The post-crisis TFP growth slowdown in CEE countries : exploring the role of global value chains.
Date: 2018-04-30
Institutions: European Central Bank
Subjects: Central and Eastern European countries, European Union, comparative study, econometrics, economic cycle, economic growth, monetary crisis, research and development, technological change

Francesco Chiacchio, Katerina Gradeva, 
Paloma Lopez-Garcia 

Working Paper Series 

The post-crisis TFP growth slowdown 
in CEE countries: 
exploring the role of 
Global Value Chains 

CompNet

The Competitiveness Research Network

No 2143 / April 2018 

Disclaimer: This paper should not be reported as representing the views of the European Central Bank 
(ECB). The views expressed are those of the authors and do not necessarily reflect those of the ECB. 

Competitiveness Research Network 

This paper presents research conducted within the Competitiveness Research Network (CompNet). CompNet is a research network 
founded in 2012 to foster the debate on competitiveness and productivity issues among partner institutions and researchers. It aims at 
providing a robust theoretical and empirical link between drivers of competitiveness and macroeconomic performance for research and 
policy analysis purposes. 

Originally founded by the European System of Central Banks (ESCB), CompNet now includes as partner institutions the European 
Central Bank (ECB), the European Commission (EC), the Leibniz Institute for Economic Research of Halle (IWH), the European Bank of 
Reconstruction and Development (EBRD), the European Investment Bank (EIB), the Tinbergen Institute and several national central 
banks. 

CompNet-related research is conducted in two main work streams: 

1) Productivity growth drivers and efficiency of resource allocation

2)

International trade, euro area rebalancing and global value chains

The views expressed in this paper are the ones of the author(s) and do not necessarily reflect those of the ECB, the ESCB, and any of 
the other organisations associated with the Network. 

ECB Working Paper Series No 2143 / April 20181Abstract 
Using micro-aggregated firm information for nine Central and Eastern European (CEE) countries and 
data  from  input-output  tables,  we  examine  the  role  of  Global  Value  Chains  (GVCs)  for  technology 
diffusion across EU countries. Our empirical results provide support for a two-stage diffusion process 
of  technology  across  countries.  In  the  first  stage,  the  most  productive  firms  in  the  host  economy 
benefit from their direct exposure to new technology created in parent firms as a result of their GVC 
participation. In the second stage, technology spills over to the rest of firms in the host economy via 
domestic  production  networks.  In  addition,  we  show  that  the  import  of  intermediate  inputs  –i.e. 
backward linkages- is the main channel of technology diffusion within GVCs. We use these results to 
explain the pronounced post-crisis drop in Total Factor Productivity (TFP) growth in CEE countries. 
We show that due to their deep integration in GVCs, CEE countries have been exposed to two recent 
developments highly correlated with their TFP performance: (i) a slowdown in TFP growth of parent 
firms  located  in  non-CEE  EU  countries;  and  (ii)  a  global  slowdown  in  the  growth  rate  of  GVC 
participation, which is evident also for CEE countries from 2011 onwards. Moreover, we find that the 
capacity of host firms in CEE countries to absorb and understand new knowledge has decreased since 
the crisis. We argue that this is related to the drop in R&D investment in the CEE region during the 
post-crisis period.  

Keywords: TFP growth, Global Value Chains, technology diffusion, Central and Eastern

       Europe

JEL codes: O33, O47, O57, C33

ECB Working Paper Series No 2143 / April 20182Non-technical executive summary 

Trade  in  general  and  Global  Value  Chains  (GVCs)  in  particular  have  been  shown  to  be  important 
channels  for  technology  transfer  across  countries.  Firms’  engagements  in  GVCs  combine  “arm’s 
length  transactions”,  which  are  normal  commercial  transactions  between  a  supplier  and  a  consumer 
(of inputs), and typical features of intragroup investments. This means that even if there are no proper 
or  formal  ownership  links  in  GVCs,  the  opportunities  for  transferring  know-how,  technology  and 
process  innovation  are  vast.  Indeed,  firms  can  access  the  new  technology  embedded  in  imported 
inputs  and  benefit  from  new  varieties  of  intermediate  goods  by  expanding  the  set  of  inputs  used  in 
production  and  reaching  a  better  complementary  between  them.  Participation  in  GVCs  also  fosters 
collaboration  in  research  and  development  (R&D),  enables  product  diversification  and  forces 
upstream firms to invest more to meet the quality standards of parent companies.  

This paper contributes to this literature by exploring the role of openness and GVCs for technology 
diffusion in Europe. The paper also adds value to the more recent literature exploring the sources of 
the global productivity growth slowdown, visible particularly since the financial crisis. A number of 
papers  have  highlighted  the  role  played  by  (the  lack  of)  technology  diffusion  from  global  frontier 
firms to the rest of firms in explaining the slowdown (OECD, 2015). In this line, this paper explores 
whether  the  disproportionate  productivity  slowdown  in  Central  and  Eastern  European  (CEE) 
countries, which are deeply integrated in GVCs, can be partially attributed to a change in the cross-
country transmission of technology within GVCs.  

The framework of the analysis follows a Neo-Schumpeterian growth model where firms’ TFP growth 
in  a  catching-up  economy  depends  on  their  exposure  to  the  global  technological  frontier  and  their 
distance to the frontier. We adapt this model to include the particular characteristics of GVCs. More 
precisely, we assume that the relevant global technology frontier firms are the parent firms operating 
in  non-CEE  EU countries and that only firms  participating in  GVCs  in the host  economy, the  most 
productive  ones,  benefit  directly  from  direct  exposure  to  the  frontier.  The  rest  of  firms  in  the  host 
economy benefit indirectly from exposure to firms engaged in GVCs. 

We test this model using CompNet data and WIOD tables to compute bilateral country-sector GVC 
links for nine CEE countries, nine non-CEE countries, nine macro-sectors and 10 years (2003-2012). 
Our results show that technology diffuses first from parent firms to the most productive firms in the 
host  economy  via  GVCs  and  in  a  second  step  to  the  rest  of  firms.  The  main  channel  of  technology 
diffusion is the import of intermediate inputs by host firms, rather than the export of inputs to parent 
firms. Finally, we find that the pronounced slowdown in TFP growth in CEE countries is related to a 
drop in the technology creation in parent firms as well as to a fall in the absorptive capacity of host 
firms. We show that the latter could be related to the drop in R&D investment in the CEE region after 
the crisis.    

ECB Working Paper Series No 2143 / April 20183 
 
 
 
 
1 Introduction  

One  of  the  long  acknowledged  sources  of  learning,  technology  transfer  and  productivity  growth  is 

related to openness to trade and production chains (Grossman and Helpman, 1991). On the one hand, 

exporting  provides  exposure  to  new  ideas  and  incentives  to  upgrade.  On  the  other  hand,  import 

activity  allows  exploiting  complementarities  between  domestic  and  foreign  capabilities  and  might 

increase access to technology and its embedded know-how.  

The  productivity  gains  from  trade  can  be  even  larger  in  the  presence  of  foreign  direct  investment 

(FDI)  given  that  the  cost  of  transferring  technology  is  reduced  within  integrated  companies.  Global 

Value Chains (GVCs), which combine typical features of intragroup investments with “arm’s length” 

transactions - normal commercial transactions between a supplier and a consumer- might also provide 

support for rapid knowledge spillovers. The reason is that GVCs offer a high degree of exposure to, 

and  learning  from,  the  fast-evolving,  technology-enabled,  business  models  that  characterize 

fragmented production chains, even without the need for participating firms to engage in ownership 

arrangements. 

This  paper  contributes  to  the  literature  exploring  the  role  of  openness  and  GVCs  for  technology 

diffusion  in  Europe.  The  analysis  of  trade,  and  especially  of  international  production  chains,  as  a 

channel  for  cross-country  knowledge  transfer  and,  therefore,  productivity  growth,  is  particularly 

relevant in Europe given its deep trade integration and tight firm-to-firm connectivity. The paper also 

adds  value  to  the  more  recent  literature  examining  the  sources  of  the  global  productivity  growth 

slowdown, visible since the financial crisis. A number of papers have highlighted the role played by 

(the  lack  of)  technology  diffusion  from  global  frontier  firms  to  the  rest  of  firms  in  explaining  the 

slowdown (e.g. OECD, 2015). In this line, this paper explores whether the productivity slowdown in 

Central  and  Eastern  European  (CEE)  countries  can  be  partially  attributed  to  a  change  in  the  cross-

country diffusion of technology within GVCs.  

Measuring technology transmission is a challenging task. Hence, in this paper we adopt the distance-

to-frontier approach (e.g. Griffith at al., 1996) and assume that knowledge created at the firm can be 

measured by its Total Factor Productivity (TFP) growth rate, while learning from the frontier can be 

approximated by the correlation between TFP growth at the firm and at a nominated frontier firm or 

set  of  firms.  This  indirect  method  of  measuring  knowledge  flows  has  the  advantage  of  implicitly 

looking at non-patentable innovations, which are the prevalent ones within GVCs. 

To  explore  these  issues  the  paper  focuses  on  CEE  countries.  The  reasoning  behind  this  choice  is 

twofold. First, total GVC participation measured as the share in gross exports of the sum of two items, 

domestic value added in third country exports (forward GVC participation) and foreign value added in 

own exports (backward GVC participation), in CEE countries has been persistently above the EU28-
average  since  2000  (Figure  1).1  Second,  CEE  countries  have  experienced  a  sharper  drop  in  annual 
labour productivity growth than other EU countries since the onset of the crisis, driven fundamentally 

by  very  weak  TFP  performance  (Figure  2).  Thus,  in  the  following  we  argue  that  due  to  their  deep 

1 See Koopman et al. (2014) for details on the decomposition of value added flows at the country level. 

ECB Working Paper Series No 2143 / April 20184 
                                                            
integration  in  GVCs,  CEE  countries  have  been  particularly  exposed  to  two  recent  developments, 

which  are  highly  correlated  with  their  TFP  growth  performance:  (i)  weaker  TFP  growth  of  parent 

firms, which has reduced the new knowledge generated in parent firms and transmitted to host firms 

through  GVCs;  and  (ii)  a  global  slowdown  in  the  growth  rate  of  GVC  participation  which  has 

decreased the opportunities for technology transfer. This slowdown is evident for CEE countries from 
2011 onwards, even after controlling for country-sector and time-specific shocks (Figure 3).2  

Figure 1: GVC participation 

Figure  2:  Difference  in  annual  labour  productivity  growth 

and its contributors between the crisis and post-crisis (2008-

2015) period and pre-crisis period (2000-2007) 

(share in gross exports) 

(in percentage points) 

EU28

CEECs

World

Capital deepening

Labour quality

TFP

Labour productivity

2

0

‐2

‐4

‐6

‐8

‐10

LV

EE

LT

SI

RO

CZ

SK

BG

HR

HU non‐CEE EU PL

0.7

0.6

0.5

0.4

2000

2002

2004

2006

2008

2010

2012

2014

Source: Authors' calculations based on WIOD (2016). 
Note:  GVC  participation  measured  as  the  share  in  gross 
exports  of  the  sum  of:  (i)  domestic  value  added  in  third 
country  exports  (forward  GVC  participation);  and  (ii)  the 
foreign  value  added 
in  own  exports  (backward  GVC 
participation). 

Source: Authors’ calculations based on Conference Board data. 
Notes:  Non-CEE  EU  refers  to  the  unweighted  average  of  Austria, 
Belgium,  Cyprus,  Denmark,  Finland,  France,  Germany,  Ireland,  Italy, 
Luxembourg, Malta, the Netherlands, Portugal, Spain, Sweden and the 
United Kingdom. 

In our empirical framework, we depart from a Neo-Schumpeterian model, assuming that a country’s 

productivity growth depends on the exposure to the global technological frontier and distance to the 

frontier,  and  expand  it  to  accommodate  the  specific  characteristics  of  GVCs.  More  precisely,  we 

assume that only the most productive firms in each sector of the host economy are directly related to 

parent  firms  through  GVCs,  and  therefore  are  exposed  to  the  technology  created  at  the  frontier.  In 

turn,  those  top  productive  host  firms  outsource  part  of  their  non-core  activities  to  other  local  firms. 

This second stage is crucial for the technology diffusion to the rest of non-frontier firms in the host 

2 Note that Figure 3 refers to the import intensity of production, rather than to the backward and forward linkages of firms, as 

shown in Figure 2. 

ECB Working Paper Series No 2143 / April 20185 
 
 
 
 
 
                                                            
economy and is consistent with the evidence of a two-stage technology diffusion process put forward 

by Bartelsman et al. (2013), Van der Wiel et al. (2008), and Iacovone and Crespi (2010).  

We test these links using CompNet data and the recent release of the World Input-Output  Database 

Figure 3: CEE countries’ import intensity 

5
1
.

1
.

5
0
.

0

5
0
.
-

2001 2002 2003 2004 2005 2006 2007

2008 2009

2010 2011

2012 2013

2014

Source:  Authors'  calculations  based  on  WIOD  (2016) 
following Timmer et al. (2016). 
Note:  The  figure  plots  estimated  coefficients  and  95% 
confidence  intervals  on  year  dummies  after  regressing  CEE 
countries’  import  intensity  on  country-sector  and  year  fixed 
effects. Coefficients are relative to 2000. 

(WIOD) for a set of nine CEE countries, nine 

macro-sectors  and  10  years  (2003-2012)  and 

find that GVC participation plays a key role in 

explaining the TFP performance of host firms 

in  these  countries.  Moreover,  we  provide 

evidence  supporting  that  technology  diffuses 

from  parent  companies  to  host  economies  in 

two stages. In addition, we show that firms in 

host economies benefit more, in terms of TFP 

growth,  from  importing  inputs  from  parent 

firms than from exporting intermediate inputs.  

The  next 

section 

reviews  briefly 

the 

interconnection  between  trade  and  technology 

transfer  to  highlight  the  possible  channels  at 

play. Section 3 presents the framework for the 

analysis,  while  Section  4 

introduces 

the 

empirical  strategy  and  describes  the  data. 

Section  5  discusses  the  econometric  results 

and  several  robustness  exercises.  Section  6  applies  the  results  to  explore  the  factors  behind  the 

productivity growth slowdown in the CEE region after the crisis and, finally, Section 7 concludes. 

2 Technology transfer through trade linkages: main channels3 

Several studies suggest that TFP growth is influenced by the ability of firms to take advantage of new 

and existing technology created at the global frontier. In the Neo-Schumpeterian models (e.g.  Aghion 

and Howitt, 2006; Saia et al., 2015), a country’s productivity growth depends on two elements. The 

first  one  is  the  “pass-through”  effect  which  is  defined  as  the  exposure  to,  and  learning  from,  new 

technology  created  at  the  frontier.  It  depends  on  the  frontier’s  rate  of  technology  creation,  which  is 

often  proxied  by  the  average  TFP  growth  of  frontier  firms.  The  second  element  is  the  so-called 

“catch-up”  effect  or  distance  to  the  global  productivity  frontier.  It  represents  the  ability  of  firms  to 

take  advantage  of  existing  technologies  and  it  is  generally  proxied  by  the  lagged  gap  in  the 

productivity  level  between  the  global  frontier  and  the  country  under  study.  The  larger  the  gap,  the 

larger  the  role  of  the  catch-up  effect  for  TFP  growth.  Comin  and  Hobijn  (2010)  provide  theoretical 

and empirical evidence for the importance of technology diffusion for TFP growth, in particular for 

emerging countries. Success stories such as the ones of the “East Asian Tigers” have coincided with a 

3 This section relies heavily on the literature review by Lopez-Garcia and Taglioni (2018). 

ECB Working Paper Series No 2143 / April 20186 
 
                                                            
 
fast catch-up in terms of technology with respect to OECD countries through substantial reduction of 

technology adoption lags. The aggregate implications of technology diffusion are far reaching: cross-

country adoption lags seem to explain at least one quarter of income per capita differentials.  

