known role. Overall, the UK is far more exposed to Brexit risks than the rest of the EU. The rest of the paper is organized as follows. In Section 2 we explain the construction of the exposure index. In Section 3 we outline the way in which the geographical disaggregation of the EU-part of WIOD as required for this study has been attained. Section 4 provides our results and Section 5 provides some brief conclusions. We propose to use a bilateral version of the domestic value added in exports (DVAiX) indicator proposed by Koopman et al. (2014) in order to measure economic exposure to Brexit. They split gross exports of a country into domestic value added, foreign value added and some (empirically small) "pure double-counting" terms. This decomposition linking trade to value added relies on global input-output tables. In a comment on this article, Los, Timmer, and de Vries (2016) provided rather simple formulas to compute the domestic value added in bilateral exports to one or more specific countries, using the same type of data. Using various extensions of the general formula proposed by Los et al. (2016), we can obtain estimates of DVA in Exports of EU regions to the UK and of DVA in exports of UK regions to the EU. When divided by regional GDP, we arrive at indicators of the share of GDP exposed to Brexit, for all regions relevant in this study. 4 It is important to emphasize that this method does not aim at quantifying the actual changes in regional GDP due to Brexit. First, such an analysis would require information about the ultimate outcome of the negotiations between the UK and the EU (which can range between trade according to WTO rules – the 'no deal' scenario – and the UK having full access to the Single Market – the 'softest' Brexit possible). Second, assumptions on the strength of interregional and international substitution patterns should be made. To what extent will industries and consumers in the UK continue to purchase products from the EU (and vice versa), after the Brexit-related trade barriers have become effective? And will trade be largely diverted to non-EU countries, or will industries on both sides of the Channel substitute imported products by domestic purchases? One could rely on trade elasticities, feeding these into a general equilibrium model. This is actually the approach adopted by Dhingra et al. (2017) in their study of the effects of Brexit on value added creation in industries in the UK. In our view, it is unclear whether such elasticities will describe behavioural changes as a consequence of Brexit well, since they have been estimated on the basis of data in a period of (generally small) reductions in trade barriers. In contrast, the UK's already deep integration in European supply chains means that Brexit might well constitute a dramatic increase in trade barriers for both the UK and the EU including barriers that are hard to quantify related to rules of origin, market regulations, administrative procedures, resurfacing cultural differences and the like. Given these problems, we opt for a different approach. Our approach should be seen as an accounting exercise, in which value added in regions is split into: (i) a part that is embodied at least once (in downstream stages of value chains, or when final products are delivered) in trade between the UK and the EU; and (ii) a part that does not cross UK–EU borders. These parts are computed using the proportionality assumptions that are common in input-output analysis. We define part (i) as regional value added that is exposed to Brexit. Let us assume that the world economy consists of C countries (c = 1,…, C). 5 Each country consists of a (variable) number of regions Rc (≥ 1 for all c), and each region is comprised of N industries. The number of industries is assumed to be identical across regions and countries. All industries in all regions in all countries could sell to each other (deliveries of intermediate products), or to final users in all regions in all countries. 6 The structure of such a global economy can be captured by a global input-output table as presented in a stylized way in Figure 1. The square matrix Z is the core of an input-output table. It contains the values of intermediate input deliveries and has N(R1 + R2 + … + RC) rows and columns. 7 Rows represent selling industries, while columns indicate purchasing industries. For the purposes of the present analysis, it is useful to consider as many as 25 submatrices of Z, each with different dimensions. Let us focus on the blocks on the main diagonal (shaded) first. Zrr is an N × N-matrix of which the typical element represents the value of sales by industry i in the focal region r to industry j in the same region. Zcc has N(Rc -1) rows and N(Rc -1) columns. The elements refer to the values of sales by industries in other regions of the country of which r is a part, to industries in other regions than r in the same country. If, for example, r refers to Île-de-France, Zcc contains deliveries of industries in Rhône-Alpes to industries in Rhône-Alpes itself, but also to industries in Auvergne and Corse (Corsica). Since we are interested in the exposure of regional GDP to Brexit, and Brexit implies that trade barriers between the regions in EU countries and regions in the UK are likely to be introduced, we