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
Date: 2016-11-09
Institutions: Directorate-General for Trade and Economic Security, European Commission
Subjects: China, anti-dumping legislation, anti-subsidy proceeding, countervailing charge, dumping, import (EU), originating product

EN 

   EN 

   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 ..................................................................................................................................... 4 

Disclaimer ................................................................................................................................. 4 

Executive Summary .................................................................................................................. 5 

Synthèse du Document ............................................................................................................. 9 

1. 

Introduction .................................................................................................................... 14 

2.  Literature survey ............................................................................................................ 16 

3.  Outline of the analysis and Graphical Representation ................................................... 19 

4. 

STEP 1: Revisiting the expected employment effects in the 10 selected AD cases ...... 21 

4.1  Benchmark Model ...................................................................................................... 21 

4.2 

Import-Demand Elasticity from EU data .................................................................... 26 

5. 

STEP 2: Assessing which sectors are likely to face dumping by Chinese firms ........... 31 

5.1  Assigning probabilities at the most detailed level ...................................................... 31 

5.2  Aggregate to the sector level ...................................................................................... 34 

5.3  Probability of dumping versus probability of AD measures ...................................... 38 

6. 

7. 

STEP 3: Direct employment effect on the entire tradable goods sector ........................ 40 

STEP 4: Adding indirect effects ..................................................................................... 46 

7.1  The signs and magnitudes of expected indirect effects .............................................. 46 

7.2  How upstream or downstream are sectors subject to EU AD duties against China? . 48 

7.3  Magnitude of upstream effects ................................................................................... 50 

7.4  Magnitude of downstream effects .............................................................................. 52 

7.5  Other effects: macroeconomic multipliers ................................................................. 55 

8.  Aggregate Effects in Three Scenarios ............................................................................ 58 

8.1  Scenario 1: Simple Extrapolation from the 10 cases .................................................. 58 

8.2  Scenario 2: Short-run effect based on cases against China currently in force ........... 59 

8.3  Scenario 3: Potential long-run scenario including hypothetical future cases ............. 60 

9.  Member State – Sector Job Effects of MES for China .................................................. 61 

9.1. Methodology for Member State-Sector Elasticities ..................................................... 61 

9.2. Job Effects by Member State and Sector ..................................................................... 63 

9.3. Benchmark Estimates (CN8) ........................................................................................ 64 

Member State Level Job Effects .................................................................................. 64 

2 

 
 
Sector Level Job Effects ............................................................................................... 67 

9.4. Product-Level Estimates .............................................................................................. 69 

Member State Level Job Effects .................................................................................. 69 

Sector Level Job Effects ............................................................................................... 71 

9.5. Summary of Country-Sector Effects ............................................................................ 74 

10.  Measures to Maintain Effectiveness of Anti-Dumping .................................................. 75 

10.1. Types of Measures ..................................................................................................... 75 

10.2. Job Effects With Different Types of Measures .......................................................... 77 

10.3. Measures Type 1: ....................................................................................................... 78 

Short-run (Scenario 2) .................................................................................................. 78 

Long-run (Scenario 3) ................................................................................................... 80 

10.4. Measures Type 2: ....................................................................................................... 81 

Short-run (Scenario 2) .................................................................................................. 81 

Long-run (Scenario 3) ................................................................................................... 81 

10.5. Summary of Measures to Maintain Effectiveness of Anti-Dumping ......................... 83 

Appendix A: Background tables for step 1 ............................................................................. 84 

Appendix B: Methodology for step 4 ..................................................................................... 87 

B.1 Calculation of the upstream effects .............................................................................. 87 

B.2 Calculation of the downstream effects ......................................................................... 89 

References ............................................................................................................................... 92 

3 

 
 
 
 
 
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” 

4 

 
 
 
 
 
 
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.1 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.2  

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%3 of total imports from China4 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-run5 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 

1 This average reduction is a combination of lower AD duties in some cases (where the investigation would 

still conclude Chinese firms are dumping) and elimination of AD duties altogether in other cases.  

2 This  is  based  on  the  unweighted  averages  arising  from  the  10  anti-dumping  cases  used  for  extrapolation 

purposes. Total current employment in sectors protected by China-related TDI is 231,000 (June 2015). 

3 Vandenbussche and Viegelahn (2011) show that the share of products imported by the EU from China and 

falling under EU anti-dumping protection reached a level of slightly higher than 2% in 2009. 

4 The methodology that we use to arrive at 2.5% is well explained in later sections (p.11), and allows us to 

assess also the indirect employment effects for which we need to turn to the WIOD database. 

5 Short-run does not refer to a one-year period, but refers to the product coverage of AD which in the short-

run can be considered unchanged. 

5 

 
 
                                                 
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 

6 

 
 
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. 

7 

 
 
 
Table 1: Summary of the Possible Employment effects of EU granting MES to China in Three 
Scenarios 

3 Scenarios: 

Percentage effect 

Benchmark 
(CN8) estimates 

Product-level 
estimates 

Job loss 
(upper 
bound) 

Chinese imports 
covered by AD 
duties a (%) 

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 

3.  Potential long-run effect 
with increased dumping 
in new product 
categories by China 

-0.121% c 

-0.193% 

< 83,100 

2.5% 

-0.294% c 

-0.469% 

< 201,8006 

5.7% 

a The share of Chinese imports covered by AD duties is calculated under the old methodology and are expected 
to  be  40%  lower  under  the  new  methodology.  The  percentage  stated  in  Table 1  is  therefore  rather  a  share  of 
dumped trade in total trade. 
b TDI sensitive sectors are sectors where TDI action is present (based on the 52 cases). 
c The percentages in Scenario 1 weigh all cases equally and are applied to the total employment in TDI sensitive 
sectors which was 231,000 (June 2015). The percentages in Scenarios 2 and 3 use a sector-specific employment 
elasticity  based  on  estimates  from  the  10  cases  and  then  constructs  a  weighted  average  effect  for  the  entire 
tradable  goods  sector  (agriculture,  mining,  manufacturing)  which  comprises  total  employment  of  43  million 
(November 2015).  

6 An  earlier  draft  of  this  report  mentioned  an  upper  bound  of  job  effects  of  211 000,  but  due  to  some 

changes in the methodology the effect has now been estimated to be somewhat smaller. 

8 

 
 
 
 
 
 
 
 
                                                 
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.7 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.8  

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 % 9 du  total  des  importations  de  Chine 10 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 terme11 de l’octroi du MES. Ils 
utilisent des méthodes d’agrégation distinctes pour extrapoler à partir de cas individuels afin 

7 Cette  réduction  moyenne  est  la  combinaison  de  droits  AD  réduits  dans  certains  dossiers  (où  l’enquête 
conclurait  encore  que  les  sociétés  chinoises  pratiquent  le  dumping)  et  de  la  suppression  des  droits  AD  dans 
d’autres cas.  

8 Ceci  est  basé  sur  les  moyennes  non  pondérées  obtenues  dans  10  dossiers  antidumping  utilisés  à  des  fins 
d’extrapolation. L’emploi total actuel dans les secteurs protégés par des TDI liés à la Chine est de 231 000 postes 
(juin 2015). 

9 Vandenbussche et Viegelahn (2011) montrent que la proportion de produits importés de Chine par l’UE et 

soumise à la protection antidumping de l’UE atteignait un niveau légèrement supérieur à 2 % en 2009. 

10 La méthodologie utilisée pour parvenir à 2,5 % est expliquée clairement dans les sections suivantes (p.11) 
et nous permet d’évaluer également les effets indirects sur l’emploi, pour lesquels nous devons nous tourner vers 
la base de données WIOD. 

11 Le court terme ne désigne pas une période d’un an, mais se réfère à la couverture AD des produits, que 

l’on peut considérer inchangée à court terme. 

9 

 
 
                                                 
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 

10 

 
 
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 

11 

 
 
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. 

12 

 
 
 
Tableau 2: Synthèse des Effets Possibles sur l’Emploi de l’Octroi par l’UE du MES à la 
Chine dans 3 Scénarios 

3 scénarios: 

Impact en pourcentage 

Estimations 
benchmark 
(CN8) 

Estimations 
produits 

Perte 
d’emplois 
(estimation 
supérieure) 

Importations 
chinoises 
couvertes par 
droits AD a (%) 

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 

-0.121% c 

-0.193% 

< 83,100 

2,5 % 

-0.294% c 

-0.469% 

< 201,80012 

5,7 % 

5.  Effets à court terme 
basés sur les 52 
dossiers existants sur 
la Chine 

6.  Effet potentiel à long 
terme avec dumping 
accru dans de 
nouvelles catégories 
de produits par la 
Chine 

a La part des importations chinoises couverte par des droits AD est calculée selon l’ancienne méthodologie et 
devrait  être  de  40 %  inférieure  sous  la  nouvelle  méthodologie.  Le  pourcentage  indiqué  dans  le  Tableau  1 
exprime donc plutôt une part des échanges qui font l’objet d’un dumping dans le total des échanges. 
b Les secteurs sensibles aux TDI sont des secteurs dans lesquels des mesures TDI sont appliquées (sur la base 
des 52 dossiers). 
c Les pourcentages dans le Scénario 1 accordent un poids égal à tous les dossiers et sont appliqués à l’emploi 
total  dans  des  secteurs  sensibles  aux  TDI,  qui  était  de  231 000  postes  (juin  2015).  Les  pourcentages  dans  les 
Scénarios 2 et 3 utilisent une élasticité de l’emploi spécifique aux secteurs, basée sur des estimations issues des 
10  dossiers,  puis  établit  un  effet  moyen  pondéré  pour  l’ensemble  du  secteur  des  biens  commercialisables 
(agriculture, activité minière, fabrication) qui comprend un total de 43 millions d’emplois (novembre 2015).  

12 Dans une version précédente, on avait un effet de 211 000 emplois, mais à cause de certains changements 

dans la méthodologie, l’effet as diminué un peu. 

13 

 
 
 
 
 
 
 
 
 
 
 
 
 
                                                 
 
1.  Introduction 

in 

Currently,  the  EU  Commission  is  analyzing  the  effects  of  changing  the  methodology  when 
investigations  regarding  China.  This  study  was 
calculating  dumping  margins 
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 
sector13 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. 

13 We focus on the tradable sector but cannot exclude that some activities in the upstream and downstream 

industry are non-tradables. 

14 

 
 
                                                 
 
In Scenario 1, we arrive at direct employment effects by extrapolating estimates based on 
10 AD-cases to all TDI-sensitive sectors.14 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. 

14 TDI stands for Trade Defense Instruments. 

15 

 
 
 
 
 
                                                 
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 

16 

 
 
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.15 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 

15 A CES model of trade predicts full pass-through of the marginal cost reduction to output price and thus 
the market expansion effect is likely to be large. A quadratic utility model of trade predicts pass-through of 50% 
whereby European producers do not fully pass on the cost savings to consumers, but instead allow their markups 
to rise (Vandenbussche et al., 2012). 

17 

 
 
                                                 
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.  

18 

 
 
 
 
 
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.16 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.  

16 44% if there are multiple cases in one HS4 product category. 

19 

 
 
                                                 
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).17 

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 

Parameter Estimations based on recent AD cases against China 

  Two complementary datasets allow for a range to be 

17 The sector classification of the ten cases cannot be linked to WIOD, Furthermore, these ten cases are a 

estimated throughout the study: 

limited snapshot in terms of sector coverage. 

Step 1: 

Lower bound 

CN-estimates 

20 

Upper bound 

Product-level 

Quantification and sectoral distribution of spread-out 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
                                                 
 
 
 
 
 
 
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.18 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. 

4.1  Benchmark 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.19 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.20 In our dataset we verified that the CN8 

18 Erkel-Rousse and Mirza (2002) discuss the importance of import-demand elasticities in trade. 
19 The elasticity estimates that we obtain are comparable to Kee et al. (2014) in most cases. 
20 The period spans from the year 2000 to 2014. 

21 

 
 
                                                 
codes  that  corresponds  to  the  AD-cases,  did  not  change  during  subsequent  classification 
revisions.21 

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 

The  estimated  coefficient   ,  provides  us  with  an  estimate  of  import-elasticity  in  every 

anti-dumping case:22 

(1) 

      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.23 

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 

21 From  Ramon,  EUROSTAT,  we  verified  all  subsequent  CN8  classification  changes  between  2000  and 

2014 to make sure product codes did not change. 

22 In the estimation we control for outliers in the price and quantity data and experimented with regressions 
in  levels  and  first  differences  where  the  latter  additionally  controls  for  measurement  error  resulting  in  lower 
estimates. 

23 These  estimates  are  at  the  CN8  product-level  (from  one  country  of  origin,  China)  and  typically  much 

lower than the (import)-demand elasticities at sector level (Broda and Weinstein, 2004). 

22 

 
 
                                                                                    
         
                
                                                 
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.24 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.  

Table 3: Tariffs and Prices of Chinese Importers (Benchmark)  

24 An example can illustrate this. Suppose the import price before protection is 100€ and that AC duty is 25% 

and MES duty 10%. Then the percentage tariff change between the two regimes is 15%. However, it would be 
wrong to say that import price under MES would be 15% lower than under AC. The correct way to think about 
this is that 15€/125€=12%. Thus, the difference paid in terms of tariffs needs to be divided by the gross price in 
the AC regime. This suggests that a 15% tariff reduction when comparing AC to MES, corresponds to a 12% 
reduction in gross prices. 

