Regional disparities in Europe : should we be concerned?
Date: 2020-08-04
Institutions: European Stability Mechanism
Subjects: EU Member State, coronavirus disease, economic consequence, economic recovery, labour market, regional disparity, value chain

Regional disparities in Europe:  
should we be concerned? 

The economic impact of the coronavirus is  likely to differ from region to 
region,  depending  on  their  sectoral  specialisation,  and  may  exacerbate 
regional  disparities.  This  paper  provides  an  overview  of  how  regional 
disparities  have  evolved  since  the  euro’s  inception  with  an  aim  to  help 
policymakers  develop  appropriate  policy  responses  to  support  recovery 
following the economic shock caused by the coronavirus, and maintain the 
convergence  process.  Modes  of  regional  support  and  policy  intervention 
are crucial to helping adjustment and boosting productivity to ensure long-
term sustainability and income convergence. 

Gergely Hudecz 
Edmund Moshammer 
Thomas Wieser 

July 2020 

 
 
 
The authors would like to thank Rolf Strauch, Markus Rodlauer, Niels Hansen, Pilar Castrillo, and 
Daragh Clancy for their support and helpful comments. 

Disclaimer: The views expressed by authors in this discussion paper do not necessarily represent those of the ESM 
or ESM policy. No responsibility or liability is accepted by the ESM in relation to the accuracy or completeness of the 
information or data presented in this paper. 

PDF 

ISBN 978-92-95085-95-4 

ISSN 2467-2025 

doi:10.2852/637082 

DW-AC-20-005-EN-N 

More information on the European Union is available on the Internet (http://europa.eu).  Luxembourg: Publications 
Office of the European Union, 2020 

© European Stability Mechanism, 2020 

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R E G I O N A L   D I S P A R I T I E S   I N   E U R O P E   |   3  

Table of contents 

Executive summary 

Introduction 

1.  A brief look at the literature 

2.  Patterns of regional disparities in Europe 

Sectoral composition 

Crisis impact and the risk of hysteresis 

Migration 

3.  The role of global value chains, specialisation,  and agglomeration 

Agglomeration 

Skills and innovation 

Policy variables 

4.  Policy implications 

Labour markets and labour mobility 

Productivity catch-up 

5.  Conclusion 

References 

Annex 

Summary statistics 

Regression model 

Acronyms 

4 

7 

10 

17 

21 

25 

26 

27 

28 

29 

29 

33 

34 

36 

39 

42 

44 

48 

51 

53 

 
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Executive summary 

The economic shock from the coronavirus may well exacerbate 
regional disparities. The impact is likely to differ from region to 
region, depending on exposures to tradable sectors, integration 
into global value chains (GVCs), and sectoral specialisation. This 
paper  provides  an  overview  of  how  regional  disparities  have 
evolved since the euro’s inception, aiming to help policymakers 
develop appropriate responses to support recovery and maintain 
the convergence process. 

Over the last 30 years we have been addressing the question of 
whether  European  integration  would  be  the  ‘convergence 
machine’ some leading economists have predicted. The internal 
market followed by Economic and Monetary Union (EMU) were 
expected  to  enhance  intra-industry  trade,  leading  to  more-
similar  economic  structures,  and 
to  better-
synchronised economic cycles. 

therefore 

to 

At  the  same  time,  numerous  economists  and  policymakers 
argued that EMU would lead to greater sectoral specialisation – 
reinforced  by  enhanced  trade  links  –  resulting  in  higher 
sensitivity 
industry-specific  shocks;  more-idiosyncratic 
business  cycles;  and  more-pronounced  growth,  employment, 
and inflation differentials. At the time, the adjustment process 
within  EMU  was  a  concern,  given  limited  price  and  wage 
flexibility,  low  labour  mobility,  and  a  lack  of  any  risk-sharing 
mechanism. 

We  examine  how  European  integration  and  globalisation  had 
affected Europe’s regions before the coronavirus shock: had they 
successfully  converged,  and  what  drove  that  success?  Or  had 
they  fallen  behind,  and  if  so,  why?  Most  disparities  between 
Member  States  have  diminished,  so  how  have  Member  States 
fared  in  regional  disparities?  Answers  to  these  questions  have 
implications for growth prospects, macroeconomic stability, and, 
potentially, political stability. This suggests policymakers should 
incorporate any understanding of these mechanisms into policy 
responses during this emergency and afterwards. 

The economic shock from the 

coronavirus may well exacerbate 

regional disparities. 

European integration had been 

expected to be a ‘convergence 

machine’… 

…but there is also a risk of 

persistent growth, employment, 

and inflation differentials. 

We examine how European 

integration and globalisation had 

affected Europe’s regions before 

the coronavirus shock. 

 
 
 
R E G I O N A L   D I S P A R I T I E S   I N   E U R O P E   |   5  

The European integration 

process and the role of trade 

relationships can appear in 

different forms as driving forces. 

The  European 
integration  process  and  the  role  of  trade 
relationships  can  appear  in  different  forms  as  driving  forces. 
Today,  GVCs  play  an  important  part  in  international trade  and 
trade specialisation, as do agglomeration effects in increasingly 
knowledge-based  economies,  and  this  may  determine  where 
highly productive firms locate. 

Our findings show regional 

disparity is indeed a serious 

concern in Europe because many 

regions are falling behind. 

Our findings show regional disparity is indeed a serious concern 
in  Europe  because  many  regions  are  falling  behind.  Diverse 
starting  positions,  varied  distances  from  growth  centres,  and 
different  capital  and  labour  mobility  mean  the  disparity  could 
intensify.  No  one  factor  explains  the  issues  involved,  but  the 
patterns emerging from our analysis have policy implications.  

  On average, poorer European Union (EU)  regions grew 
faster  from  2000  to  2016  than  those  more  advanced. 
Most of the poorest regions in new Member States were 
catching  up.  High-productivity 
such  as 
manufacturing and professional services, together with 
integration into GVCs, drove the convergence. 

sectors 

  However,  regional  disparities  have  been  on  the  rise  in 
the euro area and across the EU. We found symptoms of 
a regional ‘middle-income trap,’ with about a quarter of 
small regions in the old Member States falling behind – 
with the negative distance-to-average income widening. 

  Regional 

sectoral  compositions  and  productivity 
differences  appear  to  be  part  of  the  explanation.  The 
regional  patterns  suggest  greater  complexity  than 
urban-rural or north-south differences can explain. 

 

Inherent  advantages  flow  from  close  proximity  to  an 
economic centre of gravity. Agglomeration effects show 
a  positive  association  with  change  in  gross  domestic 
product (GDP) per capita; those further away fall behind. 

  Hysteresis  following  the  previous  crisis  has  aggravated 
regional  disparities.  The  most  affected 
regions 
experienced persistently lower employment and weaker 
investment, while less affected regions rebounded. 

  After the previous crisis, a pattern of outward migration 
inward 
emerged  from  the 
migration increased to the leading ones. We found some 
evidence  to  suggest  regions  with  higher  productivity 
growth attracted more entrants. 

lagging  regions,  while 

  Regional  government  quality  and  the  labour  force 
proportion with tertiary education have a positive link to 
GDP  per  capita  change,  while  employment  protection 
seems  to  have  an  unfavourable  impact  on  regional 
income growth within the euro area. 

 
 
 
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Regional  disparities  may  widen  even  further  in  the  absence  of 
supportive  developments  as  changes  in  the  effects  of  trade, 
value  chains,  outward  migration,  and  other  factors  bear  on 
developments.  We  draw  some  tentative  conclusions  from  our 
analysis and suggest some policies that may contribute to a more 
even regional development in Member States. 

Modes  of  regional  support  are  crucial  to  ensure  long-term 
sustainability.  Policy  intervention  that  helps  adjustment  and 
boosts productivity can set the scene for a catch-up process and 
income convergence. Policies need to be designed overall, with 
mutually  supportive  interdependencies.  National-level  policies 
will  still  be  important  but,  increasingly,  urban  policies  will 
determine  growth,  productivity,  and  high-quality  employment. 
The  EU  Member  States  and  their  regions  should  all  design 
policies  carefully  to  try  to  avoid  regional  growth-centre 
clustering, such as has emerged in the United States (US). 

A  two-track  approach  may  be  needed  to  generate  a  virtuous 
circle. One would improve the labour supply through education 
and  the  other  would  attract  investment  to  generate  labour 
demand  and  enhance  productivity.  This  will  require  education 
designed to retain qualified people. Also, the role of research and 
development  and  innovation-enhancing  policies  needs  to  suit 
the comparative advantages of the region or urban area, with EU 
funds focused, for example, on helping to develop high quality 
research and education centres. 

in  national,  regional,  and 

local  government  and 
Quality 
administration  plays  a  large  role.  Inward  investment  will  take 
place when corruption is absent, administrative procedures work 
swiftly,  and  the  outcomes  of  administrative  and 
legal 
proceedings are predictable.  

The underlying processes that cause some regions to profit more 
from  integration  and  trade  linkages,  and  others  less,  will  not 
vanish, and it would be economically and politically unacceptable 
for  regions  to  slowly  wither  away.  EU  and  national  economic 
policies  need  to  re-examine  ways  to  help  reverse  these 
processes, or at least mitigate their impact. 

Policies matter. 

Regional support must ensure 

long-term sustainability. 

A two-track approach is needed 

to generate a virtuous circle. 

Quality in national, regional, and 

local government and 

administration plays a large role. 

EU policies need to re-examine 

ways to help reverse, or at least 

mitigate, regional disparities. 

 
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Introduction 

Regional disparities and agglomeration effects were already attracting policy attention before 
the coronavirus pandemic. The issue was discussed in a flagship report by the Organisation for 
Economic Co-operation and Development (OECD), in notes to the Economic and Financial Affairs 
Council  (Ecofin),  at  the  European  Central  Bank’s  (ECB)  Sintra  Forum,  at  the  European 
Commission’s  Economic  and  Financial  directorate’s  Annual  Research  Conference,  and  in  an 
analytical chapter of the International Monetary Fund’s World Economic Outlook (WEO).1 The 
debate  is  not  new,  but  the  emergence  of  GVCs  over  the  past  decades,  and  the  most  recent 
pandemic shock, have added to the issue’s complexity. 