The possibility to learn from the global frontier depends among other factors on the existence of trade 

linkages.  Amiti  and  Konings  (2007)  find  that  the  largest  productivity  gains  from  trade  are  due  to 

lower tariffs on inputs rather than to a competition effect. Interestingly, a main channel for the gains is 

the  access  to  a  more  differentiated  variety  of  inputs,  which  seems  to  matter  more  than  the  direct 

benefits  from  lower  prices  or  higher  quality  foreign  inputs  (Goldberg  et  al.,  2010;  Halpern  et  al., 

2015). Similar results are found by Bas and Strauss-Kahn (2014), who show that an increase in the 

share of imported inputs used in production boosts firm-level productivity and exports in France by 

expanding the set of inputs used in production and reaching a better complementarity between them. 

Importing gives also rise to transfer of tacit knowledge (Csillag and Koren, 2011; MacGarvie, 2006). 

The  reason  is  that  the  newly  imported  sophisticated  machinery  and  capital  goods  require  highly 

trained operators. Importing better inputs also leads to upgrading for other firms indirectly connected 

via supply chains. Kee (2015) shows, for example, that after EU firms expanded FDI in Bangladesh, 

domestic firms that shared the same suppliers with the foreign investors expanded their product scope 

by  25  percent  and  enjoyed  productivity  gains  by  33  percent.    Çagatay  and  de  Haas  (2015)  provide 

evidence  that  in  an  emerging  country  context  (Russia  in  their  analysis)  technological  advances  are 

mostly  made  through  cooperation  with  others,  in  particular  suppliers,  highlighting  imports  as  an 

important channel of technology diffusion. 

Exporting,  on  the  other  hand,  is  associated  with  skills  upgrading,  particularly  for  domestic  frontier 

firms.  Bustos  (2011),  for  example,  finds  an  increased  demand  for  skills  after  the  creation  of 

MERCOSUR.  Furthermore,  as  firms  become  larger  and  start  exporting,  they  expand  their 

organizational capital inducing a greater need for more complex management structures, which in turn 

brings  more  skills  (Bloom  et  al.,  2016).    Exporting  also  generates  opportunities  for  learning  by 

exporting for firms and their workers (De Loecker, 2013). Demand factors play as well an important 

role  in  inducing  positive  knowledge  spillovers  from  exporting  given  that  it  enables  firms  to  learn 

about  more  sophisticated  consumers  and  competitive  markets.  Accordingly,  one  explanation  of  the 

success  of  China  in  international  markets  is  that  Chinese  firms  invested  in  capability  building  to 

improve  their  product  appeal  and  demand  (Sutton,  2007;  Brandt  et  al.,  2008;  Schott,  2008).  Also, 

Brambilla  et  al.  (2012)  show  that  firms  exporting  predominantly  to  high-income  countries  use 

relatively higher levels of skills and pay higher wages than domestic firms or exporters to middle- or 

low-income countries. 

Technology  tends  to  flow  faster  and  more  easily  as  a  result  of  the  interconnections  established  in 

GVCs.  Such  production  arrangements  link  together  multiple  firms,  usually  located  in  different 

countries, in ways similar to intra-group investment and trade. This type of engagement is based on 

deep relationships that involve ownership or licensing, franchising, joint ventures, strategic alliances, 

or other forms of non-equity modes of investment. It is the deep nature of engagement that allows the 

faster,  more  sizeable,  and  knowledge-intensive  transfer  of  capabilities  between  countries  (Mariscal 

ECB Working Paper Series No 2143 / April 20187 
and Taglioni, 2017). The capacity of GVCs to act as a channel of technology transfer is shown, for 

example, by Baldwin and Yan (2014), who controlling for self-selection estimate that Canadian firms 

witnessed  a  5%  increase  in  their  productivity  level  after  only  one  year  from  the  start  of  their 

participation into a GVC.  Moreover, Acemoglu et al. (2015) show that the existence of pronounced 

industry interlinkages might amplify significantly the macroeconomic impact of a shock. Supply-side 

shocks  in  productivity  such  as  a  TFP  shock  or  changes  in  patented  technology  frontiers  propagate 

mostly  downstream  from  supplier  to  customer  industries,  while  demand-side  shocks  have 

predominantly upstream effects.  

3 Framework of the analysis 

In  line  with  Neo-Schumpeterian  models  and  models  of  technology  diffusion  in  multiple  stages 

(Bartelsman  et  al.  2013;  Van  der  Wiel  et  al.  2008;  Iacovone  and  Crespi,  2010),  we  assume  that 

technology diffuses in two stages across countries linked through trade. In the first stage technology is 

diffused  from  the  global  frontier  firms  to  the  national  frontier  firms.  Global  frontier  firms  learn 

predominantly through own radical innovation, research and development (R&D), patenting activity, 

and  from  addressing  untapped  needs  of  sophisticated  customers.  They  are  the  most  advanced  and 

productive  firms  in  a  given  sector  at  a  global  scale.  These  firms  usually  engage  in  international 

production and trade through establishing linkages with the most productive firms in a target country, 

what  we  call  the  national  frontier  firms.    Direct  collaboration  of  national  frontier  firms  with  global 

frontier  firms  results  in  acquisition  of  new  technology,  learning  new  processes,  and  enabling 

technological  upgrading.  In  a  second  stage,  the  technology  flows  to  the  rest  of  firms  in  the  host 

economy, predominantly through domestic networks.  

To adapt this set-up to the GVC framework, we assume that in countries with very large participation 

in GVCs the relevant global frontier, that is, the source of new technology, is what we call the “GVC 

frontier”. Firms at the GVC frontier are the most productive firms in country-sectors with tight GVC 

links with host firms.  

Further, we consider three distinct types of firms in the host economies: national frontier firms, mid-

productivity firms, and laggards. The national frontier firms are those at the top 20 percent of the TFP 
distribution in the host country/macro-sector. The mid-productive firms are those between the 30th and 
the 70th percentiles of the productivity distribution, while laggards refer to the bottom 20 percent firms 
in terms of TFP. The reason for this distinction is that the role and integration in GVCs of firms in 

host  economies  depends  on  their  capabilities.  Initial  entry  into  a  GVC,  what  Mariscal  and  Taglioni 

(2017)  call  connection  stage,  implies  unstable  engagement  in  GVCs  and  involves  firms  with  basic 

capabilities (the low-middle  productive firms in  a given sector). These  firms  will eventually enter a 

process  of  upgrading,  conditioned  on  the  firm  being  able  to  manage  larger  size  and  complexity,  in 

which there is a more stable relationship with the parent firms. Lastly, mature engagement in GVCs 

implies that host firms focus  on core competencies  and outsource other tasks  to  local  firms. At  this 

stage, there is a strong focus on R&D, quality and direct connection with parent firms. Only the most 

ECB Working Paper Series No 2143 / April 20188 
 
productive firms in a given sector of the host country, what we call the national frontier firms, have 

the capabilities required to play this role. 

It  is  important  to  note  that  in  the  last  two  stages  of  the  GVC  participation  (upgrading  and  mature 

engagement), there are feedback loops between parent and host firms. Firms are exposed directly to 

new technology, skills and processes, and they can benefit and adapt as much technology as possible 

via enhancement of their absorptive capacity.  These firms are also forming their own stable network 

of domestic suppliers (Figure 4). 

Figure 4: Integration in GVCs and host firms’ capabilities 

Source: Simplified version of the taxonomy developed by Mariscal and Taglioni (2017). 

Hence, in a GVC framework knowledge flows in the first place from the GVC frontier firms to the 

national frontier firms in host economies and in a second stage from the national frontier firms to the 

rest of the domestic firms, predominantly through domestic firm-to-firm and firm-to-worker linkages. 

Domestic networks are fuelled by the outsourcing of non-core activities of the value chain. There is 

also  a  direct  channel  from  foreign  companies  to  the  medium-low  productivity  firms  in  peripheral 

economies,  which  however  grants  lower  technology  transfer;  the  engagement  involves  most  likely 

non-core  functions,  requires  capabilities  that  are  basic  in  nature,  and  it  is  based  on  “arm’s  length” 

trade relationships (Figure 5). 

ECB Working Paper Series No 2143 / April 20189 
 
 
 
 
 
 
  Figure 5: Technology diffuses from the GVC frontier to host economies in two stages 

Source: Authors’ elaboration based on Mariscal and Taglioni (2017). 

4 Empirical specification and data 

4.1 The GVC frontier 

The  GVC  frontier  is  unique  to  host  firms  operating  in  a  given  country/macro-sector  because  it 

depends on their exposure to different firms at the global frontier. The exposure to the GVC frontier 

depends  on  the  GVC  links  between  a  “parent”  country/macro-sector  and  a  “host”  country/macro-

sector measured either by the backward (downstream) position of firms in GVCs, which depends on 

their imports of intermediate inputs, or by their forward (upstream) position based on their exports of 

intermediates. As described in the previous section, the concrete channels of technology transfer vary 

depending on the type of GVC links established between parent and host firms. 

Our baseline measures of GVC links follow a “gross” definition and are based either on the imports or 

the exports of intermediates of a given host country/macro-sector: 

(cid:1833)(cid:1848)(cid:1829)	(cid:1868)(cid:1853)(cid:1870)(cid:1872)(cid:1861)(cid:1855)(cid:1861)(cid:1868)(cid:1853)(cid:1872)(cid:1861)(cid:1867)(cid:1866)(cid:3053),(cid:3037),(cid:3047) (cid:3404)

	(cid:3010)(cid:3040)(cid:3043)(cid:3042)(cid:3045)(cid:3047)(cid:3046)	(cid:3042)(cid:3033)	(cid:3036)(cid:3041)(cid:3047)(cid:3032)(cid:3045)(cid:3040)(cid:3032)(cid:3031)(cid:3036)(cid:3028)(cid:3047)(cid:3032)(cid:3046)(cid:3301),(cid:3285),(cid:3295)
(cid:3005)(cid:3042)(cid:3040)(cid:3032)(cid:3046)(cid:3047)(cid:3036)(cid:3030)	(cid:3046)(cid:3048)(cid:3043)(cid:3043)(cid:3039)(cid:3052)	(cid:3042)(cid:3033)(cid:3036)(cid:3041)(cid:3047)(cid:3032)(cid:3045)(cid:3040)(cid:3032)(cid:3031)(cid:3036)(cid:3028)(cid:3047)(cid:3032)(cid:3046)(cid:3301),(cid:3285),(cid:3295)

(cid:1833)(cid:1848)(cid:1829)	(cid:1868)(cid:1853)(cid:1870)(cid:1872)(cid:1861)(cid:1855)(cid:1861)(cid:1868)(cid:1853)(cid:1872)(cid:1861)(cid:1867)(cid:1866)(cid:3053),(cid:3037),(cid:3047) (cid:3404)

	(cid:3006)(cid:3051)(cid:3043)(cid:3042)(cid:3045)(cid:3047)(cid:3046)	(cid:3042)(cid:3033)	(cid:3036)(cid:3041)(cid:3047)(cid:3032)(cid:3045)(cid:3040)(cid:3032)(cid:3031)(cid:3036)(cid:3028)(cid:3047)(cid:3032)(cid:3046)(cid:3301),(cid:3285),(cid:3295)
(cid:3005)(cid:3042)(cid:3040)(cid:3032)(cid:3046)(cid:3047)(cid:3036)(cid:3030)	(cid:3046)(cid:3048)(cid:3043)(cid:3043)(cid:3039)(cid:3052)	(cid:3042)(cid:3033)	(cid:3036)(cid:3041)(cid:3047)(cid:3032)(cid:3045)(cid:3040)(cid:3032)(cid:3031)(cid:3036)(cid:3028)(cid:3047)(cid:3032)(cid:3046)(cid:3301),(cid:3285),(cid:3295)

(1) 

(2)

where  z  is  the  host  country,  j  is  the  sector  of  activity  and  t  is  the  year.  In  addition,  we  use  as 

robustness check the definitions of GVC links based on the value added concept following Wang et 
al. (2014).4 For our baseline specifications we opt, however, for the GVC measures based on the gross 

4 We thank an anonymous referee for this suggestion. 

ECB Working Paper Series No 2143 / April 201810 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
                                                            
concept  in  order  to  be  consistent  with  the  definition  of  the  GVC  frontier,  for  which,  as  explained 
below, we use bilateral sector-to-sector trade flows to compute the weights.5 

In Figure 6 we explain the construction of the GVC frontier on the basis of a simplified example of 

the  manufacturing  sector  in  Slovakia,  which  imports  inputs  from  two  non-CEE  EU  countries, 

Germany and France. More concretely, Slovakian manufacturing firms import 70% of their imported 

Figure  6:  Constructing 

the  GVC 

frontier 

inputs from manufacturing firms in Germany and 30% 

from firms in the professional service sector in France. 

This  means  that  the  GVC  frontier  of  Slovakian 

manufacturing firms is a weighted average of the most 

productive 

firms 

in  Germany-manufacturing  and 

France-professional  services.  The  weights  depend  on 

the particular exposure of the Slovakian-manufacturing 

firms to the two countries, which in this example is 70-

30.  If  technology  creation  at  global  frontier  firms  is 

approximated, as specified in the introduction, by their 

TFP  growth,  then  the  technology  creation  at  the  GVC 

frontier  of  Slovakian-manufacturing  firms  is  going  to 

be a weighted average (with weights being 70-30) of the TFP growth of frontier firms in Germany-

manufacturing and France-professional services. 

Thus, technology creation at the GVC frontier based on Equation 1 (imports of intermediate inputs) is 
specified as follows:6 

(cid:1846)(cid:1832)(cid:1842)	(cid:1859)(cid:1870)(cid:1867)(cid:1875)(cid:1872)(cid:1860)	(cid:1853)(cid:1872)	(cid:1833)(cid:1848)(cid:1829)	(cid:1858)(cid:1870)(cid:1867)(cid:1866)(cid:1872)(cid:1861)(cid:1857)(cid:1870)(cid:3053),(cid:3037),(cid:3047) (cid:3404)
∑
(cid:1846)(cid:1832)(cid:1842)(cid:1859)(cid:1870)(cid:1867)(cid:1875)(cid:1872)(cid:1860)(cid:3043),(cid:3036)(cid:2880)(cid:3047)(cid:3042)(cid:3043)	(cid:2870)(cid:2868)%,(cid:3047) ∙

(cid:3043)∈(cid:3017)

(cid:3010)(cid:3040)(cid:3043)(cid:3042)(cid:3045)(cid:3047)(cid:3046)	(cid:3042)(cid:3033)	(cid:3036)(cid:3041)(cid:3047)(cid:3032)(cid:3045)(cid:3040)(cid:3032)(cid:3031)(cid:3036)(cid:3028)(cid:3047)(cid:3032)(cid:3046)(cid:3291)→(cid:3301),(cid:3285),(cid:3295)

∑

(cid:3291)∈(cid:3265)

(cid:3010)(cid:3040)(cid:3043)(cid:3042)(cid:3045)(cid:3047)(cid:3046)	(cid:3042)(cid:3033)	(cid:3036)(cid:3041)(cid:3047)(cid:3032)(cid:3045)(cid:3040)(cid:3032)(cid:3031)(cid:3036)(cid:3028)(cid:3047)(cid:3032)(cid:3046)(cid:3291)→(cid:3301),(cid:3285),(cid:3295)

(3) 

where  z  is  the  host  country  (Slovakia  in  the  example),  j  is  the  macro-sector  (manufacturing  in  the 

example) and t is time. p is a pair of parent country/macro-sector (e.g.,  manufacturing of Germany) 

from  the  set  of  all  parent  countries/macro-sectors,  which  we  denote  by  P  (Germany/manufacturing 

and  France/professional  services  in  the  example  above);  and  (cid:1868) → (cid:1878), (cid:1862), (cid:1872)  is  the  flow  of  intermediate 

imports  from  parent  country/macro-sector  p  to  country  z,  macro-sector  j,  at  time  t.  Finally,  i  is  the 

productivity percentile of  a given firm. The  most  productive  firms in  a given  sector  are  assumed to 

belong  to  the  top  20%  of  the  productivity  distribution  in  the  country/macro-sector.  Hence, 

5 The decomposition proposed by Wang et al. (2014) provides unfortunately only information on value added flows  from/to 
a sector in the host country to/from a parent country, with no information on the sector of origin/destination in the parent 
country. 