23 

 
 
         
              
             
    
       
            
                                                 
Case 

AD511  
(Persulfates) 
AD516  
(FeSi) 
AD519 
(Compressors) 
AD522  
(Citric Acid) 
AD528  
(Candles) 
AD560  
(Ceramic Tiles) 
AD584  
(O. Coated Steel) 
AD585  
(MTF) 
AD598  
(Solar Glass) 
AD614  
(Tartaric Acid) 
source 

Import-
demand 
elasticy 

(1) 
-0.616** 

-1.30** 

Tariff 
under 
analogue 
country 

Tariff 
under 
MES 

Δ tariff 

Δ 
p_gross 

(2) 
72% 

26% 

(3) 
21.8% 

(4) 
-50.2% 

(5) 
-29% 

15.6% 

-10.4% 

-8% 

10.3% 

Chinese 
Import 
Increase 
due to 
MES 
(6) 
18.2% 

-0.17** 

76.5% 

13.2% 

-63.4% 

-30% 

5% 

-0.76** 

36% 

6.6% 

-29.4% 

-21% 

16% 

-0.68*** 

12% 

4.0% 

8.1% 

7% 

4.8% 

-0.32*** 

31% 

8.0% 

-23% 

-17% 

5.4% 

-0.750*** 

40% 

0.0% 

-40.2% 

-29% 

21.3% 

-1.19*** 

43% 

16.1% 

-26.9% 

-19% 

22.7% 

-2.02*** 

37% 

0.0% 

-37% 

-27% 

54% 

-0.71*** 

11.3% 

0.0% 

-11.3% 

-10% 

7.1% 

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) 

Notes: The estimates in (1) are short-run elasticities and were obtained from 8-digit product level data from 
COMEXT. The regression results are reported in the Appendix. Significance levels: * for 10% level, ** at 5% 
and *** at 1% level. In (4) is the difference in AD-tariffs if China were granted market economy status (MES). 
In (6), the import multiplier gives the percentage change in Chinese imports resulting from a gross price change 
that corresponds fully to the tariff change. 

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 

24 

 
 
 
 
 
rest of the world.25 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:  

The  EU  employment  elasticity  can  then  be  obtained  as  the  estimated  coefficient  on 

Chinese imports as follows: 

(3) 

     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).26 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 

Table 4: Employment Elasticities of EU Jobs on Chinese Imports (Benchmark) 

25 Data on employment and EU production are not available at the CN8 product-level, hence we use the EU 

confidential data. 

26 Of  course  introducing  EU  production  in  the  analysis  potentially  introduces  endogeneity.  In  part,  we 

address this by using the first year lag for EU production.  

25 

 
 
               
                                                               
                                                              
         
                
                                                 
Case 

AD511 (Persulfates) 

AD516 (FeSi) 

AD519 Compressors) 

AD522 (Citric Acid) 

AD528 (Candles) 

AD560 (Ceramic Tiles) 

AD584 (O. Coated Steel) 

AD585 (MTF) 

AD598 (Solar Glass) 

AD614 (Tartaric Acid) 

Employment 
Elasticity,27 
ρ 
(1) 
-0.346* 

Chinese Import 
Increase due to 
MES regime 
(2) 
18.2% 

-0.346* 

-0.346* 

-0.346* 

-0.346* 

-0.346* 

-0.346* 

-0.346* 

-0.346* 

-0.346* 

10.3% 

5% 

16% 

4.8% 

5.4% 

21.3% 

22.7% 

54% 

7.1% 

Employment 
Decrease 

(3) 
-6.3% 

-3.6% 

-1.7% 

-5.5% 

-1.7% 

-1.9% 

-7.4% 

-7.9% 

-19% 

-2.5% 

Source 

EU data on the cases 

Table 3 col (6) 

(col.1) * (col.2) 

Note: Column (1) * significance at 10%-level. 

4.2 

Import-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 

27  When  we  use  “import  penetration=  imports_china/(all  imports+EU  production)”  as  a  regressor  in 
specification (3) instead of import-levels, we obtain an employment elasticity of -1.15, which is similar to the 
one reported by Acemoglu et al.(2014) of -1.30 for Chinese imports into the US. 

26 

 
 
 
 
                                                 
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). 

27 

 
 
 
 
Table 5: Tariffs and Prices (Product-level EU data) of Chinese Importers  

Case 

AD511 (Persulfates) 

AD516 (FeSi) 

AD519 (Compressors) 

AD522 (Citric Acid) 

AD528 (Candles) 

AD560 (Ceramic Tiles) 

AD584 (O. Coated Steel) 

AD585 (MTF) 

AD598 (Solar Glass) 

AD614 (Tartaric Acid) 

Import demand 
elasticity EU data 
(1) 
-1.38* 

-1.38* 

-1.38* 

-1.38* 

-1.38* 

-1.38* 

-1.38* 

-1.38* 

-1.38* 

-1.38* 

Δ p_gross 

(2) 
-29% 

-8% 

-36% 

-21% 

7% 

-17% 

-29% 

-19% 

-27% 

-10% 

Chinese Import 
Increase 
(3) 
40.12% 

11.36% 

49.55% 

29.43% 

9.86% 

23.88% 

39.43% 

26.29% 

36.90% 

14.01% 

Source: 

EU data 

EU data 

col.1 * col.2 

Note: We use the same values for the employment elasticity, ρ. The regression producing (1) was run on 

confidential EU data and is reported in the Appendix (A.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, respectively28.  

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. 

Table 6: Simulated job losses for EU in Chinese MES  (in percentages) 

28 The  weighting  is  based  on  employment  shares  of  each  anti-dumping  case  in  total.  We  show  rounded 

numbers. 

28 

 
 
 
 
                                                 
Case 

Broad Sector 

Investigation 
Period (IP) 
Employment 

Job Loss in % 
IP 
(benchmark 
CN8) 

Job Loss in % 
IP 
(product-level 
EU) 

(1) 
AD511 (Persulfates) 

(2) 
Chemicals 

(3) 
152 

AD516 (FeSi) 

Iron & Steel 

1,153 

AD519 (Compressors) 

Mechanical 
Eng. 

AD522 (Citric Acid) 

Chemicals 

193 

511 

AD528 (Candles) 

Other 

4,699 

AD560 (Ceramic Tiles) 

Ceramics 

77,458 

AD584 (Coated Steel) 

Iron & Steel 

6,046 

AD585 (MTF) 

Iron & Steel 

2,366 

AD598 (Solar Glass) 

Other 

AD614 (Tartaric Acid) 

Chemicals 

857 

179 

Unweighted Av. 

Weighted Average 

(4) 
-6.3% 

-3.6% 

-1.7% 

-5.5% 

-1.7% 

-1.9% 

-7.4% 

-7.9% 

-19% 

-2.5% 

-6.11% 

-2.7% 

(5) 
-13.9% 

-3.9% 

-17.1% 

-10.1% 

-3.4% 

-8.26% 

-13.46% 

-9.1% 

-12.7% 

-4.8% 

-9.72% 

-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”. 

Table 7: Step 1: Possible Direct Employment Effects in the 10 AD-cases (absolute numbers) 

STEP 1: Summary 

Unweighted estimates 

CN8 Estimates 

Product-level Estimates 

-14,000 

-22,000 

29 

 
 
 
 
 
 
 
 
 
 
 
 
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. 

30 

 
 
 
 
 
 
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.29 

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) 

29 In microeconomic analysis, the expression 'long-term' refers to the situation where fixed and quasi-fixed 
factors of production (such as capital stocks), but also firm strategies, production technology etc. are not fixed 
anymore, but can be adjusted. This corresponds with the meaning of 'long-term' in the context used here where 
long-term  here  is  used  to  distinguish  a  time  horizon  in  which  Chinese  firms  engage  increasingly  in  dumping 
behaviour as a strategic reaction towards the lower AD tariffs which takes time to unfold and take its full effect. 
This interpretation of 'long-term' should not be confused with macro-economic analysis, in which fluctuations 
have eased out and the economy has converged to an equilibrium at its so-called natural rate of unemployment. 
In a way, our concept of 'long-term'  may even be opposed to macro-economic interpretation, as we explicitly 
exclude  macroeconomic  adjustment  from  our  analysis be it  in  a  positive  or negative direction (cf.  section 7.5 
where we discuss macroeconomic multipliers). 

31 

 
 
 
                                                 
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.30 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 database31 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.32 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.33  

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 

30 Given  that  most  investigation  periods  span  two  years,  we  use  the  average  imports  for  the  two  years. 

When a case covers products in more than one 4-digit sector, we take imports from all 4-digit sectors involved. 

31 http://trade.ec.europa.eu/tdi/ 
32 AD duties in case AD528 expired on August 7, 2015 and in case AD 539 on June 16, 2015.  
33 Ideally we would like to use production shares as we are interested in the fraction of the EU workforce 

working in these sectors, but at this detailed level of analysis import shares is all that we have available. 

32 

 
 
                                                 
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.  

Table 8: Probabilities any Product will face AD action against China 

Probability assigned 
to 4-digit HS sector 
44% 

22% 

11% 

1% 

Scenario 2 (short-run) 

Scenario 3 (potential) 

Multiple cases of type 1 

Multiple cases of type 1 

A single case of type 1 

A single case of type 1 

- 

- 

No cases of type 1, but at least a single 
case of type 2 or type 3 

No actual cases (of any type), but at 
least one case in another 4-digit sector 

33 

 
 
 
 
 
 
0% 

All other  

All other 

within the same 2-digit industry  

5.2 Aggregate 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  concordances34 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.35 

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 

34 These are available at http://unstats.un.org/unsd/cr/registry. 
35  For  “Agriculture”  (Agriculture,  Hunting,  Forestry  and  Fishing)  we  use  employment  weights  in  the 
aggregation  analysis.  This  WIOD  sector  consists  of  three  sub-sectors  in  the  ISIC,  namely  “01:  Agriculture, 
hunting and related service activities”, “02: Forestry, logging and related service activities,” and “05: Fishing, 
aquaculture and service activities incidental to fishing.” Eurostat reports separate employment statistics for each 
of the three. This breakdown of employment by sub-sectors is not available for other WIOD sectors for which 
we use import weights. 

.  

34 

 
 
 
 
                                                 
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.  

35 

 
 
 
 
Table 9: Illustration how the Probabilities/shares are calculated for WIOD sector 24 

HS4 

3905 
3906 
3907 

4002 
7104 

HS4 

2503 
2707 
2708 
28** 
29** 
… 
3905 
3906 
3907 
… 
3913 
3914 
4002 
7104 

CPC4 

34 
35 

WIOD 

24 

EU Cases 

US Cases 

Share assigned 

- 
- 
AD468 (against China) 
+ several against other 
trading partners 

AD 1014 (against China) 
- 
- 

11% 
1% 
22% 

AD472 (expired case) 
- 

- 
- 

11% 
0% 

Probabilities 

Share in CPC 34 

Scenario 2 
0 
0 
0 
0.0198 
0.1157 
… 
0 
0 
0.22 
… 
0 
0 
0 
0 

Scenario 2 
0.0725 
0.0151 

Scenario 2 
0.0455 

Scenario 3 
0 
0.01 
0.01 
0.0532 
0.1356 
… 
0.11 
0.01 
0.22 
… 
0.11 
0.01 
0.11 
0 

0.1% 
2.4% 
0.02% 
14.6% 
54.1% 
… 
0.6% 
0.8% 
3.2% 
… 
0.3% 
0.1% 
2.5% 
0.1% 

Probabilities 

Share in WIOD 24 

Scenario 3 
0.0996 
0.0303 

Probabilities 

Scenario 3 
0.0670 

53.1% 
46.9% 

Share in all tradable 
goods sector 

8.5% 

Note: WIOD sector 24 is “Chemicals”. In the top two panels, we do not list all the HS 4-digit sectors that 

fall in this industry, only a few rows to illustrate the calculations.  

36 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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. 

Table 10: Likelihood a Product in the Sector has AD measures imposed against China 

SIC 
Code 

Industry (WIOD) 

Employment 
share 

01-05  Agriculture 
10-14  Mining 
Food products 
15-16 
Textiles 
17-18 
Leather & footwear 
19 
Wood products 
20 
Paper products 
21-22 
Oil products 
23 
Chemicals 
24 
Rubber & plastics 
25 
Non-metallic minerals 
26 
Basic & fabricated metals 
27-28 
Machinery 
29 
Electrical & optical equip. 
30-33 
34-35 
Transport equipment 
36-37  Manufacturing NEC 

21.3% 
1.9% 
11.3% 
4.2% 
1.1% 
2.4% 
3.4% 
0.5% 
4.8% 
3.6% 
2.9% 
10.9% 
7.4% 
6.6% 
9.5% 
8.2% 

Probability of AD action against China 

Scenario 2 
(short-run) 
0% 
0% 
0.6% 
0% 
0% 
6.3% 
1.3% 
0% 
4.5% 
2.1% 
7.3% 
4.7% 
3.3% 
1.0% 
2.8% 
0% 

Scenario 3 
(potential) 
0.2% 
0.9% 
3.6% 
1.4% 
5.8% 
8.0% 
5.2% 
6.9% 
6.7% 
8.5% 
10.2% 
8.3% 
7.2% 
3.7% 
4.0% 
5.7% 

Note:  The  10  cases  analyzed  in  step  1  all  fall  in  industries  24,  26,  27-28,  or  29.  The  probabilities  are 

weighted using total EU imports from the 4-digit HS sectors to the WIOD sectors. 

37 

 
 
 
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.36 
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.37 

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% 

36 In these calculations we did not use the statistics from Table 11 as the detailed measures were obtained by 
aggregating  using  the  EU’s  import  shares  from  the  entire  world—as  a  stand-in  for  production  shares.  We 
recalculated all those numbers as well using EU import shares from China. 

37 Naturally,  this  adjustment  depends  crucially  on  the  decision  to  weigh  or  not.  If  we  used  a  weighted 
average there would be hardly any adjustment as AD measures are still imposed in Ceramic Tiles which is by 
far the largest of the 10 cases considered. As the 30 percentage points counterfactual reduction in AD duties is 
also obtained as an unweighted average, we also used the 3/10 ratio here.  

38 

 
 
                                                 
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. 

Table 11: Aggregate Incidence of dumping or AD measures 

(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 

EU imports from China subject to AD measures 

(b) Under the new methodology (MES) 

EU production with AD action against China 
EU imports from China subject to AD measures 

1.6% 

2.5% 

1.1% 
1.7% 

4.3% 

5.7% 

3.0% 
3.9% 

Note: The aggregate share indicated at the bottom, over all WIOD sectors, is obtained using output weights 

in the final stage of the aggregation. 