The  economic  shock  of  the  coronavirus  may  exacerbate  regional  disparities.  The  immediate 
impact is higher in urban areas because high-density regions have a greater contagion risk and 
a concentration of services directly impacted by closures. Over time, however, rural areas may 
be  increasingly  affected  when  weak  demand  and  mobility  disruptions  spread  across  the 
economy.2 Sectors  with  less  scope  for  remote  working  may  be  hit  particularly  hard,  putting 
incomes and jobs at risk.3 

The longer-term economic impact will likely differ across regions, depending on their exposure 
to  particular  trade  sectors,  their  integration  into  GVCs,  and  their  sectoral  specialisation.  This 
could  lead  to  wide  variations  in  regional  employment  and  GDP,  affecting  the  distribution  of 
economic  recovery.4 Furthermore,  regions  with smaller  buffers, fewer  savings, and  less  fiscal 
space  may  find  adaptation  more  challenging.  Recent  Bank  for  International  Settlement  (BIS) 
research  suggests  that  employment  in  southern  European  regions  and  in  France  may  be  at 
higher risk.5 

This paper aims to place this question into context with an overview of how regional disparities 
evolved  over  past  decades.  This  could  help  design  appropriate  policy  responses  to  support 
recovery and keep convergence on track; policymakers should incorporate an understanding of 
these mechanisms into policy responses during the emergency and afterwards. 

The single market and EMU, to some extent, aimed to be a ‘convergence machine.’ The Delors 
Report  that  outlined  the  path  to  EMU,  established  income  convergence  as  an  explicit  EMU 
objective.6  An  ‘endogeneity’  hypothesis  predicted  EMU  would  lead  to  greater  intra-industry 

1 OECD (2018a), Productivity and Jobs in a Globalised World: (How) Can All Regions Benefit? Demertzis M, A. Sapir and G. Wolff 
(2019),  “Promoting  sustainable  and  inclusive  growth  and  convergence  in  the  European  Union,”  Bruegel  Policy  Contribution  to 
informal Ecofin meeting; ECB (2019), 20 Years of European Economic and Monetary Union;  Bridging the growing divides: DG ECFIN’s 
Annual Research Conference 2019; IMF (2019) “Closer Together or Further Apart? Subnational Regional Disparities and Adjustment 
in Advanced Economies.” 

2 World Bank (2020), “Poverty and Distributional Impacts of COVID-19,” April 16. 

3 Bergamini, E. (2020), “How COVID-19 is laying bare inequality,” Bruegel blog post, March 31. 

4 OECD (2020), The territorial impact of COVID-19. 

5 Doerr, S. and L. Gambacorta (2020), “Covid-19 and regional employment in Europe,” BIS Bulletin 16. 

6 Delors Report (European Council, 1989) cited in Franks, JR, BB. Barkbu, R. Blavy, W. Oman and H. Schoelermann (2018), “Economic 
Convergence in the Euro Area: Coming Together or Drifting Apart?” IMF Working Paper 18/10. 

 
                                                           
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trade. Industrial structures would therefore become more-similar and economic cycles more-
synchronised. An influential study by Frankel and Rose (1998) argues that countries joining EMU 
would eventually satisfy optimum currency area conditions.7 

However, the ‘concentration’ or ‘specialisation’ hypothesis predicted that EMU would reinforce 
comparative advantages, which might lead to divergence. For instance, Krugman (1993) argues 
that  the  euro,  together  with  stronger  trade  and  financial  ties,  would  lead  to  more  sector 
specialisation.8  This  might  result  in  higher  sensitivity  to  industry-specific  shocks,  more 
idiosyncratic business cycles, and pronounced growth, employment, and inflation differentials. 
Furthermore,  the  adjustment  process  had  been  a  concern,  given  relatively  limited  price  and 
wage flexibility, low labour mobility, and the lack of a risk-sharing mechanism. The ECB’s first 
chief economist, Otmar Issing, flagged this issue at the end of his tenure in a speech entitled 
“The  euro  –  a  currency  without  a  state.”9  Going  further,  de  Larosière  (2012)  argued  that  a 
monetary  union  does  not  by  itself  create  economic  convergence,  but  tends  to  concentrate 
economic prosperity in regions better endowed with productive capital and human resources.10 

The emergence of GVCs was not foreseen at the time of the euro’s inception, nor how this would 
affect shock transmissions. The single market played a key role in trade integration, and the euro 
contributed;  recent  ECB  research  has  found  the  euro  facilitated  trade  expansion  and  the 
emergence of GVCs within the euro area, especially between ‘old’ and ‘new’ Member States 
since 2007.11  And because demand shocks are transmitted along a supply chain, trade that is 
linked by value chains will generate more synchronisation than trade in final goods. 

Along with the emergence of GVCs, studies of the US have identified agglomeration effects at 
work too, as economies become more knowledge-based. For instance, Moretti (2012) provides 
an overview of agglomeration economies and analyses the causes of productivity differences 
across local labour markets in the US. He finds that productivity differences across regions are 
unlikely to be exogenous.12 

As  these  factors  combine,  convergence  at  the  national  level  may  well  go  hand  in  hand  with 
divergence  at  the  regional  level  (Figure  1).  Value  chains  increase  both  specialisation  and 
synchronisation,  but  regions’  participation  in value  chains  varies;  some  are  more  exposed  to 
agglomeration effects than others. As national comparative advantages are exploited, regional 
advantages and specialisation may lead to some regions participating strongly in international 
trade flows and GVCs, while others lag behind. This is similar to Krugman’s argument suggesting 
this trend creates concentration and may exacerbate regional disparities. On the other hand, 
scope does exist for a different interpretation of any GVC impact, because some regions might 
catch up by joining these value chains, which could help business cycle synchronisation. 

In the meantime, regional disparities might exacerbate economic, social, and political stability 

7 Frankel, JA. and AK. Rose (1998), "The Endogeneity of the Optimum Currency Area Criteria," Economic Journal 108/449: 1009-
1025. 

8 Krugman, P. (1993), “Lessons of Massachusetts for EMU,” in Torres, F. and F. Giavazzi (eds.) Adjustment and growth in the European 
Monetary Union, Cambridge University Press, pp. 241-261. 

9 Issing, O. (2006), “The euro – a currency without a state,” Speech at an event organised by the Bank of Finland, Helsinki, BIS Review 
23. 

10 de Larosière, J. (2012), “The Long-term Outlook for the European Project and the Single Currency,” Group of Thirty, Occasional 
Paper 84. 

11 Gunnella, V, M. Fidora and M. Schmitz (2017), “The impact of global value chains on the macroeconomic analysis of the euro 
area,” ECB Economic Bulletin 8. Draghi, M. (2018), “Europe and the euro 20 years on,” speech at Laurea Honoris Causa in Economics 
by University of Sant'Anna, Pisa. ECB Working Group on Global Value Chains (2019), “The impact of global value chains on the euro 
area economy,” ECB Occasional Paper 221. 

12 Moretti, E. (2012), The New Geography of Jobs, Houghton Mifflin Harcourt. 

 
                                                           
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risks.  Differences  in  income  levels  themselves  should  not  prevent  cyclical  stabilisation,13  but 
participation in GVCs, combined with agglomeration effects, might unevenly magnify the impact 
of  exogenous  shocks.  Resulting  cyclical  heterogeneity  or  structural  divergence  can  generate 
internal imbalances and vulnerabilities to shocks. Furthermore, large and growing income gaps 
among regions may weigh on political dynamics and risk undermining both social cohesion and 
support for sound policies.  

Motivated by these considerations, this paper summarises recent trends in regional disparities 
across EU Member States, with a focus on possible drivers and implications for EMU. We start 
with a survey of related academic and policy-relevant literature, then present stylised facts to 
depict patterns of regional disparities in Europe. We explore productivity changes and sectoral 
shifts over the past 20 years to gauge what differentiates leading and lagging regions. 

The  analytical  section  aims  to  assess  the  impact  of  GVCs,  specialisation,  and  agglomeration 
effects, along with policy variables. In particular, we test variables for education, institutional 
quality, and national policies covering labour and product market regulation, to assess how they 
might  alter  regional  outcomes.  We  also  extend  the  analysis  by  examining  labour  mobility 
implications.  We  conclude  with  a  discussion  about  policy  implications,  considering  macro-
financial risks that might  emanate from regional disparities,  and identifying  policy areas that 
could help mitigate such risks.  

Figure 1 
Dispersion of regional income per capita since 2000 
(Coefficient of variation) 

Note: The  coefficient  of  variation is a  standardised measure  of dispersion.  It is the ratio of the standard deviation to the  mean. The  higher the 
coefficient of variation, the greater the level of dispersion around the mean and the larger the disparity. 1317 small regions refers to OECD regional 
breakdown of EU28. 
Source: ESM based on OECD Regional Database 

13  Cœuré,  B.  (2017),  “Convergence  matters  for  monetary  policy,”  speech  at  the  Competitiveness  Research  Network  (CompNet) 
conference on "Innovation, firm size, productivity and imbalances in the age of de-globalization" in Brussels. 

 
 
 
 
                                                           
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1.  A brief look at the literature 

 
 
 
 
 
 
 
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When the euro area was introduced, views differed as to how the economy and business cycle 
would react. It was clear from the start that the euro area did not fulfil all the optimal currency 
area criteria.14 It scored well on some measures – such as economic openness, diversification in 
production  and  consumption,  degree  of  price  stability,  and  some  elements  of  financial 
integration.  But  in  other  areas  it  scored  less  well  –  such  as  price  and wage  flexibility,  labour 
market mobility, and broader financial integration. 

It  was  generally  expected  that  monetary  integration  would  deepen  reciprocal  trade,  which 
created the basis for the ‘endogeneity’ hypothesis.15 The implication for the euro area was that 
it could turn into an optimal currency area after the launch of monetary integration, even if it 
was  not  one  beforehand.  Hence,  EMU  was  expected  to  bring  countries  closer  together,  and 
converge economies that joined. 

However, there was a risk that idiosyncratic shocks might become more frequent, and business 
cycles less correlated. In addition to Krugman’s initial concern, cited above, de Larosière (2012) 
argues  that  monetary  union  in  itself  does  not  create  economic  convergence,  but  tends  to 
concentrate economic prosperity in regions better endowed with productive capital and human 
resources.16 A natural process of economic specialisation exists in a diversified region such as 
the  euro  area,  and  belonging  to  an  economically  and  financially  integrated  zone  might 
exacerbate  this.  Conversely,  some  sectors  could  tend  to  be  sheltered  from  competition, 
reducing their efficiency and increasing the price of their services to households and industry – 
which also deters foreign investment.  

