EUROPEAN COMMISSION Brussels, 9.11.2016 SWD(2016) 372 final COMMISSION STAFF WORKING DOCUMENT Assessment of the economic impact of changing the methodology for calculating normal value in trade defense investigations against China Accompanying the document Proposal for a

REGULATION OF THE EUROPEAN PARLIAMENT AND OF THE COUNCIL amending Regulation (EU) 2016/1036 on protection against dumped imports from countries not members of the European Union and Regulation (EU) 2016/1037 on protection against subsidised imports from countries not members of the European Union {COM(2016) 721 final}

{SWD(2016) 370 final}

{SWD(2016) 371 final}



Table of Contents Abstract Disclaimer Executive Summary Synthèse du Document 1. Introduction 2. Literature survey 3. Outline of the analysis and Graphical Representation 4. STEP 1: Revisiting the expected employment effects in the 10 selected AD cases 4.1 Benchmark Model 4.2 Import-Demand Elasticity from EU data 5. STEP 2: Assessing which sectors are likely to face dumping by Chinese firms 5.1 Assigning probabilities at the most detailed level 5.2 Aggregate to the sector level 5.3 Probability of dumping versus probability of AD measures 6. STEP 3: Direct employment effect on the entire tradable goods sector 7. STEP 4: Adding indirect effects 7.1 The signs and magnitudes of expected indirect effects 7.2 How upstream or downstream are sectors subject to EU AD duties against China?

7.3 Magnitude of upstream effects 7.4 Magnitude of downstream effects 7.5 Other effects: macroeconomic multipliers 8. Aggregate Effects in Three Scenarios 8.1 Scenario 1: Simple Extrapolation from the 10 cases 8.2 Scenario 2: Short-run effect based on cases against China currently in force 8.3 Scenario 3: Potential long-run scenario including hypothetical future cases 9. Member State – Sector Job Effects of MES for China 9.1. Methodology for Member State-Sector Elasticities 9.2. Job Effects by Member State and Sector 9.3. Benchmark Estimates (CN8) Member State Level Job Effects Sector Level Job Effects 9.4. Product-Level Estimates Member State Level Job Effects Sector Level Job Effects 9.5. Summary of Country-Sector Effects 10. Measures to Maintain Effectiveness of Anti-Dumping 10.1. Types of Measures 10.2. Job Effects With Different Types of Measures

10.3. Measures Type 1: Short-run (Scenario 2) Long-run (Scenario 3) 10.4. Measures Type 2: Short-run (Scenario 2) Long-run (Scenario 3) 10.5. Summary of Measures to Maintain Effectiveness of Anti-Dumping Appendix A: Background tables for step 1 Appendix B: Methodology for step 4 B.1 Calculation of the upstream effects B.2 Calculation of the downstream effects References



Abstract This report sets out to evaluate what a change in the market economy status (MES) of China in European Trade Defense Instrument cases (TDI) would bring about in terms of potential job effects in the European market. Disclaimer

“The Information and views set out in this report are those of the authors and do not necessarily reflect the official opinion of the Commission. The Commission does not guarantee the accuracy of the data included in this report. Neither the Commission nor any person acting on the Commission’s behalf may be held responsible for the use which may be made of the information contained herein”

Executive Summary If the European Union were to apply market-economy status (MES) in Trade Defense Instruments (TDI) cases against China, this is expected to lower the imposed anti-dumping duties on imported products from China by around 30 percentage points, compared to an analogue country regime. As a result gross import prices on imported products from China that are subject to TDIs would fall by 20 percent after the duty is imposed. The lower anti-dumping duties on Chinese imports are estimated to result in lower Chinese prices on the EU market and 18 to 28 percent higher average Chinese imports than what they would be in the analogue country regime. According to our estimates, Chinese imports will rise under the MES regime and will substitute for sales of the EU import-competing industry (or competing third countries). The purpose of this study is to assess both the potential direct and indirect employment effects of this regime change in different scenarios, which we discuss in that order below. In Scenario 1, we extrapolate from ten recent AD cases for which data was provided by the Commission to the total volume of bilateral trade affected by TDI. The simulated (direct) job losses in products (or sectors) currently affected by TDI actions are estimated to lie between 14,000 and 22,000 jobs. In Scenario 2, we change our angle to take into account all the Chinese products currently subject to TDI (52 cases) (November 2015), for which less detailed data is available, though. Based on averages from recent years, we assume that 2.5% of total imports from China will be covered by TDI and extrapolate to this level. We use this historical average because in the short run the product range affected by dumping will be unchanged. We estimate the resulting (direct) job losses to range between 49,700 and 73,600. Scenarios 1 and 2 both address the short-run effects of granting MES. They use distinct aggregation methodologies to scale up from the individual cases to estimate the effect for the overall economy and thus provide a robustness check for the direct employment effects. In Scenario 1, the average employment elasticity is applied directly to the current overall level of TDI affected employment, 231,000 jobs. Scenario 2 makes projections from the existing 52 AD cases to derive the volume of affected trade for the respective HS4 sector level and adds the effects up to individual sectors first and to the total economy in a final step. It makes Scenario 2 more encompassing than Scenario 1. A second difference is that the methodology of Scenario 2 also allows for a quantification of the indirect effects, which is not possible at the level of aggregation used by Scenario 1. In Scenario 3, we consider the possibility that the MES regime may have additional long-run effects also affecting new products, not subject to EU TDIs thus far. Dumped imports from China are likely to expand into different product categories that have not been subject to EU dumping investigations before. Scenario 3 effects are not necessarily a prediction of what will happen in the future, but rather an illustration of what could potentially happen in a worst-case scenario. We use US AD cases against China and EU AD cases against other countries to inform us on the sectoral distribution of these potential new cases. In this event, the share of imports from China subject to TDIs is projected to rise to 5.7%, which we show is a sensible long-run upper bound. Adding these potential long-run effects of the MES regime, could result in EU (direct) job losses in import-competing TDI sectors that lie between 117,800 and 175,600. This is what we refer to as the long-run direct jobs at risk. Supplementary to direct employment effects, there will probably be indirect EU employment effects. The indirect effects refer to spillover effects on downstream or upstream EU producers. Downstream EU producers normally benefit from cheaper imported inputs from China, in proportion to their input use, and if EU suppliers partially match reduced Chinese import prices this benefit will be even larger. As downstream EU producers would be able to produce at lower cost, they would be able to expand production and have a higher labor demand under the MES regime for China. Using WIOD data, we estimate the positive downstream employment effects under MES to generate 13,100 (Scenario 2) to 28,400 (Scenario 3) EU jobs in, respectively, the short and long-term. In contrast, upstream EU producers will probably be adversely affected by the MES regime, as in the EU industry where they sell the bulk of their output, Chinese producers take market share away from European producers (their clients). From the WIOD database, we establish that Chinese firms use fewer EU-sourced inputs than their EU counterparts. This fact allows us to infer that demand for European upstream producers will be reduced under the MES regime. This reduced demand for the output of upstream EU producers, could result in negative EU employment effects in the upstream industry. We estimate these upstream displacement effects to cause EU job losses in a range of -15,300 to -54,600, depending on the estimates used and the timeframe considered. In most of our scenarios, our estimates suggest the indirect upstream job effects to outweigh the indirect downstream job effects. Thus, the overall indirect employment effects are most often found to be negative arising from the fact that the negative upstream employment effects dominate the positive downstream effects. Under the assumption that macroeconomic multiplier effects do not exceed one, the sum of direct and indirect employment effects constitute the economy-wide effects when the EU grants MES to China. Table 1 summarizes the possible upper bound employment effects of granting MES to China under three different scenarios. In Scenario 1 we apply estimates from 10 selected AD cases to the current employment in TDI sensitive sectors. In Scenario 2, we extend the basis of our analysis to all existing AD cases against China (52) and refer to it as “short-run” employment effects. In Scenario 3, we report the hypothetical “long-run” employment effects if dumping by Chinese firms were to become much more widespread than the products currently under investigation. In Scenario 2 and 3, we report in Table 1 the sum of hypothetical direct and indirect employment effects of granting MES to China. These are upper bounds for the entire goods sector (agriculture, mining, and manufacturing) and range between 83,100 and 201,800, respectively. When breaking these numbers down by member state, the biggest job losses would occur for Germany and Italy. Other EU countries suffer smaller losses and the impact clearly differs across countries. Larger countries tend to incur larger losses, although a member state like Italy seems disproportionately affected since countries of similar population size like UK and Spain appear to suffer much less job losses. Also, the higher the member state’s level of per capita GDP, the smaller the job losses appear to be (with the exception of Germany). In terms of sectoral breakdown, job losses are strongest in the short-run in “Basic & fabricated Metals”, in “non-metallic Minerals” and in “Machinery” and in the long-run potentially also in “Rubber & Plastics” and “Food Products”. The chemical sector seems relatively less affected by MES for China than other sectors. The numbers in Table 1 are subject to changes in the special case where the EU aims to maintain the effectiveness of TDIs. When costs and prices in China would be considered as distorted and cannot be used for the calculation of dumping margins and duties, a technique of “benchmark country cost plus union industry profit margin” can be used to calculate dumping margins. With the “lesser-duty-rule” (LDR) in place, this technique would result in an average tariff that is -3.86% lower than in the status quo and would result in EU job losses compared to status quo of around -10 300 jobs in the short-run and around - 25 100 jobs in the long-run. Removing the LDR in addition to applying benchmark country costs plus UI profit margin, would result in an average tariff increase of +7.81% compared to status quo tariffs in current TDIs and would result in additional EU jobs being saved compared to those under original duties of around +20 200 in the short-run and around +49 500 in the long-run. These would be the EU jobs that can additionally be safeguarded in comparison with the status quo. 3 Scenarios: Percentage effect Job loss (upper bound) Chinese imports covered by AD duties a (%) Benchmark (CN8) estimates Product-level estimates Limited analysis: only direct effects, only limited set of sectors 1.Simple extrapolation from the 10 cases to TDI sensitive sectors b -6.11% c -9.7% < 22,000 N/A Full analysis: direct & indirect effects, all tradable goods sectors 2.Short-run effects based on all existing 52 cases on China -0.121% c -0.193% < 83,100 2.5% 3.Potential long-run effect with increased dumping in new product categories by China -0.294% c -0.469% < 201,800 5.7%