39 

 
 
 
 
 
 
 
 
 
 
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 1.38 

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 

38 Naturally, when using the average elasticity of 0.31 and applying a 30 percentage points lower tariff we 
obtain  an  employment  reduction  of  9.3%,  very  close  to  the  result  in  Scenario  1.  It  is  not  exactly  the  same  as 
taking the product of two averages is different from taking the average of a product. 

40 

 
 
                            
     
                 
 
                                                 
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. 

Figure 1: Estimated employment elasticity by Chinese import shares 

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.39 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. 

39 The analysis in Scott and Jiang (2015) assumes all Chinese market share gains are at the expense of local 

EU producers.  

41 

0.1.2.3.40.1.2.3.4.5Chinese import shareBenchmark estimates.2.3.4.50.1.2.3.4.5Chinese import shareProduct-level estimatesEstimated employment elasticity by Chinese import shares 
 
 
 
                                                 
Figure 2: Estimated employment elasticity by “Rest of the World” import shares 

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.  

Table 12: Regression Results to fit an Adjusted, Predicted Employment Elasticity 

Dependent variable: 

Benchmark estimates 

Product-level estimates 

Constant 

China IP 

ROW IP 

Adj-R2 
Observations 

Cases excluded 

0.262** 
(0.129) 

0.123 
(0.441) 

-0.546 
(0.420) 

0.29 
9 
FeSi 

0.325*** 
(0.035) 

0.207* 
(0.119) 

-0.213* 
(0.114) 

0.29 
9 
FeSi 

Note: In both regressions we exclude case AD516 (FeSi) as the production of the domestic industry only 
accounts for 20% of demand.  Including it would lead to highly extreme and implausible point estimates on the 
import  penetration  variables.    As  the  objective  is  only  to  obtain  a  sensible  sectoral  adjustment  to  the  average 
elasticity, not to establish a stable relationship, we excluded this outlier. 

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 

42 

0.2.4.60.2.4.6ROW import shareBenchmark estimates.2.3.4.50.2.4.6ROW import shareProduct-level estimatesEstimated employment elasticity by 'Rest of the World' import shares 
 
 
     
                 
 
 
 
 
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. 

Table 13: Predicted Employment Elasticities for all WIOD sectors 

SIC 
Code 

Industry (WIOD) 

Import penetration 

China 

ROW 

Predicted employment semi-
elasticity 
Benchmark 
estimates 

Product-level 
estimates 

Agriculture 

01-05 
10-14  Mining 
15-16 

Food products 

17-18 
19 
20 

21-22 
23 

24 
25 
26 

Textiles 
Leather & footwear 
Wood products 

Paper products 
Oil products 

Chemicals 
Rubber & plastics 
Non-metallic minerals 

27-28 
29 

Basic-fabricated metals 
Machinery 

Electrical & optical eq. 
30-33 
34-35 
Transport equipment 
36-37  Manufacturing NEC 

0.5% 
0.2% 
0.6% 

17.1% 
15.6% 
1.6% 

0.3% 
0.2% 

2.3% 
4.4% 
1.9% 

1.8% 
4.2% 

16.4% 
1.8% 
3.3% 

9.9% 
64.5% 
8.1% 

23.7% 
20.8% 
5.3% 

5.3% 
16.8% 

21.1% 
7.3% 
4.2% 

9.4% 
10.7% 

16.5% 
13.0% 
12.2% 

0.21 
0.08 
0.22 

0.15 
0.17 
0.23 

0.23 
0.17 

0.15 
0.23 
0.24 

0.21 
0.21 

0.19 
0.19 
0.20 

0.30 
0.19 
0.31 

0.31 
0.31 
0.32 

0.31 
0.29 

0.29 
0.32 
0.32 

0.31 
0.31 

0.32 
0.30 
0.31 

43 

 
 
 
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. 40  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).  

Table 14: Aggregate Potential Employment Effects by Sector of granting MES to China 

SIC Code 

Industry (WIOD) 

01-05 
10-14 
15-16 
17-18 
19 
20 
21-22 
23 
24 
25 
26 
27-28 
29 
30-33 
34-35 
36-37 

Agriculture 
Mining 
Food products 
Textiles 
Leather & footwear 
Wood products 
Paper products 
Oil products 
Chemicals 
Rubber & plastics 
Non-metallic minerals 
Basic & fabricated metals 
Machinery 
Electrical & optical eq. 
Transport equipment 
Manufacturing NEC 

Predicted employment effects using the 
benchmark estimates 
Scenario 2 
(short-run) 
0% 
0% 
-0.037% 
0% 
-0.001% 
-0.443% 
-0.089% 
0% 
-0.205% 
-0.143% 
-0.532% 
-0.301% 
-0.208% 
-0.055% 
-0.163% 
0% 

Scenario 3 
(potential) 
-0.012% 
-0.013% 
-0.235% 
-0.065% 
-0.269% 
-0.561% 
-0.362% 
-0.353% 
-0.301% 
-0.582% 
-0.742% 
-0.529% 
-0.448% 
-0.216% 
-0.230% 
-0.341% 

40 We assume a constant output to employment ratio in the short run, which implies that employment and 

output effects will be equal in percentage terms.  

44 

 
 
    
               
      
 
               
                                  
  
 
                                                 
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).41  

Table  15:  Aggregate  Direct  Potential  Employment  Effects  over  the  entire  Tradable  Goods 
Sector 

(a)  In percentage terms 

Benchmark (CN8) estimates 

Product-level estimates 

(b) In number of jobs 

Scenario 2 
(short-run) 
-0.116% 

-0.171% 

Scenario 3 
(potential) 
-0.274% 

-0.409% 

Scenario 2 

Scenario 3 

Benchmark (CN8) estimates 

Product-level estimates 

-49,700 

-73,600 

-117,800 

-175,600 

Note:  is  based  on  detailed  employment  statistics  that  imply  aggregate  employment  in  the  tradable  goods 

sector of 43 mio workers in the EU28 (2014) 

41 http://ec.europa.eu/eurostat/web/structural-business-statistics  

45 

 
 
 
 
 
                                                 
7.  STEP 4: Adding indirect effects 

7.1  The 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: 

Figure 3: Fictional example of input flows in industry affected by AD 

Rubber 

(upstream) 

Tires 

Automobiles 

(directly affected by AD) 

(downstream) 

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.  

46 

 
 
 
 
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. 42  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 

42 In the direct analysis we already calculated that EU firms lost market share in directly affected sectors, 
which means that for them the loss of market share to Chinese producers outweighed the increase in sales due to 
price reductions. 

47 

 
 
                                                 
their  own  employment. 43  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. 

7.2  How 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) 

43 This is a hypothetical example, as the car industry is unlikely to produce its own tires. 

48 

 
 
                                                 
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. 

Table 16: Average Upstream-ness or Downstream-ness of Sectors with AD duties against China 

Average for EU sectors weighted 
by probability of Chinese dumping 

Scenario 2 

Scenario 3 

Average over all 
EU tradable 
sectors 

(a) Excluding trade from the calculation 
A: Inputs purchased from other 
sectors / total output 

B: Output used in other sectors 
/ total output 

A / B ratio  
(higher = more downstream) 

71.3% 

61.2% 

1.30 

75.6% 

71.8% 

69.9% 

65.6% 

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 

Note:  In  panel  (a)  we  exclude  EU  exports  of  output  and  EU  imports  of  inputs  when  we  calculate  the 
quantities  A  and  B.  In  the  calculations  in  panel  (b)  we  include  all  production  (exported  or  consumed 
domestically) and inputs (imported or sourced domestically). 

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 

49 

 
 
 
 
 
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.  

7.3  Magnitude 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. 

Table 17: Potential Upstream Indirect Employment Effects by Sector of granting MES to China  

(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 

50 

 
 
 
10-14  Mining 

15-16  Food products 

17-18  Textiles 

-0.00184 

-0.00001 

-0.00032 

-0.00000 

-0.00025 

-0.00000 

Leather & footwear 

-0.00035 

-0.00001 

-0.00167 

-0.00001 

-0.00085 

-0.00001 

-0.00063 

-0.00001 

-0.00084 

-0.00002 

Wood products 

21-22  Paper products 

Oil products 

Chemicals 

19 

20 

23 

24 

25 

26 

Rubber & plastics 

-0.00128 

-0.00001 

Non-metallic minerals 

-0.00110 

-0.00000 

27-28  Basic & fabricated metals 

-0.00205 

-0.00002 

29 

Machinery 

-0.00069 

-0.00001 

30-33  Electrical & optical eq. 

-0.00051 

-0.00001 

34-35  Transport equipment 

-0.00045 

-0.00001 

36-37  Manufacturing NEC 

-0.00070 

-0.00001 

0.00002 

0.00000 

0.00001 

0.00003 

0.00001 

0.00002 

0,00000 

0.00004 

0.00003 

0.00001 

0.00003 

0.00011 

0.00006 

0.00003 

0.00001 

-0.00183 

-0.00032 

-0.00025 

-0.00032 

-0.00167 

-0.00084 

-0.00064 

-0.00082 

-0.00126 

-0.00109 

-0.00204 

-0.00059 

-0.00046 

-0.00043 

-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. 44 In Table 18 we show the 
aggregate effects in all four possible cases. 

Table 18: Potential Upstream Indirect Employment Effects for both Scenarios and both set of 
Estimates 

(a)  In percentage terms 

Benchmark (CN8) estimates 

Product-level estimates 

(b) In number of jobs 

Scenario 2 
(short-run) 
-0.036% 

-0.052% 

Scenario 3 
(potential) 
-0.086% 

-0.127% 

Scenario 2 

Scenario 3 

Benchmark (CN8) estimates 

-15,300 

-36,900 

44 Note  that  there  are  also  negative  upstream  effects  on  the  non-tradables  sector,  which  aggregates  all 
service sectors, construction, and utilities. We did not add those effects in Table 18 as the total effect at -0.00016 
is as low as in the least affected tradable goods sector. 

51 

 
 
 
 
 
 
                                                 
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. 

7.4  Magnitude 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  (Row)  that 
measures  the  change  in  prices  for  all  products  sold  in  the  EU  market  in  directly  affected 
sectors,  both  domestically  produced  and  imported.  For  each  sector  i  we  calculate  a  column 
vector (Columni) that measures where its inputs come from, again for both EU-produced and 
imported inputs. 

Matrix multiplying the Row and Columni vectors gives the average input price reduction 

for EU sector i:  

  ̅̅̅̅̅                . 
If downstream industries passed input price changes perfectly on to consumers, the upstream 
price  changes  triggered  by  lowered  AD  duties  would  show  up  directly  into  the  final  goods 
price downstream. Alternatively, the downstream industry might keep a fraction of the price 
  ̅̅̅̅̅ with y<1. To err 
decrease as profits and only reduce prices partially, i.e.    
on the side of caution, we assumed a pass-through rate of one half (y = 0.5). Using the same 
local approximation to output change used before, the downstream impact on sector i is: 

52 

 
 
 
    
             
  ̅̅̅̅̅ 

In  order  to  gauge  the  importance  of  the  different  assumptions  we  need  to  make,  we 
calculated the downstream industry effects under several scenarios. Given the relatively low 
share of imported inputs, changing the pass-through rate y had only a relatively small impact 
on the total effect and we fixed it to one half throughout in the calculations we show. 

53 

 
 
             
 
 
Table  19:  Potential  Downstream  Indirect  Employment  Effects  by  Sector  of  granting  MES  to 
China 

(for Scenario 3 and using a pass-through rate of 0.5 downstream) 

SIC Code  Industry (WIOD) 

No price-matching 
upstream 

Partial price-matching 
upstream 
(                       

01-05 

10-14 

15-16 

17-18 

19 

20 

Agriculture 

Mining 

Food products 

Textiles 

Leather & footwear 

Wood products 

21-22 

Paper products 

23 

24 

25 

26 

Oil products 

Chemicals 

Rubber & plastics 

Non-metallic minerals 

27-28 

Basic & fabricated metals 

29 

30-33 

34-35 

36-37 

Machinery 

Electrical & optical eq. 

Transport equipment 

Manufacturing NEC 

0.00005 

0.00002 

0.00007 

0.00007 

0.00002 

0.00003 

0.00004 

0.00006 

0.00021 

0.00012 

0.00005 

0.00035 

0.00035 

0.00078 

0.00043 

0.00008 

0.00041 

0.00010 

0.00081 

0.00020 

0.00008 

0.00024 

0.00049 

0.00070 

0.00129 

0.00058 

0.00036 

0.00275 

0.00176 

0.00174 

0.00233 

0.00049 

However,  the  assumption  on  price  matching  in  the  directly  affected  sectors  was  more 
important  and  in  Table  19  we  show  results  using  two  alternative  scenarios.  A  first, 
conservative assumption is no price matching at all in directly affected sectors (x=0). It leads 
to  rather  small  effects.  A  second  assumption  of  x=0.1  is  more  optimistic,  but  still  only 
assumes that EU or RoW firms match only one tenth of the price decline of Chinese imports. 
Even with this very small price matching behavior, the effects in the last column of 18 are on 
average more than 5 times larger. This reflects the fact that EU-sourced inputs are an order of 
magnitude more important than inputs imported from China. 

The detailed results for the long-run, potential probabilities (Scenario 3) are in T9 for two 
price-matching scenarios. In Table20 we report aggregate indirect effects downstream in all 

54 

 
 
 
four possible cases.45 When we calculate aggregate effects and add the indirect effects to the 
previously obtained direct effects and the upstream effects, we use the average of the results 
obtained under either assumption, as reported in the last column of Table 20.  

Table  20:  Potential  Downstream  Indirect  Employment  Effects  for  both  Assumptions  on  Price 
Matching 

(a)  In percentage terms 

No price-matching 
Partial price-matching 
Average downstream effect 

(b) In number of jobs 

Scenario 2 
(short-run) 
0.009% 
0.052% 
0.030% 

Scenario 3 
(potential) 
0.019% 
0.115% 
0.066% 

Scenario 2 

Scenario 3 

No price-matching 

Partial price-matching 
Average downstream effect 

3,700 

22,500 
13,100 

8,600 

48,200 
28,400 

7.5  Other effects: macroeconomic multipliers  

Indirect effects 

The negative upstream effects imply that a reduction in AD duties in one sector lowers the 
input  demand  for  suppliers  to  that  sector  and  has  additional  negative  effects  on  EU 
employment (upstream). The positive downstream effects imply that a reduction in AD duties 
in one sector, will lower the input cost of that sector’s clients which will (partially) make its 
way into prices, boosting demand and raising EU employment (downstream).  