6 For details on the computation of TFP and other variables, please refer to Annex 1. 

ECB Working Paper Series No 2143 / April 201811 
 
  
 
 
 
 
                                                            
 
 
(cid:1846)(cid:1832)(cid:1842)(cid:1859)(cid:1870)(cid:1867)(cid:1875)(cid:1872)(cid:1860)(cid:3043),(cid:3037),(cid:3036)(cid:2880)(cid:3047)(cid:3042)(cid:3043)	(cid:2870)(cid:2868)%,(cid:3047)  is  the  TFP  growth  of  the  most  productive  firms  in  the  parent 
country/macro-sector p. 

In  order  to  investigate  whether  the  most  relevant  channel  of  technology  transfer  is  the  import  or 

export  of  inputs,  we  compute  an  alternative  GVC  frontier  using  as  weights  the  ratio  of  exported 

intermediate  inputs  to  total  intermediate  exports  by  a  given  host  country/macro-sector  (based  on 

Equation 2): 

(cid:1846)(cid:1832)(cid:1842)	(cid:1859)(cid:1870)(cid:1867)(cid:1875)(cid:1872)(cid:1860)	(cid:1853)(cid:1872)	(cid:1833)(cid:1848)(cid:1829)	(cid:1858)(cid:1870)(cid:1867)(cid:1866)(cid:1872)(cid:1861)(cid:1857)(cid:1870)(cid:3053),(cid:3037),(cid:3047) (cid:3404)
∑
(cid:1846)(cid:1832)(cid:1842)(cid:1859)(cid:1870)(cid:1867)(cid:1875)(cid:1872)(cid:1860)(cid:3043),(cid:3036)(cid:2880)(cid:3047)(cid:3042)(cid:3043)	(cid:2870)(cid:2868)%,(cid:3047) ∙

(cid:3043)∈(cid:3017)

	(cid:3006)(cid:3051)(cid:3043)(cid:3042)(cid:3045)(cid:3047)(cid:3046)	(cid:3042)(cid:3033)	(cid:3036)(cid:3041)(cid:3047)(cid:3032)(cid:3045)(cid:3040)(cid:3032)(cid:3031)(cid:3036)(cid:3028)(cid:3047)(cid:3032)(cid:3046)(cid:3301),(cid:3285),(cid:3295)→(cid:3291)

∑

(cid:3291)∈(cid:3265)

(cid:3006)(cid:3051)(cid:3043)(cid:3042)(cid:3045)(cid:3047)(cid:3046)	(cid:3042)(cid:3033)	(cid:3036)(cid:3041)(cid:3047)(cid:3032)(cid:3045)(cid:3040)(cid:3032)(cid:3031)(cid:3036)(cid:3028)(cid:3047)(cid:3032)(cid:3046)(cid:3301),(cid:3285),(cid:3295)→(cid:3291)

(4) 

4.2 TFP growth of national frontier firms in host economies 

According to Neo-Schumpeterian models (Aghion and Howitt, 2006; Saia et al., 2015), TFP growth 

of (host) firms depends on their direct exposure to technology creation at the global (GVC) frontier, 

measured by the correlation of their own TFP growth with that of the global (GVC) frontier firms, as 

well as on their distance to the frontier, measured by the lagged gap in TFP levels. 

Hence, TFP growth of the national frontier, i.e. the most productive firms in a given sector in the host 

economy, is estimated through the following equation: 

(cid:1846)(cid:1832)(cid:1842)	(cid:1859)(cid:1870)(cid:1867)(cid:1875)(cid:1872)(cid:1860)(cid:3053),(cid:3037),(cid:3036)(cid:2880)(cid:3047)(cid:3042)(cid:3043)	(cid:2870)(cid:2868)%,(cid:3047) (cid:3404)
(cid:2009)(cid:3053)(cid:3037) (cid:3397) (cid:2010)(cid:2869)	(cid:1846)(cid:1832)(cid:1842)	(cid:1859)(cid:1870)(cid:1867)(cid:1875)(cid:1872)(cid:1860)(cid:1833)(cid:1848)(cid:1829)(cid:1858)(cid:1870)(cid:1867)(cid:1866)(cid:1872)(cid:1861)(cid:1857)(cid:1870)(cid:3053),(cid:3037),(cid:3047) (cid:3397)
(cid:2010)(cid:2870)(cid:1864)(cid:1866)(cid:3435)(cid:1872)(cid:1858)(cid:1868)	(cid:1864)(cid:1857)(cid:1874)(cid:1857)(cid:1864)	(cid:1833)(cid:1848)(cid:1829)(cid:1858)(cid:1870)(cid:1867)(cid:1866)(cid:1872)(cid:1861)(cid:1857)(cid:1870)(cid:3053),(cid:3037),(cid:3047)(cid:2879)(cid:2869)/(cid:1872)(cid:1858)(cid:1868)	(cid:1864)(cid:1857)(cid:1874)(cid:1857)(cid:1864)(cid:3053),(cid:3037),(cid:3036)(cid:2880)(cid:3047)(cid:3042)(cid:3043)	(cid:2870)(cid:2868)%,(cid:3047)(cid:2879)(cid:2869)(cid:3439) (cid:3397) (cid:2010)(cid:2871)(cid:1833)(cid:1848)(cid:1829)	(cid:1859)(cid:1870)(cid:1867)(cid:1875)(cid:1872)(cid:1860)(cid:3053),(cid:3037),(cid:3047) (cid:3397) (cid:2012)(cid:2869)(cid:1855)(cid:1870)(cid:1861)(cid:1871)(cid:1861)(cid:1871)(cid:3047) (cid:3397)
(cid:2012)(cid:2870)(cid:1868)(cid:1867)(cid:1871)(cid:1872)(cid:1855)(cid:1870)(cid:1861)(cid:1871)(cid:1861)(cid:1871)(cid:3047) (cid:3397) (cid:1873)(cid:3053),(cid:3037),(cid:3047)    

 (5)

where the term  (cid:1846)(cid:1832)(cid:1842)	(cid:1859)(cid:1870)(cid:1867)(cid:1875)(cid:1872)(cid:1860)(cid:1833)(cid:1848)(cid:1829)(cid:1858)(cid:1870)(cid:1867)(cid:1866)(cid:1872)(cid:1861)(cid:1857)(cid:1870) captures the TFP growth of  the  GVC frontier  of  macro-

j 

in  host  country 

term 
sector 
(cid:1864)(cid:1866)(cid:3435)(cid:1872)(cid:1858)(cid:1868)	(cid:1864)(cid:1857)(cid:1874)(cid:1857)(cid:1864)	(cid:1833)(cid:1848)(cid:1829)(cid:1858)(cid:1870)(cid:1867)(cid:1866)(cid:1872)(cid:1861)(cid:1857)(cid:1870)(cid:3053),(cid:3037),(cid:3047)(cid:2879)(cid:2869)/(cid:1872)(cid:1858)(cid:1868)	(cid:1864)(cid:1857)(cid:1874)(cid:1857)(cid:1864)	(cid:3053),(cid:3037),(cid:3036)(cid:2880)(cid:3047)(cid:3042)(cid:3043)	(cid:2870)(cid:2868)%,(cid:3047)(cid:2879)(cid:2869)(cid:3439) measures the lagged distance from host 
frontier firms to the GVC frontier. The crisis and post-crisis variables equal one from 2008 to 2010 

(see  Equations  3  and  4).  The 

z  at 

time 

t 

and  from  2011  onwards,  respectively,  and  capture  cross-country  common  developments  during  the 

two periods which might explain a change in TFP growth relative to the pre-crisis period (results are 

robust to the use of year dummies). In addition, we also control for the sector’s involvement in GVCs, 

measured by the sector–specific growth of GVC participation (or by the sector–specific level of GVC 

participation) as defined by Equations 1 and 2. The underlying assumption is that if many firms in a 

given sector are already participating in GVCs, or are joining in because GVCs are expanding, there 

might be a positive externality beyond the one captured by the exposure to the frontier and the catch-

up. This  effect is due to the fact that workers are more skilled in this sector, and inputs are of better 

quality than in sectors less involved in GVCs (Kee, 2015).  

ECB Working Paper Series No 2143 / April 201812 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
  
 
4.3 TFP growth of mid-productive and laggard firms in the host economy 

According  to  the  theoretical  framework  presented  in  the  previous  section,  in  a  second  stage 

technology  diffuses  from  the  national  frontier  firms,  involved  in  GVCs,  to  the  rest  of  local  firms 

through  domestic  production  networks.  Hence,  non-frontier  firms  learn  not  directly  from  the  global 

frontier  but  rather  from  the  national  frontier  firms.  So,  TFP  growth  of  laggards  and  mid-productive 

firms in a given sector depends on their exposure to the technology already adopted by the national 

frontier and on the distance to the national frontier. As mentioned earlier, laggard and mid-productive 

firms  might  also,  in  the  initial  stages  of  their  GVC  engagement,  have  sporadic  contact  with  parent 

companies  in  non-CEE  EU  countries.  Therefore,  we  control  also  for  the  GVC  frontier  TFP  growth 

and their distance to the GVC frontier: 

(cid:1846)(cid:1832)(cid:1842)	(cid:1859)(cid:1870)(cid:1867)(cid:1875)(cid:1872)(cid:1860)(cid:3053),(cid:3037),(cid:3036),(cid:3047) (cid:3404)
(cid:2009)(cid:3053)(cid:3037) (cid:3397) (cid:2010)(cid:2869)(cid:1846)(cid:1832)(cid:1842)	(cid:1859)(cid:1870)(cid:1867)(cid:1875)(cid:1872)(cid:1860)(cid:1840)(cid:1853)(cid:1872)(cid:1861)(cid:1867)(cid:1866)(cid:1853)(cid:1864)	(cid:1858)(cid:1870)(cid:1867)(cid:1866)(cid:1872)(cid:1861)(cid:1857)(cid:1870)	(cid:3053),(cid:3037),(cid:3047) (cid:3397) (cid:2010)(cid:2870)(cid:1864)(cid:1866)(cid:3435)(cid:1872)(cid:1858)(cid:1868)	(cid:1864)(cid:1857)(cid:1874)(cid:1857)(cid:1864)(cid:1840)(cid:1853)(cid:1872)(cid:1861)(cid:1867)(cid:1866)(cid:1853)(cid:1864)	(cid:1858)(cid:1870)(cid:1867)(cid:1866)(cid:1872)(cid:1861)(cid:1857)(cid:1870)	(cid:3053),(cid:3037),(cid:3047)(cid:2879)(cid:2869)/
(cid:1872)(cid:1858)(cid:1868)	(cid:1864)(cid:1857)(cid:1874)(cid:1857)(cid:1864)(cid:3053),(cid:3037),(cid:3036),(cid:3047)(cid:2879)(cid:2869)(cid:3439) (cid:3397) (cid:2010)(cid:2871)	(cid:1846)(cid:1832)(cid:1842)	(cid:1859)(cid:1870)(cid:1867)(cid:1875)(cid:1872)(cid:1860)(cid:1833)(cid:1848)(cid:1829)(cid:1858)(cid:1870)(cid:1867)(cid:1866)(cid:1872)(cid:1861)(cid:1857)(cid:1870)(cid:3053),(cid:3037),(cid:3047) (cid:3397) (cid:2010)(cid:2872)(cid:1864)(cid:1866)(cid:3435)(cid:1872)(cid:1858)(cid:1868)	(cid:1864)(cid:1857)(cid:1874)(cid:1857)(cid:1864)	(cid:1833)(cid:1848)(cid:1829)	(cid:1858)(cid:1870)(cid:1867)(cid:1866)(cid:1872)(cid:1861)(cid:1857)(cid:1870)(cid:3053),(cid:3037),(cid:3047)(cid:2879)(cid:2869)/
(cid:1872)(cid:1858)(cid:1868)	(cid:1864)(cid:1857)(cid:1874)(cid:1857)(cid:1864)(cid:3053),(cid:3037),(cid:3036),(cid:3047)(cid:2879)(cid:2869)(cid:3439) (cid:3397) (cid:2010)(cid:2873)(cid:1833)(cid:1848)(cid:1829)	(cid:1859)(cid:1870)(cid:1867)(cid:1875)(cid:1872)(cid:1860)(cid:3053),(cid:3037),(cid:3047) (cid:3397) (cid:2012)(cid:2869)(cid:1855)(cid:1870)(cid:1861)(cid:1871)(cid:1861)(cid:1871)(cid:3047) (cid:3397) (cid:2012)(cid:2870)(cid:1868)(cid:1867)(cid:1871)(cid:1872)(cid:1855)(cid:1870)(cid:1861)(cid:1871)(cid:1861)(cid:1871)(cid:3047) (cid:3397) (cid:1873)(cid:3053),(cid:3037),(cid:3047)  

(6) 

considering  TFP 

where i is equal to the bottom 20 percent of the TFP distribution in a given macro-sector j and host 
country  z  in  year  t  in  the  case  of  the  laggard  firms,  and  between  the  30th  and  70th  deciles  of  the 
term 
growth 
distribution  when 
(cid:1846)(cid:1832)(cid:1842)(cid:1859)(cid:1870)(cid:1867)(cid:1875)(cid:1872)(cid:1860)(cid:1840)(cid:1853)(cid:1872)(cid:1861)(cid:1867)(cid:1866)(cid:1853)(cid:1864)	(cid:1858)(cid:1870)(cid:1867)(cid:1866)(cid:1872)(cid:1861)(cid:1857)(cid:1870)	(cid:3053),(cid:3037),(cid:3047)  proxies for the technology creation at the national level, while 
the  term    (cid:1864)(cid:1866)(cid:3435)(cid:1872)(cid:1858)(cid:1868)	(cid:1864)(cid:1857)(cid:1874)(cid:1857)(cid:1864)(cid:1840)(cid:1853)(cid:1872)(cid:1861)(cid:1867)(cid:1866)(cid:1853)(cid:1864)	(cid:1858)(cid:1870)(cid:1867)(cid:1866)(cid:1872)(cid:1861)(cid:1857)(cid:1870)	(cid:3053),(cid:3037),(cid:3047)(cid:2879)(cid:2869)/(cid:1872)(cid:1858)(cid:1868)	(cid:1864)(cid:1857)(cid:1874)(cid:1857)(cid:1864)(cid:3053),(cid:3037),(cid:3036),(cid:3047)(cid:2879)(cid:2869)(cid:3439)  proxies  for  the  catch-up  to  the 
national frontier.  As before, (cid:1833)(cid:1848)(cid:1829)	(cid:1859)(cid:1870)(cid:1867)(cid:1875)(cid:1872)(cid:1860)(cid:3053),(cid:3037),(cid:3047) represents GVC participation growth of a given macro-
sector,  (cid:1846)(cid:1832)(cid:1842)(cid:1859)(cid:1870)(cid:1867)(cid:1875)(cid:1872)(cid:1860)(cid:1833)(cid:1848)(cid:1829)	(cid:1858)(cid:1870)(cid:1867)(cid:1866)(cid:1872)(cid:1861)(cid:1857)(cid:1870)	(cid:3053),(cid:3037),(cid:3047) 
frontier  and 
(cid:1864)(cid:1866)(cid:3435)(cid:1872)(cid:1858)(cid:1868)	(cid:1864)(cid:1857)(cid:1874)(cid:1857)(cid:1864)	(cid:1833)(cid:1848)(cid:1829)(cid:3053),(cid:3037),(cid:3047)(cid:2879)(cid:2869)/(cid:1872)(cid:1858)(cid:1868)	(cid:1864)(cid:1857)(cid:1874)(cid:1857)(cid:1864)(cid:3053),(cid:3037),(cid:3036),(cid:3047)(cid:2879)(cid:2869)(cid:3439)  is  the  lagged  distance  to  the  GVC  frontier.  Equations  5 
and 6 are the baseline specifications for the evolution of TFP growth of the national frontier firms and 

technology  creation  at 

of  mid-productive 

firms.  The 

the  GVC 

is 

the other (non-frontier) firms in each sector of the host economies, respectively. To explore whether 

the basic links changed over the crisis and post-crisis periods, relative to the pre-crisis one, in a later 

stage  we  interact  our  proxies  for  technology  creation  and  absorption  as  well  as  GVC  participation 

growth with the crisis dummies.  