Synthèse du Document Si la Union européenne devait appliquer le statut d’économie de marché (MES) dans les dossiers d’Instruments de défense commerciale (TDI) contre la Chine, cette décision devrait réduire les droits antidumping imposés sur les produits importés de Chine d’environ 30 points de pourcentage, en comparaison d’une méthode de pays analogue. Par conséquent, les prix bruts à l’importation sur des produits importés de Chine qui sont soumis aux TDI diminueraient de 20 pour cent une fois le droit imposé. On estime que la baisse des droits antidumping sur les importations chinoises engendre alors une réduction des prix chinois sur le marché européen et 18 à 28 pour cent d’importations chinoises en plus qu’avec la méthode du pays analogue. D’après nos calculs, les importations chinoises augmenteront sous le régime MES et remplaceront les ventes du secteur concurrent dans les importations européennes (ou de pays tiers concurrents). Le but de cette étude est d’évaluer les effets directs et indirects de ce changement de régime sur l’emploi dans divers scénarios, que nous commenterons dans cet ordre ci-après. Dans le Scénario 1, nous extrapolons de 10 dossiers AD récents, dont les données nous ont été fournies par la Commission, le volume total des échanges bilatéraux affectés par des TDI. Les pertes d’emplois (directes) dans les produits (ou les secteurs) actuellement concernés par des mesures TDI atteindraient, selon les estimations, entre 14 000 et 22 000 postes, en l’absence de tout facteur d’atténuation. Dans le Scénario 2, nous changeons d’angle de vue et nous prenons en compte tous les produits chinois actuellement soumis à des TDI (52 dossiers) (novembre 2015), pour lesquels les données disponibles sont néanmoins moins détaillées. Sur la base des moyennes des années récentes, nous supposons que 2,5 % du total des importations de Chine seront couverts par des TDI et nous extrapolons jusqu’à ce niveau. Nous utilisons cette moyenne historique parce qu’à court terme, l’éventail des produits affectés par le dumping restera inchangé. Nous estimons que les pertes d’emplois (directes) consécutives se situeront entre 49 700 et 73 600 postes. Les Scénarios 1 et 2 abordent tous deux les effets à court terme de l’octroi du MES. Ils utilisent des méthodes d’agrégation distinctes pour extrapoler à partir de cas individuels afin d’estimer l’impact sur l’ensemble de l’économie et ainsi fournir un test de robustesse pour les effets directs sur l’emploi. Dans le Scénario 1, l’élasticité moyenne de l’emploi est appliquée directement au niveau global actuel de l’emploi affecté par des TDI : 231 000 postes. Le Scénario 2 effectue des projections à partir des 52 dossiers AD existants afin d’en déduire le volume sur le commerce impacté pour chaque niveau sectoriel HS4 respectif et ajoute les effets aux secteurs individuels d’abord, puis à l’ensemble de l’économie dans la dernière étape. Ceci rend le Scénario 2 plus englobant que le Scénario 1. Une seconde différence est que la méthodologie du Scénario 2 permet aussi de quantifier les effets indirects, ce qui n’est pas possible au niveau d’agrégation appliqué par le Scénario 1. Dans le Scénario 3, nous envisageons la possibilité que le régime MES puisse avoir des effets supplémentaires à long terme, avec un impact aussi sur de nouveaux produits qui ne sont pas encore soumis aux TDI de l’UE. Les importations de Chine qui font l’objet d’un dumping sont susceptibles de s’étendre dans plusieurs catégories de produits qui n’ont pas été soumises aux enquêtes de l’UE pour dumping auparavant. Les effets du Scénario 3 ne prédisent pas forcément ce qui arrivera à l’avenir, mais ils illustrent plutôt ce qui pourrait se produire dans le pire des scénarios. Nous nous servons de dossiers AD américains contre la Chine et de dossiers AD européens contre d’autres pays pour obtenir des informations sur la répartition sectorielle de ces nouveaux cas potentiels. Dans ce cas, la part des importations de Chine soumise aux TDI augmenterait jusque 5,7 %, comme nous le montrons dans une estimation supérieure raisonnable à long terme. La combinaison de ces effets potentiels à long terme et du régime MES pourrait engendrer entre 117 800 et 175 600 pertes d’emplois (directes) au sein de l’UE, dans des secteurs TDI concurrencés par les importations. Nous qualifions cet impact de potentielles pertes d’emplois directes à long terme. En plus des effets directs sur l’emploi, il y aura probablement des effets indirects sur l’emploi dans l’UE. Les effets indirects désignent les répercussions sur les producteurs européens en amont ou en aval. Les producteurs européens en aval bénéficient directement des produits moins chers importés de Chine, proportionnellement à leur utilisation, et si des fournisseurs européens s’alignent partiellement sur la réduction des prix chinois à l’importation, le bénéfice sera encore plus grand. Comme les producteurs européens en aval seront en mesure de produire à un coût moindre, ils pourront étendre leur production et la demande de main-d’œuvre augmentera sous le régime MES pour la Chine. A l’aide des données WIOD, nous estimons que les effets positifs du MES sur l’emploi en aval engendreront 13 100 (Scénario 2) à 28 400 (Scénario 3) postes dans l’UE, respectivement à court et à long terme. En revanche, les producteurs UE en amont seront probablement affectés négativement par le régime MES puisqu’au sein du secteur européen dans lequel ils vendent une majeure partie de leur production, les producteurs chinois leur prennent des parts de marché (leurs clients). A partir de la base de données WIOD, nous avons établi que les sociétés chinoises utilisent moins de produits provenant de l’UE que leurs concurrentes européennes pour alimenter leur production. Un fait qui nous permet de déduire que la demande sera réduite sous le régime MES pour les producteurs européens en amont. Ce recul de la demande pour la production des fabricants européens en amont pourra entraîner des effets négatifs sur l’emploi dans l’UE dans ces secteurs. Nous estimons que ces glissements en amont pourront provoquer des pertes d’emplois dans l’UE qui se situent entre -15 300 et -54 600 postes, selon les estimations utilisées et le cadre temporel envisagé. Dans la plupart de nos scénarios, nos estimations suggèrent que les effets indirects sur l’emploi en amont dépasseront les effets indirects sur l’emploi en aval. Dès lors, l’effet indirect total sur l’emploi s’avère le plus souvent négatif puisque l’effet négatif sur l’emploi en amont excède l’effet positif en aval. En supposant que les effets multiplicateurs macroéconomiques ne dépasseront pas un, la somme des effets directs et indirects sur l’emploi représente les effets sur l’ensemble de l’économie de l’octroi du MES à la Chine par l’UE. Le Tableau 1 résume l’estimation supérieure des effets possibles sur l’emploi de l’octroi du MES à la Chine dans le cadre de trois scénarios distincts. Dans le Scénario 1, nous appliquons des estimations issues de 10 dossiers AD sélectionnés à l’emploi actuel dans des secteurs sensibles aux TDI. Dans le Scénario 2, nous étendons la base de notre analyse à tous les dossiers AD existants contre la Chine (52) et nous y voyons les effets sur l’emploi à « court terme ». Dans le Scénario 3, nous rapportons les effets hypothétiques sur l’emploi à « long terme » si le dumping par les sociétés chinoises devait s’étendre bien au-delà des produits actuellement examinés. Dans les Scénarios 2 et 3, nous rapportons dans le Tableau 1 la somme des effets hypothétiques directs et indirects sur l’emploi de l’octroi du MES à la Chine. Il s’agit d’estimations supérieures pour l’ensemble du secteur des biens (agriculture, activité minière et fabrication), qui atteignent 83 100 et 201 800 postes, respectivement. Si l’on fait la répartition par pays membre, les pertes d’emploi les plus significatives ont lieu en Allemagne et en Italie. Les autres pays de l’Union Européenne subissent des pertes d’emploi moindres, et l’impact est clairement différencié selon les pays. Les pertes d’emploi tendent à être plus importantes dans les grands pays, même si l’Italie semble touchée de façon disproportionnée puisque les pays de taille similaire en termes de population comme le Royaume-Uni ou l’Espagne semblent subir une perte d’emploi moins importante. En outre, l’on note que les pertes d’emploi sont relativement moindres dans les pays où le PIB par tête est plus élévé (avec l’exception de l’Allemagne). En termes de répartition sectorielle, les pertes d’emploi les plus importantes concernent l’industrie des “Produits métallurgiques de base et manufacturés”, celle des “Minéraux non-métalliques” et l’industrie de la “Machine-Outil” ainsi que, à plus long terme, potentiellement l’industrie de “Caoutchouc et des Matières Plastiques” et “l’Industrie agroalimentaire”. Le secteur de la chimie est relativement moins affecté par le MES pour la Chine que d’autres secteurs. Les chiffres du premier tableau (Tableau 1) sont sujets à modification dans le cas spécifique où l’Union Européenne met en oeuvre des mesures afin de maintenir l’efficacité des Instruments de Défense Commerciale (TDI). Si l’on considère que les coûts et les prix observés en Chine sont faussés et ne peuvent être utilisés pour le calcul des marges de dumping et les droits de douane associés, alors la technique dite du ‘coût du pays de référence augmenté de la marge de profit de l’industrie de l’union’ peut être utilisée pour calculer les marges de dumping. Si la ‘règle du droit moindre’ (LDR) s’applique, cette technique résulte en un droit de douane moyen qui est de 3,86% inférieur au statu quo et une perte à court terme d’environ 10 300 emplois de plus que dans le statu quo et une perte à long terme d’environ 25 100 emplois de plus que dans le statu quo. Si l’on n’applique pas la règle du droit moindre tout en continuant à appliquer la technique du coût du pays de référence augmenté de la marge de profit de l’industrie de l’union, alors l’on obtient une augmentation de 7,81% du droit de douane moyen relativement aux droits de douane associés aux Instruments de Défense Commerciale actuellement en place. Dans ce cas de figure, +20 200 emplois européens sont sauvegardés à court terme relativement à l’emploi associé aux droits de douane actuels et +49 500 emplois supplémentaires sont sauvegardés à long terme. Ce sont ces emplois supplémentaires qui peuvent être maintenus relativement au statu quo.