Either  of  these  effects  could  dominate  and  the  net  effect  can  therefore  be  positive  or 
negative. The upstream effects depend on the magnitude of the direct employment effect and 
hence  on  the  set  of  estimates  used.  The  downstream  effects  only  depend  on  the  price 
adjustments in the directly affected sector, which depend only on the competitive responses, 
but  not  on  the  estimates  for  the  direct  effects. We  find  that  by  using  the  benchmark  (CN8) 
estimates,  which  were  smaller  in  absolute  magnitude,  the  positive  downstream  effects 
dominate, but the negative upstream effects dominate when using the product-level estimates.  

45 As for the upstream effects, we ignore the impact of lower tradable goods on the non-tradeables sector. 
Given the low share of goods inputs, the positive effect is again as low as for the least affected sectors in Table 
19. 

55 

 
 
 
 
 
 
                                                 
The “type I” multiplier effects in Scott and Jiang (2015) correspond to our upstream effects. 
The  pricing  effects  that  boost  EU  employment  downstream  might  be  included  in  those 
calculations  as  well  or  they  might  be  omitted.  What  Scott  and  Jiang  (2015)  call  “type  II” 
multiplier  effects  ,  or  respending  effects,  are  quite  different  in  nature  and  we  discuss  them 
next. 

Macroeconomic multipliers 

A final aspect to consider is to what extent the job losses calculated above will have general 
equilibrium  effects  in  the  rest  of  the  economy.  Especially  in  the  context  of  optimal  fiscal 
policy  during  recessions,  there  is  a  fierce  debate  in  the  economic  literature  as  to  the 
magnitude  of  the  appropriate  economic  multiplier  on  government  expenditures.  Ilzetzki, 
Mendoza  and  Végh  (2013)  provide  an  up-to-date  overview  to  the  recent  debate  in  the 
literature.  They  also  illustrate  that  the  exact  magnitude  of  the  (fiscal)  multiplier  is  highly 
sensitive  to countries’ particular situation,  e.g. indebtedness, exchange rate regime, level of 
development and openness.  

Ilzetzki et al. (2013) cite two extreme views from the policy debate, which ranges from 
Robert  Barro’s  assertion  that  the  peacetime  fiscal  multiplier  is  basically  zero,  to  Christina 
Romer who used a multiplier as high as 1.6 when evaluating the likely effect of President’s 
Obama  stimulus  package.  One  can  even  find  more  extreme  estimates  in  the  sprawling 
academic literature. A multiplier below 1 implies that money taken from the public (as taxes 
or borrowed) reduces GDP more than the subsequently addition to GDP when that money is 
spent by the government. The distortionary effects of the policy leave GDP lower that would 
be accomplished without the policy. A multiplier above 1 would suggest that the government 
can  create  additional  aggregate  demand  by  taxing  or  borrowing  money  and  spending  the 
proceeds. 

One  recent  entry  in  this  debate  is  Sims  and  Wolff  (2013)  who  show  that  if  the  central 
bank follows a Taylor rule the output multiplier is relatively constant over the business cycle 
and  about  0.9.  They  also  show,  however,  that  the  multiplier  is  higher  when  the  nominal 
interest rate is  pegged, which is  for example the case at  the zero lower  bound. They find a 
range  of  0.7  to  1.8,  which  includes  1,  but  does  not  rule  out  additional  effects  to  an  initial 
shock. 

Gechert, Hughes Hallett, Rannenberg (2015) conduct a meta-analysis and find a range of 
estimates. The most relevant is the multiplier associated with general public spending which 
is  around  0.75  when  economic  circumstances  are  average  or  above  average.  It  is  estimated 
higher, at approximately 1.4 during recessions. The average of 0.97 across the three regimes 
suggests  that  a  value  of  unity  would  not  be  a  bad  guide  to  formulate  policy  that  will  be  in 
force irrespective of the business cycle. 

The  magnitude  of  the  multiplier  is  an  ongoing  debate  in  macroeconomics  with  no 
consensus  in the literature. Moreover, it has  generally (or ‘been’) to  applied to  government 
spending  and  the  existing  evidence  is  unlikely  to  generalize  directly  to  changes  in  private 
economic activity. It matters in our context as we need to consider to what extent displaced 
workers  are  likely  to  quickly  find  new  jobs  or  to  what  extent  the  reduction  in  employment 

56 

 
 
will  lower  aggregate  demand  in  the  economy  and  lead  to  further  job  losses  as  displaced 
workers reduce their consumption. If the first case dominates, the job losses calculated before 
are overestimated. If the second effect dominates, the results are underestimates.  

We have little to add to this debate as it goes beyond trade policy. As both effects work in 
opposite  direction,  they  will  cancel  out  to  some  extent.  We  feel  the  appropriate  course  of 
action  is  to  not  adjust  our  calculations  to  reflect  these  concerns.  As  these  effects  are 
macroeconomic in nature, they will affect all displaced workers similarly and they can simply 
be  adjusted  by  applying  a  multiplier  on  the  final  estimates.  We  see  no  reason  to  use  a 
multiplier  different  from  unity,  but  if  one  is  convinced  that  a  macroeconomic  multiplier 
above one is more appropriate—implying that for every job lost due to a change in the AD 
policy there will be some additional job destruction due to general equilibrium effects—one 
can  straightforwardly  multiply  all  the  estimated  effects  by  this  multiplier.  We  feel  such 
calculations  are  ad-hoc  and,  most  importantly,  are  considerations  that  are  entirely 
independent from the trade and market equilibrium effects we have concentrated on. 

57 

 
 
 
 
8.  Aggregate Effects in Three Scenarios 

Finally,  we  summarize  the  aggregate  effects  that  we  obtained  under  different  modelling 
assumptions in a comparative table. In the three sub-sections we show the results for the three 
different  Scenarios.  In  each  case  we  use  two  sets  of  results,  once  using  the  benchmark 
estimates  from step 1, which estimates the elasticities at  the CN8 level, and once using the 
product-level  estimates  which  estimate  the  elasticities  using  the  more  detailed  product  data 
from the cases, but which allows for less heterogeneity in the model. 

8.1 

Scenario 1: Simple Extrapolation from the 10 cases 

In this scenario we simply extrapolate directly the results from the 10 AD cases to a broader 
group of sectors. We applied the estimated average percentage response to the 231,000 total 
employment in “TDI sensitive” sectors. We thus obtain an estimated aggregate employment 
effect  on  these  particular  sectors.  Note  that  these  estimates  are  limited  to  a  subset  of  the 
economy and that indirect effects are not yet taken into account. 

Table 21: Summary of Potential Employment Effects in Scenario 1 

(only direct effects) 

Benchmark (CN8) estimates 

Product-level estimates 

Unweighted 
Weighted 

In percentage 

In jobs 

In percentage 

In jobs 

-6.0% 
-2.7% 

-14,000 
-6,000 

-9.7% 
-8.5% 

-22,000 
-19,000 

Note: Percentage employment reduction applied to 231,000 employees in “TDI sensitive” sectors. 

58 

 
 
 
 
 
 
 
 
8.2 

Scenario  2:  Short-run  effect  based  on  cases  against  China  currently  in 
force 

In Scenario 2 that we develop below, we now consider all the ongoing EU TDI cases against 
China  (52  cases  in  total).  Based  on  the  products  involved  in  all  these  cases,  we  construct 
“best  estimates”  of  the  likelihood  that  any  particular  product  from  China  is  subject  to  AD 
duties.  Thus  scenario  2  matches  the  current  state  of  affairs  in  terms  of  existing  product 
coverage  of  dumped  imports  from  China.  This  allows  us  to  construct  an  estimate  of  the 
counterfactual  employment  effects  in  the  MES  regime,  if  the  current  range  of  cases  and 
products  were  to  remain  unchanged,  but  if  the  assigned  duties  would  now  be  determined 
when giving China MES. 

 
 

 

In 2009, 2% of Chinese imports into the EU was subject to AD measures 
In this scenario, 2.5% of Chinese imports into the EU will be subject to AD duties 
(under the old methodology) 
In  this  scenario,  1.6%  of  domestic  EU  production  is  in  sectors  where  Chinese 
imports are subject to AD duties. 

Estimated aggregate effects on employment are: 

Table 22: Summary of Potential Employment Effects in Scenario 2 

Benchmark (CN8) estimates 

Product-level estimates 

In percentage 

In jobs 

In percentage 

In jobs 

Direct effect 
Indirect effects upstream 
Indirect effects downstream 

Total effect 

-0.116% 
-0.036% 
 +0.030% 

-0.121% 

-49,700 
 -15,300 
+13,100 

-51,900 

-0.171% 
-0.052% 
 +0.031% 

-0.193% 

-73,600 
-22,600 
+13,100 

-83,100 

Note:  is  based  on  detailed  employment  statistics  that  imply  aggregate  employment  in  the  tradable  goods 

sector of 43 mio workers in the EU28 (2014). 

59 

 
 
 
 
 
 
 
 
 
8.3 

Scenario 3: Potential long-run scenario including hypothetical future cases 

This  scenario  differs  from  Scenario  2  as  it  uses  different  probabilities  in  step  2  on  the 
likelihood that any particular product is affected by Chinese dumping. The probabilities used 
now reflect a possible future expansion of dumping by Chinese firms, even though in some of 
these cases the new (MES) adjudication methodology will not lead to the imposition of AD 
duties anymore.  

Two points of reference to assess the scope of this scenario are as follows: 

 
 

 

In 2009, 2% of Chinese imports into the EU was subject to AD measures 
In  this  scenario,  5.7%  of  Chinese  imports  into  the  EU  will  be  subject  to  AD 
measures under the old methodology 
In  this  scenario,  3.4%  of  domestic  EU  production  is  in  sectors  where  Chinese 
imports are subject to AD duties 

Estimated aggregate effects on employment are: 

Table 23: Summary of Potential Employment Effects in Scenario 3 

Benchmark (CN8) estimates 

Product-level estimates 

In percentage 

In jobs 

In percentage 

In jobs 

Direct effect 
Indirect effects upstream 
Indirect effects downstream 

Total effect 

-0.274% 
-0.086% 
+0.066% 

-0.294% 

-117,800 
-36,900 
+28,400 

-126,300 

-0.409% 
-0.127% 
+0.066% 

-0.469% 

-175,600 
 -54,600 
 +28,400 

-201,800 

Note:  The  percentage  employment  reduction  is  applied  to  detailed  employment  statistics  that  imply 

aggregate employment in the tradable goods sector of 43 mio workers in the EU28 (2014). 

60 

 
 
 
 
 
 
 
9.  Member State – Sector Job Effects of MES for China 

9.1. Methodology for Member State-Sector Elasticities 

In this section we perform a breakdown of aggregate EU job losses from MES for China in 
TDI cases, by EU member state (MS) and by sector. Throughout the section we describe job 
effects  that  would  result  from  a  reduction  in  tariffs  of  around  -30%,  which  is  the  tariff 
decrease  that  can  be  expected  when  moving  from  current  duty  calculation  towards 
counterfactual duties in the case of MES for China. 

Below  we  explain  the  methodology  we  performed  to  obtain  sectoral  elasticities.  The  next 
section holds all the employment results obtained with these elasticities. 

As  described  earlier,  we  calculate  sectoral  elasticities  using  the  regression  coefficients 
obtained  by  fitting  an  equation  on  the  results  from  the  case-level  analysis  –  which  was 
inherently an EU-level analysis.   

Extending the step 1 results of individual cases to all sectors at the EU-level, we could use a 
sectoral elasticity for the EU as a whole and use the following equation to impute the relevant 
elasticity for sector s: 

  ̅̅̅̅̅̅̅̅ 
  ̅̅̅̅̅̅̅                

Now, for the calculation of the member state-sector import elasticities below, we adjusted the 
equation as follows:  

  ̅̅̅̅̅̅̅          [      

  ̅̅̅̅̅̅̅]  

  ̅̅̅̅̅̅̅̅       

[(      

)   (      

̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅)] 

The  three  coefficients    ,      and      are  estimated  based  on  the  10  cases  analyzed  in 
before  and  the  results  from  the  regressions  are  reported  there.  We  obtained  2  sets  of 
coefficients, one using the CN8 estimates and one using the product-level estimates, and we 
will work with two sets of elasticities throughout. We use the coefficient estimates obtained 
at  the  EU-level  to  construct  an  elasticity  that  varies  by  member  state  by  exploiting  the 
member state-specific variation in import penetration ratios.  

,       

 and        

 are  the  member  state-specific  import  penetration  ratios  from  the 
three respective destinations: China, Rest-of-the-World, and Rest-of-the-EU. At the EU-level, 
the  last  ratio  is  by  construction  non-existent,  but  at  the  member  state-level  it  does  drive  a 
wedge between the size of the domestic industry and the imports from outside the EU.  

  ̅̅̅̅̅̅̅̅ are the sectoral import penetration ratios for the EU as a whole, from China 
  ̅̅̅̅̅̅̅ and       
and the Rest-of-the-World respectively. These are the only variables one would need to use 

61 

 
 
                              
                                
          
            
 
 
           
 
             
  
      
 
 
 
      
̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅ 
when  working  at  the  EU-level.    The  final  variable  entering  the  equation  is       
which  is  the  employment  weighted  average  of  the  sum  of  the  national  import  penetration 
ratios  from  RW  and  REU.  All  the  import  penetration  ratios  vary  at  the  sector  level,  with 
sectors defined at the level of WIOD. 