split the set of countries to which the focal region r does not belong to (regions in) other EU countries, regions in the UK, and non-EU countries. Zee contains the values of all transactions between industries in regions of EU countries to which r does not belong. Continuing our example for Île-de-France, this matrix provides quantitative information about the values of intermediate input flows between Navarra (a region in a different EU country) and Alentejo (also a region in an EU country other than France), among many other flows. Zuu is the part of the matrix that contains the values of intermediate input flows between industries in the regions of the UK. Finally, the matrix Zoo represents values of intermediate flows among industries in countries that do not belong to the EU. Deliveries of Chinese manufacturers of components used by car manufacturing plants in the US, for example, are included in this part. The off-diagonal blocks within Z refer to trade in intermediate inputs between industries in different types of geographical entities. The elements in Zrc, for example, indicate the values of intermediate input sales by industries in the focal region r to industries in other regions in the same country. Hence, it quantifies linkages between suppliers in Île-de-France and users of intermediate inputs in, for example, Rhône-Alpes. In a similar vein, Zre presents values of intermediate input sales by industries in Île-de-France to regions in other EU countries, such as Stuttgart or Stockholm. Zer contains flows in the opposite direction, intermediate inputs imports of the focal region from regions elsewhere in the EU. The matrices and vectors in the block labelled F have a similar interpretation in terms of the regions and countries involved, but refer to deliveries of final products. In our analysis, we do not distinguish between final uses, as a consequence of which consumption demand by households, government consumption, gross fixed capital formation and changes in inventories for the output of industries in regions/countries have been aggregated into single numbers. This is reflected by the fact that final demand as exerted in region r is represented by column vectors fr, rather than by matrices with multiple columns. Row-wise summation of deliveries for intermediate use and for final use gives gross output of industries in all regions, represented by the last column, x. Double-entry bookkeeping ensures that the values in the bottom row are equal to the values in this rightmost column: payments by an industry for the intermediate inputs and for production factors (value added, including profits) in the corresponding column equal the value of sales by that industry. Value added by industries in each of the regions and countries is contained in the row vectors w′. 8 In the empirical section, we present results for some extensions of this general case. First, we will not only quantify the exposure of EU regions to Brexit, but also the exposure to Brexit of UK regions. In this case, the global input-output tables in Figure 1 would have a slightly different setup. Region r would be one of the UK regions and the superindex u would refer to the other UK regions. 11 Next, the parts of the derived matrices A and F labelled Arc, Are, Auc, Aue, Frc, Fre, Fuc and Fue are set to zero. Second, we will make a distinction between 'direct' and 'total' GDP exposure to Brexit. The total indicator is based on the matrices in (6b). The direct indicator, however, focuses on region r's value added contained in its own exports to UK regions. It does not include the part of r's GDP contained in exports to the UK by other (domestic and foreign) EU regions. Hence, the direct indicator cannot be larger than the total indicator. It is obtained in a way similar to equations (6–6b), but only the blocks Aru and Fru are set to zero. Finally, we will not only report results for aggregate regional economies, but also for broad sectors within these. The results are obtained by modifying the vector vr in (3) and in (6a). Only the value added coefficients corresponding to region r's industries that are part of the broad sector considered are retained, all other elements of vr are set to zero. The results reported in this paper have been obtained on the basis of data combining data from two types of sources. First, the world input-output tables of the WIOD 2013 release, in which 40 countries (accounting for about 85% of world GDP) plus a composite 'super-country' labelled 'Rest of the World' are represented (Timmer, Dietzenbacher, Los, Stehrer, & de Vries, 2015). All 27 EU member states as of 2009 are included. These WIOD data have been merged with the second type of data, from regional sources: Data from Eurostat's regional economic accounts, a number of survey-based regional supply and use tables or input-output tables produced in a subset of countries, and estimates of interregional goods and services trade based on freight and airline business passenger statistics (Thissen, van Oort, Diodato, & Ruijs, 2013). The merging of the information contained in these data sources allows us to incorporate regional details regarding production structure and trade at the NUTS 2-level for all major EU-countries in global input-output tables for 2000–2010. 