The proposed specifications (Equations 5 and 6) are estimated using fixed-effects, thus, exploiting the 

within  country/macro-sector  time  variation  of  the  variables.  Standard  errors  are  clustered  at  the 

country/macro-sector  level  to  adjust  for  common  group  effects.  Our  estimation  strategy  suffers 

potentially from an endogeneity bias because we cannot rule out the case that more productive sectors 

are  participating  more  intensively  in  GVCs  in  the  first  place.  An  omitted  variable  could  also  be 

responsible for the changes in TFP growth and GVC participation. Hence, we do not claim causality 

but  rather  correlation.  Nevertheless,  we  still  believe  that  investigating  the  relationship  between  the 

dynamics  of  the  GVC-related  variables  and  TFP  growth  of  host  firms  is  informative  and  offers 

interesting and important insights.  

ECB Working Paper Series No 2143 / April 201813 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
4.4 Data  

Given the specific country case which we analyse in this paper, we assume that CEE countries are the 
host economies and that parent firms are located in non-CEE EU countries.7 

Data on TFP growth of the frontier and non-frontier firms in parent and host countries are taken from 

the CompNet micro-aggregated database. CompNet is a research network created in 2012 within the 

European  System  of  Central  Banks  and  devoted  to  the  analysis  of  competitiveness  from  a 

multidimensional prospective. The CompNet database is based mainly on administrative data from the 

firm registries and constructed following a micro-distributed approach due to the confidential nature 

of  firm-level  information  in  most  countries  (Bartelsman  et  al.,  2004).  The  database  provides 

harmonised  cross-country  information  on  all  deciles  of  the  distribution  of  a  number  of  variables 

related to firm performance and competitiveness, including productivity, in a given country, macro-

sector  and  year.  In  total,  CompNet  covers  about  18  EU  countries,  nine  macro-sectors,  including 
manufacturing, construction and seven service sectors8, for the time period 2001-2013. Lopez-Garcia 
et al. (2015) provides further information on the dataset and coverage. 

CompNet data used  in this paper refer to  non-financial corporations with at least one employee (20 
employees  in  Poland,  Romania  and  Slovakia).  Our  final  sample  includes  nine  CEE  countries9,  nine 
non-CEE EU countries10 (used for constructing the GVC frontier), nine macro-sectors and 10 years, 
2003-2012, in order to achieve the most consistent coverage across countries, sectors and time. The 

final dataset is an unbalanced panel and covers, on average, around half of the employment and value 

added in each country (see Table A1 in Annex 1). 

Regarding  the  GVC-related  variables,  both  the  weights  to  compute  the  GVC  frontier  and  the 

participation  in  GVCs  are  computed  using  data  from  the  recent  release  of  the  WIOD  (2016).  The 

WIOD  combines  national  supply  and  uses  tables  with  bilateral  international  trade  data  so  that  it 

provides information on the final consumption and inter/intra-country intermediate supply and usage 

of goods and services. The sample covers 43 countries in total, including all EU28 countries (as well 
as  a  model  for  the  rest  of  the  world),  and  56  mainly  two-digits  sectors11  for  the  time  period  2000-
2014. Based on these data we construct the relative importance (weight) of each non-CEE EU country 

in  total  intermediate  imports/exports  of  CEE  countries  by  macro-sector  and  year  according  to 

Equations 3 and 4.  In addition to the GVC participation measures defined in Equations 1 and 2, we 

use  as  well  GVC  participation  indicators  based  on  the  value  added  concept  following  Wang  et  al. 

(2014).  These  capture  either  sector’s  backward  linkages,  depending  on  the  foreign  value  added 

incorporated in the host country-sector’s gross exports, or its forward linkages, which depend on the 

7 This assumption seems reasonable given that on average, during the period 2003-2012, CEE imports of intermediate inputs 
amounted to 43% of their domestic  intermediate input usage with half of them (about 22%) originating from other non-
CEE EU countries. 

8 Wholesale and retail trade; transportation and storage; accommodation and food service activities; information and 

communication; real estate activities; professional, scientific and technical services; and administrative and support 
service activities. 

9 Croatia, Estonia, Hungary, Latvia, Lithuania, Poland, Romania, Slovakia and Slovenia. 
10 Austria, Belgium, Denmark, Finland, France (sample 20+ employees), Germany, Italy, Portugal and Spain. 
11 Including three agricultural sectors, mining, 19 manufacturing sectors, construction, three utilities sectors, and 27 services 

sectors. 

ECB Working Paper Series No 2143 / April 201814 
                                                            
domestic value added in third countries’ gross exports (for details refer to Annex 1). Unless otherwise 

indicated, in the following we refer to the GVC measures based on the gross definition as our baseline 

indicators. 

Table 1 presents the descriptive statistics of our main variables, namely, growth of TFP of all different 

sets  of  host  firms,  as  well  as  of  the  GVC  frontier  and  participation  in  GVCs,  for  the  pre-  and 

crisis/post-crisis periods separately. In general, as expected, in the post-2008 period TFP growth rates 

have been significantly lower for all types of firms compared to their pre-crisis dynamics, while the 

slowdown of GVC participation in the post-crisis is evident if we include data up to 2014. 

Table 1: Descriptive statistics 

2003-
2007 

2008-
2010 

2011-
2012 

2003-
2012 

Bonferroni comparison of means by period 

2008-2010 
minus 2003-
2007 

2011-2012 
minus 2003-
2007 

2011-2012 
minus 2008-
2010 

TFP growth GVC 
frontier 
TFP growth sector 
TFP growth national 
frontier 
TFP growth mid-prod. 

TFP growth laggards 
GVC participation 
growth 
GVC participation 
growth up to 2014 

3.0 

4.7 

3.9 

3.8 

4.0 

1.4 

1.4 

-0.4 

-3.2 

-2.9 

-3.1 

-5.0 

-0.0 

-0.0 

-1.5 

2.8 

2.8 

2.5 

3.1 

2.0 

1.0 

0.9 

1.3 

1.1 

0.9 

0.4 

1.1 

-3.346*** 

-4.473*** 

-1.127 

-7.883*** 

-6.829*** 

-6.88*** 

-8.986*** 

-1.405*** 

-1.865 

-1.118 

-1.313 

-0.915 

0.639 

6.017*** 

5.711*** 

5.567*** 

8.071*** 

2.044*** 

Note:  Averages  across  periods.  Last  three  columns  show  the  pairwise  period  mean  difference  for  each  variable.  All  TFP 
growth  variables  are  defined  as:  growth(x)=ln(x)t-ln(x)t-1  and  refer  to  TFP  growth  of  the  respective  set  of  firms.  GVC 
participation based on imported inputs as a share of domestic intermediate usage.  
Bonferroni mean pairwise comparison test: *** p<0.01, ** p<0.05, * p<0.10. 

5 Results 

5.1. TFP growth of a host country/macro-sector  

We  start  off  by  exploring  TFP  growth  of  firms  in  a  given  host  country/macro-sector,  without 
distinguishing  between  national  frontier  firms  and  laggards.12  Then,  we  move  on  to  analyse  TFP 
growth of different types of firms in the host economy.  

According  to  our  results,  during  the  crisis  and  post-crisis  periods  sectoral  TFP  growth  in  the  CEE 

countries has been significantly lower compared to its pre-crisis level:  during the crisis period (2008-

2010)  TFP  growth  was  8.2pp  lower  than  in  pre-2008  average,  while  after  2010  (post-crisis)  it 

recovered  slightly  to  be  only  2.3pp  below  the  pre-crisis  period  (Table  2,  column  1).  Interestingly, 

when we control for the dynamics of the GVC-related variables, the crisis period is associated with a 

smaller drop in TFP growth and the post-crisis dummy is not significant anymore.  

12 Sector TFP growth is defined as the weighted average of TFP growth of the frontier, mid-productive and laggard firms in 

the sector, where weights depend on their respective employment share. 

ECB Working Paper Series No 2143 / April 201815 
 
 
 
 
 
 
                                                            
Table 2: TFP growth of the host country/macro-sector 

Only crisis 
dummies 

GVC 
participation 
based on 
gross 
imports 

GVC 
participati
on based 
on gross 
exports 

(1) 

(2) 

(3) 

GVC 
participation 
based on 
gross 
imports and 
exports 
(4) 

GVC 
participation 
based on 
value added 
(backward)  

GVC 
participation 
based on 
value added 
(forward) 

(5) 

(6) 

2008-2010 dummy 

Post-2010 dummy 

TFP growth GVC frontier based on 
imports (backward linkages) 

Lagged TFP gap GVC frontier  based 
on imports (backward linkages) 

TFP growth GVC frontier based on 
exports (forward linkages) 

Lagged TFP gap GVC frontier based on 
exports (forward linkages) 

GVC participation growth - based on 
imports (backward linkages) 

GVC participation growth - based on 
exports (forward linkages) 

-0.082*** 
(0.011) 
-0.023** 
(0.010) 

-0.051*** 
(0.014) 
-0.001 
(0.014) 

0.483*** 

(0.063) 

0.521*** 

(0.136) 

-0.048*** 
(0.015) 
-0.003 
(0.013) 

-0.044*** 
(0.013) 
0.002 
(0.013) 

-0.053*** 
(0.014) 
-0.003 
(0.014) 

-0.056*** 
(0.012) 
-0.003 
(0.013) 

0.438*** 

0.462*** 

(0.065) 

(0.068) 

0.402*** 

0.515*** 

(0.120) 

(0.137) 

0.167*** 

-0.020 

(0.054) 

(0.053) 

0.426*** 

0.133** 

(0.103) 

(0.055) 

0.182* 

(0.105) 

0.294** 

0.543** 

(0.126) 

(0.214) 

0.057 

-0.082 

(0.058) 

(0.055) 

0.208*** 

(0.062) 

0.421*** 

(0.095) 

-1.539 

(0.977) 

-1.231*** 
(0.286) 

Constant 

0.049*** 
(0.006) 

-1.662*** 
(0.439) 

-1.252*** 
(0.315) 

-1.678*** 
(0.441) 

-1.638*** 
(0.444) 

Observations 
Adjusted R-squared 

613 
0.062 

613 
0.345 

613 
0.267 

613 
0.353 

613 
0.350 

613 
0.299 

Note: *** p<0.01, ** p<0.05, * p<0.10. 
Robust standard errors in parentheses, clustered at the country-sector level. Country-sector FE included.  

Regarding our variables of interest, we find that both technology creation at the GVC frontier as well 

as the distance to the frontier are significantly correlated with sectoral TFP growth in CEE countries 

(Table  2,  columns  2-3).  Interestingly,  the  definition  of  GVC  participation,  based  either  on  import 

content of inputs (column 2) or on exports of inputs (column 3), matters. First, the correlation between 

sectoral TFP growth and technology creation at the GVC frontier is higher when the definition of the 

GVC  frontier  is  based  on  CEE  countries’  imports  of  intermediate  products  rather  than  on  exports. 

Second, while sectors which engage more in importing intermediate products grow faster than other 

sectors  in  terms  of  TFP,  this  is  not  the  case  for  sectors  which  display  higher  growth  of  exporting 

ECB Working Paper Series No 2143 / April 201816 
 
 
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
intermediate  inputs.  This  result  points  towards  the  existence  of  positive  externalities  for  firms 

operating  in  a  sector  with  a  growing  participation  in  GVCs  through  importing  intermediate  inputs, 

rather  than  exporting  inputs.  Column  4  (Table  2)  confirms  this  finding  by  regressing  sectoral  TFP 

growth on both measures of GVC participation simultaneously: only the linkages through importing 

intermediate inputs remain significant. 

Finally, columns 5 and 6 (Table 2) use the alternative definitions of GVC participation based on the 

foreign value added incorporated in the host country’s exports (backward) or on the domestic value 

added  included  in  a  third  countries’  exports  (forward).  Results  are  consistent  and  show  the  same 
picture.13  

Table  3  provides  further  evidence  for  the  robustness  of  the  results.  Columns  1  and  2  include  as  an 

additional control variable the R&D intensity of the host country/macro-sector, as spending in  R&D 

is  fundamental  for  TFP  growth.  Results  do  not  change,  and  the  R&D  intensity  variable  is  not 

significantly different from zero. Next, we substitute the GVC frontier by the EU frontier or add it as 

an  additional  control  variable  (columns  3  to  6).  The  EU  frontier  is  defined  as  the  unweighted  TFP 

growth  of  frontier  firms  in  non-CEE  EU  firms  and  proxies  technology  creation  in  non-CEE  EU 

countries  independently  of  their  GVC  links  with  the  host  economies.  In  columns  3  and  4  the  GVC 
frontier and distance to the frontier are substituted by the EU frontier14, whereas in columns 5 and 6 
both the GVC and EU frontiers (and distances to the respective frontier) are included.  

We find that when the GVC frontier is substituted by the EU frontier, TFP growth of the EU frontier 

and  distance  to  the  EU  frontier  still  matter  for  TFP  growth  of  the  host  economy,  though  the 

correlation  is  of  a  lower  magnitude.  Clearly,  there  are  trade  links  between  CEE  and  non-CEE  EU 

countries which facilitate technology transfer but they are not as tight as the ones  established  under 

the  umbrella  of  GVCs,  as  predicted  by  the  literature.  When  both  the  GVC  and  the  EU  frontier  are 

included together, we find that GVC linkages through importing inputs are the only ones which are 

important for technology transfer (EU frontier is not significant in column 5). Interestingly, in the case 

of forward linkages, i.e. exports of intermediates, what matters is the EU frontier rather than the GVC 

frontier  (column  6).  We  read  this  result  as  support  for  the  fact  that  the  impact  of  exports  on 

productivity  growth  comes  via  the  degree  of  sophistication  of  destination  markets  rather  than  via 

participation in GVCs (Brambilla et al. 2012). This result is further supported by the fact that, when 

distinguishing  between  different  types  of  host  firms,  this  result  holds  only  for  the  most  productive 

host firms –the ones exporting-, rather than for host non-frontier ones (see below). 

13 Note, however, that only the GVC participation indicators differ in this specification, relative to the one including GVC 
measures based on gross exports and imports. The GVC frontier has to be measured using gross flows due to the 
unavailability of data on value added flows from host country-sector to parent country-sector. 

14 The EU frontier is the same in columns 3 and 4 but column 3 includes the growth in GVC participation of the 

country/sector using the gross import definition and column 4 using the gross export one. 