3 scénarios: Impact en pourcentage Perte d’emplois (estimation supérieure) Importations chinoises couvertes par droits AD a (%) Estimations benchmark (CN8) Estimations produits Analyse limitée : effets directs uniquement, nombre limité de secteurs seulement 4.Simple extrapolation de 10 dossiers à des secteurs sensibles aux TDI b -6.11 % c -9.7 % < 22 000 N/A Analyse complète : effets directs et indirects, tous secteurs de biens commercialisables 5.Effets à court terme basés sur les 52 dossiers existants sur la Chine -0.121% c -0.193% < 83,100 2,5 % 6.Effet potentiel à long terme avec dumping accru dans de nouvelles catégories de produits par la Chine -0.294% c -0.469% < 201,800 5,7 %



1.Introduction Currently, the EU Commission is analyzing the effects of changing the methodology when calculating dumping margins in investigations regarding China. This study was commissioned to contribute to the debate on the potential economic effects of such a change in the methodology in view of the impending expiration of some provisions in China’s Protocol of Accession to the WTO on December 11, 2016. If China were granted market economy status (MES), the Commission would need to change the way it calculates the “normal value” of Chinese exports, which is then compared to the export prices in order to establish the existence of dumping. Currently, the normal value in China is usually calculated using prices and/or costs from a third country that is considered a market economy, a so-called “analogue country.” In current anti-dumping (AD) investigations, the Commission can grant market economy treatment (MET) to individual Chinese exporters which file a request and satisfy several criteria. If that is the case, prices and costs provided by the interested Chinese producers themselves are used to calculate the “normal value”, either the price at which the product is sold in China or an estimate of the production cost with an added margin to cover trading costs and a profit margin. Under MES, it would become the norm to use Chinese prices and costs in determining whether dumping is actually taking place and in determining the applicable dumping margin. In this report we analyze the likely effects on EU employment in the tradable goods sector if all dumping cases involving Chinese firms would be assessed under the MES methodology. The starting point of our calculations is the re-analysis of 10 recent AD cases, for which the EU Commission could compile sufficient data to construct a baseline and a counterfactual set of measures under MES. Currently, the average duty imposed in these cases is 38.5% and the Commission estimates that under the new methodology, duties would not have been imposed in 3 cases and would have been lower in 7 cases. On average, the duties imposed under the MES methodology would be 30 percentage points lower than the actual situation under the old methodology. This average reduction is a combination of lower AD duties in some cases (where the investigation would still conclude Chinese firms are dumping) and the elimination of AD duties altogether in other cases. Based on this information, we develop a methodology to evaluate the broader employment effects of such a change in methodology to assess dumping involving Chinese firms. Note that we only aim to evaluate the changes in employment in the new regime. We do not aim to predict the absolute effect of AD enforcement on employment. We only estimate the counterfactual, the differential effect of the new (MES) versus the old (analogue country) methodology. In Scenario 1, we arrive at direct employment effects by extrapolating estimates based on 10 AD-cases to all TDI-sensitive sectors. However, in view of the limited number of cases, this is only an incomplete analysis. In Scenario 2, we consider all the Chinese products currently subject to TDI cases (52 cases). In Scenario 3, we additionally consider the possibility that in the future, AD enforcement could spread to more products (a hypothetical long-run scenario). In the last two scenarios we consider not only the direct employment effects in sectors directly affected by AD duties, but also the indirect employment effects arising from spillover effects on upstream sectors that supply inputs and on downstream sectors that use the directly affected products as inputs. Once we obtain the overall job effects, we engage in a breakdown of the overall job effects by sector and by EU member state. In the final section of the report, we investigate the job effects in the special case where the EU aims to maintain the effectiveness of TDIs. When costs and prices in China would be considered as distorted and cannot be used for the calculation of dumping margins and duties, a technique of “benchmark country cost plus union industry profit margin” can be used to calculate dumping margins. We consider job effects under this technique both when keeping the “lesser-duty-rule” (LDR) in place and when removing the LDR. Job effects will be estimated for both the short-run and the long-run. The remainder of this report is organized as follows. In Section 2 we provide a brief literature review. In Section 3 we outline the four analytical steps that our analysis will take, which is followed by a more detailed description of each step of the analysis in Sections 4 to 7. In Section 8 we summarize our findings and provide an overview of the estimated potential employment effects for the 3 scenarios that vary in product-coverage. In section 9, we discuss the estimated job effects at sector-level and for each EU member state. Finally, in section 10, we discuss measures that the EU can apply to maintain the effectiveness of TDIs.





2.Literature survey There is a small but growing literature on the employment effects of trade policy changes. Most studies have focused mostly on the import-competing industries. For example, Konings and Vandenbussche (2008) have shown that anti-dumping protection helps smaller European firms to maintain their market share and avoid layoffs. But TDI can have additional effects beyond the direct effects on products that are protected. This has been shown by several papers. While Vandenbussche and Zanardi (2008) estimated the EU’s imports subject to AD duties to lie around 2%, they point out that the total amount of trade affected by the trade policy is larger due its deterrent effect on other trade flows. The imports under protection have a “chilling” effect on other existing trade flows, not subject to measures. As a result, EU trade flows would be 8% higher if anti-dumping policy was lifted altogether. The finding that anti-dumping protection results in “missed trade” is confirmed by Prusa and Besedes (2013) who show the “death of trade flows” as a result of trade protection. This suggests any reduction in tariffs, does not only affect the intensive margin of affected trade flows e.g. “how much” is shipped, but also affects the extensive margins of trade e.g. “how many” products that are subject to protection (Klenow and Rodriguez-Clare, 1997; Goldberg et al., 2010). Another study showing that TDI have an effect on the extensive margin was given by Vandenbussche and Viegelahn (2011). They show that for the last decade, the EU product coverage of anti-dumping cases has increased, now including sectors and products that were not subject to protection before. The share of products covered with anti-dumping has gone up from 1.2% in 1999 to over 2% in 2009. Migyagiwa et al. (2015) show that the future evolution of anti-dumping cases against a trade partner heavily depends on the relative market size. Countries of equal economic size are less likely to start anti-dumping wars, due to the costly effects of reciprocal measures, which often occur (Blonigen and Bown, 2003). All these papers show that trade policy is a dynamic phenomenon and its coverage tends to vary over time with opposing forces. The outcome of these trends is ultimately an empirical issue. A recent paper by Acemoglu et al. (2014) on the employment effects of trade with China for the US, estimates an import penetration elasticity that is negative and significant, suggesting that an increase in Chinese imports displaces US workers in industries with high import penetration. The more downstream effects of a change in the import regime have been studied even less. Viegelahn and Vandenbussche (2015) is one of the few papers that get into this. They show that European anti-dumping protection has negative effects on downstream industries that use the protected product as an input. The imposition of antidumping measures on the inputs used in production raise marginal costs. This increase in marginal cost is then imperfectly passed-through to output prices which subsequently results in lower markups of input-using firms. Reversing their result, it suggests that trade liberalization or a regime switch in which imports become cheaper is likely to have beneficial effects on downstream European producers. Access to cheaper Chinese inputs is likely to raise demand for downstream EU producers and hence raise labour demand in these firms. A similar conclusion was reached by Blonigen (2015) for the steel sector who showed that industrial policy on upstream US producers, negatively impacts the competitiveness of downstream steel exporters. Likewise, studies by Konings and Vandenbussche (2013), Hoekman and Leidy (2001) found that input-users suffer from upstream TDI protection. The literature thus seems to suggest that the indirect effects of TDI are important to study and quantify. As countries’ value chains are becoming increasingly international, trade policy is likely to have an impact on sourcing as well as offshoring patterns (Van Biesebroeck and Zhang, 2014). From a theoretical point of view, several trade models developed in the literature can guide our priors (Melitz, 2003). A reduction in tariffs, as can be expected from the granting of MES to China, induces consumers to substitute away from relatively expensive European products towards cheaper Chinese imported products. The magnitude of this substitution effect in demand will depend on the elasticity of substitution between the European and the Chinese products. Thus, for import-competing European firms, the substitution effect will dominate and will result in a decline of output for these firms as a result of a more liberal trade policy regime towards China. Based on a standard trade model’s prediction, we expect the direct employment effects to the EU industry to be negative and strongest in those firms selling a similar product to the Chinese one (Willig, 1996). The question that remains is however, to what extent indirect effects add to or offset the direct negative employment effects. What are the effects on upstream firms that are supplying the EU import-competing firms? Lower AD duties on China, will have a negative demand and output effect on EU import-competing industries, which in turn will spillover to all its suppliers in the value chain. In the absence of an offsetting force, lower Chinese duties thus result in lower demand and output effects on upstream European suppliers. These indirect upstream effects will add to the direct negative employment effects faced by EU import-competing firms. What are the effects on downstream firms? Whenever EU firms use Chinese imports as inputs, theory predicts that a market expansion effect will occur for these downstream industries. The mechanism runs as follows. When European downstream producers use cheaper Chinese inputs as inputs in their production, this lowers their marginal cost of production and hence lowers their output price. This reduction in output price will raise demand for downstream European products. This results in an increase in labour demand and positive employment effects in downstream sectors (Goldberg et al., 2010). Even when European downstream producers continue to use European inputs in production after a duties’ reduction on similar Chinese inputs, the price of these European produced inputs is likely to fall. The reason is that European input producers will be subject to downward price pressures as a result of the stronger competition from Chinese like products. This “matching” of European prices to Chinese input prices will add to the downstream positive employment effects as all downstream EU producers will now have lower input prices, including the ones that use European inputs. In sum, the literature has shown that TDI have both direct and indirect employment effects. Thus, part of the challenge in this report lies in empirically identifying the sign and size of the upstream and downstream employment effects and to what extent they can offset or add to the direct employment effects in TDI sectors directly affected by the MES trade regime towards China. In addition, evidence suggests that trade policy is not static, but has extensive margin effects on product coverage. Thus another challenge consists in making a reasonable assessment of the long-run extensive margin effects of trade policy, where we allow the product coverage of China in TDI to vary in the future from what it is currently.



3.Outline of the analysis and Graphical Representation The analysis proceeds in four consecutive steps with a natural progression, as each step takes the output of the previous step as input. We present a graphical representation of the steps involved at the end of this section. The objective is to obtain an estimate for the differential employment effects on the EU if it were to grant China MES in AD investigations. Step 1: Revisiting the expected employment effects in the 10 selected AD cases We start with an independent assessment of the possible direct employment effects in the 10 anti-dumping cases the Commission selected for the purpose of this study which allowed us to use detailed (confidential) information. For this purpose we estimate behavioural elasticities on (a) bilateral import demand and (b) employment with respect to bilateral imports for the ten recent AD cases where counterfactual duties under MES could be established.