245 NUTS 2 European regions are represented and 14 industries can be identified for all regions and countries. All transactions (in current prices) have been converted to euro values, using market exchange rates. A detailed description of the construction methodology can be found in Thissen, Los, Lankhuizen, Van Oort, and Diodato (2017), so here we will just provide a brief account of its main characteristics. The inter-country trade flows in WIOD's international SUTs have only been adjusted to include the actual origin and destination of so-called re-exports. The existing trade patterns between countries were used to determine the trade patterns of the re-exports. In order to be able to regionalize these national SUTs they also have been trade-linked, thus the exports to a specific country equal the imports of that country on the commodity level (see Thissen, Lankhuizen, & Jonkeren, 2015, for more details). In order to construct the interregional trade flows, regional SUTs (supply and use tables) were created which were subsequently combined into regional IO tables using the 'industry technology' (Miller & Blair, 2009). This method was chosen because the SUTs have unequal numbers of commodities and industries. 12 The reason for regionalizing SUTs instead of IO tables is that the different sources of regional information used are both commodity (i.e. information on trade) and industry based (i.e. information on industry value added). The multiregional IO tables are an update from the perspective of interregional trade where multiregional trade data from the PBL Netherlands Environmental Assessment Agency (based on freight data and business class travel data) is used as prior information in the estimation (Thissen et al., 2013). An important feature of the data is that no a-priori gravity-type interaction behaviour is imposed (Thissen et al., 2015). The multiregional IO-tables were then constructed in several steps. In the first step, following Isard (1953), the SUTs are regionalized using additional information from Eurostat regional accounts on value added and regional income and demand. In the second step these regionalized national coefficients are used as prior information for the subsequent non-linear optimization estimation of the multiregional NUTS 2 SUTs. Information on regional coefficients of the SUTs is added by using available regional tables for Italy, Scotland and Wales (NUTS 1), Spain (NUTS 2) and Finland (NUTS 3). These were the only regional tables available to us that were survey-based and not derived from a regionalization of national tables. In those cases information for which only a few years were available, the coefficients of the closest available year are used as a proxy. It proved important to use the regional information as additional prior information only, and not to impose the absolute regional values, because using the absolute numbers in the regional tables commonly would have resulted in extreme deviations from national coefficients in regions from the same country without regional tables. This is due to inconsistencies between regional and national tables. To complete the prior information, the multiregional trade patterns from the PBL interregional trade data were added. The prior information has been preserved as much as possible in a constrained quadratic minimization process, under constraints posed by national totals and totals on bilateral international trade taken from the adapted international SUTs from WIOD (see Thissen et al., 2017, for details). In the final step, the global SUTs with interregional detail were converted to global input-output tables with regional detail, using the methods described in Dietzenbacher, Los, Stehrer, Timmer, and de Vries (2013). As far as we are aware, this database is the first one that provides geographical disaggregation of multiple countries within global input-output tables. 13 Still, the database has its weaknesses. Ideally, the tables would have had more industry detail, since indicators derived from relatively aggregated tables tend to be affected by aggregation bias (see Miller & Blair, 2009). Based on current knowledge of aggregation biases, we cannot judge whether our indicators are biased in a specific direction. Another weakness is related to the fact that computations based on the database implicitly assume that exporters and non-exporters in a specific industry share the same technology. Bernard, Eaton, Jensen, and Kortum (2003) showed that technologies are often quite different (see Tybout, 2003, for an overview article). At this stage of the development of global input-output tables, it is impossible to come up with well-founded speculations about the magnitude and direction of biases. One of the reasons for this is that differences between exporters and non-exporters are not confined to input requirements per unit of output, but also relate to prices paid for production factors. These differences interact in a myriad of ways, affecting the cost shares represented by the elements of the matrix A. The results as reported in the rest of the paper are based on the tables for 2010. Currently these are the most recent data available. As Timmer, Los, Stehrer, and de Vries (2016) show, the international fragmentation of production processes has been rather modest since 2011, which