To  understand  the  logic  of  the  equation  for  the  member  state-sector  elasticities,  one  only 
needs to realize that the (employment) weighted average of both terms in square brackets are 
zero when averaged over all EU member states.  As a result, the average member state-sector 
elasticities (over  countries)  will equal  the  sectoral  elasticity.    However, the  elasticities  vary 
across countries to the extent that the terms in the square brackets are positive or negative. I.e. 
they differ to the extent that countries differ from the EU-wide import penetration. 

For  the  import  penetration  from  China  this  is  straightforward.  The  EU-average  IP  simply 
drops out and we could use the member state-specific IP directly. However, the adjustment is 
important  for  the  adjustment  to  the  Rest-of-the-World  IP.  At  the  EU  level,  domestic  sales 
with imports from China and RW aggregate to the entire market. At the member state-level, 
imports  from  the  rest  of  the  EU  are  yet  an  alternative  way  to  satisfy  demand,  but  also  an 
alternative  channel  where  increased  imports  from  China  does  not  hurt  the  domestic  sector, 
but instead lowers imports. By using the member state-specific deviation in square brackets 
we are able to use the     coefficient that we necessarily had to estimate at the EU-level to 
make the adjustment. Given that the import penetration ratios are much larger and vary much 
more when adding REU to RW, we only use one half of the variation in the adjustment. 

In the remainder of section 9, we describe the results in terms of job effects arising from the 
member  state-sector  elasticities  explained  above.  We  first  discuss  the  benchmark  (CN8) 
estimates of a breakdown of employment effects of MES in TDI cases against China, by EU 
member state and sector. Afterwards, we also discuss the product-level estimates by member 
state and sector.  

62 

 
 
             
  
 
 
9.2. Job Effects by Member State and Sector  

Below we discuss results on job effects of MES for China, for individual EU countries and 
individual sectors. We consider short-run (scenario 2) and long-run (scenario 3) effects which 
we define as follows. 

In the short-run scenario 2, we take into account all the Chinese products currently subject 
to  TDI  (52  cases)  (November  2015)  and  based  on  these  products,  we  determine  the 
probability  that  each  sector  will  be  subject  to  TDI  in  the  nearby  future.  Thus,  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.  

In  the  long-run  scenario  3,  we  consider  the  possibility  that  the  MES  regime  may  have 
additional long-run effects also affecting new products, not subject to current EU TDIs thus 
far. As 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  incorporates 
what is products that are likely to be affected in the future. For this purpose, we use US AD 
cases against China and EU AD cases against other countries than China to inform us on the 
sectoral  distribution  of  these  potential  new  cases  and  products.  In  Scenario  3,  we  thus 
additionally consider the possibility that in the future, AD enforcement could spread to more 
products (a hypothetical long-run scenario which can be regarded as the worst case scenario). 

We  develop  short-run  and  long-run  job  effects  under  two  alternative  sets  of  product 
definitions  which  differ  from  each  other  in  terms  of  aggregation:    i)  we  consider  publicly 
available 8-digit product codes (CN8) from Eurostat (COMEXT data), which we refer to as 
our  “Benchmark  CN8  estimates”  and  ii)  we  consider  more  detailed  Product-level 
definitions made available to us by the EU commission and resulted from confidential data 
where products are typically more narrowly defined than in the publicly available data. 

In  our  discussion  below,  we  each  time  first  discuss  the  short-run  (scenario  2)  and 
subsequently  the  long-run  (scenario  3)  job  effects.  In  each  of  the  Tables  we  give  separate 
results for the “direct effects”, and “indirect effects” and “total effects”. We further split the 
“indirect  effects”  into  “indirect  upstream”  and  “indirect  downstream”  job  effects.  As 
anticipated,  “direct  employment  effects”  and  “indirect  upstream”  employment  effects  are 
negative, while “indirect downstream” employment effects are positive. Together, they result 
in  total  negative  job  losses  of  MES  for  China.  This  holds  for  every  individual  EU  member 
state,  and  for  every  EU  sector  and  for  every  scenario.  Overall,  “direct  effects”  on 
employment tends to be the largest component in the total. 

In  general,  job  effects  are  more  negative  under  the  long-run  scenario  than  in  the  short-run 
which  is  plausible.  Also,  job  effects  tend  to  be  more  negative  when  using  Product-level 
estimates rather than CN8 estimates. 

63 

 
 
We start by discussing Benchmark estimates (CN8) where we first show a breakdown of job 
losses by individual EU member state and afterwards a breakdown of job losses by individual 
EU sectors.46 

9.3. Benchmark Estimates (CN8) 
Member State Level Job Effects  

The  total  aggregate  EU  employment  loss  based  on  CN8  short-run  estimates  lies  around  -
52 000 jobs. When we break this number down by individual EU member state, like we do in 
Table 23 below, it can be noted that especially Germany would suffer job losses, followed by 
Italy, France and Poland. It can also be noted from Table 24 that while all countries would 
suffer losses, the impact is clearly differential across countries. Larger countries tend to carry 
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. 
Results however also  suggest that sectoral composition of TDI cases may disproportionally 
affect some countries but that by no means job losses are  limited to one or a few individual 
countries. With the exception of Germany, one observation that arises is that the higher per 
capita  GDP  of  a  MS,  the  bigger  the  indirect  (and  positive)  downstream  job  effects  and  the 
lower the overall job loss. 

The  total  aggregate  EU  employment  loss  based  on  CN8  long-run  estimates  lies  around  -
126 000  jobs.  Also,  in  the  long-run,  especially  Germany  and  Italy  would  be  negatively 
affected, followed by France and Poland. The impact on other EU countries is smaller. 

46 Small divergences in numbers with the Final Report can occur due to rounding issues. 

64 

 
 
 
 
 
 
                                                 
Table 24: Short-run (Scenario 2) Job Effects by Member state using CN8 estimates 

Direct 
Effects 

Indirect 
upstream 
Effects 

Indirect 
downstream 
Effects 

Total effect 

Austria 
Belgium 
Bulgaria 
Cyprus 
Czech Republic 
Germany 
Denmark 
Spain 
Estonia 
Finland 
France 
United Kingdom 
Greece 
Hungary 
Ireland 
Italy 
Lithuania 
Luxemburg 
Latvia 
Malta 
Netherlands 
Poland 
Portugal 
Romania 
Slovakia 
Slovenia 
Sweden 
Total 

-928 
-588 
-629 
-36 
-2,082 
-12,864 
-358 
-3,474 
-129 
-591 
-4,903 
-3,752 
-427 
-909 
-258 
-7,312 
-202 
-12 
-189 
-17 
-736 
-4,446 
-948 
-2,053 
-675 
-247 
-905 
-49,668 

-331 
-240 
-239 
-15 
-632 
-3,158 
-137 
-957 
-56 
-165 
-1,403 
-1,220 
-224 
-362 
-87 
-1,878 
-95 
-7 
-67 
-8 
-326 
-1,739 
-313 
-1,063 
-252 
-113 
-253 
-15,339 

270 
215 
161 
9 
587 
3,415 
119 
799 
33 
128 
1,246 
1,088 
125 
348 
74 
1,549 
48 
5 
31 
7 
261 
1,148 
215 
678 
234 
92 
219 
13,104 

-989 
-612 
-708 
-41 
-2,126 
-12,607 
-375 
-3,632 
-152 
-628 
-5,060 
-3,884 
-526 
-923 
-272 
-7,640 
-248 
-14 
-226 
-18 
-801 
-5,036 
-1,047 
-2,438 
-693 
-268 
-938 
-51,903 

65 

 
 
 
  
 
 
 
Table 25:Long-run (Scenario 3) Job Effects by Member state using CN8 estimates 

Austria 
Belgium 
Bulgaria 
Cyprus 
Czech Republic 
Germany 
Denmark 
Spain 
Estonia 
Finland 
France 
United Kingdom 
Greece 
Hungary 
Ireland 
Italy 
Lithuania 
Luxemburg 
Latvia 
Malta 
Netherlands 
Poland 
Portugal 
Romania 
Slovakia 
Slovenia 
Sweden 

Total 

Direct 
Effects 

Indirect 
upstream 
Effects 

Indirect 
downstream 
Effects 

-2,065 
-1,266 
-1,758 
-90 
-4,442 
-28,368 
-809 
-8,488 
-283 
-1,362 
-11,824 
-9,482 
-1,196 
-2,303 
-607 
-17,779 
-563 
-25 
-366 
-54 
-2,144 
-11,125 
-2,439 
-5,072 
-1,465 
-575 
-1,844 

-763 
-540 
-639 
-38 
-1,374 
-6,882 
-324 
-2,384 
-130 
-392 
-3,328 
-2,861 
-681 
-862 
-237 
-4,313 
-255 
-17 
-169 
-20 
-782 
-4,609 
-784 
-3,158 
-565 
-264 
-561 

582 
460 
372 
21 
1,236 
7,262 
261 
1,738 
74 
283 
2,704 
2,352 
295 
775 
174 
3,324 
114 
11 
72 
16 
568 
2,538 
471 
1,550 
497 
198 
460 

Total effect 
-2,246 
-1,346 
-2,025 
-108 
-4,579 
-27,988 
-872 
-9,134 
-339 
-1,471 
-12,448 
-9,991 
-1,582 
-2,390 
-670 
-18,768 
-704 
-31 
-464 
-57 
-2,357 
-13,197 
-2,752 
-6,680 
-1,533 
-642 
-1,944 

-117,791 

-36,933 

28,407 

-126,317 

66 

 
 
  
 
 
 
Sector Level Job Effects 

Below we discuss job losses of MES for China for each individual EU sector based on CN8 
estimates.  

Based on Table 26, we conclude that in the short-run, most job losses would be suffered by 
the  “Basic  and  fabricated  metals”  sector,  followed  by  “non-metallic  minerals”,  “wood 
products” , “Transport equipment” and “Chemicals”. Incidentally, these sectors are typically 
the  ones  in  which  most  Antidumping  activity  takes  place  and  therefore  do  not  come  as  a 
surprise as sectors where most of the employment action is going on.  

Based on Table 27, we conclude that in the long-run, most job losses would occur in sectors 
such as “Basic Metals”, “non-metallic Minerals” which is similar to the short-run, but now 
also sectors like ”Machinery”, “Rubber and Plastics” and even “Agriculture” would suffer 
net job losses in the long-run scenario.47   

The  occurrence  of  “Agriculture”  warrants  a  deeper  look  at  this  sector  whose  full-length 
description is “Agriculture, Hunting, Forestry and Fishing”. Most TDI action is coming from 
the sub-sector “Fishing & Aquaculture”, with anti-dumping cases in this sector on products 
such  as  farmed  salmon,  trout  and  shrimp  but  with  relatively  little  EU  employment  in  the 
“Fishing  &  Aquaculture”  sub-sector.  The  other  sub-categories  in  this  sector  involving 
products of Agriculture, Hunting and Forestry occur far less in TDIs. 

47 Sector 36 is shown in Table 27 to suffer higher job losses than some of those mentioned in this paragraph. 
However,  since  the  sector  is  a  diverse  residual  aggregate  rather  than  a  clearly  defined  sector  we  chose  not  to 
highlight it. 

67 

 
 
 
 
 
 
                                                 
Table 26: Short-Run (Scenario 2) Job Effects by Sector using CN8 Estimates 

Indirect 
upstream 
Effects 

Indirect 
downstream 
Effects 

Sector 
Agriculture 
Mining 
Food products 
Textiles 
Leather & footwear 
Wood products 
Paper products 
Oil products 
Chemicals 
Rubber & plastics 
Non-metallic minerals 
Basic & fabricated 
metals 
Machinery 
Electrical & optical 
equip. 
Transport equipment 
Manufacturing NEC 

1-5 
10-14 
15-16 
17-18 
19 
20 
21-22 
23 
24 
25 
26 

27-28 

29 

30-33 

34-35 
36 

Total 

Direct 
Effects 
0 
0 
-1,803 
0 
-3 
-4,475 
-1,306 
0 
-4,235 
-2,234 
-6,695 

-2,170 
-391 
-310 
-113 
-16 
-914 
-376 
-34 
-773 
-757 
-846 

-14,114 

-5,027 

-6,610 

-1,555 

-6,637 
0 

-896 

-525 

-1,138 
-1,052 

Total effect 
-1,364 
-370 
-1,455 
-53 
-16 
-5,297 
-1,546 
-20 
-4,183 
-2,739 
-7,391 

-15,288 

-5,917 

-865 

-4,822 
-577 

806 
21 
658 
60 
4 
92 
136 
14 
825 
252 
150 

3,853 

1,589 

1,215 

2,953 
475 

   -49,668 

-15,339 

13,104 

-51,903 

Notes: (1) For “Agriculture” (Agriculture, Hunting, Forestry and Fishing) we use employment weights in the 
aggregation  analysis.  This  WIOD  sector  consists  of  three  sub-sectors  in  the  ISIC,  namely  “01:  Agriculture, 
hunting and related service activities”, “02: Forestry, logging and related service activities,” and “05: Fishing, 
aquaculture and service activities incidental to fishing.” Eurostat reports separate employment statistics for each 
of the three. This breakdown of employment by sub-sectors is not available for other WIOD sectors for which 
we use import weights. 

68 

 
 
  
 
 
 
 
Table 27: Long-Run (Scenario 3) Job Effects by Sector using CN8 Estimates 

Sector 
Agriculture 

1-5 
10-14  Mining 
15-16  Food products 
17-18  Textiles 

Leather & footwear 

19 
20  Wood products 
21-22  Paper products 

23 

24 

25 

26 

Oil products 

Chemicals 

Rubber & plastics 

Non-metallic minerals 

27-28  Basic & fabricated metals 

29  Machinery 

30-33  Electrical & optical equip. 
34-35  Transport equipment 
36  Manufacturing NEC 

Direct 
Effects 

-1,089 

-107 

-11,357 

-1,177 

-1,282 

-5,671 

-5,282 

-686 

-6,242 

-9,087 

-9,349 

-24,776 

-14,225 

-6,112 

-9,367 

-11,983 

Indirect 
upstream 
Effects 

Indirect 
downstream 
Effects 

-8,214 

-1,493 

-1,544 

-461 

-154 

-1,686 

-1,234 

-125 

-1,696 

-1,975 

-1,375 

-9,589 

-1,882 

-1,317 

-1,737 

-2,452 

2,106 

47 

2,140 

243 

22 

137 

389 

73 

1,561 

549 

259 

7,279 

3,376 

3,591 

5,636 

999 

Total effect 
-7,197 

-1,553 

-10,762 

-1,395 

-1,413 

-7,220 

-6,127 

-737 

-6,377 

-10,512 

-10,465 

-27,085 

-12,731 

-3,838 

-5,468 

-13,436 

Total 

-117,791 

-36,933 

28,407 

-126,317 

9.4. Product-Level Estimates 

In this section we discuss job effects based on confidential product-level data which is more 
disaggregate than CN8 level data. We start by discussing member state-level job effects and 
afterwards sector-level job effects.  