Beyond the cyclical synchronisation issue, real income convergence is crucial for political and 
social cohesion in a monetary union. The dominant empirical approach to understand income-
per-capita differences lies within the neoclassical framework: relatively poor regions with less 
capital and lower productivity are expected to attract  capital from rich and capital-abundant 
areas.  In  theory,  such  capital  flows  facilitate  a  productivity  catch-up  by  spreading  new 
technologies  from  the  world’s more-advanced  areas to  the  less-advanced.  Hence,  the model 
posits real income convergence – the tendency of lower income countries to grow faster than 
higher  income  ones  to  converge  at  higher  incomes.  However,  this  model’s  emphasis  on  the 
dynamics of capital stock offers limited insight into efficiency differences across regions within 
countries, given the assumed mobility of physical capital inside national boundaries.17 

Sala-i-Martin (1996) marshals empirical evidence  about  the convergence  of real incomes. He 
analyses long time series that include a cross-section of 110 countries (1960-1990), US states 
(1880-1990), prefectures of Japan (1955-1990), and regions within selected European countries 
(1950-1990).18 This work finds that per capita income and product in poor areas tended to grow 
faster than in rich ones, both in terms of aggregates and within sectors. The rate of convergence 
is surprisingly similar across the data sets, but not rapid; the gap between poor and rich states 
declines by roughly 2% a year.19 Sala-i-Martin and his co-authors use the neoclassical growth 

14 Mundell, R. (1961), “A Theory of Optimum Currency Areas”, American Economic Review, 51: 657-65. 

15 Several authors brought forward concepts about  the possible sources of “endogeneities.” Artis and Zhang have discussed the 
endogeneity of symmetry of shocks; Blanchard and Wolfers, and Saint Paul and Bentolila, have discussed the endogeneity of labour 
market institutions; Kalemli-Ozcan, Sørensen, and Yosha discuss the effects of sharing a single currency on financial markets; cited 
in Issing, O. (2006) “The euro – a currency without a state,” Speech at an event organised by the Bank of Finland, Helsinki, 24 March 
2006, BIS Review 23. 

16 de Larosière, J. (2012). 

17  Acemoglu,  D.  and  M.  Dell  (2010),  “Productivity  Differences  Between  and  Within  Countries.”  American  Economic  Journal: 
Macroeconomics 2/1: 169–88. 

18 Sala-i-Martin, X. (1996), “The Classical Approach to Convergence Analysis,” Economic Journal, 106/437. 

19 This implies that 50% difference of the distance disappears in 35 years; even after 70 years 25% of the income difference remains. 

 
                                                           
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model  as  a  framework,  but  note  the  findings  could  be  reconciled  quantitatively  with  the 
neoclassical model only if diminishing returns to capital set in very slowly.20 Sala-i-Martin (1996) 
points to the technological diffusion hypothesis as a possible explanation.21 

In contrast, Rodrik (2011) argues that convergence depends on sustaining structural change in 
the direction of tradables such as manufacturing and modern services.22 Policies that successful 
countries have used to achieve this are hard to emulate. More recently, Buti and Turrini (2015) 
argue  that  sustainable  real  convergence  is  linked  to  structural  convergence,  notably  an 
economy’s sectoral composition, productivity, and labour costs.23 

Several euro area countries have achieved significant progress in real convergence, but others 
have  not  managed  to  bridge  the  gap  in  real  incomes.  Both  an  ECB  article  (2015)24  and  an 
International Monetary Fund (IMF) paper (2018)25 showed that real convergence of per-capita 
income has been uneven across euro area countries. In a note to the Ecofin, Daniel Gros (2018) 
finds that the convergence process in Europe has bifurcated; new Member States are catching 
up but the north and south have diverged within the euro area.26 However, he argues that the 
lack of north-south convergence has roots other than the euro. Similarly, Diaz del Hoyo et al. 
(2017) argue that the main reason for the lack of convergence in the South represents a gradual 
reduction in total factor productivity growth, which began long before the introduction of the 
euro.27 

Empirical  results  on  convergence  across  regions  in  Europe  are  mixed.  Sala-i-Martin  (1996) 
reports  that  the  convergence  process  across  European  regions  between  1950  and  1990  was 
similar to that for US states.28 This work finds evidence that poorer regions within a country tend 
to  grow  faster  than  richer  ones,  relative  to  the  respective  country’s  average.29  Similarly, 
economic historians have documented a narrowing of regional inequality during most of the 
20th century – but from the 1980s this convergence stopped, and partly reversed.30 

Magrini  (2004)  cautions  that  different  methods  deliver  different  results.  He  argues  that  “a 
substantial lack of convergence” exists across Europe. Indeed, many studies found a persistence 
in  income  disparities,  rather  than  convergence,  for  a  long  time.  Recognition  of  a  European 
‘regional  problem’  led  to  the  devotion  of  substantial  resources  to  try  to  mitigate  its 
manifestations. Magrini depicts a polarisation process, within which a group of richer regions 
have drawn away from the rest of the cross-sectional distribution. He suggests spatial factors 

20 Barro, RJ, and X. Sala-i-Martin (1992), “Convergence,” Journal of Political Economy, 100/2: 223-251. 

21 Sala-i-Martin, X. (1996), “Regional cohesion: evidence and theories of regional growth and convergence,” European Economic 
Review 40/6. 

22 Rodrik, D. (2011), “The Future of Economic Convergence” National Bureau of Economic Research (NBER) Working Paper 17400. 

23 Buti, M. and A. Turrini (2015), “Three waves of convergence. Can Eurozone countries start growing together again?” voxeu.org 

24 “Real convergence in the euro area: evidence, theory and policy implications,” ECB Bulletin 2015/5. 

25 Franks, JR, BB. Barkbu, R. Blavy, W. Oman and H. Schoelermann (2018), “Economic Convergence in the Euro Area: Coming Together 
or Drifting Apart?” IMF Working Paper 18/10. 

26 Gros, D. (2018), “Convergence in the European Union: Inside and outside the euro,” CEPS for Ecofin. 

27 Diaz del Hoyo, JL, E. Dorrucci, F. Heinz and S. Muzikarova (2017), “Real convergence in the euro area: a long-term perspective,” 
ECB Occasional Paper 203. 

28 Sala-i-Martin, X. (1996). 

29  Barro,  RJ,  X.  Sala-i-Martin,  OJ.  Blanchard  and  RE.Hall  (1991),  “Convergence  Across  States  and  Regions”  Brookings  Papers  on 
Economic Activity 1991/1: 107-182. 

30 Rosés, JR and N. Wolf (2018), “The Economic Development of Europe's Regions,” Routledge. 

 
                                                           
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could explain much of the cross-sectional per-capita income distribution.31 

An OECD paper (2012) finds that aggregate inequality in Europe rose between 1985 and 2008.32 
An  inequality  increase  within  countries  dominated  this  trend,  although  some  countries 
converged  to  higher-income  levels  and  cross-country  inequality  might  have  declined.  And  a 
more recent OECD report (2018b) also highlighted increasing regional disparities in countries hit 
hardest by the global financial crisis.33 

Goecke and Hüther (2016) argue that NUTS334 regions have travelled down a convergence path 
since  2000,  but  with  huge  differences.  Many  eastern  European countries,  along  with  several 
Spanish and Portuguese regions, are characterised by a convergence process, but the opposite 
holds  for  many  regions  in  Greece,  Italy,  and  the  United  Kingdom.  Their  findings  identify  a 
region’s manufacturing size as important for this convergence process.35 (Figure 12 in Annex.) 

A  Centre  for  European  Policy  Studies  (CEPS)  study  (2018)  also  finds  considerable  regional 
convergence  heterogeneity  within  EU  countries  from  2000  to  2015.36  A  related  article 
challenged the argument that deeper economic integration would lead to income convergence, 
arguing  that  economic 
income 
concentration, which are difficult to reconcile with income convergence.37 

leads  to  production  agglomeration  and 

integration 

A  recent  National  Bank  of  Belgium  analysis  concludes  that  intra-country  income  disparities 
remain substantial and  have  widened over time. Metropolitan regions  – especially capitals – 
have grown faster than average, contributing to regional convergence across EU countries and 
to  intra-country  disparities.  Agglomeration  effects  have  likely  played  a  role  here,  notably  a 
concentration of higher-productivity activities and innovation.38 

At the same time, Kalemli-Özcan suggests poorer euro area regions catch up with the richer ones 
on average, controlling for standard growth determinants such as demographic variables and 
education.39 However, these factors might be endogenous to regional characteristics. A section 
in  the  European  Investment  Bank’s  (EIB)  2018/2019  Investment  Report  shows  the  strong 
variation in research and development expenditures, patenting activities, and productivity in EU 
regions and argues that a good mix of institutional quality and a skilled labour force is crucial for 
regional catch up.40  

Dijkstra et al. (2015) point out that growth patterns in and across Europe differ from the simple 
city-led  growth  patterns.  The  previous  crisis  led  to  big  contractions,  especially  in  urban  and 
remote  rural  regions,  while  intermediate  and  rural  regions  close  to  a  city  displayed  more 
resilience.41  A  Centre  for  European  Reform  (CER)  note  (2019)  also  argues  that  the  much-

31 Magrini, S. (2004), “Regional (di) convergence,” Handbook of Regional and Urban Economics 4: 2741-2796. 

32 Fredriksen, KB. (2012), “Income Inequality in the European Union,” OECD Working Paper 952. 

33 OECD (2018b) Regions and Cities at a Glance. 

34 Nomenclature of Territorial Units for Statistics (NUTS) embraces NUTS3, which covers the EU statistical regions. 

35 Goecke, H. and M. Hüther (2016), “Regional Convergence in Europe,” Intereconomics 51/3, pp. 165-171. 

36  Alcidi.  C,  JN  Ferrer,  R.  Musmeci,  M.  Di  Salvo  and  M.  Pilati  (2018),  “Income  Convergence  in  the  EU:  Within-country  regional 
patterns,” CEPS. 

37 Alcidi. C. (2019), “Economic Integration and Income Convergence in the EU,” Intereconomics. 

38 Bisciari, P, D. Essers and E. Vincent (2020), “Does the EU convergence machine still work?” National Bank of Belgium Economic 
Review, June 2020. 