suggests that our results are likely to be not too distant from what we would find for 2017 if the data would have been available. In this section we present the regional results primarily in the form of maps, and the detailed regional data underlying each of the maps are reported in Tables A1-A4 in the Appendix. Figure 2 depicts the GDP exposure to Brexit of European regions. The highest levels are found for many of the UK's non-core regions in the Midlands and the North of England, many of which voted for Brexit. For London and Scottish regions, the exposure rates are still much higher than for regions outside the UK, but clearly lower than for most of the UK. These results are in line with the results based on UK regions' dependency on EU consumption and investment demand reported by Los et al. (2017). 14 Figure 2 also shows that regions in Ireland are the only other ones as exposed to Brexit as some UK regions. UK regions typically exhibit Brexit trade-related risks exposure of the order of 10–17 per cent of regional GDP, with Irish regions also displaying values of the order of 10 per cent of GDP. The Irish regions therefore have levels of Brexit-related risk exposure which are similar to the UK regions with the lowest levels of Brexit exposure, namely London and parts of Northern Scotland. The patterns in Figure 2 are also largely visible in Figure 3, in which we consider regional labour incomes (rather than GDP) exposed to Brexit-related risks in all EU regions. In Figure 4 and 5 we depict the exposure to Brexit related risks for EU regions, regarding GDP and labour income, respectively. In these maps, we excluded all the UK regions in order to allow for a more fine-grained observation of the impacts on the non-UK regions. We see that, after Irish regions, German regions are the next most exposed regions, with Brexit risks exposure levels of the order of 4.5 per cent–6.4 per cent of regional GDP (with southern German regions 15 in particular displaying the higher levels), followed by regions in The Netherlands (3.5%–5% of regional GDP) and regions in Belgium (2.8%–4% of regional GDP), and in France (1.8%–2.7% of regional GDP). If we consider regional labour incomes rather than GDP, we see that the Brexit exposure patterns and orders of magnitude across EU regions are generally very similar indeed. The notable exceptions are Irish regions, where the labour income exposure levels are some 2.5 percentage points below their GDP exposure levels. This result mainly relates to the strong presence of FDI-related activities in Ireland, which have a much higher capital income to labour income ratio than regular activities. In terms of labour income, Irish regions are typically half as exposed to Brexit as the typical UK region. These regional differences are also reflected in the national levels of exposure to Brexit. As can be seen in Table 1, the Republic of Ireland's national level of Brexit trade-related risk exposure is over 10 per cent of GDP, Germany's level of exposure is just over 5 per cent of GDP, the exposure of The Netherlands is just over 4 per cent of GDP, and that of Belgium is 3.5 per cent of GDP. 16 The other three large EU economies, namely France, Spain and Italy, face a level of GDP exposure to Brexit of only just over 2 per cent, just over 0.7 per cent and 0.5 per cent, respectively. Apart from tiny Malta, the rest of the EU member states face levels of Brexit exposure which are below that of France. Figure 4 shows that there is considerable variation in regional GDP exposure to Brexit within countries. The region Stuttgart, for example, has an index of 6.4 per cent, which is considerably larger than the German average of just below 5.5 per cent. At the other end of the spectrum in Germany, we find the Eastern regions like Brandenburg-Nordost and Mecklenburg-Vorpommern, with exposure levels of around 4.5 per cent. The rightmost column in Table 1 presents the within-country population standard deviation of GDP exposure to Brexit levels for those countries for which regional detail is available. Besides Germany, The Netherlands and Belgium appear to be heterogeneous. In the Netherlands, Zeeland and Groningen have the highest exposure levels, probably as a consequence of the energy produced in these regions. In Belgium, Brabant-Wallonne and Limburg are most dependent on trade between the EU and the UK. So far, we have presented results for 'total' GDP and labour income exposure to Brexit levels. These intentionally incorporate all effects related to value chains that span regions and countries. Banks based in Inner London, for example, might not be exporting a lot of their financial services directly, but might offer lots of services to manufacturing firms all over the UK, which might be active in exporting to the EU. Such effects would not enter the analysis if the interregional input-output structure would not play a role in the analysis and we would only focus on 'direct' exports. In Section 2, we explained how we can compute 'direct' exposure to Brexit, by computing how much domestic value added and labour income are contained in the exports to the EU of the UK region itself (or in the exports to the UK of the European region itself). This implies that intra-regional input-output linkages are taken into account, but