Member State Level Job Effects 

The short-run job loss for the EU as a whole when using product-level estimates amounts to 
around – 83 000 jobs lost. A breakdown by individual EU member state predicts the same as 
before e.g. countries like Germany and Italy would suffer most in terms of job losses of MES 
for  China,  followed  by  France  and  Poland.  Losses  are  smaller  for  other  individual  EU 
countries. This holds both in the short-run, shown in Table 28 below as well as in the long-
run, shown in Table 29 below. But overall, long-run job losses would be larger, as expected. 

69 

 
 
  
  
 
 
 
 
 
Table 28: Short-run (Scenario 2) Job Effects by Member state using Product-level Estimates 

Austria 
Belgium 
Bulgaria 
Cyprus 
Czech Republic 
Germany 
Denmark 
Spain 
Estonia 
Finland 
France 
United Kingdom 
Greece 
Hungary 
Ireland 
Italy 
Lithuania 
Luxemburg 
Latvia 
Malta 
Netherlands 
Poland 
Portugal 
Romania 
Slovakia 
Slovenia 
Sweden 
Total 

Direct 
Effects 

Indirect 
upstream 
Effects 

Indirect 
downstream 
Effects 

-1,552 
-1,273 
-939 
-52 
-3,254 
-18,622 
-708 
-4,728 
-217 
-817 
-7,111 
-6,034 
-604 
-1,684 
-424 
-9,701 
-328 
-29 
-275 
-35 
-1,385 
-6,597 
-1,363 
-2,900 
-1,207 
-483 
-1,322 
-73,644 

-485 
-357 
-348 
-21 
-933 
-4,712 
-202 
-1,407 
-80 
-241 
-2,077 
-1,807 
-321 
-535 
-130 
-2,762 
-135 
-11 
-96 
-12 
-480 
-2,518 
-454 
-1,523 
-371 
-166 
-371 
-22,556 

270 
216 
161 
9 
588 
3,418 
119 
800 
33 
128 
1,247 
1,089 
125 
348 
74 
1,550 
48 
5 
31 
7 
261 
1,150 
215 
678 
234 
92 
219 
13,115 

Total effect 
-1,768 
-1,415 
-1,126 
-65 
-3,599 
-19,916 
-792 
-5,335 
-264 
-930 
-7,940 
-6,752 
-800 
-1,871 
-479 
-10,912 
-415 
-35 
-339 
-40 
-1,604 
-7,965 
-1,602 
-3,745 
-1,344 
-557 
-1,474 
-83,084 

70 

 
 
  
 
 
 
 
Table 29: Long-run (Scenario 3) Job Effects by Member state using Product-level Estimates 

Austria 
Belgium 
Bulgaria 
Cyprus 
Czech Rep. 
Germany 
Denmark 
Spain 
Estonia 
Finland 
France 
United Kingdom 
Greece 
Hungary 
Ireland 
Italy 
Lithuania 
Luxemburg 
Latvia 
Malta 
Netherlands 
Poland 
Portugal 
Romania 
Slovakia 
Slovenia 
Sweden 
Total 

Direct 
Effects 

Indirect 
upstream 
Effects 

Indirect 
downstream 
Effects 

-3,491 
-2,797 
-2,641 
-138 
-7,074 
-41,663 
-1,642 
-11,529 
-508 
-1,901 
-17,033 
-15,226 
-1,682 
-4,184 
-1,037 
-23,459 
-908 
-67 
-577 
-109 
-3,913 
-16,481 
-3,574 
-7,375 
-2,701 
-1,081 
-2,806 
-175,597 

-1,124 
-806 
-947 
-56 
-2,045 
-10,291 
-481 
-3,509 
-191 
-577 
-4,925 
-4,272 
-981 
-1,279 
-350 
-6,389 
-369 
-25 
-245 
-30 
-1,155 
-6,769 
-1,155 
-4,578 
-841 
-390 
-829 
-54,608 

582 
460 
372 
21 
1,236 
7,262 
261 
1,738 
74 
283 
2,704 
2,352 
295 
775 
174 
3,324 
114 
11 
72 
16 
568 
2,538 
471 
1,550 
497 
198 
460 
28,407 

Total effect 
-4,033 
-3,143 
-3,215 
-173 
-7,883 
-44,692 
-1,862 
-13,299 
-625 
-2,195 
-19,254 
-17,146 
-2,368 
-4,689 
-1,214 
-26,523 
-1,163 
-81 
-751 
-123 
-4,500 
-20,712 
-4,258 
-10,403 
-3,044 
-1,273 
-3,175 
-201,797 

Sector Level Job Effects 

When we consider job effects obtained with product-level estimates at sector-level, the same 
conclusions arise as  the ones  obtained with  CN8 estimates. From Table 30 below it can  be 
noted  that  sectors  that  would  endure  most  employment  losses  in  the  short-run  would  be 

71 

 
 
  
 
 
 
 
 
 
“Basic  and  fabricated  Metals”,  “non-metallic  Minerals”,  “Transport  Equipment”  and 
“Machinery” and to a lesser extent also “Wood products” and “Chemicals”. 

From  Table  31,  where  we  show  long-run  results,  also  “Rubber  and  Plastics”  and 
“Agriculture” are sectors that would additionally lose jobs from MES for China.  

Table 30: Short-run (Scenario 2) Job Effects by Sector using Product-level Estimates 

Sector 
Agriculture 

1-5 
10-14  Mining 
15-16 
17-18 
19 
20 
21-22 
23 
24 
25 
26 
27-28 
29 
30-33 
34-35 
36 

Food products 
Textiles 
Leather & footwear 
Wood products 
Paper products 
Oil products 
Chemicals 
Rubber & plastics 
Non-metallic minerals 
Basic & fabricated metals 
Machinery 
Electrical & optical equip. 
Transport equipment 
Manufacturing NEC 

Total 

Direct 
Effects 
0 
0 
-2,552 
0 
-5 
-6,039 
-1,758 
0 
-8,051 
-3,128 
-8,871 
-20,458 
-9,841 
-2,616 
-10,324 
0 

-73,644 

Indirect 
upstream 
Effects 

Indirect 
downstream 
Effects 

-3,013 
-551 
-465 
-171 
-24 
-1,251 
-538 
-55 
-1,298 
-1,144 
-1,167 
-7,401 
-1,348 
-826 
-1,770 
-1,534 

806 
21 
658 
60 
4 
92 
136 
14 
827 
253 
150 
3,853 
1,589 
1,215 
2,957 
478 

Total effect 
-2,207 
-530 
-2,358 
-111 
-26 
-7,198 
-2,160 
-41 
-8,522 
-4,018 
-9,887 
-24,007 
-9,599 
-2,227 
-9,137 
-1,055 

-22,556 

13,115 

-83,084 

Table 31: long-run (Scenario 3) Job Effects by Sector using Product-level Estimates 

Sector 

Agriculture 

1-5 
10-14  Mining 

Indirect 
upstream 
Effects 

-11,566 

-2,360 

Indirect 
downstream 

Effects  Total effect 

2,106 

-11,051 

47 

-2,716 

Direct 
Effects 

-1,591 

-402 

72 

 
 
 
  
  
 
 
 
 
 
 
  
-16,073 

-2,246 

2,140 

-16,179 

Food products 

15-16 
17-18  Textiles 

19 

20 

Leather & footwear 

Wood products 

21-22 

Paper products 

23 

24 

25 

26 

Oil products 

Chemicals 

Rubber & plastics 

Non-metallic minerals 

-2,375 

-1,934 

-7,653 

-7,111 

-1,163 

-11,866 

-12,725 

-12,387 

-768 

-274 

-2,381 

-1,739 

-205 

-2,751 

-2,953 

-1,920 

27-28  Basic & fabricated metals 

-35,912 

-14,179 

29 

Machinery 

30-33  Electrical & optical equip. 
34-35  Transport equipment 
Manufacturing NEC 

36 

-21,178 

-10,282 

-14,570 

-18,374 

-2,842 

-2,118 

-2,692 

-3,616 

243 

22 

137 

389 

73 

1,561 

549 

259 

7,279 

3,376 

3,591 

5,636 

999 

-2,900 

-2,186 

-9,898 

-8,460 

-1,295 

-13,056 

-15,129 

-14,048 

-42,811 

-20,644 

-8,809 

-11,626 

-20,991 

Total 

-175,597 

-54,608 

28,407 

-201,797 

73 

 
 
  
 
 
 
 
 
9.5. Summary of Country-Sector Effects 

In  this  section  9  we  pursue  a  breakdown  of  counterfactual  EU  employment  effects  at 
individual EU member state level as well as at sector-level, if MES for China were granted in 
European TDI cases on Chinese imports. 

We consider results for two sets of product-definitions and under both a short-run and long-
run scenario. Here we mainly summarize the upper-bound results at individual member state-
level and at sector-level, respectively. 

For the short-run, the upper bound total EU job loss lies around -83 000 jobs48 that would be 
lost when moving to MES for China.  

When  breaking  this  number  down  by  member  state,  the  biggest  job  losses  would  occur  for 
Germany and Italy. 

For the long-run, the upper bound total EU job loss lies around – 202 000 jobs losses from 
moving to MES for China, again featuring Germany and Italy as suffering most of the losses 
in absolute levels. 

Other EU countries suffer smaller losses and the impact is clearly differential across countries. 
Larger  countries  tend  to  carry  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,  short-run  job  losses  are  strongest  in  “Basic  &  fabricated 
Metals”, in “non-metallic Minerals” and in “Machinery”. 

The  chemical  sector,  which  has  seen  many  antidumping  cases  in  the  past,  perhaps 
surprisingly seems relatively less affected by MES for China than other sectors. 

In the long-run, additional job losses can also be expected to be high in “Rubber & Plastics” 
and “Food Products”. 

48 Differences with numbers in final report are due to rounding issues. 

74 

 
 
 
 
 
 
 
                                                 
10.  Measures to Maintain Effectiveness of Anti-Dumping  

10.1. Types of Measures 

In this Section 10, we discuss measures to maintain the effectiveness of Anti-dumping. Thus, 
we  compare  job  effects  reported  in Section  8  earlier to  what  they  would  be  in  the event  of 
applying  measures  aimed  at  maintaining  the  effectiveness  in  anti-dumping  cases  against 
China .  

In  the  Table  32  below  we  report  for  the  10  cases  given  to  us  by  the  EU  Commission  both 
actual  duties  imposed  in  these  cases  (col  1)  as  well  as  hypothetical  duties  under  the  MES 
regime  without  additional  measures  (col  2).  The  last  two  columns  report  duties  under  the 
counterfactual  scenario  that  prices  in  China  are  not  used  for  the  calculation  of  the  EU 
antidumping  duty  against  China  but  instead  a  technique  of  “benchmark  cost  from  a  third 
country  plus  a  reasonable  profit  margin”  is  used  to  determine  normal  values  and  hence 
dumping margins and duties. This practice enters into force whenever the EU decides that the 
normal  value  of  imports  cannot  be  based  on  domestic  prices  and  costs  in  the  exporting 
country  because  they  are  distorted.    Column  3  shows  duty  levels  in  this  counterfactual 
scenario where the lesser-duty rule (LDR) is applied, while column 4 goes one step further 
and reports counterfactual duty levels with the LDR removed. In the counterfactual scenario, 
the application of the LDR makes a difference and usually implies that duty rates are lower, 
as we can see from column (3).  

In the analysis in section 8, we have compared the effects on jobs of moving in Table 32 from 
column  1  duties  to  column  2  duties  e.g.  by  comparing  the  original  duty  rates  based  on 
analogue  country  prices  that  apply  in  EU  cases  against  China  with  duty  rates  that  would 
apply in case of MES. This resulted in an average duty decrease of around -30% which can 
be seen from bottom Table 33. 

Currently in this section 10, we investigate to what extent the benchmark country technique 
and the LDR could alter duty rates in EU antidumping cases against China. We consider i) 
the  calculation  of  normal  value  and  dumping  margin  on  the  basis  of  the  benchmark  costs 
instead  of  exporters  prices  or  costs  and  under  the  continued  application  of  the  “LDR” 
(column  3  Table  32);  ii)  the  benchmark  cost  technique  but  without  the  “LDR”  (Column  4 
Table 32). This results in two “counterfactual Measures” which we describe below.  

75 

 
 
 
 
Table 32: Original and Counterfactual Duty Rates Towards China 

Duty 
Original 

Duty MES 

Duty with 
Benchmark 
cost + UI 
profit) 

Duty: benchmark cost 
+ UI profit 

Persulfates 

Fe Si 

Compressors 

Citric Acid 

Candles 

Ceramic Tiles 

OCS 

MTF 

Solar Glass 

Tartaric Acid 

LDR 
(1) 

72.00% 

26.00% 

76.50% 

36.00% 

12.10% 

31.00% 

40.20% 

43.00% 

37.00% 

11.30% 

LDR 
(2) 

21.80% 

15.60% 

13.20% 

6.60% 

4.00% 

8.00% 

0.00% 

16.10% 

0.00% 

0.00% 

LDR 
(3) 

72.00% 

26.00% 

76.50% 

36.00% 

12.10% 

18.50% 

40.20% 

16.90% 

37.00% 

11.30% 

No LDR 
(4) 

76.10% 

29.00% 

76.50% 

58.00% 

27.10% 

18.50% 

51.90% 

16.90% 

71.70% 

37.50% 

(1)  This is the original duty for China without MES 
(2)  This is the duty for China with MES but without additional measures 
(3)  This is duty for China MES with adjusted costs and prices and with the LDR still in place 
(4)  This is duty for China MES with adjusted costs and prices but with the LDR removed 

Measures 1: Whenever the EU decides that costs and prices in a trade partner are distorted, it 
calculates  normal  value  and  dumping  margin  on  the  basis  of  “benchmark  costs  plus  a 
reasonable profit margin”. In this calculation the Union Industry (UI)’s profit margin is added 
to  the  costs  of  production  incurred  in  the  benchmark  country  for  the  product  under 
consideration. The EU is one of the few countries that applies the LDR in antidumping (AD) 
cases. This involves that the antidumping duty is set at the smallest of the dumping margin or 
injury margin. It will equal the injury margin whenever this is lower than the dumping margin. 
As a result, duties in col (3) are lower than those in col (4). 