ECB Working Paper Series No 2143 / April 201817 
 
 
 
                                                            
Table 3: Sectoral TFP growth - robustness 

GVC 
participation 
based on 
gross 
imports 

GVC 
participation 
based on 
gross 
exports 

(1) 

(2) 

GVC 
participation 
based on 
gross 
imports 
with EU 
frontier 
(3) 

GVC 
participation 
based on 
gross 
exports  
with EU 
frontier 
(4) 

GVC 
participation 
based on 
gross 
imports and 
EU frontier 

GVC 
participation 
based on 
gross 
exports and 
EU frontier 

-0.045** 
(0.020) 
0.009 
(0.020) 

-0.031* 
(0.018) 
0.032* 
(0.019) 

-0.033* 
(0.019) 
0.036** 
(0.018) 

2008-2010 dummy 

Post-2010 dummy 

TFP growth GVC frontier based on 
imports (backward linkages) 

Lagged TFP gap GVC frontier  based 
on imports (backward linkages) 

TFP growth GVC frontier based on 
exports (forward linkages) 

Lagged TFP gap GVC frontier based on 
exports (forward linkages) 

GVC participation growth - based on 
imports (backward linkages) 

GVC participation growth - based on 
exports (forward linkages) 

R&D intensity 

TFP growth EU frontier  

Lagged TFP gap EU frontier to sector 

-0.045** 
(0.020) 
0.022 
(0.019) 

0.547*** 

(0.081) 

0.572*** 

(0.090) 

0.212* 

(0.114) 

-0.473 
(0.566) 

(5) 

-0.039** 
(0.015) 
0.018 
(0.015) 

0.404*** 

(0.070) 

0.361** 

(0.137) 

(6) 

-0.033* 
(0.018) 
0.027 
(0.017) 

0.051 

(0.058) 

0.192** 

(0.073) 

0.044 

(0.058) 

0.183*** 

(0.066) 

0.472*** 

(0.075) 

0.136** 

(0.053) 

-0.152 
(0.638) 

0.330*** 

(0.122) 

0.179 

(0.112) 

0.049 

(0.054) 

0.327** 
(0.134) 
0.482*** 
(0.118) 

-1.842*** 
(0.463) 

0.365*** 
(0.126) 
0.490*** 
(0.115) 

-1.872*** 
(0.452) 

0.136 
(0.099) 
0.182** 
(0.074) 

-1.853*** 
(0.459) 

0.261** 
(0.107) 
0.335*** 
(0.093) 

-1.849*** 
(0.435) 

Constant 

-1.861*** 
(0.300) 

-1.418*** 
(0.232) 

Observations 
Adjusted R-squared 
Note: *** p<0.01, ** p<0.05, * p<0.10. 
Robust standard errors in parentheses, clustered at the country-sector level. Country-sector FE included. R&D intensity measured as fixed capital 
formation out of value added. 

613 
0.297 

613 
0.352 

613 
0.313 

350 
0.410 

350 
0.337 

613 
0.306 

ECB Working Paper Series No 2143 / April 201818 
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
 
5.2 TFP growth of host national frontier firms 

Turning to the analysis of different types of firms in the host economy, Table 4 shows the results for 

the TFP growth of the most productive firms in the sector – the national frontier firms (Equation 5).  

The  baseline  results  in  Table  4,  columns  2  and  4,  are  consistent  with  the  ones  for  the  sectoral  TFP 

growth  (Table  2)  and  confirm  the  importance  of  GVCs:  both  technology  creation  by  the  parent 

companies  as  well  as  the  catching-up  process  are  strongly  correlated  with  the  TFP  growth  of  the 

national frontier firms in the host economy. Also, similar to Table 2, the effect of the crisis dummy 

drops  by  a  third  when  including  the  rest  of  GVC-related  explanatory  variables.  The  findings  are 

robust  to  the  definition  of  GVC  participation  in  terms  of  backward  or  forward  linkages.  Again,  the 

correlation between the GVC-related variables and the TFP growth of the CEE frontier firms is lower 

when  considering  the  exports  of  intermediate  inputs,  rather  than  the  imports.  The  results  are  also 

robust to the definition of GVC linkages using value added trade flows (Table A3.1 in Annex 3).  

In Annex 3 we show the estimation of Equation 5 including also the EU frontier. Similar to the case 

of  sectoral  TFP  growth,  we  find  that  importing  inputs  is  the  main  channel  of  technology  transfer 

within GVCs. However, the GVC frontier loses significance in favour of the EU frontier when trade 

links are measured by the exports of intermediate inputs. This result confirms that for firms exporting 

intermediate inputs what matters is the export destination market rather than having direct linkages to 

the frontier firms in the parent countries. 

Columns  3  and  5  of  Table  4  interact  all  explanatory  variables  with  the  crisis  and  post-crisis  period 

dummies.  The  purpose  is  to  explore  whether  the  capacity  to  learn  from  direct  exposure  to  new 

technology and to catch-up have changed over the crisis and post-crisis period relative to the pre-crisis 

period  (reference  category).  The  results  suggest  that  the  absorptive  capacity  of  CEE  frontier  firms 

dropped significantly in the post-2008 period relative to the pre-crisis one. The results disclose as well 

that sectors with relatively high backward linkages were actually more resilient than the rest of sectors 

to  the  crisis  (and  post-crisis)  TFP  slowdown.  Interestingly,  this  result  does  not  hold  for  sectors 

involved in exporting of intermediates.  

ECB Working Paper Series No 2143 / April 201819 
 
 
 
 
 
 
 
 
 
Table 4: TFP growth of national frontier firms 

2008-2010 dummy 

Post-2010 dummy 

TFP growth GVC frontier 

TFP growth GVC frontier 
*2008-2010 dummy 

TFP growth GVC frontier 
*post-2010 dummy 

(1) 

-0.069*** 
(0.010) 
-0.012 
(0.009) 

GVC participation based 
on gross imports 
(3) 
(2) 

GVC participation based 
on gross exports 
(5) 
(4) 

-0.046*** 
(0.010) 
0.004 
(0.011) 
0.430*** 
(0.058) 

0.033 
(0.032) 
0.073* 
(0.039) 
0.445*** 
(0.118) 

0.045 

(0.145) 

-0.245* 

(0.143) 

-0.049*** 
(0.010) 
0.001 
(0.010) 
0.156*** 
(0.044) 

0.010 
(0.029) 
0.052 
(0.034) 
0.182*** 
(0.062) 

0.031 

(0.108) 

-0.232** 

(0.089) 

Lagged gap TFP GVC frontier to national 
frontier 

0.364*** 

0.386*** 

0.281*** 

0.294*** 

Lagged gap*2008-2010 dummy 

Lagged gap*post-2010 dummy 

GVC participation growth 

GVC participation growth 
*2008-2010 dummy 

GVC participation growth 
*post-2010 dummy 

Constant 

(0.054) 

0.199** 
(0.079) 

0.040*** 
(0.005) 

-0.991*** 
(0.148) 

(0.052) 
-0.030*** 
(0.010) 
-0.028** 
(0.012) 
-0.080 
(0.107) 

0.358*** 

(0.134) 

0.317* 

(0.181) 
-1.048*** 
(0.145) 

(0.044) 

0.079** 
(0.036) 

-0.691*** 
(0.112) 

Observations 
Adjusted R-squared 
Note: *** p<0.01, ** p<0.05, * p<0.10. 
Robust standard errors in parentheses, clustered at the country-sector level. Country-sector FE included.  

642 
0.355 

642 
0.087 

642 
0.334 

642 
0.224 

(0.041) 
-0.024** 
(0.010) 
-0.020 
(0.013) 
0.027 
(0.069) 

0.094 

(0.089) 

-0.003 

(0.082) 
-0.721*** 
(0.106) 

642 
0.235 

5.3 TFP growth of host laggard firms 

The  next  step  in  the  empirical  analysis  is  to  investigate  the  determinants  of  TFP  growth  of  non-

frontier  CEE  firms  (Equation  6)  considering  firms  at  the  bottom  percentiles  of  the  productivity 

distribution.  Following the crisis, TFP growth of the laggard firms has been about 9pp lower relative 

to the pre-crisis period (Column 1, Table 5), though, this effect drops considerably once including the 

GVC-related variables in the regression. 

Two  key  messages  emerge  from  Table  5.  First,  technology  creation  at  the  GVC  frontier  is 

substantially  less  important  for  TFP  growth  of  laggards  than  technology  creation  at  the  national 

ECB Working Paper Series No 2143 / April 201820 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
frontier in the host economy, confirming the two-step diffusion process put forward by Bartelsman et 

al. (2013). Second, firms at the bottom of the productivity distribution also display higher TFP growth 

rates when they operate in sectors with high GVC participation growth rates (relative to laggard firms 

in  sectors  with  low  GVC  participation).  Hence,  the  larger  the  integration  of  sectors  in  GVCs,  the 

higher the exposure of laggard firms to the frontier technology.  

Table A3.2 in Annex 3 shows the results  of interacting all explanatory variables with the crisis  and 

post-crisis  dummies  in  order  to  explore  whether  the  basic  relations  changed  during  the  post-2008 

period. Differently from what we found for frontier firms in host economies, the interactions are not 

significant while the main results still hold. Thus, the basic correlation did not change during and after 

the crisis relative to the pre-crisis period for the laggard firms.   

Table 5: TFP growth of host laggard firms 

2008-2010 dummy 

Post-2010 dummy 

TFP growth national frontier 

Lagged gap TFP national frontier to laggards 

TFP growth GVC frontier 

Lagged gap TFP GVC frontier to laggards 

GVC participation growth 

Constant 

(1) 

-0.090*** 
(0.011) 
-0.009 
(0.012) 

0.040*** 
(0.005) 

GVC 
participation 
based on gross 
imports 
(2) 

GVC 
participation 
based on gross 
exports 
(3) 

-0.025*** 
(0.008) 
-0.022** 
(0.008) 
0.920*** 
(0.049) 
0.569*** 
(0.080) 

0.151*** 
(0.041) 
0.010 
(0.024) 

0.203** 
(0.079) 
-1.054*** 
(0.113) 

-0.029*** 
(0.008) 
-0.027*** 
(0.008) 
0.947*** 
(0.051) 
0.560*** 
(0.077) 

0.060* 
(0.036) 
0.041 
(0.026) 

0.068** 
(0.032) 
-1.168*** 
(0.111) 

642 
0.727 

Observations 
Adjusted R-squared 
Note: *** p<0.01, ** p<0.05, * p<0.10. 
Robust standard errors in parentheses, clustered at the country-sector level. Country-sector FE included.  

642 
0.736 

642 
0.092 

Table A3.3 in Annex 3 presents the results for laggard firms in the host economy: (i) including at the 

same  time  backward  and  forward  linkages;  (ii)  including  value-added-based  indicators  of  GVC 

ECB Working Paper Series No 2143 / April 201821 
 
  
 
  
  
  
  
 
  
  
  
  
  
 
  
  
  
 
  
  
  
 
 
 
 
 
 
participation;  and  (iii)  including  the  EU  frontier  in  addition  to  the  GVC  frontier.  None  of  the 

robustness  checks  changes  our  main  findings.  Differently  from  the  results  for  the  national  frontier 

firms, all variables related to the EU frontier are not significant. An explanation for this finding might 

be the fact that laggard  firms are probably engaged  to a lesser extent in trade with frontier  firms in 

non-CEE EU countries.  

The results for mid-productive firms in the host economy are very similar to the laggard ones and are 

shown in Table A3.4 of Annex 3. Annex 3 shows also several other robustness exercises. Table A3.5 

includes the  results from  estimating the baseline specification  for each  set  of  firms (sector, frontier, 

laggard  and  mid-productive)  including  country-  and  sector-fixed  effects  separately  as  well  as  year-

fixed  effects  instead  of  controlling  for  country-sector  fixed  effects  and  the  three  time  periods  (pre-, 

post-  and crisis). This specification,  which also  corrects for the  cyclicality of TFP  growth, provides 

very  similar  findings  to  our  baseline.  In  Table  A3.6  we  include  as  a  control  the  level  of  GVC 

participation  in  the  host  country/macro-sector  instead  of  its  growth  rate.  The  findings  provide 

evidence that not only operating in sectors with growing participation in GVCs results in higher TFP 

growth, but also in sectors with a higher level of GVC integration. 

6 Drivers of the post-crisis TFP growth slowdown in CEE countries 

6.1 Shift-share analysis 

In this section, we use the results of our parametric exercise to explore the sources of the TFP growth 

slowdown in CEE economies in the post-crisis period relative to the pre-crisis one. In order to assess 

which are the main contributing factors – a decrease in the value of the regressor between the pre- and 

post-crisis  period  or  a  change  in  the  estimated  elasticities  -  we  carry  out  a  shift-share  exercise.  The 

shift-share analysis decomposes the aggregate growth rate of a given variable, in this case the change 

in TFP growth from the pre- to the post-crisis period, in the following components: (i) change in the 

value of the given determinant holding its coefficient constant at the pre-crisis level; (ii) change in the 

elasticity  of  each  determinant,  holding  its  value  constant  at  the  pre-crisis  level;  and  finally  (iii)  a 

covariance term between the change in the determinant and the coefficient. We depart from Equation 

5, the TFP growth specification for the national frontier firms, and expand it to include all interactions 

with  the  period  dummies  considered  in  Table  4,  columns  3  and  5.  The  predicted  change  in  TFP 

growth  of  national  frontier  firms  in  host  country  z  from  the  pre-crisis  period  t-1  to  the  post-crisis 
period t can be expressed then as follows15: 

∆(cid:1846)(cid:1832)(cid:1842)(cid:1859)(cid:1870)(cid:1867)(cid:1875)(cid:1872)(cid:1860)(cid:3053),(cid:3047)

(cid:3006) (cid:3404) ∑

(cid:3049)∈(cid:3023)

∆(cid:1856)(cid:3053),(cid:3047)

(cid:3049) (cid:3400) (cid:1875)(cid:3047)(cid:2879)(cid:2869)

(cid:3049)

(cid:3397) ∑

(cid:3049)∈(cid:3023)

(cid:3049)
(cid:1856)(cid:3053),(cid:3047)(cid:2879)(cid:2869)

(cid:3049)
(cid:3400) ∆(cid:1875)(cid:3047)

(cid:3397) ∆(cid:1856)(cid:3053),(cid:3047)

(cid:3049) (cid:3400) ∆(cid:1875)(cid:3047)

(cid:3049) 	 (cid:3397) ∆(cid:1870)(cid:3047)																		 

(7)

Change in predicted 
TFP growth of 
national frontier firms 
from the pre-crisis to 
the post-crisis period 

Change in value of 
the regressor, 
holding its 
coefficient at the 
pre-crisis level 

Change in the 
coefficient of the 
regressor, holding its 
value at the pre-
crisis level 

Covariance 
between value of 
regressor and 
coefficients 

Residual 

15 The predicted TFP change is first estimated for each macro-sector j and then aggregated to the country level using the 

sectoral value added as weights. 

ECB Working Paper Series No 2143 / April 201822 
 
 
 
 
 
 
                                                            
(cid:3049)   represents  the  aggregated  value  of  the  variable  v  at  time  t  in 
(cid:3049) is the estimated coefficient of v at time t, r is the residual and ∆ is the change between 

where  V  is  the  set  of  regressors,  (cid:1856)(cid:3053),(cid:3047)
country z, (cid:1875)(cid:3047)
the pre-crisis and the post-crisis period.  

We find that the main contributor to the decline in predicted TFP growth of the host national frontier 

firms is the decrease in absorptive capacity of host firms, captured by the decrease in the coefficient 

of  the  TFP  gap  to  the  GVC  frontier  (red  bars  with  diagonal  pattern  in  Figure  7).  The  TFP  growth 

slowdown in non-CEE EU parent companies (solid light blue bars) also has played an important role. 

Figure 7: Shift-share analysis of national frontier predicted TFP growth, 2011-2012 vs. 2003-2007 
(in percentage points) 

Change in constant/residual

GVC frontier growth

Gap with GVC frontier

GVC participation

Change in predicted TFP growth

Change in data TFP growth

HR

EE

HU

LV

LT

PL

RO

SK

SI

15

10

5

0

-5

-10

-15

Legend
Constant/Residual Data ch. Coeff. ch. Covariance Data ch. Coeff. ch. Covariance Data ch. Coeff. ch. Covariance

Gap with GVC frontier

GVC participation

GVC frontier

Note: Changes in contributions can be split into (i) a change in the period data averages, times the previous coefficient; (ii) a 
change in the coefficient, times the previous period data average; (iii) a covariance term. 

Not sign.