interregional linkages and international linkages within the EU are not. The differences between the 'total' and 'direct' exposure levels are sizable. On average, the total GDP exposure levels are more than 68 per cent higher than the direct exposure levels for non-UK regions in the EU and more than 33 per cent higher for UK regions. The differences are most marked for many Polish and Italian regions, while the differences for Irish regions are relatively modest. Finally, these national and regional differences in Brexit risk exposure also imply large exposure differences between the UK and the rest of the EU. The UK's national level of Brexit exposure is 12.2 per cent of UK GDP and 11.3 per cent of UK labour income. In contrast, the rest of EU in aggregate face an exposure to Brexit, which is only 2.64 per cent of their combined GDP and 2.62 per cent of their combined labour income. In other words, the Brexit trade-related exposure of the UK economy is 4.6 times greater than that of the rest of the EU. 17 The figures reported here suggest three major points. First, the UK and its regions are far more vulnerable to trade-related risks of Brexit than other EU member states and their regions. Our results also mirror the broad thrust of the arguments of other analyses (Dhingra et al., 2017). As such, the UK is far more dependent on a relatively seamless and comprehensive free trade deal than the other EU member states. Mercantilist arguments popular in the UK media, which posit that the UK trade deficit with the rest of Europe implies that on economic grounds other EU member states will be eager to agree a free trade deal with the UK, are not correct. When we consider the real trade-demand impacts on the EU member states and their regions, allowing for both domestic and international input-output relationships which capture the complex global value-chains which crisscross borders many times (Bailey & De Propris, 2017), the emerging picture is very different. The UK's exposure to Brexit is some 4.6 times greater than that of the rest of other EU as a whole, and the UK regions are far more exposed to Brexit risks than regions in other EU countries, except for those in Ireland. As such, in all likelihood the potential impacts of either no deal (Springford & Tilford, 2017) between the UK and the EU or a bad deal whereby the UK's access to the Single Market and the Customs Union is heavily curtailed, are far more damaging for the UK than for the rest of the EU. Second, it is many of the UK's economically weaker regions which are especially exposed to Brexit. Third, across Europe there is a strong core-periphery type of economic geography to these patterns with the highly urbanized regions in northern and western Europe being more exposed to Brexit risks than regions in southern or eastern Europe. This is also reflected in the national levels of Brexit related risks exposure. As such, our analysis suggests that on purely economic grounds at least, the Republic of Ireland, Germany, the Netherlands and Belgium, will have more to gain from a relatively seamless and comprehensive UK–EU free trade deal than will other EU countries. Finally, we can ask whether our analytical approach, which essentially involves setting all UK–EU trade linkages to zero, represents an upper bound for the potential Brexit-related exposure risks faced by regions. On this point, the evidence on the 'no-deal' scenario (HoC, 2017) suggests that the legal basis of many of the UK's cross-border exchanges (Dunt, 2016; UKICE, 2017) including all air travel (Guardian, 2017), sea-borne logistics, and even health and energy systems, will become insecure (UKICE, 2017), while the EU rules of origin will make UK–EU high value-added just-in-time systems in manufacturing and retail all but impossible to maintain (Bailey & De Propris, 2017). In all likelihood most existing and complex UK–EU supply chains, which also tend to be in knowledge-intensive and high value-adding sectors, will be either heavily disrupted or completely severed. From these perspectives our analytical approach would appear to be very realistic. Moreover, our analysis has not even considered the impacts on foreign direct investment, human capital-migration, and the additional trade disruptions or complications related to the UK's commercial relationships which do not directly cross any UK-EU borders. The fact that the EU also has some 40 or so different trade or cooperation agreements with third countries 18 means that in total the UK will need to negotiate well over 700 new trade agreements (Financial Times, 2017b). In our analysis we have treated these relationships as being unaffected by Brexit. Yet, the evidence regarding the likely impacts of 'no deal' allied with the additional complications related to the UK's non-EU trade relationships suggests that our analysis may not represent an upper bound, and that the actual Brexit-related exposure risks facing the UK and its regions are even greater than those reported here. The research undertaken for this paper is part of the Research Project: "The Economic Impacts of Brexit on the UK, its regions, its cities and its sectors". The project is funded through the ESRC Economic and Social Research Council under grant reference: 35587 and Council reference number: ES/R00126X/1.