Measures 2: We also consider the case where costs and prices in a trade partner are distorted 
and  a  “benchmark  technique”  is  applied  but  where  the  EU  would  additionally  remove  the 
LDR.    In  this  scenario  (No  LDR),  the  antidumping  duty  is  set  at  the  level  of  the  dumping 
margin and is typically higher than in the presence of LDR (col 4 Table 32).  

In Table 33 below, we now compare the duty levels under  status quo with the duties in the 
various  counterfactual  Measures  scenarios.  We  start  by  comparing  the  original  AD  duty  to 
the MES scenario (col 1 and col 2 of Table 32), which would result in an average drop of the 

76 

 
 
  
 
  
 
 
AD tariff of -29.99%, shown at the bottom of Table 33. Next, we compare the original duties 
to  the  Measures  1 scenario with the duty under  “benchmark cost +UI profit” and applying 
the LDR rule (comparing col 1 and col 3 of Table 32). In that case, we find the average duty 
drops by about -3.86% compared to the status quo.  

And finally, we compare the original duties with the counterfactual scenario of “benchmark 
cost + UI profit” but without applying the LDR rule (comparing col 1 to col 4 in Table 32). 
Under  Measures  2,  the  average  counterfactual  duty  would  be  7.81%  higher  than  under 
original duties which can be seen from the last column of Table 33. The correct interpretation 
is that average tariffs would be 7.81% higher under Measures 2 compared to status quo, as 
reported in Column (3) of Table 33. As we will see below, this will result in the prediction 
that EU jobs are additionally saved on top of those that are already protected by current TDI. 

Table 33: Factors to Maintain Effectiveness of Duties against China 

Duty MES - 
Duty Original 

Measures 1 

Measures 2 

Case Name 

Persulfates 
FeSI 
Compressors 
Citric Acid 
Candles 
Ceramic tiles 
OCS 
MTF 
Solar glass 
Tartaric acid 

-50.2% 
-10.4% 
-63.4% 
-29.4% 
-8.1% 
-23.0% 
-40.2% 
-26.9% 
-37.0% 
-11.3% 

Tariff Change 

-29.99% 

(a)  We compare the original duties with MES duties 
(b)  We compare original duties with Measures 1 duties 
(c)  We compare the original duties with Measures 2 duties 

0.0% 
0.0% 
0.0% 
0.0% 
0.0% 
-12.5% 
0.0% 
-26.1% 
0.0% 
0.0% 

-3.86% 

4.1% 
3.0% 
0.0% 
22.0% 
15.0% 
-12.5% 
11.7% 
-26.10% 
34.7% 
26.2% 

7.81% 

10.2. Job Effects With Different Types of Measures 

The short-run job effects with MES without benchmark technique were listed in Table 22 in 
section  8.2.  while  those  for  the  long-run  job  effects  were  listed  in  Table  23  in  section  8.3. 
Below  we  re-draft  these  tables  under  each  of  the  Measures  scenarios  when  applying  the 
benchmark technique with and without applying LDR. Based on the average duty changes in 
scenarios Measures 1 and Measures 2 compared to the status quo, we now calculate the job 
effects  under  the  short-run  scenario  (Scenario  2)  as  well  as  job  effects  under  the  long-run 
scenario (Scenario 3).  

77 

 
 
 
  
  
  
  
 
In the short-run scenario 2, we take into account all the Chinese products currently subject 
to  TDI  (52  cases)  (November  2015)  and  based  on  these  products,  we  determine  the 
probability  that  each  sector  will  be  subject  to  TDI  in  the  nearby  future.  Thus,  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.  

In  the  long-run  scenario  3,  we  consider  the  possibility  that  the  MES  regime  may  have 
additional long-run effects also affecting new products, not subject to current EU TDIs thus 
far. As 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  incorporates 
what is products that are likely to be affected in the future. For this purpose, we use US AD 
cases against China and EU AD cases against other countries than China to inform us on the 
sectoral  distribution  of  these  potential  new  cases  and  products.  In  Scenario  3,  we  thus 
additionally consider the possibility that in the future, AD enforcement could spread to more 
products (a hypothetical long-run scenario which can be regarded as the worst case scenario). 

We  develop  short-run  and  long-run  job  effects  under  two  sets  of  product  definitions  which 
differ  from  each  other  in  terms  of  aggregation  i)  we  consider  publicly  available  8-digit 
product codes (CN8) from Eurostat (COMEXT data), which we refer to as our “benchmark 
CN8  estimates”; ii) we consider more detailed  product-level definitions made available to 
us by the EU Commission and resulting from confidential data where products are typically 
more narrowly defined than in the publicly available data. 

We  would  like  to  point  out  that  the  direct  effects  of  measures  for  a  particular  sector  and 
member  state  are  exactly  proportional  to  the  change  in  AD,  in  case  one  would  like  to 
evaluate  measures  that  maintain  the  effectiveness  of  AD.  Indirect  employment  effects  are 
almost proportional.49  

10.3. Measures Type 1: 
Short-run (Scenario 2) 

In Table 34 below, we re-do the Table 22 of Section 8.2. in which job effects are listed (direct, 
indirect up-and downstream effects and total job effects). But instead, we now use an average 
tariff reduction that applies in case of Measures 1 e.g. an average reduction of  -3.86%.  By 
comparing  the  original Table  22  under  MES  with  Table  34  under  Measures  1  scenario,  we 
see that job effects are much smaller in Measures 1 than under MES. Where the upper-bound 
total effect in the original report by moving to MES was -83 100 jobs lost, the short-run job 
loss  in  scenario  2  under  Measures  1  compared  to  the  status  quo  of  the  original  duties  is  -
10 300 jobs as shown below. 

49 As  the  employment  elasticities  and  input-output  weights  differ  by  sector  and  countries  have  different 
sector-weights, the indirect effects are not exactly proportional with tariffs, not at the country-sector level (but 
close to it). 

78 

 
 
 
                                                 
Table 34: Re-doing Table 22 of Section 8.2. ( for Measures 1): Summary of Employment Effects 
in Scenario 2 

Benchmark (CN8) estimates 

Product-level estimates 

In percentage 

In jobs 

In percentage 

In jobs 

Direct effect 

-0.015% 

Indirect effects upstream 

-0.005% 

-6,400 

-2,000 

-0.022% 

-0.007% 

-9,500 

-2,900 

Indirect effects 
downstream 

Total effect 

+0.005% 

+2,100 

+0.005% 

+2,100 

-0.015% 

-6,300 

-0.024% 

-10,300 

79 

 
 
 
  
  
 
Long-run (Scenario 3) 

In Table 35 below, we re-do Table 23 for the long-run job effects but now with an average 
tariff reduction of  -3.86%. By comparing the original Table 23 to Table 35, we clearly see 
that  job  effects  are  much  smaller  under  Measures  1  in  Table  35.  Where  the  total  effect  in 
Table 23 of section 8.3. was -201 800 jobs lost as a result of moving from status quo to MES, 
the long-run job losses under Measures 1 compared to status quo amount to -25 100 jobs lost, 
which can be seen below. 

Table 35: Re-doing Table 23 of Section 8.3. (for Measures 1): Summary of Employment Effects 
in Scenario 3 

Benchmark (CN8) estimates 

Product-level estimates 

In percentage 

In jobs 

In percentage 

In jobs 

-0.035% 

-0.011% 

-15,200 

-4,700 

-0.053% 

-0.016% 

-22,600 

-7,000 

+0.011% 

+4,600 

+0.011% 

+4,600 

Direct effect 

Indirect effects upstream 
Indirect effects 
downstream 

Total effect 

-0.036% 

-15,300 

-0.058% 

-25,100 

80 

 
 
 
  
  
 
 
 
 
10.4. Measures Type 2: 
Short-run (Scenario 2) 

The  Measures  2  scenario  results  in  more  jobs  being  saved  than  under  the  status  quo  of  the 
original duty. These jobs saved under Measures 2 were not protected under the current TDI 
legislation and therefore lost, but would be saved in case of removing the LDR. The fact that 
average tariffs would be 7.81% higher under Measures 2 compared to status quo, as reported 
in Column (3) of Table 33, results in EU jobs that are saved in addition to the jobs that are 
already protected by current TDI.  

In Table 36 below, we now re-do Table 22 of Section 8.2. but with an average tariff increase 
compared  to  status  quo  of  7.81%.  In  the  original  Table  23,  moving  from  status  quo  to  the 
MES resulted in -83 000 jobs lost. When now comparing the status quo with the Measures 2 
in Table 36, we see that the removal of LDR saves an additional 20 200 EU jobs compared to 
the jobs protected in the original duty scenario.  

Table 36: Re-doing Table 22 of Section 8.2. (for Measures 2): Summary of Employment Effects 
in Scenario 2 

Direct effect 

Indirect effects upstream 
Indirect effects 
downstream 
Total effect 

Benchmark (CN8) estimates 
In percentage 

In jobs 

Product-level estimates 
In jobs 

In percentage 

0.030% 

0.009% 

+12,900 

+4,000 

+0.045% 

+0.014% 

+19,200 

+5,900 

-0.011% 

-4,800 

-0.011% 

-4,800 

 0.028% 

+12,100 

+0.047% 

+20,200 

Long-run (Scenario 3) 

In  Table  37,  we  re-do  Table  23  for  the  long-run  job  effects  but  now  with  an  average  tariff 
increase  of  7.81%  compared  to  status  quo,  this  results  in  different  estimates  than  in  the 
original Table 23 where we compared MES to status quo. By now comparing the status quo 
with Measures 2 duties, where we remove the LDR, we clearly see below that job effects are 

81 

 
 
 
  
  
 
 
 
 
 
 
 
 
 
positive. The removal of the LDR in combination with measures taken under the Measures 1 
scenario (Measures 2) saves an extra 49 500 jobs that would otherwise be lost in the face of 
Chinese import competition under status quo TDI duties which can be seen below. 

Table 37: Re-doing Table 23 of Section 8.3. (for Measures 2): Summary of Employment Effects 
in Scenario 3 

Direct effect 

Indirect effects upstream 
Indirect effects 
downstream 
Total effect 

Benchmark (CN8) estimates 

Product-level estimates 

In percentage 

In jobs 

In percentage 

In jobs 

+0.071% 

+0.022% 

+30,700 

+9,600 

+0.106% 

+0.033% 

+45,700 

+14,200 

-0.024% 

-10,400 

-0.024% 

-10,400 

+0.069% 

+29,800 

+0.115% 

+49,500 

When  interpreting  the  "Measures  type  2"  results,  the  reader  should  keep  in  mind  that  our 
methodology  is  tailored  to  only  study  the  sectors  directly  and  indirectly  affected,  not  the 
economy  as  a  whole.  The  resulting  job  increases  may  therefore  not  be  misinterpreted  as  a 
reduction  of  unemployment  at  the  macro-level  that  can  be  achieved  by  increasing  import 
tariffs.  

82 

 
 
 
  
  
 
 
 
 
 
 
 
10.5. Summary of Measures to Maintain Effectiveness of Anti-Dumping 

In this section 10, we presented a set of alternative job effects of granting MES to China in 
TDI cases. Where the main report in Section 8 calculates job effects based on costs and prices 
of Chinese firms, in this section 10 we supplemented this by calculating EU job effects in the 
event that costs and prices in China would be considered as distorted and cannot be used for 
the  calculation  of  dumping  margins  and  duties.  But  instead  a  technique  of  “benchmark 
country  cost  plus  union  industry  profit  margin”  would  be  used  for  dumping  margins.  We 
evaluated job effects under this counterfactual both with and without application of the LDR. 
As before, we considered results in the short-run and long-run. 

Applying  this  technique  while  still  maintaining  the  LDR,  would  result  in  an  average  tariff 
that is -3.86%  lower than in the status  quo and results 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 are the 
EU jobs that can additionally be safeguarded in comparison with the status quo. 

83 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
Appendix A: Background tables for step 1 

Table A. 1: Import-Demand Elasticities from CN8 Data (Step 1A) 

Case AD 

(511) 
dlnImpchina 

(516) 
dlnImpchina 

(519) 
dlnImpchina 

(522) 
dlnImpchina 

(528) 
dlnImpchina 

(560) 
dlnImpchina 

(584) 
dlnImpchina 

(585) 
dlnImpchina 

(598) 
dlnImpchina 

(614) 
dlnImpchina 

dlnPricechin 

Control 

Year Dum 

Obs. 
R2 

-0.616** 
(0.278) 

Price of 
Row 
Yes 

201 

0.154 

-1.30** 
(0.55) 

Price of 
Row 
Yes 

115 

0.288 

-0.175** 
(0.07) 

Price of 
Row 
Yes 

451 

0.113 

-0.76** 
(0.344) 

Price of 
Row 
Yes 

202 

0.029 

-0.68*** 
(0.08) 

-0.32*** 
(0.06) 

-0.75*** 
(0.202) 

-1.18*** 
(0.162) 

-2.03*** 
(0.300) 

-0.71*** 
(0.240) 

Price of 
Row 
Yes 

627 

0.364 

Price of 
Row 
Yes 

1028 

0.138 

Price of 
Row 
Yes 

281 

0.270 

Price of 
Row 
Yes 

326 

0.207 

Price of 
Row 
Yes 

66 

0.504 

Price of 
Row 
Yes 

163 

0.163 

Note: Standard errors in brackets. We insert of prices from the RoW as a control as well as time dummies. Prices are unit values obtained from 8-digit product-level data from 
COMEXT, EUROSTAT. These are short-run elasticities estimated on the bilateral import volumes at CN8 level from China into the individual member states of the EU 
between 2000 and 2014. We did not instrument for price and cannot exclude endogeneity effects which may bias elasticities downward resulting in lower bound effects in 
scenario 1. Estimates are reported in first differences on trimmed data to account for potential measurement error but are typically lower than in levels. */**/*** are 
respectively 1%, 5% and 10% significance levels.