Using two alternative sources for trade data, COMEXT (CN8) and the data collected by case handlers (product-level), allows for the establishment of two alternative sets of parameters, which allow to sketch a confidence range rather than a point estimate throughout the entire subsequent analysis. An extrapolation from these 10 cases to the entire employment currently affected by bilateral TDIs allows for a first, rather rough quantification of the overall direct effects, referred to as scenario 1 in the study. Step 2: Assessing the likelihood each EU sector will face dumping by Chinese firms In order to receive more realistic, comprehensive results, three elements are missing: 1.An overall quantification of the spread out: Earlier research shows that 2.5% and, respectively 5.7% of all bilateral trade before measures are a reasonable order of magnitude for the short and long term. 2.Sectoral distribution of the spread-out: In the ten cases described in step 1, on average 22% of bilateral imports under the corresponding 4-digit HS product categories are affected. This number is used to sketch a counterfactual distribution of potential spread out under MES: In the short run, 22% of all sectors currently affect by bilateral TDI are assumed to be dumped. In the long run, other sectors are assumed to be affected on top of that, though by a lesser percentage of the bilateral trade. The distribution of these sectors is inferred by TDI cases by either the EU or China with third countries. The resulting overall percentage of bilateral trade is the one mentioned above under point 1. The short-run scenario assumes that product coverage remains the same which we refer to as scenario 2. The long-run scenario assumes the possibility of new AD-cases in the future in products that thus far have not occurred under EU TDI cases, which we refer to as scenario 3. 3.In order to link to upstream and downstream sectors, the information at the detailed 4-digit HS product category level is aggregated up to the sector classification used by the World Input Output Database (WIOD). Step 3: Generalizing the direct employment effects of Chinese MES for the entire economy We estimate sector-specific relations between bilateral tariff changes and employment by adjusting the estimates from step 1 estimates for sector characteristics. Sector specific parameters, so-called semi-elasticities are estimated. These describe the quantitative relationship between bilateral tariff reductions and sectoral employment. Taking into account sector specific import penetration rates by China and by third countries, allows for sector specific parameters. Subsequently, these parameters are used to simulate the employment effects of lower AD tariffs under MES. Step 4: Adding indirect employment effects WIOD Input-Output-coefficients are applied to quantify indirect employment effects in upstream and downstream sectors



4.STEP 1: Revisiting the expected employment effects in the 10 selected AD cases As a first step we engage in an independent analysis based on the 10 anti-dumping cases especially chosen for this purpose by the Commission. Our analysis is a two-step partial equilibrium model. First we develop a model to estimate import-demand elasticities of Chinese imports into the EU with respect to Chinese import prices. Next, we estimate EU employment elasticities in the 10 AD-cases with respect to Chinese imports. Together, these elasticities will provide us with the necessary multipliers that allow us to calculate the EU employment effects of a change in the status of China under the anti-dumping law which we call Scenario 1. This change in trade policy regime is likely to result in a lower incidence of dumping, in lower AD duties on Chinese imports and in lower Chinese border prices. Our objective is to assess the direct employment effects of this potential regime shift, on EU firms operating in similar sectors compared to the Chinese imported products. The information derived from the 10 AD- cases in this first step will then serve as an input in the following steps of the analysis. The sample of ten cases is part of a full population of 52 cases with measures currently in force vis-à-vis China. These are all cases for which it was possible to establish a hypothetical dumping margin under MES. While they constitute only about 20% of the total number of current cases, they account for more than 40% of the current employment safeguarded by bilateral AD measures. 4.1Benchmark Model STEP 1A: Elasticity of Import-demand for Chinese imports First, we estimate “import-demand elasticities” that capture the sensitivity of Chinese imports into the EU to a change in the Chinese import price. In the event of a tariff decrease, the Chinese import price is likely to fall, which will stimulate EU demand and will have a positive effect on Chinese imports. For each of the 10 cases we were handed, we estimate expected import effects of granting MES to China. The data that we use for this purpose are data at the 8-digit product-level (COMEXT, EUROSTAT) that correspond with the product classification of the products involved in the 10 anti-dumping cases. We use annual data from 2000 and 2014 which gives us meaningful results on the elasticities. From Comext we obtain all the bilateral trade flows in the CN8 products affected by AD-protection from any bilateral trade partner into any European member state. Then we kept only the import flows in these antidumping products coming from China. In our dataset we verified that the CN8 codes that corresponds to the AD-cases, did not change during subsequent classification revisions. We then use these data to regress Chinese imports in volumes into the EU in each anti-dumping-case i, on Chinese import prices as well as on an average import price from all the other countries in the world importing the same product into the EU, in the following way (1) The estimated coefficient , provides us with an estimate of import-elasticity in every anti-dumping case: import demand elasticity of Chinese import prices Controlling for the average price level of imports from the rest-of-the-world as we do in (1), does not substantially affect the estimated coefficients. The reason for this is that in the 10 AD-cases under scrutiny, the share of Chinese imports in total imports of the product is particularly high and much higher than for other products in the same sector. For comparison, the average Chinese import penetration at the Nace rev.2, 2-digit sector level to which these products belong is much lower. Therefore the rest-of-the-world’s import share in the 10 AD-cases is much smaller than in other products. We also experimented with the inclusion of year dummies to account for unobserved shocks over time, but this did not substantially alter the estimates on.

The import-demand elasticities that we get on the basis of (1) can usefully be compared with available import-demand elasticities at HS6 level in the literature (Kee et al, 2008, World Bank) and the results are not that different. In our estimates of the import-demand elasticities we control for the country of origin (e.g. China) in the trade flows and we turn to a more detailed product level e.g. CN8 versus HS6-level. Estimates range from an elasticity of -0.17 in the case of “Compressors” (case AD519) to an elasticity of -2.02 in the case of “Solar Glass” (case AD598) which can be seen from column 1 in Table 3. In the Appendix, we report the regression results for each case. Next, we multiply the import-demand elasticity, β, with the “percentage change of the Chinese import price that would happen under the MES regime”. In the event of MES, we expect the AD-duties would be lower. This can be verified in Table 3, where we show in column (2) the AD-duties that apply under the analogue country methodology where we rounded the decimals, and in column (3), where we show the AD duties that would apply if China would obtain MES. What Table 3 shows is that duties are typically lower under the MES regime than in the analogue country regime. This implies that Chinese imports (import duty paid or after the duty) would become cheaper to EU consumers when moving from the analogue country regime (AC) to an MES regime. The extent to which gross prices (after the duty) are lower with a lower imposition of the AD-duty is given by the following formula: (2) Rewriting (2) implies that . This results in a percentage change in gross Chinese import prices (after the duty) that corresponds with the percentage point changes in the EU AD-duty. The changes in gross output prices after the duty that correspond with a fall in AD-duties, is given in column (5) of Table 3. Next, we multiply the expected Chinese gross import price change in column (5) in Table 3 by the import-demand elasticity, β, to obtain a Chinese imports multiplier in column (6). This multiplier gives us a “percentage increase in Chinese imports as a result of MES”, whose expression is shown below and whose value per case can be found in the last column of Table 3. Chinese Import Multiplier = * = percentage increase in Chinese import volumes into EU, resulting from granting MES Important to note is that in Table 3, we assume full pass-through of AD duties into Chinese prices in all trade policy regimes. This assumption corresponds most closely to the legal interpretation of the anti-dumping law e.g. the imposition of the tariff is expected to raise Chinese prices to EU consumers by at least the full amount of the tariff. The EU anti-dumping law rules out “absorption” of tariffs by Chinese exporters (see article 12 of Regulation 1225/2009). The import-demand multiplier that corresponds with this benchmark scenario thus reflects a full pass-through of tariffs to consumers. Case Import-demand elasticy Tariff under analogue country Tariff under MES Δ tariff Δ p_gross Chinese Import Increase due to MES (1) (2) (3) (4) (5) (6) AD511 (Persulfates) -0.616** 72% 21.8% -50.2% -29% 18.2% AD516 (FeSi) -1.30** 26% 15.6% -10.4% -8% 10.3% AD519 (Compressors) -0.17** 76.5% 13.2% -63.4% -30% 5% AD522 (Citric Acid) -0.76** 36% 6.6% -29.4% -21% 16% AD528 (Candles) -0.68*** 12% 4.0% 8.1% 7% 4.8% AD560 (Ceramic Tiles) -0.32*** 31% 8.0% -23% -17% 5.4% AD584 (O. Coated Steel) -0.750*** 40% 0.0% -40.2% -29% 21.3% AD585 (MTF) -1.19*** 43% 16.1% -26.9% -19% 22.7% AD598 (Solar Glass) -2.02*** 37% 0.0% -37% -27% 54% AD614 (Tartaric Acid) -0.71*** 11.3% 0.0% -11.3% -10% 7.1% source 8-digit CN data on Chinese imports EU data on the cases EU data on the cases (col.3) – (col.2) (col.1) * (col.5) STEP 1.B.: Elasticity of European Employment with respect to Chinese Imports Once we obtain the effect of lower AD duties on Chinese imports, we turn to the estimation of employment elasticities. This will tell us how rising Chinese imports affect EU employment in the AD-cases we have at hand. To estimate the sensitivity of EU employment to Chinese imports, we use confidential EU data for the period before protection. Next to Chinese imports, we include additional control variables that are likely to affect EU employment such as EU production, (domestic EU sales + EU exports) and imports from the rest of the world. This way, when estimating how a change in Chinese imports affects EU employment, we control for the additional sources of variation that can account for EU employment changes but that cannot be ascribed to the evolution of Chinese imports. The estimated regression is in levels with the following specification: (3)