39 Şebnem Kalemli-Özcan (2019), “Twenty Years of Convergence ” Discussion at ECB Forum on Central Banking. 

40 EIB’s Investment Report 2018/2019, pp. 115-126. 

41 Dijkstra, L, E. Garcilazo and P. McCann (2015), “The effects of the global financial crisis on European regions and cities”, Journal of 

 
                                                           
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discussed  rural-urban  divide  is  too  simplistic,  and  does  not  fit  the  data.42  Some  industrial 
heartlands suffered relative decline but industrial output growth was stronger in the countryside 
and towns of western Europe, and spread evenly across regions in central and eastern Europe. 
Some  large  cities  and  regions  managed  to  replace  shrinking  industrial  production  with  high-
value services, especially tradable services such as finance, technology, culture, and advertising, 
and in some cases with high-technology manufacturing. 

Collier  (2017)  argues  that  an  interaction  of  trade  with  spatial  agglomeration  can  produce 
powerful transfers, with costs to greater trans‐national connectivity. An increase in international 
trade can make everyone better off, but those who gain do not necessarily compensate those 
who lose, generating adverse effects.43 This is corroborated by the World Bank’s Global Value 
Chain Development Report (2017), which provides some evidence that the benefits from GVC-
related trade were distributed unevenly within the respective countries.44 

An OECD report (2018a) identifies drivers of contrasting trends in productivity within countries. 
On  average,  regions  with  a  higher  specialisation  in  the  tradable  sector  –  implying  a  higher 
exposure to international competition – or located near a city experience faster catch-up to the 
most prosperous regions in their country.45 The report also notes some European regions seem 
to fall into a regional ‘middle income trap.’46 

In  terms  of  the  possible  agglomeration  effects,  Moretti  (2019)  consistently  finds  that 
manufacturing productivity gains also benefit employment and earnings in other sectors.47 Local 
manufacturing productivity growth reduces local inequality because it raises earnings of local 
less-skilled workers more than the earnings of local more-skilled workers. So the benefits from 
productivity growth mainly depend on where workers live. 

At the ECB Central Banking Forum in 2019 two papers offered additional insights for Europe. 
Imbs and Pauwels (2019) show that pairs of sectors with high export intensity are significantly 
more  correlated,  which  explains  aggregate  convergence  in  GDP  growth  at  a  country  level.48 
Alfaro et al. (2019) indicate that greater manufacturing agglomeration in 2004 in the euro area 
regions was associated with higher average real GDP growth between 2005 and 2017.49 Analysis 
by  Jacob  and  Mion  (2020)  suggests  higher  prices  mainly  drive  the  productivity  advantage  of 
denser areas, while firms in denser areas can also sell greater quantities.50 

Iammarino et al. (2018) complement the analysis by highlighting the role of technology shocks.51 

Economic Geography 15/5: 935-949. 

42 Odendahl, Ch, J. Springford, S. Johnson, J. Murray (2019), “The big European sort? The diverging fortunes of Europe’s regions,” 
CER Policy brief. 

43 Collier, P. (2017) “The downside of globalisation: Why it matters and what can be done about it” World Economy 41/4: 967-974. 

44  World  Bank  (2017),  Global  Value  Chain  Development  Report  “Measuring  and  Analyzing  the  Impact  of  GVCs  on  Economic 
Development.” 

45 OECD (2018a). 

46 Gill, I. and H. Kharas (2015), “The Middle-Income Trap Turns Ten”, World Bank Policy Research Working Paper 7403. 

47  Moretti,  E,  and  R.  Hornbeck  (2019),  “Estimating  Who  Benefits  From  Productivity  Growth:  Direct  and  Indirect  Effects  of  City 
Manufacturing TFP Growth on Wages, Rents, and Inequality.” 

48 Imbs, J. and Pauwels, L. (2019), “Real convergence in the EMU”, in 20 Years of European Economic and Monetary Union, ECB. 

49 Alfaro, L, M. Chen and H. Fadinger (2019), “Superstar firms and spatial agglomeration: An exploration of effects in Europe”, in 20 
Years of European Economic and Monetary Union, ECB. 

50 Jacob, N. and G. Mion (2020), “On the Productivity Advantage of Cities,” LSE CEP Discussion Paper 1687. 

51 Iammarino, S, A. Rodríguez-Pose and M. Storper (2018) “Regional inequality in Europe: Evidence, theory and policy implications,” 
voxeu.org  Iammarino,  S,  A.  Rodríguez-Pose  and  M.  Storper  (2018),  “Regional  inequality  in  Europe:  evidence,  theory  and  policy 

 
                                                           
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They  argue  that  increasing  inter-regional  inequality  in  Europe  is  partly  due  to  a  wave  of 
technological innovation that stimulated the concentration of high-technology and knowledge-
intensive sectors in large metropolitan areas, coupled with the demise of the more traditional 
industrial jobs across most of Europe. This is corroborated by the analytical chapter of the IMF’s 
2019  autumn  WEO,  which  finds  technology  shocks  raise  unemployment  in  regions  more 
vulnerable to automation, with the more-exposed lagging regions particularly hurt.52 

Rodríguez-Pose (2018) highlights the negative externalities of agglomeration and the social costs 
of  economic  distress  in  many  non-agglomerated  areas.53  Furthermore,  the  capacity  and 
willingness of individuals to move may be overestimated, because those who stay in lagging or 
declining  regions  may  be  unlikely  to  relocate  due  to  emotional  attachment,  age,  or  lack  of 
sufficient  skills  and  qualifications.  Hence,  encouraging  mobility,  or  making  it  easier  to  find 
housing in dynamic areas, may not be sufficient to attract people. 

Meanwhile,  the evidence of business  cycle  synchronisation remains inconclusive. De  Grauwe 
and Ji (2016)54 find the bilateral business cycle correlation is higher within the euro area than 
outside,  but  do  not  consider  whether  the  degree  of  synchronisation  has  evolved  over  time. 
Enderlein et al. (2016)55 note a lack of improvement in business cycle synchronisation since the 
start of EMU. Bayoumi and Eichengreen (2017)56 find the euro area falling short of an optimal 
currency  area  in  its  response  to  demand  and  supply  shocks,  with  little  evidence  of  the 
“endogeneity of the optimal currency area criteria.” Recent IMF analysis shows that business 
cycles have become increasingly synchronised across euro area countries, but the amplitude of 
these cycles remain different.57 

Blanchard and Katz (1992) show that in the US, the dominant adjustment mechanism to labour 
demand shocks is labour mobility, rather than job creation or job migration.58 They suggest the 
conventional adjustment mechanism, in which countries that suffer adverse shocks restore full 
employment through real depreciation, is virtually absent in the US. Wages decline in states that 
suffer adverse shocks, but there is no tendency for states to recover lost jobs. Instead, workers 
move – and the unemployment rate falls. 

Following a similar approach, Beyer and Smets (2015) find that in Europe labour mobility is a 
less  important  adjustment  mechanism  than  in  the  US,  but  they  detect  a  rise  in  the  role  of 
migration.59  However,  this  does  not  address  Krugman’s  initial  concern  that  a  combination  of 
single market and EMU would produce a European economy that is “likely to exhibit greater 
disparity in regional growth rates, because with increased factor mobility regions will tend to 
adjust to shocks by adding or shedding resources rather than by adding or shedding industries.” 
In  keeping  with  this,  “Europe  will  have  a  problem  if  it  starts  to  experience  American-style 

implications,” Journal of Economic Geography. 

52 IMF (2019). 

53 Rodríguez-Pose, A. (2018), “The revenge of the places that don’t matter (and what to do about it)”, Cambridge Journal of Regions, 
Economy and Society 11/1. 

54 De Grauwe, P. and Y. Ji (2016), “Synchronisation in business cycles: An endogenous explanation,” voxeu.org. 

55 Enderlein, H, E. Letta et al. (2016), “Repair and Prepare: Growth and the Euro after Brexit,” Gütersloh, Berlin, Paris: Bertelsmann 
Stiftung, Jacques Delors Institut – Berlin and Jacques Delors Institute in Paris. 

56 Bayoumi, T. and B. Eichengreen (2017), “Aftershocks of Monetary Unification: Hysteresis with a Financial Twist,” NBER Working 
Paper 23205. 

57 Franks et al. (2018). 

58 Blanchard, O. and L. Katz (1992), “Regional Evolutions,” in Brookings Papers in Economic Activity 1. 

59 Beyer, RCM. and F. Smets (2015), “Labour market adjustments in Europe and the US: How different?” ECB Working Paper 1767. 

 
                                                           
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regional slumps without American-style fiscal federalism.” Hence, “Some kind of policy reform 
will be necessary if the increasingly unified European economy is not to pay an even higher price 
for that unification than the US does.”60 

60 Krugman (1993). 

 
 
 
                                                           
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2.  Patterns of regional disparities in Europe 

 
 
 
 
 
 
 
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Our  analysis  suggests  European  regional  disparities  intensified  from  2000  to  2016.  In  this 
section, we look at regional patterns between 2000 and 2016 both in the 19 euro area member 
states (EA19) and then more broadly across the 28 EU Member States (EU28). We analyse data 
for  large  regions  with  an  average  population  around  2.5  million  and  small  regions  with  an 
average  of  around  400,000,  based  on  Eurostat  and  the  OECD  Regional  Database.  The  EA19 
covered 141 large and 924 small regions, while the EU28 as a whole gathers 213 large and 1,317 
small  regions  into  our  sample.61  Figure  2,  left  panel,  depicts  the  coefficient  of  variation,  a 
standardised measure of dispersion, for the EA19 and the EU28.62 

Regional disparities in Europe compare to those in the US. By some measures, disparities in the 
EA19 and the EU28 are even larger, with both the coefficient of variation and the 90/10 ratio63 
is higher when comparing large regions in Europe and regions in the US. The 90/10 ratio is about 
1.8  for the US, 2.4 for EA19 and 2.8 for EU28. The regional distribution of GDP per capita in 
Europe is less concentrated around the mean and has ‘fatter tails’, suggesting a larger share of 
regions at the extremes (Figure 2, right panel). 