6.2 The role of research and development 

Given  the  relevant  role  played  by  the  drop  in  absorptive  capacity  of  national  frontier  firms  in 

explaining CEE countries’ slowdown in TFP growth, this sub-section explores possible factors behind 

that  development.  The  seminal  work  of  Cohen  and  Levinthal  (1989)  shows  that  firms’  absorptive 

capacity  depends  crucially  on  their  investment  in  human  capital  and  intangibles,  particularly  R&D 

(Cohen and  Levinthal, 1989;  Griffith et  al.,  2004).  According to the  descriptive  evidence,  shown in 

Annex 2 (Figures A2.1 and A2.2), R&D spending as a share of GDP (or of population) has decreased 

or flattened in CEE countries during the 2011-2015 period. This development might have hindered the 

ECB Working Paper Series No 2143 / April 201823 
 
 
 
 
absorptive capacity of national frontier firms in CEE countries, thus, amplifying their post-crisis TFP 

growth slowdown.  

In  order  to  investigate  the  role  of  R&D  for  TFP  growth  of  frontier  firms,  we  follow  the  approach 

suggested by Rajan and Zingales (1998) given the lack of firm-level information on R&D. The idea is 

that  the  drop  in  R&D  investment  by  firms  in  the  host  economy  will  affect  relatively  more  the 

absorptive capacity of  firms operating in sectors which are more R&D-intensive. In other sectors, less 

dependent  on  R&D,  the  absorptive  capacity  of  firms  might  not  suffer  as  much  due  to  the  lower 

investment in intangibles. Therefore, we split sectors in a given host country-year according to their 

R&D  intensity  (defined  as  fixed  capital  formation  in  R&D  as  a  share  of  sectoral  value  added).  

Sectors with R&D intensity above the country-year median are labelled R&D-intensive sectors, while 
the sectors below the median are less R&D-intensive.16 

Table 6 shows the results of re-estimating Equation 5 considering separately R&D-intensive and less 

intensive sectors. Because we do not have data on R&D investment for all countries, sectors and years 

used in the previous analysis, we first show in column 1 the baseline results of Table 4 (pooling all 

sectors together) but using the sub-sample of country-sector-years with R&D data. Baseline results in 

this sub-sample are confirmed providing confidence that the results do not suffer from a sample bias. 

We find that the drop in absorptive capacity of host firms in the post-crisis period is significant only 

in R&D-intensive sectors, while there is no evidence of a change in elasticities for firms operating in 

less  R&D-intensive  sectors  (columns  2  and  3).  Furthermore,  the  results  referring  to  the  pre-crisis 

period  are  quite  similar  for  R&D-intensive  and  less  intensive  sectors.  Table  6  provides,  thus, 

suggestive  evidence  that  the  observed  drop  in  absorptive  capacity  of  host  frontier  firms  during  the 
post-crisis period might be the result of their reduction in R&D investment.17  

As  a  robustness  check,  Table  A3.7  in  Annex  3  uses  the  intellectual  property  products  (IPP)  as  a 

different measure of investment in intangibles. Although less significant, we still find that the drop in 

absorptive  capacity  of  frontier  firms  during  the  crisis  period  is  relatively  more  pronounced  in  IPP-

intensive sectors. 

16 The results are robust to using different measures of R&D intensity such as the level of gross fixed capital formation in 

R&D or its share of sectoral output. 

17 Another possible explanation, provided by Acemoglu et al. (2006), is that economies closer to the frontier switch to more 
R&D-intensive activities, while at the early stages of development they follow a rather investment-based strategy. 

ECB Working Paper Series No 2143 / April 201824 
 
 
 
 
 
 
 
                                                            
Table 6: TFP growth of national frontier firms, sectors split by R&D intensity (as % of value added) 

Baseline for R&D 
sample (pooled 
sectors) 
(1) 

R&D-intensive 
sectors 

Less R&D-
intensive sectors 

(2) 

(3) 

2008-2010 dummy 

Post-2010 dummy 

TFP growth GVC frontier 

TFP growth GVC frontier*2008-2010 dummy 

TFP growth GVC frontier*Post-2010 dummy 

Lagged gap TFP GVC frontier to national frontier 

Lagged gap*2008-2010 dummy 

Lagged gap*2011-2012 dummy 

GVC participation growth 

GVC participation growth*2008-2010 dummy 

GVC participation growth*2011-2012 dummy 

Constant 

-0.045*** 
(0.014) 
0.016 
(0.015) 
0.464*** 
(0.065) 

0.481*** 
(0.063) 

0.204** 
(0.096) 

-1.361*** 
(0.183) 

0.027 
(0.038) 
0.088* 
(0.043) 
0.677** 
(0.255) 
-0.320 
(0.250) 
-0.494** 
(0.212) 
0.530*** 
(0.090) 
-0.024* 
(0.014) 
-0.023* 
(0.012) 
-0.183 
(0.148) 
0.618** 
(0.250) 
0.344* 
(0.191) 
-1.447*** 
(0.258) 

Observations 
Adjusted R-squared 
Note: *** p<0.01, ** p<0.05, * p<0.10. 
Robust standard errors in parentheses, clustered at the country-sector level. Country-sector FE included.  

184 
0.461 

369 
0.413 

0.013 
(0.084) 
0.084 
(0.087) 
0.416 
(0.265) 
0.005 
(0.347) 
0.028 
(0.274) 
0.511*** 
(0.108) 
-0.027 
(0.023) 
-0.036 
(0.029) 
-0.538 
(0.444) 
1.217* 
(0.631) 
1.069+ 
(0.713) 
-1.480*** 
(0.327) 

185 
0.408 

7 Conclusion 

The literature has long emphasized the role of GVCs for spurring productivity growth. However, less 

is known about the mechanisms through which GVCs contribute to the development process.  

In this paper, we focus on the role of GVCs as a channel of technology transfer from parent firms to 

host economies. In particular, we explore the case of CEE countries, which are deeply integrated in 

GVCs and, therefore, depend to a large extent on technology creation at the parent firms, what we call 

the  GVC  frontier.  We  show  that  the  main  channel of  technology diffusion  between  parent  and  host 

firms is backward linkages, that is, the import of intermediate inputs, rather than the forward linkages. 

According  to  the  literature,  host  firms  importing  inputs  are  exposed  to  more  variety  and  better 

complementarities as well as better technology embedded in the imported inputs. Moreover, in order 

ECB Working Paper Series No 2143 / April 201825 
  
  
  
  
  
  
  
  
  
 
  
 
 
  
 
  
 
  
 
 
  
 
  
 
  
 
 
  
 
  
  
 
to  include  foreign,  more  complex,  inputs  in  the  production  function  host  firms  might  need  to  hire 

skilled workers which increase their productivity. We find as well that firms exporting inputs to non-

CEE EU  countries, independently of their  GVC links with  host economies, also feature  higher  TFP 

growth. This second result is in line with the findings of Brambilla et al. (2012) on the importance for 

productivity of the technological sophistication of export destinations.  

Our results confirm the idea that technology created at the global level is diffused to the rest of the 

economies  via  a  two-stage  process.  In  particular,  frontier  firms  are  directly  involved  in  GVCs  and 

exposed  to  new  technology,  while  non-frontier  firms  mainly  benefit  from  their  participation  in 

domestic  production  networks,  as  well  as,  to  a  lesser  extent,  from  direct  contact  with  parent 

companies.  

We  use  these  results  to  explain  the  slowdown  in  TFP  growth  of  CEE  countries  in  the  post-crisis 

period.  We  find  that  the  main  contributor  to  this  decline  is  the  decrease  in  host  firms’  absorptive 

capacity of new knowledge, possibly caused by a drop in R&D investment, as well as the slowdown 

in technology creation of parent firms.  

ECB Working Paper Series No 2143 / April 201826 
 
 
References 

Acemoglu,  D.,  F.  Zilibotti  and  P.  Aghion  (2006):  “Distance  to  Frontier,  Selection,  and  Economic 
Growth”, Journal of the European Economic Association, Vol. 4(1), 37-74. 

Acemoglu,  D.,  U.  Akcigit  and  W.  Kerr  (2015):  “Networks  and  the  Macroeconomy:  An  Empirical 
Exploration”, NBER Macroeconomics Annual, Vol. 30, 273-335. 

Ackerberg,  D.,  K.  Caves  and  G.  Frazer  (2006):  “Structural  Identification  of  Production  Functions”, 
MPRA Paper No. 38349. 

Aghion,  P.  and  P.  Howitt  (2006):  “Joseph  Schumpeter  Lecture  Appropriate  Growth  Policy:  A 
Unifying Framework”, Journal of the European Economic Association, Vol. 4 (2-3), 269-314. 

Amiti,  M.  and  J.  Konings  (2007):  "Trade  Liberalization,  Intermediate  Inputs,  and  Productivity: 
Evidence from Indonesia ”, The American Economic Review, Vol. 97 (5), 1611-1638. 

Baldwin J. and B. Yan (2014): “Global Value Chains and the Productivity of Canadian Manufacturing 
Firms”, Economic Analysis Research Paper Series, No. 90. 

Bartelsman,  E.  J.,  J.  Haltiwanger  and  S.  Scarpetta  (2013):  “Cross-Country  Differences  in 
Productivity: The Role of Allocation and Selection”, The American Economic Review, Vol. 103 (1), 
305-334. 

Bartelsman,  E.  J.,  J.  Haltiwanger  and  S.  Scarpetta  (2004):  “Microeconomic  Evidence  of  Creative 
Destruction in Industrial and Developing Countries”, World Bank Policy Research Paper No. 3464. 

Bas,  M.  and  V.  Strauss-Kahn  (2014): “Does  Importing  More  Inputs  Raise  Exports?  Firm-level 
Evidence from France”, Review of World Economics, Vol. 150 (2), 241–275. 

Bloom,  N.,  K.  Manova,  J.  Van  Reenen,  S.  Sun  and  Z.  Yu  (2017):  “Managing  Trade:  Evidence 
from China and the US”, mimeo. 

Brambilla,  I.,  D.  Lederman,  and  G.  Porto  (2012):  “Exports,  Export  Destinations,  and  Skills”,  The 
American Economic Review, Vol. 102 (7), 3406–3438. 

Brandt,  L.,  G.  T.  Rawski,  and  J.  Sutton  (2008):  “China’s  Industrial  Development”,  in  Brandt  and 
Rawski (eds.), China’s Great Economic Transformation, 593–604. 

Bustos,  P.  (2011):  “Trade  Liberalization,  Exports,  and  Technology  Upgrading:  Evidence  on  the 
Impact  of  MERCOSUR  on  Argentinian  Firms”,  The  American  Economic  Review,  Vol.  101  (1), 
304-340. 

Çagatay,  B.  and  R.  de  Haas  (2015):  “The  Limits  of  Lending:  Banks  and  Technology  Adoption 
across Russia”, CESifo Working Paper No. 5461. 

Cohen, W. M. and D. A. Levinthal (1989): “Innovation and Learning: The Two Faces of R&D”, The 
Economic Journal, Vol. 99 (397), 569-596. 

Comin,  D.  and  B.  Hobijn  (2010):  “An  Exploration  of  Technology  Diffusion”,  The  American 
Economic Review, Vol. 100 (5), 2031-2059. 

CompNet Taskforce (2014): “Micro-based evidence of EU competitiveness: The CompNet database”, 
ECB Working Paper No. 1634.  

Csillag, M. and M. Koren (2011): “Machines and Machinists: Capital-Skill Complementarity from an 
International Trade Perspective”, CEPR Discussion Paper No. 8317. 

De  Loecker,  J.  (2013):  “Detecting  Learning  by  Exporting”,  The  American  Economic  Journal: 
Microeconomics, Vol. 5 (3), 1-21. 

Galuščák,  K.  and  L.  Lízal  (2011):  “The  Impact  of  Capital  Measurement  Error  Correction  on 
Firm-Level Production Function Estimation”, Czech National Bank Working Paper No. 9. 

ECB Working Paper Series No 2143 / April 201827 
Goldberg, P. K., A. K. Khandelwal, N. Pavcnik and P. Topalova (2010): “Imported Intermediate 
Inputs  and  Domestic  Product  Growth:  Evidence  from  India”,  The  Quarterly  Journal  of 
Economics, Vol. 125 (4), 1727-1767. 

Griffith, R., S. Redding and J. Van Reenen (2004): “Mapping the Two Faces of R&D: Productivity 
Growth in a Panel of OECD Industries”, The Review of Economics and Statistics, Vol. 86 (4), 883-
895. 

Grossman,  G.  M.  and  E.  Helpman  (1991):  “Trade,  Knowledge  Spillovers,  and  Growth”,  European 
Economic Review, Vol. 35 (2-3), 517-526. 

Halpern,  L.,  M.  Koren  and  A.  Szeidl  (2015):  “Imported  Inputs  and  Productivity”,  The  American 
Economic Review, Vol. 105 (12), 3660-3703. 

Iacovone,  L.  and  G.  Crespi  (2010):  “Catching  Up  with  the  Technological  Frontier:  Micro-level 
Evidence on Growth and Convergence”, Industrial and Corporate Change, Vol. 19 (6), 2073-2096. 

Kee, H. L. (2015): “Local Intermediate Inputs and the Shared Supplier Spillovers of Foreign Direct 
Investment”, Journal of Development Economics, Vol. 112, 56-71. 

Koopman, R., Z. Wang and S.-J. Wei (2014): “Tracing Value-Added and Double Counting in Gross 
Exports”, The American Economic Review, Vol. 104 (2), 459-494. 

Levinson,  J.  and  A.  Petrin  (2003):  “Estimating  Production  Functions  Using  Inputs  to  Control  for 
Unobservables”, The Review of Economic Studies, Vol. 70 (2), 317-341. 

Lopez-Garcia,  P.,  F.  di  Mauro  and  the  CompNet  Task  Force  (2015):  “Assessing  European 
Competitiveness: The New CompNet Micro-Based Database”, ECB Working Paper No. 1764. 

Lopez-Garcia, P. and D. Taglioni (2018): “Knowledge Transfers from International Openness in 
Trade  and  Investment:  The  European  Case”  in  D.  M.  Gould,  (ed.),  Critical  Connections:  Why 
ECA’s Regional and Global Network of Interconnections Matter for Growth and Stability, World 
Bank, forthcoming. 

MacGarvie, M. (2006): “Do Firms Learn from International Trade?”, The Review of Economics and 
Statistics, Vol. 88 (1), 46-60. 

Mariscal, A. and D. Taglioni (2017): “GVCs as Source of Firm Capabilities”, mimeo. 

OECD (2015): “The Future of Productivity”, OECD Publishing. 

Olley,  G.  S.  and  A.  Pakes  (1996):  “The  Dynamics  of  Productivity  in  the  Telecommunications 
Equipment Industry”, Econometrica, Vol. 64 (6), 1263-1297. 

Rajan, R. G. and L. Zingales (1998): “Financial Dependence and Growth”, The American Economic 
Review, Vol. 88 (3), 559-586. 

Saia, A., D. Andrews and S. Albrizio (2015): “Productivity Spillovers from the Global Frontier and 
Public Policy: Industry-Level Evidence”, OECD Economics Department Working Paper No. 1238. 

Schott,  P.  K.  (2008):  “The  Relative  Sophistication  of  Chinese  Exports”,  Economic  Policy,  Vol.  23 
(53), 5–49. 

Sutton,  J.  (2007):  “Quality,  Trade  and  the  Moving  Window:  The  Globalisation  Process”,  The 
Economic Journal, Vol. 117 (524), F469-F498. 

Timmer,  M.,  B.  Los,  R.  Stehrer  and  G.  J.  de  Vries  (2016):  “An  Anatomy  of  the  Global  Trade 
Slowdown based on the WIOD 2016 Release”, GGDC Research Memorandum No. 162, University of 
Groningen. 

Van der Wiel, H., H. Creusen, G. van Leeuwen and E. van der Pijll (2008): "Cross Your Border and 
Look Around", DEGIT Conference Papers, DEGIT, Dynamics, Economic Growth, and International 
Trade. 

ECB Working Paper Series No 2143 / April 201828 
Wang, Z., S.-J. Wei and K. Zhu (2014): “Quantifying International Production Sharing at the Bilateral 
and Sector Levels”, Office of Economics of the U.S. International Trade Commission, Working Paper 
No. 2014-04A. 