The EU employment elasticity can then be obtained as the estimated coefficient on Chinese imports as follows: overall employment elasticity of Chinese imports Due to the short time span that we have, with observations up to three years prior to the investigation period, we cannot estimate the employment elasticities on a case-by-case basis since that would only give us 4 observations per case (i) which we consider too few for a reliable estimate. Instead, we estimate the employment elasticity, , across all ten cases. Its value is listed in column (1) of Table 4. The resulting elasticity is -0.346 when controlling for EU production (which is the sum of EU domestic sales and EU exports). This regression result is shown separately in the Appendix Table A.3. The employment elasticity is then multiplied with the “percentage change in Chinese imports under the MES regime compared to the analogue regime”, listed in column (1) of Table 4. This gives us the “percentage change in EU employment resulting from the percentage change of Chinese imports”, whose expression is shown below and whose empirical values are reported for each AD-case in the last column of Table 4. EU Employment Multiplier = * = * Chinese Import Increase = percentage decrease of in EU jobs/percentage change in tariffs wrt China under MES Case Employment Elasticity, ρ Chinese Import Increase due to MES regime Employment Decrease (1) (2) (3) AD511 (Persulfates) -0.346* 18.2% -6.3% AD516 (FeSi) -0.346* 10.3% -3.6% AD519 Compressors) -0.346* 5% -1.7% AD522 (Citric Acid) -0.346* 16% -5.5% AD528 (Candles) -0.346* 4.8% -1.7% AD560 (Ceramic Tiles) -0.346* 5.4% -1.9% AD584 (O. Coated Steel) -0.346* 21.3% -7.4% AD585 (MTF) -0.346* 22.7% -7.9% AD598 (Solar Glass) -0.346* 54% -19% AD614 (Tartaric Acid) -0.346* 7.1% -2.5% Source EU data on the cases Table 3 col (6) (col.1) * (col.2) 4.2Import-Demand Elasticity from EU data A downside of using CN8 level data from the publicly available trade statistics is that these product definitions are at a somewhat higher level of aggregation than the actual products under investigation in the 10 anti-dumping cases under scrutiny. Given the existence of confidential data on import volumes and import prices, available from the Commission, we also estimate an alternative import demand elasticity based on EU data. After all, we cannot exclude the possibility that “at the product-level under investigation”, the products have a very different elasticity than the one at the more aggregated CN8 level. The EU confidential data that were handed to us for this purpose, involve four observations per case, corresponding with the four years prior to the initiation year of the case. Due to the limited number of years, these data do not allow us to estimate a case-by-case import demand elasticity of Chinese import volumes on Chinese prices. Instead, we pool the data across cases and estimate one overall import-demand elasticity at the product-level involved in the AD-cases. The value of the elasticity obtained this way is reported in the first column of Table 5. The exact specification and regression result is listed in the Appendix in Table A.2. This alternative elasticity is listed in column (1) of Table 5 and will allow us to arrive at an alternative set of multipliers. In column (2) of Table 5, we list the change in the gross Chinese import prices after the duty. The import multiplier, listed in the last column of Table 5, is now obtained by multiplying the alternative elasticity at product-level from the EU data in column (1) by the gross price changes that can be expected under a regime switch in column (2).



Case Import demand elasticity EU data Δ p_gross Chinese Import Increase (1) (2) (3) AD511 (Persulfates) -1.38* -29% 40.12% AD516 (FeSi) -1.38* -8% 11.36% AD519 (Compressors) -1.38* -36% 49.55% AD522 (Citric Acid) -1.38* -21% 29.43% AD528 (Candles) -1.38* 7% 9.86% AD560 (Ceramic Tiles) -1.38* -17% 23.88% AD584 (O. Coated Steel) -1.38* -29% 39.43% AD585 (MTF) -1.38* -19% 26.29% AD598 (Solar Glass) -1.38* -27% 36.90% AD614 (Tartaric Acid) -1.38* -10% 14.01% Source: EU data EU data col.1 * col.2 In Table 6 we apply the multipliers obtained above and we calculate the effects on EU job losses. The estimates arising from our independent analysis are reported in the last two columns. Using the same employment elasticity as in Table 4, we perform again the calculation from sector imports to sectoral employment.

In column (4) of Table 6, we present the results obtained in our independent analysis under the “CN8 benchmark” scenario with full pass-through, where we use multipliers of Table 4 and 5 reported earlier. Based on these estimates we find job losses, in the EU industry from the 10 AD-cases which amount to between 2.7% to 6.11%, for weighted and unweighted percentage changes, respectively . In column (5) of Table 6, we present the results obtained in our independent analysis under the “product-level” estimation where we use the multipliers of Table 5 as inputs in the analysis. Based on these upper bound estimates we find that job losses in the EU industry from the 10 AD-cases under scrutiny amount to between 8.41% and 9.72%, the unweighted and weighted percentage changes, respectively. Case Broad Sector Investigation Period (IP) Employment Job Loss in % IP (benchmark CN8) Job Loss in % IP (product-level EU) (1) (2) (3) (4) (5) AD511 (Persulfates) Chemicals 152 -6.3% -13.9% AD516 (FeSi) Iron & Steel 1,153 -3.6% -3.9% AD519 (Compressors) Mechanical Eng. 193 -1.7% -17.1% AD522 (Citric Acid) Chemicals 511 -5.5% -10.1% AD528 (Candles) Other 4,699 -1.7% -3.4% AD560 (Ceramic Tiles) Ceramics 77,458 -1.9% -8.26% AD584 (Coated Steel) Iron & Steel 6,046 -7.4% -13.46% AD585 (MTF) Iron & Steel 2,366 -7.9% -9.1% AD598 (Solar Glass) Other 857 -19% -12.7% AD614 (Tartaric Acid) Chemicals 179 -2.5% -4.8% Unweighted Av. -6.11% -9.72% Weighted Average -2.7% -8.41% When we apply these percentage changes in job loss from the 10 cases and extrapolate that to the entire employment in Trade Defense sectors, which is currently estimated at 231,000 jobs, then we find potential job losses due to Chinese MES between around 6,000 and 14,000 jobs in the benchmark scenario, where we round numbers. When we do the same for the alternative elasticity estimate, we find possible job losses of between 19,000 and 22,000. Table 7 below provides a summary of our analysis and findings under step 1. Independent of the elasticities that were used, or the weighting scheme that was applied, the results appear to be in the same “order of magnitude”. STEP 1: Summary CN8 Estimates Product-level Estimates Unweighted estimates -14,000 -22,000 Weighted estimates -6,000 -19,000 It should be noted however, that the job losses listed in Table 7, capture only the employment effect in EU firms that directly compete with the Chinese products and sell a similar product. The negative employment effects that we find for these firms are in fact what we expected. Whenever a Chinese firm exports a similar product to the EU produced one, theories of demand would predict that a substitution effect will take place, away from the EU product and towards the Chinese product whenever this Chinese product becomes cheaper than it was before. This will negatively impact the labour demand in EU firms which will then have a negative effect on employment in the import-competing EU firms. The employment effects shown in step 1 do not take into account the potential spillover effects that the change in the anti-dumping methodology may have on other more downstream or more upstream producers (see step 4). EU firms producing downstream from the products affected by anti-dumping may use the cheap Chinese imported product as an input. The MES regime makes the Chinese inputs cheaper than they were under the analogue country regime and EU downstream firms may pass some of that cost reduction into their output price, which will decrease. This will result in a positive demand for EU products which in turn result in an increase of labor demand in input-using EU firms. Thus, in a downstream EU industry, the employment effects of cheaper Chinese imports are likely to be positive. EU firms producing products which are more upstream than the Chinese imported product, are however likely to suffer negative employment effects. The reason is that they face a reduction in the demand for their output. Upstream EU firms producing the same type of inputs as the Chinese will lose business and employment in these upstream EU firms is likely to go down. A more detailed analysis of upstream and downstream effects is left for step 4. Here in step 1, we concentrate only on the direct employment effects of EU firms selling similar products to the imported Chinese ones.



5.STEP 2: Assessing which sectors are likely to face dumping by Chinese firms The analysis of employment effects for the entire economy, in the next Section, will be conducted at a level of aggregation where we observe import penetration of Chinese and Rest-of-the-World (ROW) firms in the EU market as well the input-output flows between sectors. The World Input-Output Database (WIOD) contains this information and we will introduce it then. What is needed as well is an assessment of the likelihood that any product in a WIOD sector is subject to anti-dumping measures on Chinese imports. We will consider two scenarios. First, in Scenario 2 we will generalize the detailed analysis conducted for 10 cases in step 1 to the entire economy using the universe of cases (52) where the EU currently imposes AD duties on imports from China. Second, in Scenario 3 we will further consider that the incidence of Chinese dumping might increase in the future, because of Chinese firms changing their behavior when the EU grants it market economy status in anti-dumping investigations. We will assign probabilities of AD action in different sectors based on observable cases of three different types, namely: 1.Type 1: EU cases against China (in force on August 2015)

2.Type 2: Third country cases against China (in the period 2000-2015) 3.Type 3: EU cases against other countries (in the period 2000-2015) In Scenario 2 only cases of the first type are used. In Scenario 3, we additionally exploit the information of the second and third type of cases to identify sectors most at risk of potential future cases. The additional cases considered in the Scenario 3 will help us to identify sectors most at risk for future dumping. We will rely on the academic literature to obtain an estimate of the likely overall magnitude on the potential future increase in dumping. 5.1 Assigning probabilities at the most detailed level Scenario 2 (short-term) Incorporating the first type of cases is straightforward. For these products anti-dumping measures are currently in force. From the information on the 10 cases analyzed in step 1, we observe how large Chinese imports are of the products subject to AD measures during the investigation period. For the same period, which differs across the 10 cases, we collect information on the total value of Chinese imports in the 4-digit HS sector that contains the affected products from the UN Comtrade database. We find that Chinese imports of the affected products in each case account, on average, for 22% of total Chinese imports in the affected 4-digit sector. Note that this share is calculated over the investigation period, thus before any AD measures came in force. From DG Trade’s online TDI database and its August 2015 report on AD measures, we retrieved an exhaustive list of all cases where AD measures against China are currently imposed, including undertakings. We also added two cases that were in force at the start of 2015 but for which duties expired in the following months. In total these cover 52cases and we identified all the 4-digit HS sectors involved. When a sector has a single active case it gets assigned a 22% probability, when more than one case is in force, the sector is assigned a 44% probability. The interpretation is that 22% of the output in a 4-digit sector is in product categories where Chinese imports are subject to AD measures. Sectors that contain products subject to a type 1 case would with certainty have been affected if the EU had decided to grant MES to China and had analyzed these cases differently. Therefore our first analysis only considers the employment effects in these sectors. One can think of these as short-run effects. Scenario 3 (potential long-term) When we take a more long-run perspective, type 1 AD cases are not the only ones that are relevant. To take into account that the focus of trade defensive actions evolves over time, we additionally identify “sensitive AD sectors” using type 2 and 3 cases: (type 2) anti-dumping actions from third country jurisdictions (e.g. the United States or Australia) against China, and (type 3) anti-dumping actions from the EU against other countries than China. The change in the EU’s methodology to adjudicate anti-dumping cases against China might change Chinese firms’ pricing behavior on the EU market. We draw on the academic literature to assess what the additional incidence of dumping might be when the EU’s enacts a less stringent standard. The type 2 and 3 cases are used to determine which sectors are most likely at risk of potential future anti-dumping cases. In particular, we assign some positive probability of future dumping happening in sectors that contain type 2 cases, which cover actions of the United States against China. It is not implausible that in sectors where Chinese firms are currently dumping, the same type of behavior might spread to the EU market. It might even be the case that dumping is already happening, but the affected EU sector has not yet filed a case or injury has not yet materialized. Cases from other jurisdictions are informative about likely future dumping in the EU because firms tend to enter export markets sequentially, and large (Arkolakis, 2010), nearby (Eaton and Kortum, 2004), and less uncertain markets (Albornoz et al. 2012) will be entered first. The EU might be entered, and be subject to dumping, only at a later time, while more attractive markets, due to size or proximity, are already affected sooner. In principle we could include AD actions of other countries besides the United States as well, but there is a strong overlap in the industries that different countries target, see for example Blonigen and Prusa (2015). Hence, each additional third country we include will gradually have fewer and fewer impact on the probabilities. We also incorporate information from type 3 cases, namely those of the EU against other countries than China. These types of actions are also informative given that many products exhibit a natural lifecycle in production (Antràs, 2005). Some of the current imports of other countries to the EU are likely to be replaced by Chinese imports in the future as the Chinese industry matures and productivity increases. Current anti-dumping problems with other countries might move with them. Even more general, we also assigned a small, but positive probability to 4-digit sectors that did not experience any type of AD case in the last years, but are in a 2-digit industry where other 4-digit sectors experienced AD cases. We must also take into account that dumping patterns might spread to sectors that are similar in technology or where the same firms are active. Naturally, we only assign a low probability in this case. In Table 8 we summarize the probabilities we have used for the different cases in the two scenarios, as discussed above.