Figure 2 
Regional disparities in Europe and the US 
Dispersion of income per capita, coefficient of variation 

Distribution of regions by GDP per capita, as % of average 

Note: The left panel is based on data for small regions, 924 in EA19 and 1,317 in EU28. The right panel is based on data for large regions to ensure 
comparability with the US, 141 in EA19, 213 in EU28 and 179 in the US. The chart depicts latest data available: 2017 for Europe and 2016 for the US. 
Source: ESM based on OECD Regional Database 

However, the dynamics were different inside and outside EMU. In the euro area, dispersion was 
stable  until  the  previous  crisis,  and  lower  income  regions  were  catching  up  to  some  extent. 
Divergences  emerged  afterwards,  when  both  the  top-income  and  bottom-income  regions 
moved further away from the average. In other EU countries, low-income regions continued to 
catch up during the crisis. Overall, the share of regions moving away from the average in the 
euro area from 2000 to 2016 was higher than in other EU countries (Figure 3). 

61 In this section, we follow the OECD’s classification for better data coverage. The large/small region classification is similar, but not 
identical to the Eurostat’s NUTS2/3 level classification. Notably, there is a difference between the OECD’s large regions for Germany 
(16 states) and the Eurostat’s NUTS2 regions (41). 

62 The coefficient of variation is a standardised measure of dispersion. It is the ratio of the standard deviation to the mean. The 
higher the coefficient of variation, the greater the level of dispersion around the mean and the larger the disparity. 

63 The 90/10 ratio is the ratio of real GDP per capita, PPP-adjusted, of the 90th percentile subnational region to the 10th percentile 
subnational region, calculated for each country. 

 
 
 
                                                           
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Figure 3 
Change in distance to average GDP per capita 
Top and bottom regions’ GDP per capita, as % of average  Change in distance to average between 2000–2016 

Note: The left panel is based on data for 924 small regions in the euro area. The right panel is based on data for 924 euro a rea and 393 other EU 
regions. The horizontal axis depicts the change in distance of a region’s GDP per capita to the EU average GDP per capita expressed in percentage 
points. The vertical axis shows the share of regions in the respective bracket. 
Source: ESM based on OECD Regional Database 

Also,  the  catching-up  effect  in  new  EU  Member  States  may  contribute  to  different  regional 
dynamics. When we split our sample between the 12 ‘old’ euro area member states (EA12) and 
seven ‘new’ euro area member states (EA7), we find the regional dispersion increases across the 
EA12,  but  declines  across  the  EA7,  reflecting  the  catch-up  in  regions  with  an  initially  lower 
income (Figure 4). 

Figure 4 
Regional disparities and catching up in old and new euro area member states 
Dispersion of income per capita, coefficient of variation   Income per capita in poorest regions, as % of EA19 average 

Note: Both charts are based on data for small regions, 881 in old EA12 and 43 in new EA7. The right panel depicts the regions with GDP per capita 
falling into the lowest 10% in a given year. 
Source: ESM based on OECD Regional Database 

For a more granular analysis, we distinguish four groups of regions, based on how their GDP per 
capita evolved between 2000 and 2016. These four groups are: (1) those pulling away (initially 
above-average  and rising faster than average); (2) those  ‘catching down’  (above  average  but 
rising slower than average); (3) those catching up (below average rising faster than average); 
and (4) those lagging (below average rising slower than average). The GDP per capita is based 
on US dollar per head, at current prices and purchasing power parity (PPP), and we compare 
euro area member states to the euro area average and non-euro countries to the EU average. 

In Europe, about a quarter of the small regions are falling behind, while around 15% are pulling 
away. In the EU28 group, 26% of the small regions recorded a below-average GDP per capita in 
2000 and fell further below  average  by 2016, while 14%  were  above  the  average  and pulled 
further ahead. In the EA19 group, 28% fell further behind, with 17% pulling away (Table 5 in 
Annex). Once again, a striking difference emerges between the old and new Member States; in 
the new Member States, the overwhelming majority of small regions are catching up, but the 

 
 
 
 
 
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picture is more uneven in the old Member States (Figure 5, comparing the left and right panels). 

Urban-rural differences do not seem to fully explain regional variations.64 Urban regions are the 
most likely to be pulling away, but this only represents a minority of urban regions in the EA12 
(about 25%), while 42% of urban areas have been catching down and 17% have been lagging. 
Rural regions close to a city are most likely to be catching up (45%), while remote rural areas are 
the  most  likely  to  be  lagging  (54%)  in  the  EA12.  Intermediate  regions,  which  are  the  most 
common  in  the  EA12  at  383  out  of  881,  are  relatively  evenly  distributed  across  the  four 
categories, with  a  modest  skew  towards catching  up  (31%). The  pattern  is  broadly  similar  in 
Denmark, Sweden, and the United Kingdom (UK) – the three old EU Member States that did not 
introduce  the  euro  –  with  a  relatively  bigger  share  of  catching  down  regions  as  opposed  to 
catching up, consistent with the higher initial income level of these three countries (Figure 5, 
top left and bottom left). 

Figure 5 
Small regions by urban/rural typology and by relative change in GDP per capita between 2000 and 2016 
Initial euro area members (12 countries) 

New euro area members (seven countries) 

Old non-euro members (three countries) 

New non-euro members (six countries) 

Note: The number of regions in each category are shown in brackets. 
Source: ESM based on OECD Regional Database 

Taking a closer look at the initial 12 euro area countries, it is apparent that regional dynamics 
are not necessarily about north-south differences. Pulling-away regions represent a higher share 
in some northern countries, such as Ireland, Austria, Germany, and Belgium at above 20%, but 
the split between catching up and lagging regions is more nuanced. Catching-up regions are in a 
relative majority in Portugal, Austria, Germany, Spain, and Finland, standing above 40%, while 
more regions are lagging in Greece, France, Italy, and Belgium, at over 40%; in particular, the 
share of lagging regions in France is a strikingly high 72%65 (Figure 6). 

64 We follow the classifications in the OECD’s Regional Database. 

65 The basis for comparison in this case is the average of the 12 countries. 

 
 
 
 
 
 
 
                                                           
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Figure 6 
Share of small regions in each country by relative change in GDP per capita between 2000 and 2016 
Compared to full-sample cross-country average of the initial 12 euro area member states 

Note: Based on a total sample of 881 small regions. Luxembourg is omitted due to the limited number of regions. 
Source: ESM based on OECD Regional Database 

If we consider the GDP-weighted and employment-weighted ratios, a similar picture emerges, 
but with notable differences. In the GDP-weighted iteration, the relative share of pulling-away 
and lagging regions increases, suggesting a pattern of polarisation; the pulling-away and lagging 
regions  appear  to  be  economically  more  significant.  In  particular,  over  80%  of  GDP  is 
concentrated in pulling away regions in Ireland and in lagging Italian regions. The balance also 
tilts towards lagging regions in Portugal. On the other hand, when weighted by employment the 
relative share of lagging regions is lower at some 50% in France and 30% in Italy, but the weight 
of catching-down regions is higher (Figure 11 in Annex).66 

Sectoral composition 

Manufacturing  appears  to  play  an  important  role  when  considering  possible  differentiating 
factors.  In  the  OECD  Regional  Database  we  find  sectoral  data  for  agriculture,  industry, 
manufacturing  and  construction  in  small  regions, and  a  more  comprehensive  breakdown  for 
large regions. Based on this, we analyse the sectoral composition of regional economic activity. 
At  first  glance,  the  change  in  manufacturing  employment  shows  some  correlation  with  the 
change  in  GDP  per  capita  relative  to  the  average  both  in  the  euro  area  and  in  the  EU  more 
broadly (Figure 7). 

66 Weighting is based on GDP and employment at the beginning of the period, in 2000. 

 
 
 
                                                           
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Figure 7 
Change in manufacturing employment and in relative GDP per capita between 2000 and 2016 
Initial 12 euro area member states 

European Union (28 countries) 

Note: The left panel is based on data for 881 small regions in EA12. The right panel is based on 1,317 small regions in EU28. The horizontal axes in 
both panels depict the change in a region’s GDP per capita relative to the sample average GDP per capita in percentage points. 
Source: ESM based on OECD Regional Database 

Regions that could retain manufacturing and so benefit from productivity gains, turned out to 
be  the  more  successful.  As  a  general  pattern, manufacturing  employment  fell  from the  year 
2000, while its gross value added (GVA) increased. However, the manufacturing employment 
decline  was  more  modest  and  the  GVA  increase  more  pronounced  in  the  pulling-away  and 
catching-up regions.  

A shift to professional services is another driver in leading regions. In the pulling-away regions, 
gains in professional-services employment, including finance and information technology (IT), 
more than offset losses in manufacturing employment,  and contributed considerably to GVA 
gains.  Catching-down  regions  recorded  comparable  employment  gains  in  services,  but  with 
more modest GVA gains. The lagging and catching-up regions experienced shifts from agriculture 
to services, but the resulting GVA gains were more meaningful in the catching-up regions. This 
seems to suggest weaker sectoral productivity gains in catching-down regions compared to the 
lagging ones (Figure 8). 

Figure 8 
Contribution to regional growth by sector in the initial 12 euro area member states between 2000 and 2016 
Contribution to employment growth, in percentage points     Contribution to GVA growth, in percentage points 

Note: Based on data  for 131 large regions.  Retail services include  distributive trade, repairs, transport, accommodation, food service activities; 
professional services include scientific, technical, administrative, support, information, communication, financial and insurance service activities. 
Source: ESM based on OECD Regional Database 

Rising intra-sector productivity differentials contribute to regional disparities. Overall, regions 
with higher initial total labour productivity seem to gain productivity at a slower pace than the 
initially less-productive regions, presumably approaching the technology frontier. However, this 
does not appear to be consistent within sectors across regions. Leading regions seem to have 
had  higher  sectoral  labour  productivity  to  begin  with,  and  then  recorded  more  pronounced 
sectoral productivity gains than lagging regions. Consequently, the productivity gaps between 

 
 
 
 
 
 
 
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leading and lagging regions appear to have been widening in most sectors (Figure 9). 