84 

 
 
 
 
Table A. 2: Import-Demand Elasticity from EU data (Step 1A) 

Dependent Variable 

Ln(Imp)China 

Ln(P)China 

Ln(P)Row 

constant 

Year dummies 
Observations 
R2 

-1.38* 
(0.821) 
0.477 
(0.681) 
16.07*** 
(3.73) 
Yes 
36 
0.99 

EU 

data 

confidential 

production  
EU 
Source: 
Note: Standard errors in brackets. Variables are all in logs. The insignificance of the coefficient on imports of 
the RoW is probably specific to the 10 AD-cases under scrutiny given the large role of Chinese imports in most 
of  these  cases  which  much  higher  than  for  other  products  in  the  same  sector.  Product-fixed  effects  which 
account  for  the  cross-section  variation  were  also  included  when  estimating  and  estimates  thus  represent  the 
within variation. 

employment, 

imports 

and 

on 

85 

 
 
 
 
Table A. 3: Employment Elasticity ( Step 1B) 

Dependent Variable 

Ln(Emp)EU 

Ln(Emp)EU 

Ln(imports)China 

Ln(imports)Row 

Ln(prod)EU 

Constant 

Year dummies 
Observations 
R2 

(1) 
-0.346* 
(0.197) 
0.0325 
(0.117) 
0.72*** 
(0.157) 
1.60*** 
(0.800) 
No 
40 
0.67 

(2) 
-0.399* 
(0.221) 
0.047 
(0.12) 
0.75*** 
(0.166) 
1.8** 
(0.932) 
Yes 
40 
0.68 

Source: EU confidential data on EU employment, imports and production. 
Note: variables are all in logs. Standard errors in brackets. In (1), lagging EU production with one year to 
address potential endogeneity, results in qualitatively similar results but that would mean losing many 
observations. The insignificance of the coefficient on imports of the RoW is probably specific to the 10 AD-
cases under scrutiny given the large role of Chinese imports in most of these cases. In our calculations, we use 
the employment elasticity reported in (1) which is the more prudent estimate. 

86 

 
 
 
 
 
 
Appendix B: Methodology for step 4 

B.1 Calculation of the upstream effects 

Before  we  show  the  matrix  calculations  that  generated  the  results,  we  first  introduce  and 
define different elements that enter these calculations: 

  The WIOD matrix has a generic element 

[   ] 

which  captures  the  output  of  sector  i  that  is  consumed  by  sector  j  as  input.  We 
collapsed the matrix50 to have only 51 columns: 48 (3x16) columns capture the output 
of  sector  i  that  is  consumed  by  the  16  tradable  sectors  from  the  three  regions  of 
interest  EU,  ROW,  and  China  and  the  last  3  columns  are  the  final  demands  of  the 
three regions which include input use by non-tradable sectors not directly subject to 
AD actions. The first 16 elements capture input use by the 16 tradable EU sectors and 
  } .  The  next  16 
we  sometimes  make  this  clear  by  a  subscript,  e.g.  {    
elements capture similar input use of sector i (in practice we will only look at the first 
16 EU sectors) by ROW {    

   } and China {    

  }. 

  Total output of sector i can be obtained either from the demand side, the summation 
of where its output goes, or from the production side, the summation of its own input 
use, value added, and adjustments. Here we use the first approach and define 

     ∑    

summing over the 48 sectors (16 in each region) that use i’s inputs as well as 3 final 
demand sectors (one for each region). 

      equals the estimated reduction in production in EU sector 1 due to the introduced 
MES  of  China.  It  incorporates  (i)  the  likelihood  the  sector  is  affected  and  (ii)  the 
relevant  employment  elasticity  for  the  sector  which  incorporates  both  the  negative 
substitution effect and the positive demand-boosting effect.51 
   ̃  equals  the  corresponding  effect  on  the  imports  of  ROW  sector  1  into  the  EU. 
How this compares to the effect on EU firms depends on the type of competition and 

 

50 The original WIOD matrix is gigantic. It covers 35 production sectors for 40 countries plus a rest-of-the-
world  residual  country.  There  are  3  additional  columns  for  final  consumption  by  households,  non-profit 
organizations, and government, and 2 columns for gross fixed capital formation and change in inventory for a 
total  of  (35+5)  x  (40+1)  or  1640  columns.  In  addition  to  the  35x41  rows  that  distinguish  where  each  sector 
obtains  its  inputs  from,  there  are  6  additional  rows  for  taxes-subsidies,  cif/fob  adjustments,  direct  purchases 
abroad and foreign purchases in the local area, value added and transport costs for a total of 1441 rows or an 
entire matrix of a 1441x1640 dimensionality or approximately 2.36 million elements. See Dietzenbacher, Los, 
Stehrer, Timmer and de Vries (2013) for details about its construction and content. 

51 In  the  absence  of  output  elasticities  by  employment,  we  use  the  standard  assumption  of  a  constant 

proportion between output and employment. 

87 

 
 
            
             
            
           
   
 
                                                 
market  clearing  we  assume.  One  option  is  to  use  the  same  effect  as  on  EU  terms, 
which  is  not  implausible  as  the  percentage  effect  is  expressed  as  an  elasticity.  This 
would be the appropriate assumption if competition was monopolistically competitive 
with  horizontally  differentiated  varieties.  Alternatively,  we  could  make  a 
proportionality assumption, multiplying the estimated effect in EU sector 1 with the 
     is 
relative  import  penetration      

      of  ROW  producers,  where     

defined  as         

   .  In  that  case  we  would  use   ̃      

      We 

performed the calculations both ways, but given the extremely small effect on RoW 
firms,  this  assumption  turns  out  to  be  quantitatively  entirely  unimportant.  To  err  on 
the  save  side  and  not  underestimate  any  possible  negative  effect,  we  use  the  first 
assumption in the final calculations and use  ̃      . 

  As the prices of Chinese imports fall, the market for their products expands and this 
effect is denoted by   . Assuming a constant local effect, the total output increase for 
each  sector  is  proportional  to  the  price  decline    ̅ and  the  likelihood  a  sector  has 
AD duties imposed (Pri). Using a Constant Elasticity Demand assumption as a local 
approximation, the market expansion can be calculated as 

        ̅       Pr   

Note that the revenue increases by less, using (     ) in the above formula, due to the 
price decline. However, we are interested in the output change as this is assumed to be 
proportional  to  the  employment  change.  We  use  a  demand  elasticity  of  1.38  in  the 
calculations, in line with the assumption used in step 1.52 

  The total effect on Chinese imports combines the market stealing effect from EU and 
RoW  competitors  in  addition  to  the  market  expansion.  In  order  to  combine  the 
previous  effects  which  are  in  percentage  terms,  we  rescale  the    and  ̃  percentage 
effects  to  be  appropriate  multipliers  for  the  absolute  level  of  Chinese  imports: 

    and similarly for  ̃ . 

The upstream indirect effects on sector 1 (the first EU tradable goods sector) are found by 
multiplying a row-vector of its input use expressed in output shares with a column vector of 
similar  dimensions  (18)  that  captures  the  changed  demand  in  48  client  sectors.  The 
substitution  effects  will  lower  the  demand  from  EU  and  ROW  sectors,  but  boost  demand 
from Chinese sectors. The pure demand effect will boost demand in all 48 sectors, but most 
strongly from Chinese sectors as price declines in the EU market will be strongest on inputs 
from China which directly benefit from lower AD duties, while other sectors only see a price 
decline to the extent they respond to the lower prices of their Chinese competitors. 

Using the previously defined elements we can define the row vector for sector 1 as: 

52 As before, a reduction in the tariff by 30% would lead to a price decline of 0.30/1.30 or 23%. 

88 

 
 
       
         
   
   
   
  
      
   
   
   
 
                                                 
Row :      [

   ]  

A similar row vector exists for all 16 EU tradable goods sectors and one for the non-tradables 
sector.  

We  need  to  adjust  these  shares,  because  the  elements  in  this  Row  vector  capture  the 
amount of good 1 produced in the EU that is used as an input by all 48 tradable good sectors 
around  the  world.  However,  not  all  of  that  output  is  destined  for  the  EU  market.  EU 
production  that  is  exported  will  not  be  affected  by  the  lower  downstream  EU  demand 
resulting from lower Chinese prices on the EU market. Similarly, Chinese production that is 
locally  consumed  will  also  not  be  affected.  Therefore,  we  have  to  multiply  Row  1  with  a 
diagonal matrix, denoted by A, that measures the fraction of the output of each sector that is 
destined for the EU market, either as input in any of the EU sectors or as final demand: 

    [

], with      

(∑

   ). 

The term in brackets sums all EU bound output of sector i for use in tradable sectors, services, 
or  consumed  as  final  demand  and  is  calculated  similarly  for  all  sectors  i,  whether  they  are 
located in the EU or abroad. It only tracks where a sector’s output goes. 

The relevant column vector, which is the same for each row vector, measures the change 

in size in each sector’s output and thus the different input demands: 

Col   :   [               ̃       ̃            

     ̃ 

     ̃ 

     ]  

Note that the row straightforwardly measures both the output of sector 1 going into other 
EU sectors as well as output consumed by the different RoW and Chinese sectors. To obtain 
the absolute change in the size of the extra-EU sectors, rows 17-32 and 33-48 in the column 
vector, we need some additional calculations. 

Matrix  multiplying  the  row  vector  with  matrix  A  to  strip  out  exports  and  then  with  the 
column  vector  produces  the  aggregate  indirect  upstream  effect  for  sector  1  due  to  changed 
MES treatment of China in the entire EU tradable goods sector:  

Indirect upstream effect on sector 1 = Row1 * A * Column 

B.2 Calculation of the downstream effects 

Most  elements  we  need  to  calculate  the  market  expansion  effect  downstream  have  already 
been defined in the previous section. A few more are listed here:  

  We again need the total output for a sector. We could calculate this summing over all 
rows, the 48 rows a sector gets its inputs from, plus 1 rows for the value added plus 
adjustments produced within the sector,      ∑
is the same as the row-total    calculated before. 

, but by construction this 

89 

 
 
    
  
  
    
  
  
  
     
   
  
 
     
   
  
  
     
  
  
 
     
  
  
      
 
 
 
 
 
        
 
  
    
  
  
   
      
          
           
   
           
   
  The price reduction of Chinese producers on the EU market will be given by        
    assuming  perfect  pass-through  of  tariff  changes  into  prices,  as  required  by  AD 
laws (0.23 = 0.30/1.30). 

  For EU and  ROW producers, the effect depends  on the price adjustment which will 
vary  according  to  the  type  of  market  competition  that  is  assumed.  In  the  extreme 
scenario  of  Bertrand  price  competition  they  would  match  Chinese  price  reductions 
one-for-one.  In  the  other  extreme  of  monopolistic  competition  and  a  small  market 
penetration  of  Chinese  imports  (so  that  their  impact  on  the  sectoral  price  index  is 
negligible)  prices  of  EU  or  ROW  producers  would  not  change  at  all.  The  truth  is 
somewhere in the middle and we denote the price decline by             . We will 
perform some sensitivity check on the appropriate value of x. In particular, in the most 
conservative  scenario  we  set  x=0  in  which  case  the  downstream  industries  only 
benefit from price changes on imports imported from China. In the second scenario, 
we put x=0.1. It means that the maximum price adjustment for EU producers to a 23% 
price  reduction  on  Chinese  imports  we  consider  is  2.2%.  Using  a  larger  value  for  x 
would make the indirect downstream effects more positive. 

  The export share of a EU sector i is denoted by    

Using  all  these  elements  we  can  again  define  a  row  and  column  vector  that  need  to  be 
multiplied to find the total effect. Now, the column vector for sector 1 captures input use by 
EU sector 1 from each of the 48 potential input sectors, either domestic inputs (the first 16 
elements) or imported inputs. The vector contains the input shares and the sum would be one 
minus the value added share in output: 

              [

   ]

In contrast with the calculations of the upstream effect, we do not need to focus only on 
output destined for the EU market. If cheaper inputs allows EU sector 1 to raise its exports, 
that is still a positive indirect downstream effect that we want to incorporate in the total. As 
such,  we  do  not  need  a  multiplication  with  a  matrix,  like  A  before,  that  downscales  the 
column  vector.  We  do,  however,  need  to  adjust  the  price  decline  of  imported  inputs  from 
 ), because AD duties on inputs are rebated if the 
China for the export share of the sector (   
final  output  is  exported.  As  a  result,  export  sales  will  not  benefit  directly  from  lower  AD 
duties  on  Chinese  inputs,  only  by  the  potential  price  reductions  of  domestic  of  RoW 
producers  to  the  extent  they  match  the  Chinese  price  declines,  which  is  indicated  by  the  x 
factor. 

The  corresponding  row  vector,  which  is  now  the  same  for  each  of  the  16  EU-sector 

columns, is 

Row                    [    Pr      Pr        Pr      Pr    Pr  Pr     ] 

90 

 
 
  
 
    
  
  
    
  
  
  
     
   
   
 
     
   
   
      
       
  
   
    
       
  
   
 
 
It  measures  the  price  decline  in  each  sector,  as  discussed  above,  which  is  scaled  by  the 
likelihood a product in each sector is subject to dumping by Chinese firms (Pri).  

91 

 
 
 
 
 
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