Probability assigned to 4-digit HS sector Scenario 2 (short-run) Scenario 3 (potential) 44% Multiple cases of type 1 Multiple cases of type 1 22% A single case of type 1 A single case of type 1 11% - No cases of type 1, but at least a single case of type 2 or type 3 1% - No actual cases (of any type), but at least one case in another 4-digit sector within the same 2-digit industry 0% All other All other 5.2Aggregate to the sector level Sectoral incidence of dumping The probabilities in Table 8 are assigned at the 4-digit HS level, which comprises approximately 1200 different sectors. They are aggregated up to the WIOD sectors at which level we will study the production and employment impacts. To map the detailed HS codes into WIOD sectors we used some existing concordances and constructed one ourselves, according to the following chain: •4-digit HS (at this level of aggregation the EU classification corresponds to the UN’s) •2 or 4-digit CPC (Central Product Classification ver. 1.0) •2-digit ISIC (International Standardized Industry Classification) •16 tradable WIOD sectors (out of 35 input sectors) To aggregate from the 4-digit HS codes to the WIOD sectors we necessarily used import shares as weights because production or employment is not available at such a detailed level. As the objective is to measure the fraction of EU workers who are employed in sectors where Chinese products are subject, or could become subject, to AD duties, we used the shares of EU imports from the entire world rather than Chinese shares to aggregate from the detailed 4-digit categories to the more aggregate WIOD sectors. Only in the final aggregation, from the WIOD sectors to the aggregate for all tradable goods sectors we can use production shares as weights. In Table 9 we provide an illustration of how we obtained the sectoral probabilities used in the two scenarios, starting from a number of observed AD cases. We show for a number of cases in the WIOD sector 24 “Chemicals,” how the assigned probabilities are aggregated using observed shares to the aggregate probabilities a given product in sector 24 is subject to AD measures, either in the short term (Scenario 2) or potentially in the future (Scenario 3). Magnitude of the probabilities Assigning a magnitude to the probability of a 4-digit sector to have a dumping case in the future, even though there are currently no EU measures in force against China, is necessarily subjective. We do have a few objective benchmarks that informed our choices. First, for type 1 cases, the share of Chinese trade in products subject to current AD duties as a fraction of total imports from China in the entire 4-digit sector is a natural percentage to use. Second, we draw on the literature of the “chilling” effect of anti-dumping statutes (Egger and Nelson, 2011; Vandenbussche and Zanardi, 2010) to obtain an upper bound on the fraction of aggregate trade that is likely to be affected by a change in anti-dumping enforcement. These papers estimate what fraction of a countries’ trade is affected indirectly by the mere enactment of an AD statute. While only a small fraction of trade is actually subject to AD measures, especially for new users, importers in other sectors might change their behavior merely because of the threat of AD action. We can use the point estimate from Vandenbussche and Zanardi (2010) to perform a similar calculation as in their footnote 34 for the average “tough” AD user. They show that a country that newly enacts an AD statute and launches 6 cases per year would see direct and indirect effects on 5.9% of its aggregate trade. As the EU had 9 cases per year over the same time period, the comparable share of trade affected would be 7.2%. Given that the current exercise is not the same as the EU enacting AD legislation for the first time, but merely changing its implementation, this fraction should be considered a large upper-bound on the total fraction of trade that could be affected, even in the long-run.





HS4 EU Cases US Cases Share assigned 3905 - AD 1014 (against China) 11% 3906 - - 1% 3907 AD468 (against China) + several against other trading partners - 22% 4002 AD472 (expired case) - 11% 7104 - - 0% HS4 Probabilities Share in CPC 34 Scenario 2 Scenario 3 2503 0 0 0.1% 2707 0 0.01 2.4% 2708 0 0.01 0.02% 28** 0.0198 0.0532 14.6% 29** 0.1157 0.1356 54.1% … … … … 3905 0 0.11 0.6% 3906 0 0.01 0.8% 3907 0.22 0.22 3.2% … … … … 3913 0 0.11 0.3% 3914 0 0.01 0.1% 4002 0 0.11 2.5% 7104 0 0 0.1% CPC4 Probabilities Share in WIOD 24 Scenario 2 Scenario 3 34 0.0725 0.0996 53.1% 35 0.0151 0.0303 46.9% WIOD Probabilities Share in all tradable goods sector Scenario 2 Scenario 3 24 0.0455 0.0670 8.5%



Third, Vandenbussche and Viegelahn (2011) have shown that EU anti-dumping policy in recent years has increasingly targeted the developing world, with China taking a very prominent position as the main target country. In particular, they find that the share of products imported by the EU from China and falling under EU anti-dumping protection has roughly tripled in 2004-2009, reaching a level of more than 2% at the end of the period. As a country with a large overlap in its product-mix compared with that of the EU, China is more likely to be targeted under EU anti-dumping policy than other developing countries. In Table 10 we illustrate the sectoral implications of the detailed probabilities we assigned in Table 9. The WIOD contains 35 sectors, but only 16 of them are tradable goods where AD measures are possible, the rest being service sectors. As the two scenarios use different probabilities at the 4-digit level, they generate different aggregate probabilities. In the short-run Scenario 2 that only uses existing EU cases against China, many aggregate sectors have no AD actions at all. In Scenario 3, where US cases against China and EU cases against other countries also receive some weight, all 16 tradable goods sectors show a positive probability, but they range from 0.9% in the mining sector to a more than 10-fold higher probability of 10.2% in non-metallic minerals. SIC Code Industry (WIOD) Employment share Probability of AD action against China Scenario 2 (short-run) Scenario 3 (potential) 01-05 Agriculture 21.3% 0% 0.2% 10-14 Mining 1.9% 0% 0.9% 15-16 Food products 11.3% 0.6% 3.6% 17-18 Textiles 4.2% 0% 1.4% 19 Leather & footwear 1.1% 0% 5.8% 20 Wood products 2.4% 6.3% 8.0% 21-22 Paper products 3.4% 1.3% 5.2% 23 Oil products 0.5% 0% 6.9% 24 Chemicals 4.8% 4.5% 6.7% 25 Rubber & plastics 3.6% 2.1% 8.5% 26 Non-metallic minerals 2.9% 7.3% 10.2% 27-28 Basic & fabricated metals 10.9% 4.7% 8.3% 29 Machinery 7.4% 3.3% 7.2% 30-33 Electrical & optical equip. 6.6% 1.0% 3.7% 34-35 Transport equipment 9.5% 2.8% 4.0% 36-37 Manufacturing NEC 8.2% 0% 5.7% 5.3 Probability of dumping versus probability of AD measures It is important to realize that the statistics shown in Table 10 are a reflection of the current AD regime that the EU is enforcing. We have calculated the probabilities for the actual current cases against China (Scenario 2) or for the potential rise in future dumping cases (Scenario 3). These numbers are intended to capture the likelihood a product is subject to Chinese dumping under the current methodology. In the analysis in step 3 we will make a counterfactual comparison calculating how many jobs would be lost if these cases were assessed under the new methodology versus the old methodology. As shown in step 1, the new methodology might not change the outcome, might result in lower AD duties (the case in 7 of the 10 cases), but it might also lead to a decision of “no dumping taking place.” Under the old methodology, these sectors would be considered dumped, but not anymore under the new methodology. Therefore the probabilities in Table 10 are accurate for the old methodology, but might overestimate the results for some of the sectors under the MES methodology. The effect of the new methodology leading to some decisions where the investigation will not find evidence of dumping anymore is already incorporated in the counterfactual tariff change that we will use. We will study the employment effects under the situation that the new methodology leads to a reduction of around 30 percentage-points in AD duties, but this is a combination of some cases seeing the AD duty lowered, while other cases seeing the AD duty abolished entirely. This distinction does not matter for the calculations in step 3 as the effects enter linearly, but it naturally implies that under the new methodology fewer sectors will be deemed to be victim of Chinese dumping, even under unchanged pricing behavior in the face of the new methodology. In Table 11 we show the aggregate incidence of dumping taking place, in the first row. These statistics correspond to the incidence of AD measures being in place under the old methodology. We show two sets of numbers. First, we use production shares as weights, which is the relevant way of aggregating when we calculate aggregate employment effects. Second, we use Chinese import shares as weights, which is the relevant calculation to assess how accurate the probabilities in Scenario 2 are or how expansive the Scenario 3 really is. At the bottom, we adjust these numbers to reflect that the analysis in step 1 suggested that in 30% of the sectors no AD measures would be imposed anymore under the new methodology. At the bottom we see that under Scenario 2, representing the current state, 2.5% of Chinese trade is affected by EU AD measures. This statistic is relatively close to the 2% number Vandenbussche and Zanardi (2010) calculated for 2009. As AD action is more likely in sectors where Chinese imports are large, it is not surprising that EU production in the same sectors accounts for a smaller fraction, 1.6%, of the total (of the entire tradable goods sector). The corresponding statistics for Scenario 3, which includes a possible future expansion of EU AD actions to new products, show that this scenario represents a situation where 5.7% of Chinese trade would be subject to EU AD action under the old methodology. This is more than double from Scenario 2, the current state of affairs, and a tripling compared to 2009, which itself was already a strong increase over the 2004-2009 period. It suggests that Scenario 3 is a very expansive scenario where EU AD enforcement would spread to many more sectors than has been historically be the case. Employment effects estimated in this case are likely to be upper bound estimates. (weighted averages of the probabilities) Probability of AD action against China Scenario 2 (short-run) Scenario 3 (potential) (a)Under the old methodology EU production with AD action against China 1.6% 4.3% EU imports from China subject to AD measures 2.5% 5.7% (b)Under the new methodology (MES) EU production with AD action against China 1.1% 3.0% EU imports from China subject to AD measures 1.7% 3.9%