Figure 9 
Regional productivity change by sector in the initial 12 euro area member states between 2000 and 2016 
Initial productivity and subsequent growth 

Regional productivity, as % of sectoral average 

Note: Based on data for 131 large regions. Productivity is measured by gross value added per employee. In the left panel, the size of the bubbles 
reflects the contribution of employment growth in the respective sector to overall employment growth; empty bubbles show negative contribution. 
Retail  services  include  distributive  trade,  repairs,  transport,  accommodation,  food  service  activities;  professional  services  include  scientific, 
technical, administrative, support, information, communication, financial and insurance service activities. 
Source: ESM based on OECD Regional Database 

To  obtain  some  econometric  evidence  on  this  pattern,  and  on  the  importance  of  starting 
conditions, we run a set of fixed-effects panel regressions based on annual data for large regions. 
We split the regions according to their initial (2000) productivity levels into five quintiles, and 
run  estimates  for  total,  manufacturing,  and  professional-services  productivity  growth.  As  an 
explanatory variable, we include the lagged productivity level respectively (Table 1). This also 
provides some insights into the stability of sectoral productivity dynamics for different groups 
of regions. The results for the full sample (c) corroborate that regions with higher initial total 
labour  productivity  gain  productivity  at  a  slower  pace  than  initially  less-productive  regions. 
However, the results are not significant for manufacturing and services productivity separately. 
Looking  at  the  breakdown  by  quintiles,  results  are  stronger  for  the  first  quintile,  the  least 
productive,  and  weaker  for  the  fifth  and  most  productive  quintile  (a  and  b).  This  seems  to 
suggest  that  the  lowest  productivity  regions  achieve  some  catch-up  to  the  average,  but 
productivity growth is unlikely to slow for the most productive.  

Agglomeration may be another factor driving regional differentiation. To explore this possibility 
we introduce a gravity index following Odendahl et al. (2019). The gravity equation from physics 
is the basis of this measure: the product of the masses of two objects, divided by the square of 
the  distance  between  them  determines  gravity.  Translated  to  our  setting,  we  calculate  this 
measure  based on GDP for all possible region-pairs, and then sum it up for each region. The 
closer a region is to other economically large regions, the higher the gravity index (Figure 13 in 
Annex).67  This  measure  proves  to  be  significant  and  robust  across  most  of  the  iterations 
estimating productivity growth. 

67 See Odendahl et al. (2019) Chart 14 pp. 14-15. 

 
 
 
 
 
 
                                                           
2 4   |   D I S C U S S I O N   P A P E R   S E R I E S   |   J u l y   2 0 2 0    

Table 1 
Panel data analysis of regional productivity growth between 2000 and 2016 
(Annual productivity growth) 

EU28 

EA19 

EA12 

Overall  Manu-

facturing 
sector. 
(2) 

(1) 

Service 
sector 

Overall  Manu-

Overall  Manu-

Service 
sector 

facturing 
sector. 
(5) 

facturing 
sector. 
(8) 

Service 
sector 

(9) 

(3) 

(4) 

(6) 

(7) 

)
a
(
1
Q

)
b
(
5
Q

)
c
(

l
l

u
F

Productivity in t-1 

Gravity index (ln) 

-0.420*** -0.720***  -0.763***  -0.339**  -0.431***  -1.246***  -0.558*** -0.273*** -1.061*** 
(-5.11) 

(-7.84) 

(-3.86) 

(-6.41) 

(-3.11) 

(-6.70) 

(-8.48) 

(-7.32) 

(-7.29) 

35.40***  41.52***  24.02**  27.99***  30.23***  48.16***  26.23***  38.63**  33.54*** 
(3.72) 

(13.14) 

(2.98) 

(8.67) 

(5.05) 

(3.69) 

(4.05) 

(7.11) 

(5.60) 

GDP per capita in t-1 (ln) 

-40.63***  -32.90** 

14.95 

-32.88***  -50.16*** 

-31.03*  -20.44*** -69.43*** 

-19.77 

(-5.74) 

(-3.43) 

(1.48) 

(-6.76) 

(-4.72) 

(-2.08) 

(-6.15) 

(-4.03) 

(-1.65) 

Productivity in t-1 

-0.109 
(-1.73) 

-0.0542* 
(-2.22) 

-0.144* 
(-2.14) 

-0.138 
(-1.92) 

-0.0564** 
(-2.85) 

-0.110 
(-1.91) 

-0.184 
(-1.94) 

-0.219* 
(-2.25) 

-0.118 
(-1.56) 

Gravity index (ln) 

47.39** 

100.8 

25.54***  55.70** 

121.8 

24.72***  31.04**  30.80***  24.60*** 

(3.26) 

(1.91) 

(9.29) 

(3.38) 

(2.01) 

(7.63) 

(3.60) 

(3.77) 

(4.00) 

GDP per capita in t-1 (ln) 

-65.71** 

-145.1 

-25.83***  -68.78** 

-154.0 

-25.84***  -34.09** 

-30.50* 

-28.03* 

(-2.91) 

(-1.95) 

(-4.85) 

(-2.90) 

(-1.94) 

(-4.40) 

(-2.85) 

(-2.17) 

(-2.73) 

Productivity in t-1 

-0.185*** 
(-4.24) 

-0.117 
(-1.88) 

-1.632 
(-1.48) 

-0.202*** 
(-4.14) 

-0.101 
(-1.87) 

-2.546 
(-1.24) 

-0.196***  -0.0953 
(-1.76) 

(-3.66) 

-2.633 
(-1.22) 

Gravity index (ln) 

29.38***  42.33*** 

51.10 

28.98***  42.64* 

88.06 

29.19***  44.77* 

94.36 

(8.15) 

(3.61) 

(1.94) 

(5.73) 

(2.41) 

(1.50) 

(5.37) 

(2.35) 

(1.47) 

GDP per capita in t-1 (ln) 

-42.16***  -65.66*** 

-24.21 

-36.76***  -67.26** 

-13.55 

-37.73***  -75.02* 

-17.19 

(-8.02) 

(-3.98) 

(-1.97) 

(-4.66) 

(-2.68) 

(-0.55) 

(-4.13) 

(-2.58) 

(-0.72) 

Full sample 

3601 

3439 

3439 

2387 

2274 

2274 

2193 

2087 

2087 

EA19 member dummy 

Crisis dummy for BMS* 

Region and year fixed effects 

yes 

yes 

yes 

yes 

yes 

yes 

yes 

yes 

yes 

yes 

yes 

yes 

yes 

yes 

yes 

yes 

yes 

yes 

yes 

yes 

yes 

Note: Cluster-robust t-statistics in parentheses. The table presents the estimated coefficients of 27 independent time and region fixed effects panel 
regressions, sampled by region group (EU28, EA19, and EA12), by starting productivity (first quintile, fifth quintile, full sample), and by sector (full 
economy, manufacturing sector, services sector). T-1 refers to previous year. Ln refers to the natural logarithm. *ESM beneficiary Member States. 
Source: ESM calculations based on Eurostat and OECD data 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
R E G I O N A L   D I S P A R I T I E S   I N   E U R O P E   |   2 5  

Crisis impact and the risk of hysteresis 

Hysteresis after the previous crisis aggravated regional disparities. The crisis demonstrated that 
the  growth  models  of  several  regions  were  not  sustainable.  Many  experienced  persistently 
lower employment and weaker investment, raising the spectre of protracted stagnation. But, at 
the same time, leading regions were less affected or rebounded faster.68 Part of this difference 
may be due to flexible exchange rate adjustments (in non-euro area countries), or with a longer-
term trend of catching up from a low-income starting point (in new Member States). However, 
the  difference  between  the  most-  and  least-affected  regions  within  the  initial  12  euro  area 
countries is still striking, given these countries are homogeneous in their exchange rate regime 
and relatively more similar in income levels (Figure 10, left panel). 

An OECD report finds that regions with the largest non-tradable sector expansions suffered the 
strongest employment losses.69 Tradable sectors may appear more exposed to external shocks, 
but, in fact, post-2008 employment declined more in regions that expanded their non-tradable 
sectors relative to tradable activities in the pre-crisis period 2000–2007. Hence, non-tradable 
activities are not immune from external shocks; local links tie tradable and non-tradable sectors 
together.  Furthermore,  non-tradable  sectors  depend  more  on  local  demand,  while  tradable 
sectors have more opportunities to find alternative markets. 

From  a  different  perspective,  a  recent  ECB  analysis70  shows  sizeable  heterogeneity  across 
regions in the evolution of total hours worked in the euro area between 2007 and 2018. Richer 
regions were more insulated during the recession period, and poorer regions did not fully catch 
up  during  the  recovery  period.  By  2018 total  hours worked  had  recovered to their  pre-crisis 
levels only in the regions at the top of the GDP per capita distribution, while in the remaining 
regions they still stood below their 2007 levels. The smaller decline in total hours worked in the 
richer  regions  during  the  downturn  can  be  partly  attributed  to  regional  differences  in  the 
employment  rate  and  in  population  growth consistent  with  labour migrating  from  poorer  to 
richer regions. 

Figure 10 
Regional employment and migration in the initial 12 euro area member states between 2000 and 2016 
Employment, 2000=100 

Migration rate, % 

Note: In the left panel, regions with smallest GVA contraction had no decline in GVA, while regions with largest GVA contraction had 10% or more 
contraction peak-to-trough. In the right panel, migration rate is net migration during the year in % of average population in that year. 
Source: ESM based on Eurostat 

68 See also the European Commission’s 8th progress report on economic, social and territorial cohesion, 2013. 

69 OECD (2018a), p. 76. 

70 Botelho, V, C. Foroni and L. Vivian (2020), “Regional labour market developments during the great financial crisis and subsequent 
recovery,” ECB Economic Bulletin 4. 

 
 
 
 
 
                                                           
2 6   |   D I S C U S S I O N   P A P E R   S E R I E S   |   J u l y   2 0 2 0    

Migration  

Migration has shifted people away from lagging regions towards leading ones. Migration rates71 
were broadly similar until 2008, but after the  previous crisis, across the euro area, migration 
rates in lagging regions declined and in leading ones increased. Eventually, around 2014, the 
rate turned negative in lagging regions and increased in leading ones, also presumably reflecting 
the shock of the crisis (Figure 10, right panel).72 

An  IMF  study  finds  that  the  better-educated  or  employed  are  more  likely  to  move  within 
countries, which can exacerbate unemployment and skill shortages in lagging regions.73 Also, a 
section in the EIB’s 2019/2020 Investment Report argues that technological transformation also 
aggravates the spatial polarisation of European labour markets.74 

Overall, we observe the following key patterns of regional disparities in Europe:  

  Regional disparities have been rising both in the euro area and across the EU more broadly, 
especially following the crisis. The magnitude of regional disparities in Europe is comparable 
to the US, and larger by some measures. 