Wooldridge, J. M. (2009): “On Estimating Firm-level Production Functions Using Proxy Variables to 
Control for Unobservables”, Economics Letters, Vol. 104 (3), 112-114. 

ECB Working Paper Series No 2143 / April 201829 
 
 
Annex 1 

Table A1.1: Data coverage  

Country 

Sample period 

Missing sectors  

Exclusion rule 

Coverage vis-à-vis 
National Accounts 1 
Employment 

VA 

56% 
58% 
56% 
59% 
59% 

38% 

75% 
34% 
49% 
43% 
36% 

29% 

Croatia 
Estonia 
Hungary 
Latvia 
Lithuania 

Poland  

2003-2012 
2003-2012 
2004-2012 
2006-2012 
2003-2011 

2005-2012 

none  
none 
none 
none 
none 

>19 employees 

none 
none 
none 
none 
none 
Accommodation and 
food service activities 
none 
none 
none 

2004-2012 
2003-2012 
2003-2012 

Romania 
Slovakia  
Slovenia 
1 Source: Eurostat – National Accounts Series. 
Note:  CompNet data for Poland, Romania and Slovakia refer to firms with 20 employees or more. Average coverage over 
the respective sample period. 

>19 employees 
>19 employees 
none 

41% 
38% 
55% 

47% 
67% 
44% 

Variable definitions 

a.  Sector TFP growth: 

(cid:1846)(cid:1832)(cid:1842)	(cid:1859)(cid:1870)(cid:1867)(cid:1875)(cid:1872)(cid:1860)(cid:3053),(cid:3037),(cid:3047) ≅ ∑

(cid:3036)(cid:2880)(cid:3043)(cid:2869)(cid:2868)(cid:2879)(cid:2870)(cid:2868),(cid:3043)(cid:2871)(cid:2868)(cid:2879)(cid:2875)(cid:2868),(cid:3043)(cid:2876)(cid:2868)(cid:2879)(cid:2877)(cid:2868)

(cid:1875)(cid:3053),(cid:3037),(cid:3036),(cid:3047)(cid:1872)(cid:1858)(cid:1868)	(cid:1859)(cid:1870)(cid:1867)(cid:1875)(cid:1872)(cid:1860)(cid:3053),(cid:3037),(cid:3036),(cid:3047)

where  z  stands  for  country,  j  for  sector,  t  for  year  and  i  for  the  set  of  firms  belonging  to  either  the 
average of the 10th and 20th percentiles (laggard firms), the average of the  30th to 70thpercentiles (mid-
productive  firms),  or  the  average  of  the  80th  and  90th  percentiles  of  the  TFP  distribution  (frontier 
firms).18  w captures the (time-varying) share of employment of each set of firms.19  

b.  TFP level and growth: 

TFP  is  computed  as  the  difference  between  observed  and  expected  firm-level  output,  given  the 

industry-specific coefficients of labour and capital estimated using a semi-parametric approach. More 

concretely, we depart from a Cobb-Douglas production function: 

(cid:1844)(cid:1848)(cid:1827)(cid:3036)(cid:3047) (cid:3404) 	 (cid:1827)(cid:3036)(cid:3047)(cid:1837)(cid:3036)(cid:3047)

(cid:3080)(cid:1838)(cid:3036)(cid:3047)

(cid:2869)(cid:2879)(cid:3080) 

where RVA is real value added of firm i at time t operating in a given two-digit industry (according to 

the  NACE  rev.2  sector  classification),  K  is  its  real  book  value  of  net  capital  and  L  its  total 

employment. The above production function is estimated in logs building on the approach developed 

by  Olley  and  Pakes  (1996),  Levinshon  and  Petrin  (2003),  Ackerberg  et  al  (2006)  and  using  an 

alteration of Wooldridge (2009) proposed by Galuščák and Lízal (2011). The approach uses (lagged 

value  of)  inputs  to  control  for  unobserved  productivity  shocks  to  address  the  simultaneity  bias 

18 Results are robust to different definitions of frontier firms, e.g. including 99th percentile as well. 
19 Results are robust to a time-invariant definition of w.  

ECB Working Paper Series No 2143 / April 201830 
  
  
  
  
 
  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
                                                            
stemming  from  the  fact  that  the  firm  knows  its  productivity  when  choosing  inputs  for  production.20 
Because  labour  and  TFP  are  simultaneously  determined,  while  capital  takes  time  to  build,  labour  is 

instrumented  by  its  first  lag.  In  addition,  several  higher  order  and  interaction  terms  between  capital 

and  materials  are  included  in  order  to  control  for  non-linearities.  A  full  set  of  year  dummies  is 

included to control for sector-specific trends. Standard errors are clustered at the firm-level. Following 

the  estimation,  firm-level  TFP  is  retrieved  as  the  difference  between  (log)  real  value  added  and  the 

fitted values for (log) real capital, (log) labour and a year trend: 

(cid:1846)(cid:1832)(cid:1842)(cid:3036)(cid:3047) (cid:3404) (cid:1844)(cid:1848)(cid:1827)(cid:3036)(cid:3047) (cid:3398) (cid:3435)(cid:2010)(cid:4632)

(cid:2868) (cid:3397) (cid:2010)(cid:4632)

(cid:2869)(cid:1863)(cid:3036)(cid:3047) (cid:3397) (cid:2011)(cid:3548)(cid:1851)(cid:1857)(cid:1853)(cid:1870)(cid:3047) (cid:3397) (cid:2033)(cid:3549)(cid:1838)(cid:3036)(cid:4666)(cid:3047)(cid:2879)(cid:2869)(cid:4667)(cid:3439) 

c.  GVC participation: value added concept (robustness) 

Wang  et  al.  (2014)  develop  a  bilateral  trade  accounting  framework  in  order  to  decompose  gross 

exports flows at the sectoral level into their value added components, taking into account the direct 

and  indirect  trade  partners  along  the  value  chain  as  well  as  double-counting  of  trade  flows.  In  this 

framework, two possible indicators for sectoral GVC participation emerge: 

(cid:1828)(cid:1853)(cid:1855)(cid:1863)(cid:1875)(cid:1853)(cid:1870)(cid:1856)	(cid:1833)(cid:1848)(cid:1829)	(cid:1868)(cid:1853)(cid:1870)(cid:1872)(cid:1861)(cid:1855)(cid:1861)(cid:1868)(cid:1853)(cid:1872)(cid:1861)(cid:1867)(cid:1866)(cid:3053),(cid:3037),(cid:3047) (cid:3404)

	(cid:1832)(cid:1867)(cid:1870)(cid:1857)(cid:1861)(cid:1859)(cid:1866)	(cid:1874)(cid:1853)(cid:1864)(cid:1873)(cid:1857)	(cid:1853)(cid:1856)(cid:1856)(cid:1857)(cid:1856)	(cid:1861)(cid:1866)	(cid:1857)(cid:1876)(cid:1868)(cid:1867)(cid:1870)(cid:1872)(cid:1871)(cid:3053),(cid:3037),(cid:3047)
(cid:1833)(cid:1870)(cid:1867)(cid:1871)(cid:1871)	(cid:1857)(cid:1876)(cid:1868)(cid:1867)(cid:1870)(cid:1872)(cid:1871)(cid:3053),(cid:3037),(cid:3047)

(cid:1832)(cid:1867)(cid:1870)(cid:1875)(cid:1853)(cid:1870)(cid:1856)	(cid:1833)(cid:1848)(cid:1829)	(cid:1868)(cid:1853)(cid:1870)(cid:1872)(cid:1861)(cid:1855)(cid:1861)(cid:1868)(cid:1853)(cid:1872)(cid:1861)(cid:1867)(cid:1866)(cid:3053),(cid:3037),(cid:3047) (cid:3404)

	(cid:1841)(cid:1875)(cid:1866)	(cid:1874)(cid:1853)(cid:1864)(cid:1873)(cid:1857)	(cid:1853)(cid:1856)(cid:1856)(cid:1857)(cid:1856)	(cid:1861)(cid:1866)	(cid:1872)(cid:1860)(cid:1861)(cid:1870)(cid:1856)	(cid:1855)(cid:1867)(cid:1873)(cid:1866)(cid:1872)(cid:1870)(cid:1861)(cid:1857)(cid:1871)′	(cid:1857)(cid:1876)(cid:1868)(cid:1867)(cid:1870)(cid:1872)(cid:1871)(cid:3053),(cid:3037),(cid:3047)
(cid:1833)(cid:1870)(cid:1867)(cid:1871)(cid:1871)	(cid:1857)(cid:1876)(cid:1868)(cid:1867)(cid:1870)(cid:1872)(cid:1871)(cid:3053),(cid:3037),(cid:3047)

where z is the given country, j the sector and t the year. The forward GVC participation captures also 
the own value added, which is exported and then returns back to the country as third countries’ 
exports, excluding this component does not change the results. 

20 For more information on TFP estimation in CompNet, please refer to the CompNet taskforce (2014) reference paper as 
well as Lopez-Garcia et al. (2015). See also Olley and Pakes (1996), Levinsohn and Petrin (2003), and Wooldridge 
(2009). 

ECB Working Paper Series No 2143 / April 201831 
   
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
                                                            
Annex 2: R&D in host economies 

Figure A2.1: R&D as a share of GDP 

(in percentage points) 

non-CEE EU

EU28

CEE sample (right scale)

1.3

1.2

1.1

1.0

0.9

0.8

0.7

0.7

0.6

0.5

0.4

0.3

0.2

2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015

Source: Authors’ calculations based on Eurostat. 
Note:  R&D  captures  R&D  expenditure  of  the  business  enterprise  sector.  Non-CEE  EU  refers  to  the 
unweighted  average  of  Austria,  Belgium,  Cyprus,  Denmark,  Finland,  France,  Germany,  Greece,  Ireland, 
Italy, Luxembourg, Malta, the Netherlands, Portugal, Spain, Sweden and the United Kingdom. CEE sample: 
Croatia, Estonia, Hungary, Latvia, Lithuania, Poland, Romania, Slovakia and Slovenia. 

ECB Working Paper Series No 2143 / April 201832 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Figure A2.2: R&D as a share of population 

(in percentage points) 

non-CEE EU

EU28

CEE sample (right scale)

500

450

400

350

300

250

200

100

80

60

40

20

2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015

Source: Authors’ calculations based on Eurostat. 
Note:  R&D  captures  R&D  expenditure  of  the  business  enterprise  sector.  Non-CEE  EU  refers  to  the 
unweighted  average  of  Austria,  Belgium,  Cyprus,  Denmark,  Finland,  France,  Germany,  Greece,  Ireland, 
Italy, Luxembourg, Malta, the Netherlands, Portugal, Spain, Sweden and the United Kingdom. CEE sample: 
Croatia, Estonia, Hungary, Latvia, Lithuania, Poland, Romania, Slovakia and Slovenia. 

ECB Working Paper Series No 2143 / April 201833 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Annex 3: Robustness of econometric results  

Table A3.1: TFP growth of host frontier firms - robustness 

GVC 
participation 
imports/ 
exports 

Backward 
GVC 
participation  

Forward 
GVC 
participation  

GVC 
participation 
based on 
imports and 
EU frontier 

GVC 
participation 
based on 
exports and 
EU frontier 

(1) 

(2) 

(3) 

(4) 

(5) 

2008-2010 dummy 

-0.043*** 

-0.050*** 

-0.051*** 

-0.038*** 

-0.037*** 

Post-2010 dummy 

(0.010) 

0.007 

(0.010) 

(0.010) 

0.002 

(0.010) 

(0.010) 

0.002 

(0.010) 

TFP growth GVC frontier imports/backward 

0.404*** 

0.392*** 

(0.057) 

(0.060) 

0.308*** 

0.352*** 

(0.055) 

(0.049) 

0.006 

(0.042) 

0.070* 

(0.039) 

0.259** 

0.733*** 

(0.174) 

(0.101) 

-0.052 

(0.037) 

0.170*** 

(0.045) 

0.285*** 

(0.044) 

-0.448 

(0.291) 

Lagged TFP gap GVC frontier  
imports/backward to national frontier 

TFP growth GVC frontier exports/forward 

Lagged TFP gap GVC frontier 
exports/forward to national frontier 

GVC participation growth - 
imports/backward 

GVC participation growth - exports/forward 

TFP growth EU frontier  

Lagged TFP gap EU frontier to national 
frontier 

(0.011) 

0.019 

(0.012) 

0.376*** 

(0.065) 

0.253*** 

(0.077) 

0.192** 

(0.081) 

0.102 

(0.072) 

(0.011) 

0.025** 

(0.010) 

0.072 

(0.048) 

0.115** 

(0.054) 

0.065* 

(0.034) 

0.206*** 

(0.066) 

0.127** 

0.249*** 

(0.054) 

(0.049) 

Constant 

-1.013*** 

-0.955*** 

-0.696*** 

-1.118*** 

-1.116*** 

(0.158) 

(0.154) 

(0.114) 

(0.149) 

(0.146) 

Observations 

Adjusted R-squared 

642 

0.337 

642 

0.355 

642 

0.225 

642 

0.341 

642 

0.271 

Note: *** p<0.01, ** p<0.05, * p<0.10. 
Robust standard errors in parentheses, clustered at the country-sector level. Country-sector FE included.  

ECB Working Paper Series No 2143 / April 201834 
 
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
 
 
 
 
Table A3.2: TFP growth of host laggard firms – interaction with period dummies 

2008-2010 dummy 

Post-2010 dummy 

TFP growth national frontier 

TFP growth national frontier 
*2008-2010 dummy 

TFP growth national frontier *post-
2010 dummy 

Lagged gap TFP national frontier to 
laggards 

Lagged gap TFP national frontier to 
laggards *2008-2010 dummy 

Lagged gap TFP national frontier to 
laggards *post-2010 dummy 

TFP growth GVC frontier 

TFP growth GVC frontier *2008-
2010 dummy 

TFP growth GVC frontier *post-
2010 dummy 

Lagged gap TFP GVC frontier to 
laggards 

Lagged gap TFP GVC frontier to 
laggards *2008-2010 dummy 

Lagged gap TFP GVC frontier to 
laggards *post-2010 dummy 

GVC participation growth 

GVC participation growth *2008-
2010 dummy 

GVC participation growth *post-
2010 dummy 

Constant 

GVC participation based on 
imports 

GVC participation based on 
exports 

(1) 

(2) 

(3) 

(4) 

(5) 

-0.090*** 
(0.011) 
-0.009 
(0.012) 

-0.025*** 
(0.008) 
-0.022** 
(0.008) 
0.920*** 
(0.049) 

-0.029*** 
(0.008) 
-0.027*** 
(0.008) 
0.947*** 
(0.051) 

-0.009 
(0.042) 
-0.016 
(0.038) 
0.905*** 
(0.074) 

0.063 
(0.099) 

-0.127 
(0.110) 

-0.032 
(0.038) 
-0.004 
(0.036) 
0.886*** 
(0.073) 

0.149 
(0.101) 

-0.137 
(0.107) 

0.569*** 
(0.080) 

0.587*** 
(0.079) 

0.560*** 
(0.077) 

0.564*** 
(0.078) 

-0.009 
(0.021) 

-0.027 

(0.023) 
0.074 
(0.091) 

0.147 
(0.103) 

-0.100 
(0.140) 

0.001 
(0.028) 

-0.000 
(0.010) 

0.010 
(0.012) 
0.191** 
(0.096) 

-0.051 
(0.089) 

0.151*** 
(0.041) 

0.010 
(0.024) 

0.203** 
(0.079) 

0.060* 
(0.036) 

0.041 
(0.026) 

0.068** 
(0.032) 

-0.012 
(0.020) 

-0.022 

(0.024) 
0.029 
(0.041) 

0.084 
(0.064) 

-0.061 
(0.098) 

0.039 
(0.030) 

0.006 
(0.010) 

0.004 
(0.012) 
0.048 
(0.060) 

-0.005 
(0.055) 

0.040*** 
(0.005) 

-1.054*** 
(0.113) 

-0.143 
(0.159) 
-1.044*** 
(0.109) 

-1.168*** 
(0.111) 

0.013 
(0.069) 
-1.165*** 
(0.106) 

Observations 
Adjusted R-squared 
Note: *** p<0.01, ** p<0.05, * p<0.10. 
Robust standard errors in parentheses, clustered at the country-sector level. Country-sector FE included. 