6.STEP 3: Direct employment effect on the entire tradable goods sector In the third step, we calculate an expected employment reduction in the EU if China were granted MES in AD investigations. The change in adjudication methodology would lead to lower AD duties than are imposed in the current regime. We use the estimates from step 1 as a starting point to construct a sector-specific employment semi-elasticity. Naturally not every product will be subject to this changing AD regime. Therefore we will multiply the expected employment effects with the probability that a product in a given sector is subject to AD action, i.e. the statistics calculated in step 2. We first need to generalize the expected employment effects from the 10 cases to the entire tradable goods sector. We need to make two adjustments, first, for the fact that the different cases entail a different change in AD duties, and second, that sectors are structurally different and even the same change in AD duty is likely to have different effects. We accomplish the first adjustment by not working with the percentage employment reduction directly, but by constructing an employment semi-elasticity, i.e. the percentage change in employment by the EU industry for each percentage point change in AD duty on Chinese products. For each of the 10 cases in step 1, we take the predicted change in employment due to the different status of China, the statistics in column (4) of Table 6, and we divide them by the percentage point AD duty changes in column (4) of Table 3. The ratio we work with is thus: For the benchmark estimates from step 1, this elasticity averages 0.23 over the 10 cases, with a median of 0.20 and a range of [0.02 – 0.34] once we drop the top and bottom outliers and limit attention to the 8 cases in the middle. For the estimates obtained using the product-level estimates, the mean is higher at 0.35, with a median of 0.35 and a range of [0.27 – 0.36], again omitting the two extreme values. When we use the average of these elasticities for all sectors to simulate a counterfactual change in AD duties for an “average” sector, it is natural to also perform this counterfactual calculation using the same average reduction in AD duties in each sector, which was 30 percentage points in the 10 cases of step 1. When using this average elasticity on the 16 WIOD sectors, we will not use the simple average directly as sectoral heterogeneity will affect the relevant employment elasticity in a predictable way. In step 1 those sectoral differences, in particular in terms of import penetration, could be taken into account explicitly, but the lack of detailed information at the aggregate level requires a reduced form approach. We adjust the employment elasticity using two observable sector characteristics that have predictable effects on the expected employment effect. First, a reduction in AD duty in a sector with a higher Chinese import penetration should have a larger effect as the same proportional adjustment of Chinese firms will have a larger quantity effect on the domestic industry in absolute terms. We plot in Figure 1 the elasticities of the ten cases (on the vertical axis) against Chinese import penetration (on the horizontal axis) for the two sets of elasticities: the step 1 benchmark estimates (left) and the second set of estimates obtained at the more detailed product level (right). The regression lines confirm the predicted positive relationship: sectors with higher Chinese import penetration see larger employment effects per percentage point reduction in AD duties. The adjustment is not very large though. We perform the same exercise using import penetration from the Rest-of-the-World (ROW). Here we expect a negative relationship as a higher import penetration of other countries will, ceteris paribus, let the burden of market share adjustment fall to a greater extent on foreign firms, and thus less on the domestic EU industry. This is indeed what surfaces from the results in Figure 2. For both sets of estimates, the relationship between EU employment effects and ROW import penetration is strongly negative. Both adjustments are intuitive, but they are naturally not independent. Sectors with higher Chinese import penetration are likely to also be sectors with lower ROW import penetration and the two effects from Figures 1 and 2 will to some extent offset. In order to calculate sector-specific semi-elasticities that incorporate both adjustments, we run a multivariate regression, and report the results in Table 12. Dependent variable: Benchmark estimates Product-level estimates Constant 0.262** 0.325*** (0.129) (0.035) China IP 0.123 0.207* (0.441) (0.119) ROW IP -0.546 -0.213* (0.420) (0.114) Adj-R2 0.29 0.29 Observations 9 9 Cases excluded FeSi FeSi

Given that Chinese and ROW import penetration is observable for each of the WIOD sectors, we can use the regression results from Table 12 to calculate a full set of adjusted, sector-specific elasticities. As the import penetrations at this more aggregate level tend to be a lot lower than in the 10 cases we analyzed, the predicted semi-elasticities are somewhat closer to the intercepts in Table 12 which is the applicable semi-elasticity in a sector with no imports whatsoever. The last two columns of Table 13 report the sector-specific semi-elasticities in the two cases, starting from the two different sets of estimates from step 1. An adjustment was made for the mining elasticity when using the benchmark estimates as the predicted elasticity was negative—due to the excessively large import penetration. We replaced the estimate with one half of the lowest elasticity across the other sectors. SIC Code Industry (WIOD) Import penetration Predicted employment semi-elasticity China ROW Benchmark estimates Product-level estimates 01-05 Agriculture 0.5% 9.9% 0.21 0.30 10-14 Mining 0.2% 64.5% 0.08 0.19 15-16 Food products 0.6% 8.1% 0.22 0.31 17-18 Textiles 17.1% 23.7% 0.15 0.31 19 Leather & footwear 15.6% 20.8% 0.17 0.31 20 Wood products 1.6% 5.3% 0.23 0.32 21-22 Paper products 0.3% 5.3% 0.23 0.31 23 Oil products 0.2% 16.8% 0.17 0.29 24 Chemicals 2.3% 21.1% 0.15 0.29 25 Rubber & plastics 4.4% 7.3% 0.23 0.32 26 Non-metallic minerals 1.9% 4.2% 0.24 0.32 27-28 Basic-fabricated metals 1.8% 9.4% 0.21 0.31 29 Machinery 4.2% 10.7% 0.21 0.31 30-33 Electrical & optical eq. 16.4% 16.5% 0.19 0.32 34-35 Transport equipment 1.8% 13.0% 0.19 0.30 36-37 Manufacturing NEC 3.3% 12.2% 0.20 0.31 With two sets of employment semi-elasticities from Table 13, and two sets of probabilities that each sector is affected under two alternative scenarios from Table 10, we can calculate 4 different expected output or employment effects for each sector. The employment change is calculated as follows: In Table 14 we report the results using the benchmark estimates in the two scenarios at the sectoral level. Sectors with large effects have a higher than average elasticity, such as “food products” (15), a particularly high probability of being affected, such as “basic and fabricated metals” (27-28), or both, such as “non-metallic minerals” (26). SIC Code Industry (WIOD) Predicted employment effects using the benchmark estimates Scenario 2 (short-run) Scenario 3 (potential) 01-05 Agriculture 0% -0.012% 10-14 Mining 0% -0.013% 15-16 Food products -0.037% -0.235% 17-18 Textiles 0% -0.065% 19 Leather & footwear -0.001% -0.269% 20 Wood products -0.443% -0.561% 21-22 Paper products -0.089% -0.362% 23 Oil products 0% -0.353% 24 Chemicals -0.205% -0.301% 25 Rubber & plastics -0.143% -0.582% 26 Non-metallic minerals -0.532% -0.742% 27-28 Basic & fabricated metals -0.301% -0.529% 29 Machinery -0.208% -0.448% 30-33 Electrical & optical eq. -0.055% -0.216% 34-35 Transport equipment -0.163% -0.230% 36-37 Manufacturing NEC 0% -0.341% In Table 15 we show the aggregate effects over the entire tradable goods sector in all 4 possible cases. These aggregate effects were obtained from aggregating detailed changes at country-sector-level. The aggregate employment over all countries and tradable goods sectors was 43 million workers in the EU28 in 2014 (Structural Business Statistics of Eurostat). (a)In percentage terms Scenario 2 (short-run) Scenario 3 (potential) Benchmark (CN8) estimates -0.116% -0.274% Product-level estimates -0.171% -0.409% (b)In number of jobs Scenario 2 Scenario 3 Benchmark (CN8) estimates -49,700 -117,800 Product-level estimates -73,600 -175,600 7.STEP 4: Adding indirect effects

7.1The signs and magnitudes of expected indirect effects From the analysis in the first three steps we obtained the employment effects in the sectors directly related with current anti-dumping action. We now incorporate indirect employment effects as the adjustment of directly affected sectors as a result of the change in methodology has spillover effects on upstream and downstream sectors. Lower AD duties in a sector will lead to the following indirect effects in other sectors: Effects on upstream sectors –Substitution: Demand falls as downstream EU production is replaced by Chinese imports which use fewer EU inputs in their production (negative) –Demand: Demand rises as lower prices downstream boost output and more inputs are needed by domestic and foreign producers (positive) Effects on downstream sectors –Substitution: Lower input prices for the downstream sector might induce firms to substitute away from labor and towards intermediates (negative) –Demand: Lower input prices will filter into lower output prices downstream, expand the market and raise sales, and thus raising labor demand (positive) We expect the substitution effect to dominate in the upstream sectors for an overall negative effect and the demand effect to dominate in the downstream sectors for an overall positive effect. We now discuss why this will be the case, but to facilitate the discussion and keep the terminology straight, we will sometimes use the following fictional example to make upstream-downstream unambiguous: In this example, the tire sector is directly affected by AD action against China and in step 3 we already calculated the magnitude of the negative employment effects for the tire sector when AD duties are lowered if the EU grants MES to China. We will now estimate employment effects in the rubber and automobile industry even when these are not directly affected by AD actions. In reality, each sector will experience both direct and indirect effects (especially in Scenario 3) because each sector will be upstream for some and downstream for other sectors. We discuss the quantitative implementation in the sub-sections below, but first illustrate the nature and relative importance of the four different effects that we listed above. In particular, we are interested in quantifying the indirect employment effects stemming from a change in AD duties in the tire sector both on the upstream sectors that provide inputs to tires, such as the rubber sector, and on the downstream sectors that use tires as inputs in their own products, such as the automobile sector. Upstream The upstream rubber producers will be affected negatively as Chinese producers take market share away from EU based tire manufacturers. As all firms source disproportionately local, this change in market share will hurt the EU rubber industry and benefit the Chinese rubber industry. The magnitude of this effect is determined by the difference in sourcing of the different tire sectors, which we can observe in the WIOD tables. The fact that many Chinese exports are produced under the processing trade regime which relies heavily on imported intermediaries is likely to cushion the blow somewhat as the EU rubber industry will increase exports to the Chinese tire industry. An opposite, positive, employment effect will simultaneously affect the rubber industry as lower tire prices in the EU market raise demand and output for all market participants. The increase in production will help all input suppliers including the domestic, upstream rubber industry. We expect this effect to be muted, though, as the price decline will be weaker for EU tire producers than for Chinese producers. The exact effect will depend on the elasticity of the demand for tires and the nature of competition. The more differentiated the goods directly affected by AD action are, the weaker the positive upstream demand effect is likely to be. In all, we expect the first substitution effect to dominate upstream. The loss in market share of the domestic tire industry will be the dominant force. EU producers will lose more sales to Chinese firms than they gain from lower prices. For Chinese producers, the substitution and demand effect work in the same direction, but their expansion in production is unlikely to help the upstream EU input industry enough. As we will show in the next section, most EU sectors where Chinese products are affected by AD duties are relatively upstream and the EU industry is only a marginal input provider to the Chinese industry. Downstream