  We find symptoms of a regional ‘middle income trap’, with most lagging regions – those with 
an increasing negative distance to average income – in old Member States, while most of the 
poorest regions in new Member States seem to be catching up. 

  The  regional  disparity  pattern  suggests  an  urban-rural  divide  or  north-south  differences 
cannot offer simple explanations; regional sectoral composition and productivity differences 
appear to form part of the story. 

  Regions that could retain manufacturing, and so benefit from productivity gains, turned out 
to be more successful. A shift to professional services is another driver in leading regions and 
it would appear regions benefitted from trade  integration, given that manufacturing and a 
large part of professional services can be considered ‘tradables’.  

  The intra-sector productivity gap between leading and lagging regions has been widening in 

most sectors, which could be explained by agglomeration effects and GVC integration. 

  Hysteresis following the financial crisis may have aggravated regional disparities. The most-
affected regions experienced persistently lower employment and weaker investment, while 
other regions were less affected, or rebounded more swiftly. 

  A clear pattern of outward migration from lagging regions emerged after the financial crisis, 

while inward migration to leading ones increased. 

71 Migration rate is net migration during the year in % of average population in that year. 

72 The refugee crisis in 2015 may have also contributed to the spike in leading regions. 

73 IMF (2019), pp. 77-78. 

74 EIB Investment Report 2019/2020, p. 374. 

 
 
 
 
                                                           
R E G I O N A L   D I S P A R I T I E S   I N   E U R O P E   |   2 7  

3.  The role of global value chains, specialisation,  

and agglomeration 

 
 
 
 
 
 
 
2 8   |   D I S C U S S I O N   P A P E R   S E R I E S   |   J u l y   2 0 2 0    

Empirical  evidence  corroborates  the  importance  of  the  GVC  integration  and  agglomeration 
effects. In this section, we undertake a multivariate analysis to overlay the increase in regional 
disparities with GVC integration, specialisation, agglomeration effects and policy variables, such 
as government quality and labour market protection. Our analysis aims to test the hypotheses 
on the impact of GVC and agglomeration for the EU and the euro area. 

We estimate the change in regional GDP per capita compared to sample average expressed in 
percentage point change.75 We run the regressions for the 28 EU Member States, for the current 
19 members of euro area and for the initial 12 members of the euro area, using the respective 
averages  as  bases  for  comparison.  All  three  estimation  periods  cover  2000  to  2016,  in  two 
different approaches. The first focuses on the overall change during this period (Table 1), while 
the second uses annual data during this period for the explanatory variables where the time-
series is available (see Annex for details.) 

Regional data are available from the OECD’s regional database, and from the Productivity and 
Jobs in a Globalised World76 report that is based on regional input-output tables and provides 
information on regional GVC integration. We also calculate a sectoral specialisation indicator, 
the so-called Balassa-Hoover index77 and the gravity index cited above. Additionally, we include 
proxies for education and patenting, and control for the initial income level. In an alternative 
specification (Table 1/7-8), we include a region’s distance to the respective national capital, and 
its  interaction  term  with  the  country’s  federal  structure  to  gauge  its  impact  on  the 
agglomeration structure. 

As policy variables, we use the  regional European Quality of Government Index and EU fund 
allocations, as well as national-level indicators for labour and product market regulation. We 
include dummy variables for euro area membership depending on the time of accession, and 
dummies for ESM beneficiary Member States (BMS) to gauge the possible financial crisis impact.  

The results from our analysis from 2000 to 2016 suggest the following: 

  There seems to be a significant negative association between initial GDP per capita and the 
subsequent change across most iterations, suggesting poorer regions grow faster on average, 
and  that  income  convergence  is  indeed  taking  place  overall.  Notably,  in  the  cross-section, 
results are only significant for the EA12 if we control for government quality (Table 1/3-4). 

  The  share  of  value-added  from  GVCs  in  the  total  regional  value-added  has  a  significantly 
positive association with change in GDP per capita across all iterations (Table 1). This suggests 
that stronger integration into GVCs is a growth-enhancing factor that can support catching up. 
However, the simple specialisation indicators, like the Balassa-Hoover index, do not appear to 
be significant in the panel data analysis, for neither manufacturing nor services (Table 2).78 

Agglomeration 

  The gravity index, which is a proxy for agglomeration effects, shows  a significantly positive 
association  with  change  in  GDP  per  capita  throughout  the  euro  area  in  the  cross-sectional 
analysis  (Table  1),  and  also  more  broadly  in  the  panel  regressions  (Table  2).  This  seems  to 

75 Although the exercise if focused on EU member states, we use the USD-based series from the OECD to improve data availability 
and facilitate possible comparisons beyond Europe. 

76 OECD 2018a, Figures 3.3 and 3.5 on pp. 101 and 116. 

77 Based on Rusticelli, E, D. Haugh, A. Arquie and L. Demmou (2018), “Going local: A regional perspective on how trade affects labour 
markets and inequality,” OECD Working Paper 1530, Annex D. 

78 These results are in line with the findings of Rusticelli et al (2018). 

 
                                                           
R E G I O N A L   D I S P A R I T I E S   I N   E U R O P E   |   2 9  

support de Larosiere’s argument that there may be an inherent advantage in being close to an 
economic  centre  of  gravity  that  determines  outcomes.  The  interaction  term  of  the  gravity 
index  and  productivity  growth  is  also  significantly  positive  (Tables  2/6-8),  suggesting 
agglomerations  may  be  amplifying  the  impact  of  labour  productivity  growth  on  change  in 
income more broadly. 

  Consistent  with  this  result,  the  alternative  measure  of  distance-to-capital  proves  to  be 
significantly  negative,  especially  in  centralised  states  (Tables  1/7-8).  This  may  also  be 
interpreted as different models of development – development of the metropolitan capital 
against development across regions.  

Skills and innovation 

  The share of the labour force with tertiary education has a significantly positive association 
with change in GDP per capita in the cross-sectional analysis for the EU as a whole. This holds 
for  the  2000–2016  average  ratio  (Table  1/1)  and  for  the  initial  ratio  in  2000  (Table  1/5), 
underlining the importance of the starting conditions, as suggested by de Larosiere. Similarly, 
the impact of patenting activity is significantly positive (Table 1/4).  

  However, the results do not appear to hold in the panel regressions (Table 2). This may also 
be explained by a non-direct causal link (e.g. longer studies because of unemployment) or a 
lagged transmission of educational achievements to productivity gains. 

Policy variables 

  Employment  protection  seems  to  have  an  unfavourable  impact  within  the  euro  area;  it  is 
negatively  associated  with  change  in  GDP  per  capita  (Table  1/2-3).  For  the  EU28,  the 
association only turns significant if we introduce an interaction term with euro membership, 
suggesting  that  the  importance  of  appropriate  labour  market  regulation  becomes  more 
important when joining EMU (Table 1/6,8). 

  The results for product market regulation are similarly negative for GDP per capita growth in 

the EA12 (Table 1/3), but the results are weaker for other iterations. 

  If we add the quality of government variable (which is available for a subset of our sample so 
we focus on the EA12), it shows a favourable impact, being positively associated with change 
in GDP per capita, while the significance of labour and product market regulation weakens. 

  EU fund allocations turn out to be significantly positive only for the EU28 iterations in the panel 
data  analysis  (Table  2/1,  6),  suggesting  they  may  have  a  more  meaningful  impact  for  new 
Members outside the euro area. 

 
 
 
3 0   |   D I S C U S S I O N   P A P E R   S E R I E S   |   J u l y   2 0 2 0    

Table 2 
Cross-section regression analysis of regional income developments  

(Change in regional GDP per capita relative to average over 2000-2016, in percentage points) 

Change in regional GDP per capita relative to average 

(1) 

(2) 

(3) 

(4) 

(5) 

(6) 

(7) 

(8) 

Share of value-added from  
GVCs in total value-added 

1.541***  1.195** 
(3.00) 

(5.24) 

0.792*** 
(4.27) 

0.499* 
(2.13) 

1.493*** 
(4.73) 

0.947* 
(2.55) 

1.018** 
(3.31) 

0.840** 
(2.66) 

Gravity index (ln)- average 
(agglomeration proxy) 

-0.0461 
(-0.09) 

0.912 
(1.20) 

1.610***  1.267*** 

(3.77) 

(3.50) 

1.217 
(1.89) 

78.30*** 
(4.25) 

41.81* 
(2.12) 

17.03 
(1.19) 

9.031 
(0.64) 

-0.221 
(-0.35) 

61.62** 
(3.32) 

58.43** 
(3.15) 

39.33** 
(2.79) 

35.58** 
(2.62) 

Country-Level Employment  
Protection Legislation Index 

-5.248 
(-0.95) 

-19.02***  -17.59*** 

(-3.63) 

(-3.79) 

0.0605** 

(3.29) 

-3.546 
(-0.85) 

-11.19 
(-1.72) 

18.98* 
(2.17) 

-2.951 
(-0.60) 

8.847 
(1.46) 

4.217 
(0.70) 
52.04 
(1.11) 

-9.757 
(-1.38) 
19.03 
(0.38) 

-15.78*** 
(-3.58) 
22.45 
(0.44) 

8.509 
(1.12) 
-23.93 
(-0.84) 

7.168 
(1.05) 
72.25 
(1.25) 

0.314*** 
(6.29) 

-39.98*** 
(-3.93) 

-3.064 
(-0.47) 
64.78 
(1.24) 

5.779 
(1.09) 
15.20 
(0.39) 

-20.25** 
(-2.90) 

1.407 
(0.27) 
51.22 
(1.05) 

-3.664***  -3.414*** 

(-3.60) 
-16.18 
(-1.86) 

(-3.43) 
-15.49 
(-1.81) 

5.237***  4.875*** 

(3.51) 

(3.40) 

-17.54*** 
(-3.35) 
1.279 
(0.28) 
0.571 
(0.16) 
132.8* 
(2.41) 
EU28 
188 
0.489 

-17.01* 
(-2.51) 

-8.774 
(-1.13) 

-34.15*** 
(-6.84) 