642 
0.740 

642 
0.727 

642 
0.736 

642 
0.092 

642 
0.732 

ECB Working Paper Series No 2143 / April 201835 
 
  
  
  
  
  
  
  
  
  
  
Table A3.3: TFP growth of host laggard firms - robustness 

GVC 
participation 
imports/ 
exports 

Backward 
GVC 
participation  

Forward 
GVC 
participation  

GVC 
participation 
based on 
imports and 
EU frontier 

GVC 
participation 
based on 
exports and 
EU frontier 

(1) 

(2) 

(3) 

(4) 

(5) 

2008-2010 dummy 

-0.023*** 

-0.027*** 

-0.028*** 

-0.023** 

-0.029*** 

Post-2010 dummy 

TFP growth GVC frontier 
imports/backward 

Lagged TFP gap GVC frontier  
imports/backward to laggards 

TFP growth GVC frontier exports/forward 

Lagged TFP gap GVC frontier 
exports/forward to laggards 

(0.008) 

(0.008) 

(0.008) 

-0.020** 

-0.020** 

-0.026*** 

(0.009) 

(0.008) 

(0.008) 

0.130*** 

0.166*** 

(0.044) 

(0.040) 

-0.034 

0.012 

(0.025) 

(0.042) 

0.031 

(0.034) 

0.057 

(0.042) 

0.051 

(0.036) 

0.038 

(0.025) 

(0.009) 

-0.017 

(0.010) 

0.131*** 

(0.047) 

-0.036 

(0.055) 

(0.008) 

-0.028*** 

(0.009) 

0.062 

(0.040) 

0.042 

(0.045) 

TFP growth national frontier 

0.916*** 

0.918*** 

0.954*** 

0.915*** 

0.947*** 

Lagged TFP  gap national frontier to 
laggards 

(0.049) 

(0.049) 

(0.050) 

(0.049) 

(0.051) 

0.566*** 

0.569*** 

0.564*** 

0.561*** 

0.560*** 

GVC participation growth - 
imports/backward 

GVC participation growth - 
exports/forward 

TFP growth EU frontier  

Lagged TFP gap EU frontier to laggards 

(0.081) 

0.210* 

(0.121) 

(0.080) 

0.190* 

(0.110) 

0.006 

(0.058) 

(0.077) 

(0.079) 

(0.074) 

0.207 

(0.221) 

0.211** 

(0.081) 

0.003 

(0.045) 

0.055 

(0.056) 

0.070** 

(0.032) 

-0.015 

(0.048) 

-0.001 

(0.048) 

Constant 

-1.150*** 

-1.064*** 

-1.162*** 

-1.117*** 

-1.166*** 

(0.217) 

(0.118) 

(0.112) 

(0.146) 

(0.139) 

Observations 

Adjusted R-squared 

642 

0.737 

642 

0.734 

642 

0.726 

642 

0.737 

642 

0.727 

Note: *** p<0.01, ** p<0.05, * p<0.10. 
Robust standard errors in parentheses, clustered at the country-sector level. Country-sector FE included.  

ECB Working Paper Series No 2143 / April 201836 
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
 
 
 
Table A3.4: TFP growth of mid-productive firms –robustness  

2008-2010 dummy 

Post-2010 dummy 

TFP growth national frontier 

TFP growth national frontier 
*2008-2010 dummy 

TFP growth national frontier 
*post-2010 dummy 

Lagged gap TFP national frontier to 
middle 

Lagged gap TFP national frontier to 
middle*2008-2010 dummy 

Lagged gap TFP national frontier to 
middle*post-2010 dummy 

TFP growth GVC frontier 

TFP growth GVC frontier 
*2008-2010 dummy 

TFP growth GVC frontier 
*post-2010 dummy 

Lagged gap TFP GVC frontier to 
middle 

Lagged gap TFP GVC frontier to 
middle*2008-2010 dummy 

Lagged gap TFP GVC frontier to 
middle*post-2010 dummy 

GVC participation growth 

GVC participation growth 
*2008-2010 dummy 

GVC participation growth 
*post-2010 dummy 

Constant 

GVC participation based on 
imports 

GVC participation based on 
exports 

(1) 

(2) 

(3) 

(4) 

(5) 

-0.069*** 
(0.009) 
-0.014 
(0.009) 

-0.004 
(0.005) 
-0.004 
(0.005) 
0.862*** 
(0.028) 

-0.019 
(0.022) 
-0.032 
(0.029) 
0.782*** 
(0.034) 

0.142*** 

(0.046) 

-0.008 

(0.060) 

-0.004 
(0.004) 
-0.006 
(0.005) 
0.884*** 
(0.029) 

-0.033 
(0.021) 
-0.030 
(0.028) 
0.792*** 
(0.037) 

0.161*** 

(0.045) 

-0.027 

(0.060) 

0.593*** 

0.549*** 

0.610*** 

0.560*** 

(0.072) 

(0.068) 

(0.072) 

(0.068) 

0.095*** 
(0.019) 

0.032* 

(0.018) 

0.065* 
(0.037) 

0.022 

(0.018) 

0.014 

(0.021) 
0.077 
(0.047) 

0.020 

(0.053) 

-0.009 

(0.067) 

0.036* 

(0.020) 

-0.001 

(0.005) 

0.004 

(0.006) 
0.057 
(0.064) 

-0.021 

(0.062) 

0.027 

0.024 
(0.018) 

0.024 

(0.016) 

0.018 

(0.019) 
0.029 
(0.025) 

0.013 

(0.036) 

-0.059 

(0.051) 

0.035** 

0.040** 

(0.017) 

(0.017) 

0.019 
(0.016) 

0.002 

(0.005) 

0.003 

(0.007) 
0.020 
(0.033) 

-0.011 

(0.034) 

-0.004 

(0.032) 
-0.597*** 
(0.062) 

642 
0.863 

0.038*** 
(0.004) 

-0.608*** 
(0.064) 

(0.111) 
-0.585*** 
(0.069) 

-0.626*** 
(0.063) 

Observations 
Adjusted R-squared 
Note: *** p<0.01, ** p<0.05, * p<0.10. 
Robust standard errors in parentheses, clustered at the country-sector level. Country-sector FE included. 

642 
0.093 

642 
0.865 

642 
0.861 

642 
0.857 

ECB Working Paper Series No 2143 / April 201837 
 
 
 
 
 
 
 
 
 
  
  
  
 
 
 
  
 
 
 
 
  
 
 
  
 
 
 
  
 
 
 
 
  
 
  
 
 
 
  
 
 
 
 
  
 
 
  
 
 
 
  
 
 
 
 
  
 
  
 
 
 
  
 
 
 
 
  
 
  
  
 
 
 
Table A3.5: Baseline with country, sector, and year dummies 

TFP growth national frontier 

Lagged gap TFP national frontier to middle 

Lagged gap TFP national frontier to laggards 

TFP growth GVC frontier 

Lagged gap TFP GVC frontier to sector 

Lagged gap TFP GVC frontier to national frontier 

Lagged gap TFP GVC frontier to middle 

Lagged gap TFP GVC frontier to laggards 

GVC participation growth 

Constant 

Sector 
(1) 

Frontier 
(2) 

Mid-
productive 
(3) 

0.816*** 
(0.033) 
0.054*** 
(0.017) 

0.161* 
(0.090) 
0.018 
(0.016) 

0.095 
(0.083) 

0.043 
(0.027) 

0.002 
(0.008) 

0.009*** 
(0.003) 

0.287*** 
(0.086) 
-0.055 
(0.042) 

0.155** 
(0.063) 
-0.001 
(0.023) 

0.084** 
(0.042) 
-0.082*** 
(0.020) 

Laggards 
(4) 

0.878*** 
(0.052) 

0.015* 
(0.008) 
0.050 
(0.053) 

0.024*** 
(0.006) 
0.247** 
(0.094) 
-0.147*** 
(0.030) 

Observations 
Adjusted R-squared 
Note: *** p<0.01, ** p<0.05, * p<0.10. 
Robust standard errors in parentheses, clustered at the country-sector level. Country, sector, and year FE included.   

642 
0.199 

642 
0.812 

613 
0.101 

642 
0.636 

ECB Working Paper Series No 2143 / April 201838 
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Table A3.6: Baseline with level of GVC participation instead of growth in GVC participation 

Sector 

Frontier 

(1) 

(2) 

Mid-
productive 
(3) 

Laggards 

(4) 

2008-2010 dummy 

Post-2010 dummy 

TFP growth national frontier 

Lagged gap TFP national frontier to middle 

Lagged gap TFP national frontier to laggards 

TFP growth GVC frontier 

Lagged gap TFP GVC frontier to sector 

Lagged gap TFP GVC frontier to national frontier 

Lagged gap TFP GVC frontier to middle 

Lagged gap TFP GVC frontier to laggards 

GVC participation 

Constant 

-0.054*** 
(0.014) 
-0.014 
(0.017) 

-0.049*** 
(0.011) 
-0.002 
(0.012) 

-0.005 
(0.005) 
-0.007 
(0.006) 
0.859*** 
(0.029) 
0.597*** 
(0.070) 

0.493*** 
(0.057) 
0.542*** 
(0.137) 

0.452*** 
(0.056) 

0.101*** 
(0.018) 

0.380*** 
(0.056) 

0.040** 
(0.019) 

0.270*** 
(0.089) 
-1.815*** 
(0.461) 

0.169*** 
(0.060) 
-1.089*** 
(0.164) 

0.086** 
(0.039) 
-0.671*** 
(0.065) 

Observations 
Adjusted R-squared 
Note: *** p<0.01, ** p<0.05, * p<0.10. 
Robust standard errors in parentheses, clustered at the country-sector level. Country-sector FE included. 

642 
0.336 

613 
0.354 

642 
0.863 

-0.028*** 
(0.009) 
-0.026** 
(0.010) 
0.921*** 
(0.050) 

0.572*** 
(0.080) 
0.176*** 
(0.039) 

0.023 
(0.026) 
0.122** 
(0.054) 
-1.157*** 
(0.114) 

642 
0.735 

ECB Working Paper Series No 2143 / April 201839 
 
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
 
 
 
 
 
 
 
 
 
 
 
 
Table A3.7: TFP growth of national frontier firms, sectors split by IPP intensity 

2008-2010 dummy 

Post-2010 dummy 

TFP growth GVC frontier 

TFP growth GVC frontier*2008-2010 dummy 

TFP growth GVC frontier*Post-2010 dummy 

Lagged gap TFP GVC frontier to national frontier 

Lagged gap*2008-2010 dummy 

Lagged gap*2011-2012 dummy 

GVC participation growth 

GVC participation growth*2008-2010 dummy 

GVC participation growth*2011-2012 dummy 

Constant 

IPP 
intensive 
sectors 
(1) 

0.046 
(0.041) 
0.074* 
(0.044) 
0.931** 
(0.358) 
-0.476 
(0.320) 
-0.731** 
(0.320) 
0.535*** 
(0.092) 
-0.024* 
(0.014) 
-0.018 
(0.011) 
-0.142 
(0.116) 
0.504*** 
(0.180) 
0.359** 
(0.142) 
-1.455*** 
(0.263) 

Less IPP 
intensive 
sectors 
(2) 

-0.046 
(0.067) 
0.054 
(0.080) 
0.453* 
(0.233) 
-0.043 
(0.308) 
0.016 
(0.240) 
0.466*** 
(0.094) 
-0.004 
(0.020) 
-0.022 
(0.025) 
-0.397 
(0.364) 
1.225** 
(0.514) 
0.986 
(0.625) 
-1.378*** 
(0.289) 

Observations 
Adjusted R-squared 
Note: *** p<0.01, ** p<0.05, * p<0.10. 
Robust standard errors in parentheses, clustered at the country-sector level. Country-sector FE included. 

188 
0.447 

229 
0.417 

ECB Working Paper Series No 2143 / April 201840 
 
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
 
 
 
 
 
 
 
 
 
 
 
Table A3.8: TFP growth of national frontier firms, sectors split by R&D intensity and GVC 

participation based on exports 

Baseline for R&D 
sample 
(1) 

R&D intensive 
sectors 
(2) 

R&D less 
intensive sectors 
(3) 

2008-2010 dummy 

Post-2010 dummy 

TFP growth GVC frontier 

TFP growth GVC frontier*2008-2010 dummy 

TFP growth GVC frontier*Post-2010 dummy 

Lagged gap TFP GVC frontier to national frontier 

Lagged gap*2008-2010 dummy 

Lagged gap*2011-2012 dummy 

GVC participation growth 

GVC participation growth*2008-2010 dummy 

GVC participation growth*2011-2012 dummy 

Constant 

-0.044*** 
(0.013) 
0.013 
(0.015) 
0.170*** 
(0.058) 

0.365*** 
(0.053) 

0.120*** 
(0.033) 

-0.938*** 
(0.141) 

0.018 
(0.030) 
0.073* 
(0.040) 
0.138 
(0.221) 
0.369 
(0.278) 
-0.180 
(0.279) 
0.329*** 
(0.064) 
-0.029*** 
(0.010) 
-0.024* 
(0.012) 
-0.018 
(0.044) 
0.247*** 
(0.070) 
0.078 
(0.103) 
-0.802*** 
(0.164) 

Observations 
Adjusted R-squared 
Note: *** p<0.01, ** p<0.05, * p<0.10. 
Robust standard errors in parentheses, clustered at the country-sector level. Country-sector FE included.  

0.371 

0.290 

369 

184 

0.038 
(0.067) 
0.071 
(0.101) 
0.230** 
(0.105) 
-0.130 
(0.171) 
-0.389** 
(0.160) 
0.362*** 
(0.071) 
-0.030+ 
(0.019) 
-0.024 
(0.038) 
-0.044 
(0.263) 
-0.168 
(0.466) 
0.069 
(0.266) 
-0.967*** 
(0.195) 

185 

0.241 

ECB Working Paper Series No 2143 / April 201841 
 
  
  
  
  
  
  
  
  
  
 
  
 
 
  
 
  
 
  
 
 
  
 
  
 
  
 
 
  
 
  
  
 
Acknowledgements 

The first version of this paper benefitted substantially from the important contribution of our colleague Elisa Gamberoni. We are grateful 
to her as well as to Ettore Dorrucci, Daria Taglioni, Vanessa Gunnella, an anonymous referee and various conference participants for 
insightful discussions and comments. The views expressed are those of the authors and do not necessarily reflect those of the 
European Central Bank or Bruegel. 

Francesco Chiacchio 
Bruegel, Brussels, Belgium; email: francesco.chiacchio@bruegel.org 

Katerina Gradeva 
European Central Bank, Frankfurt am Main, Germany; email: katerina.gradeva@ecb.europa.eu 

Paloma Lopez-Garcia 
European Central Bank, Frankfurt am Main, Germany; email: paloma.lopez-garcia@ecb.europa.eu 

© European Central Bank, 2018 

Postal address 
Telephone 
Website 

60640 Frankfurt am Main, Germany 
+49 69 1344 0
www.ecb.europa.eu

All rights reserved. Any reproduction, publication and reprint in the form of a different publication, whether printed or produced 
electronically, in whole or in part, is permitted only with the explicit written authorisation of the ECB or the authors.  

This paper can be downloaded without charge from www.ecb.europa.eu, from the Social Science Research Network electronic library or 
from RePEc: Research Papers in Economics. Information on all of the papers published in the ECB Working Paper Series can be found 
on the ECB’s website. 

ISSN 
ISBN 

1725-2806 (pdf) 
978-92-899-3248-6 (pdf)

DOI 
EU catalogue No 

10.2866/73697 (pdf) 
QB-AR-18-023-EN-N (pdf)