The substitution mechanism that affects the downstream industry is entirely different from upstream, but again contributes negatively to the indirect employment prospects. As prices decline in the EU tire industry, other industries that use tires as inputs, such as the automobile industry, might substitute between purchased intermediates and direct employment. Rather than make some parts in-house by combining raw materials with their own workers, downstream firms might instead purchase finished parts from the upstream sector and reduce their own employment. This is a corollary from the positive demand effect that we discussed in the upstream sector. Greater output and greater employment of tires, will be partially offset by lower employment and less in-house production of tires by automobile companies downstream. But also in the downstream automobile industry, there is an opposite, offsetting effect from the demand side. As inputs (tires) become cheaper, it lowers the marginal cost of the downstream industry (automobiles) which will eventually translate in lower prices, higher quantities sold and greater employment. Whether the negative substitution effect or the positive demand effect dominates depends on the elasticity of substitution in production downstream and the elasticity of demand. Especially in the short run, there is often very little scope to substitute between inputs. Much of the literature assumes a Leontief technology, see for example Van Biesebroeck (2003). We therefore expect the positive demand effect to dominate. As the indirect employment effects are expected to be negative upstream, but positive downstream, the net impact of MES for China will hinge on the average “downstreamness” of Chinese inputs. To quantify the exact indirect effects we need information on the source of inputs for different industries and the demand elasticities of upstream and downstream sectors. We will use the World Input-Output Database and make demand elasticity assumptions to evaluate which effects dominate, but first we provide some direct information on the nature of the sectors particularly prone to AD dumping action against China. 7.2How upstream or downstream are sectors subject to EU AD duties against China? In our simple example in Figure 3 each sector, rubber, tires, and automobiles, can unambiguously be ranked as upstream or downstream. Of course, in the broader economy, industries are subject to both upstream and downstream effects, e.g. AD dumping action in the rubber or automobile sectors would have opposite effects on the tire industry. Moreover, the rubber industry will also use trucks in its logistics chain and to some extent the automobile industry will even be upstream from the rubber industry. To assess whether sectors particularly prone to EU AD action against Chinese imports are mostly upstream or downstream, we calculate a summary measure over the entire tradable goods industry. For each sector we calculate two quantities, which we express as a share of total sector output merely for convenience: A.The amount of inputs purchased by industry i from all other industries (the complement of value added in gross output) B.The amount of output of the same industry i used as input by all other industries (and not as final demand) We then compare the ratio A/B for the average EU sector, using production weights of each sector (as shown in Table 10), with the weighted average of the same ratio where we additionally use the probability a product is affected by Chinese dumping (as calculated in step 2 and shown in the last columns of Table 10) in the weights. Average over all EU tradable sectors Average for EU sectors weighted by probability of Chinese dumping Scenario 2 Scenario 3 (a) Excluding trade from the calculation A: Inputs purchased from other sectors / total output 71.3% 75.6% 71.8% B: Output used in other sectors / total output 61.2% 69.9% 65.6% A / B ratio (higher = more downstream) 1.30 1.20 1.21 (b) Alternative measure: Using total output in the calculations, i.e. including trade A / B ratio (higher = more downstream) 1.25 1.09 1.17 Lower numbers for the A/B ratio, such as in Scenario 2 (column 2) and 3 (column 3), compared to the average for all the EU tradable sectors (column 1) indicate that an industry is more important as a provider of intermediates to other sectors (its role as an upstream sector) rather than as a purchaser of inputs (its role as a downstream sector). The results indicate clearly that the average EU sector hit by AD duties against Chinese imports is a lot more upstream than the average tradable EU sector. While for the average EU sector A exceeds B by 30% (the 1.30 value in Table 16), the difference for sectors with AD duties against China is only 20% (the 1.20 value in Table 16). When we include EU exports of output and EU imports of inputs in these calculations, in panel (b), the difference is similar. The A/B ratio is a lot smaller for sectors with AD duties against China (1.09) than for the average sector (1.25). These results directly indicate that sectors where the EU currently imposes AD duties on China tend to have fewer upstream sectors than the average EU manufacturing sector. At the same time, sectors where the EU currently imposes AD duties on China tend to have more downstream sectors than the average EU manufacturing sector. Recall also that the lowering of the AD duties under the market economy methodology is expected to negatively affect upstream sectors, but positively affect downstream sectors, As a result, the indirect effect of changing towards MES for China—which exports products into the EU that tend to be rather upstream—will be on balance less negative than a similar change in status for a different country with a production structure that is more like the EU. 7.3Magnitude of upstream effects As discussed above, we expect the sum of the substitution and demand effect on upstream sectors to be negative. In Appendix B we outline in detail the methodology based on the WIOD table that we used to assess the quantitative importance of these effects. The key ingredients in the analysis are (1) a comparison of sourcing patterns between EU and Chinese sectors, which we can observe directly in the WIOD, and (2) the increased demand for inputs produced in the EU due to higher imports of Chinese products downstream, which requires an assumption on the demand elasticity. The final results are obtained by a matrix multiplication of three elements. First is a row vector Row1 that measures the input use of upstream sector i. Second is a matrix A that controls for the share of sector i output that is exported and thus unaffected. Third is a column vector, which is the same for each upstream sector i, and which contains the downstream change in demand. Multiplying these three elements produces the aggregate indirect upstream effect for sector i due to changed MES treatment of China in the entire EU tradable goods sector:

Indirect upstream effect on sector i = Rowi * A * Column In Table 17 we separately report the negative effects from the EU industry (the first 16 terms in the sum, as obtained by taking only the first 16 elements of Rowi), RoW imports (then next 16 terms), and the positive effects from increased input deliveries to Chinese sectors increasing their production as their exports to the EU expand. All three columns contain the net effect of Chinese products gaining market share at the expense of EU and RoW products, as well as the demand expansion downstream due to lower prices. The results clearly indicate that the negative effects dominate in each sector in the EU. The greater intensity of EU input use by EU firms compared to Chinese firms is by far the most important element. In some sectors, such as Leather & footwear or Machinery, the increased sales by Chinese firms also lead to higher sales of EU inputs, but it is never enough to compensate for the lower input use by EU clients. (for Scenario 3, benchmark CN8 employment elasticities, and assuming σ=1.38) SIC Code Industry (WIOD) Effect from EU clients Effect from RoW clients Effect from CH clients Total 1-5 Agriculture -0.00090 -0.00000 0.00001 -0.00090 10-14 Mining -0.00184 -0.00001 0.00002 -0.00183 15-16 Food products -0.00032 -0.00000 0.00000 -0.00032 17-18 Textiles -0.00025 -0.00000 0.00001 -0.00025 19 Leather & footwear -0.00035 -0.00001 0.00003 -0.00032 20 Wood products -0.00167 -0.00001 0.00001 -0.00167 21-22 Paper products -0.00085 -0.00001 0.00002 -0.00084 23 Oil products -0.00063 -0.00001 0,00000 -0.00064 24 Chemicals -0.00084 -0.00002 0.00004 -0.00082 25 Rubber & plastics -0.00128 -0.00001 0.00003 -0.00126 26 Non-metallic minerals -0.00110 -0.00000 0.00001 -0.00109 27-28 Basic & fabricated metals -0.00205 -0.00002 0.00003 -0.00204 29 Machinery -0.00069 -0.00001 0.00011 -0.00059 30-33 Electrical & optical eq. -0.00051 -0.00001 0.00006 -0.00046 34-35 Transport equipment -0.00045 -0.00001 0.00003 -0.00043 36-37 Manufacturing NEC -0.00070 -0.00001 0.00001 -0.00070 In Table 17 we reported the detailed results for one particular case, i.e. the hypothetical Scenario 3 and using benchmark (CN8) semi-elasticity estimates. In Table 18 we show the aggregate effects in all four possible cases. (a)In percentage terms Scenario 2 (short-run) Scenario 3 (potential) Benchmark (CN8) estimates -0.036% -0.086% Product-level estimates -0.052% -0.127% (b)In number of jobs Scenario 2 Scenario 3 Benchmark (CN8) estimates -15,300 -36,900 Product-level estimates -22,600 -54,600 Finally, it should be noted that these calculations adjust for the fraction of output from each sector not destined to the EU market, through the multiplication with the A matrix. If, alternatively, we would assume that inputs sourced from Europe are disproportionately used in products destined to be exported back to Europe the indirect effects would be somewhat more positive. This seems especially plausible in the case of China, where the export processing sector uses a lot more imported inputs than firms producing for the domestic market (Koopman, Wang, and Wei, 2012). As a result, the findings above might overestimate the negative indirect upstream effect as the output expansion by Chinese firms might not reduce the use of inputs sourced from upstream EU industries as much as is assumed here. Given the small magnitude of the effects in the second last column of Table 17, any such change would be minor. 7.4Magnitude of downstream effects The calculations to estimate the downstream effects also exploit the information in the WIOD, but capture fundamentally different effects. One important element we have no information on, and which is likely to be unimportant in practice, is the input-substitution downstream between employment and purchased inputs. We follow the maintained hypothesis underlying the construction of the input-output matrices and assume a Leontief technology. It implies that the input shares in the production process are fixed and determined by technology. There is no substitution between employment and purchased intermediates. In the short-run this is surely the only reasonable assumption, but even in the long run it is difficult for most industries to change their production technology radically. What is left to calculate is how important the upstream market expansion is for employment in downstream sectors. The details of these calculations are again delegated to Appendix B. Using the information in the WIOD, we calculate a row vector (R