4.747 
(1.17) 
191.7** 
(2.78) 
EA19 
134 
0.460 

9.568** 
(3.13) 
115.8 
(1.45) 
EA12 
126 
0.481 

1.499 
(0.38) 
285.6*** 
(5.51) 
EA12 
89 
0.729 

-19.13***  -19.83***  -18.79*** 
(-5.92) 
-2.772 
(-0.61) 
-5.759 
(-1.79) 

(-5.62) 
46.98** 
(2.70) 
-6.809* 
(-2.01) 

185.3***  157.9*** 

-16.12* 
(-2.56) 
5.309 
(0.93) 
-1.944 
(-0.45) 
137.8* 
(2.24) 
EU28 
176 
0.466 

(-3.79) 
97.77*** 
(4.19) 
-0.00399 
(-0.00) 
102.4 
(1.95) 
EU28 
188 
0.543 

(5.38) 
EU28 
188 
0.615 

(4.34) 
EU28 
188 
0.633 

Sample 
N 
r2 
Note: Robust t-statistics in parentheses. Ln refers to the natural logarithm. N refers to the number of observations. r2 refers to the coefficient of 
determination. 
Source: ESM calculations based on European Commission, OECD, and University of Gothenburg data 

Gravity index (ln) - initial 
(agglomeration proxy) 
Share of labour force with  
tertiary education - average 

Share of labour force with  
tertiary education - initial 

Patent cooperation treaty 
patent applications per  
million habitants 

Employment protection x  
euro area membership 

Country-Level Product  
Market Regulation Index 
EU Fund allocations 

European Quality of  
Government Index 

Distance to capital 

Federal state dummy 

Distance to capital x  
federal state dummy 

GDP per capita in 2000 (ln) 

Euro area dummy 

Crisis dummy for BMS 

Constant 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
  
  
  
  
 
 
  
  
  
  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
  
  
  
 
  
 
  
  
  
  
 
  
 
  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
  
 
 
 
 
 
 
  
 
 
 
 
 
  
 
 
 
 
 
 
  
 
 
 
 
 
  
 
 
 
 
 
  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
R E G I O N A L   D I S P A R I T I E S   I N   E U R O P E   |   3 1  

Table 3 
Panel regression analysis of regional income developments 

(Annual change in regional GDP per capita relative to average, in percentage points) 

Change in regional GDP per capita relative to average 

(1) 

(2) 

(3) 

(4) 

(5) 

(6) 

(7) 

(8) 

Gravity index (ln) 
(agglomeration proxy) 

Balassa-Hoover Index  
manufacturing 
Balassa-Hoover Index  
services 

EU Fund allocations 

Share of labour force with  
tertiary education 
Productivity growth 

Productivity growth x  
Gravity index 

22.50***  21.66***  21.87***  22.33***  22.18***  10.66***  5.319*** 
(6.18) 

(6.26) 

(8.06) 

(5.81) 

(8.74) 

(6.65) 

(4.49) 

1.101 
(0.57) 
0.966 
(0.73) 

15.63*** 
(4.10) 

-6.661 
(-1.65) 

2.802 
(1.05) 
3.141 
(1.34) 

10.99* 
(2.48) 

-1.326 
(-0.30) 

3.718 
(1.34) 
2.680 
(1.18) 

5.876 
(1.26) 

3.772 
(1.54) 
2.804 
(1.26) 

9.069 
(1.88) 

4.463 
(1.73) 
2.206 
(1.03) 

7.016 
(1.43) 

-1.313 
(-0.29) 

-1.291 
(-0.32) 

-0.890 
(-0.22) 

-1.407 
(-1.15) 
1.197 
(1.09) 

8.582* 
(2.59) 

-2.721 
(-0.86) 
-0.377 
(-1.53) 

-0.585 
(-0.50) 
1.688 
(1.02) 

1.954 
(0.56) 

-1.295 
(-0.50) 
0.00430 
(0.03) 

4.853*** 
(3.99) 

-0.191 
(-0.16) 
0.718 
(0.48) 

-1.498 
(-0.42) 

-0.788 
(-0.32) 
0.0475 
(0.31) 

0.0577**  0.0490***  0.0467*** 
(3.39) 

(3.39) 

(3.07) 

(-6.77) 

Euro area dummy 

(-6.76) 

(-5.94) 

-12.89*** 
(-6.97) 

GDP per capita in t-1 (ln) 

-34.77***  -38.09***  -40.96***  -35.24***  -37.16***  -16.93*** 

(-9.43) 
-0.102 
(-0.11) 

(-7.12) 
1.054 
(1.10) 
-2.866***  -3.127***  -3.005*** 
(-9.37) 
53.17**  77.95*** 
(3.32) 
EA19 
yes 
no 
2116 
0.327 

(-8.13) 
11.22 
(0.92) 
EU28 
yes 
no 
3171 
0.316 

(4.65) 
EA12 
yes 
no 
1978 
0.338 

Crisis dummy for BMS 
(from 2008) 
Constant 

-3.228*** 
(-11.07) 
56.89*** 
(7.00) 
EA12 
Sample 
yes 
Region and year fixed eff. 
no 
Crisis dummy -time-varying  
1978 
N 
r2 
0.666 
Note: Cluster-robust t-statistics in parentheses. t-1 refers to previous year. Ln refers to the natural logarithm. N refers to the number of observations. r2 
refers to the coefficient of determination. 
Source: ESM calculations based on European Commission and OECD data 

32.38 
(1.86) 
EA12 
yes 
yes 
1978 
0.400 

12.81 
(0.84) 
EA19 
yes 
yes 
2116 
0.390 

(-8.68) 
-0.108 
(-0.14) 
-3.181*** 
(-10.59) 
10.13 
(1.15) 
EU28 
yes 
no 
3171 
0.520 

-12.33*** 
(-7.17) 
1.403* 
(2.03) 
-3.305*** 
(-11.36) 
42.39*** 
(4.80) 
EA19 
yes 
no 
2116 
0.648 

(-9.07) 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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It appears that regions with higher productivity growth attract labour from other regions. To 
shed some light upon the drivers of regional labour mobility, we run a set of panel regressions 
on  regional  migration.  Most  results  are  quite  intuitive,  showing  that  the  net  migration  rate 
(arrivals less departures over population) is lower when the unemployment rate is higher, and 
entrants are attracted by higher employment growth. Nevertheless, there is also some tentative 
evidence  that  regions  with  higher  productivity  growth  attract  more  entrants  from  the  same 
country  (Table  4/5).  Our  results  are  inconclusive  regarding  the  role  of  education  and  labour 
market regulation in migration. 

Table 4 
Possible drivers of regional labour mobility  

Employment (ln) 

Unemployment rate 

Productivity growth  

Gravity index (ln) 
(agglomeration proxy) 
EU Fund allocations 

Country-Level Employment  
Protection Legislation Index 
Share of labour force with  
tertiary education 
Euro area dummy 

Crisis dummy for BMS 
(from 2008) 
Constant 

Net migration 
rate t+1 

Net migration 
rate t+1 

Net migration 
rate t+1 

Net migration 
rate t+1 

Entrants from 
same country 
ln t+1 

Leavers to 
same country 
ln t+1 

(1) 

(2) 

(3) 

(4) 

(5) 

(6) 

0.360 
(1.46) 

-0.380 
(-0.78) 

0.799** 
(3.28) 

-0.0116*** 
(-3.42) 

-0.0136* 
(-2.39) 

0.00443 
(1.25) 

0.00274* 
(2.33) 

0.00215 
(1.92) 

0.00343 
(1.22) 

0.00359 
(1.16) 

0.00377** 
(3.04) 

-0.00229 
(-0.71) 

0.130 
(1.13) 
0.109 
(0.13) 
-0.0402 
(-0.24) 
-1.835 
(-1.48) 
0.0369 
(0.35) 
0.00601 
(0.09) 
-6.327 
(-1.63) 
EU28 
yes 
1539 
0.0246 

0.0527 
(0.33) 
-0.307 
(-0.33) 
-0.109 
(-0.69) 
-2.036 
(-1.61) 
0.0422 
(0.37) 
0.0849 
(1.16) 
0.0616 
(0.03) 
EU28 
yes 
1520 
0.0417 

0.323 
(1.64) 
0.316 
(0.31) 
-0.0185 
(-0.07) 
-2.530 
(-1.51) 
-0.00360 
(-0.03) 
-0.0356 
(-0.45) 
0.565 
(0.09) 
EA19 
yes 
1008 
0.0337 

0.0152 
(0.06) 
-0.340 
(-0.37) 
-0.217 
(-0.95) 
-2.664 
(-1.49) 
0.0462 
(0.34) 
0.103 
(1.13) 
1.134 
(0.28) 
EA19 
yes 
989 
0.0506 

0.154 
(1.89) 
-0.689 
(-1.39) 
-0.0412 
(-0.64) 
-0.305 
(-0.88) 
-0.0584 
(-1.15) 
0.00802 
(0.23) 
-3.287 
(-1.08) 
EA19 
yes 
1008 
0.233 

0.321 
(1.24) 
-0.118 
(-0.15) 
0.00748 
(0.04) 
1.160 
(1.04) 
-0.120 
(-0.78) 
-0.0507 
(-1.37) 
4.266 
(1.16) 
EA19 
yes 
1037 
0.0274 

Sample 
Region and year fixed eff. 
N 
r2 
Note: Cluster-robust t-statistics in parentheses; t+1 refers to consecutive year. Ln refers to the natural logarithm. N refers to the number of observations. r2 
refers to the coefficient of determination. 
Source: ESM calculations based on European Commission and OECD data 

 
 
 
 
 
 
 
 
 
 
  
  
  
 
  
  
  
 
  
  
  
  
  
  
 
 
 
 
 
 
 
  
  
  
 
  
  
  
 
  
  
  
  
  
  
 
 
 
 
 
 
 
 
 
  
  
  
  
  
  
 
 
 
 
 
R E G I O N A L   D I S P A R I T I E S   I N   E U R O P E   |   3 3  

4.  Policy implications 

 
 
 
 
 
 
 
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Our findings show that regional disparity is indeed a serious concern in Europe and deserves a 
higher  degree  of  policy  attention.  Regional  disparities  can  raise  both  cyclical  and  structural 
challenges,  so  looking  at  national  aggregate  variables  is  not  always  enough  to  ensure  the 
economic, social and political impact and sustainability of policy settings. 

In cyclical terms, deeper integration into GVCs can strengthen business cycle synchronisation 
across  the most  integrated  regions.  This  is  good  news  when  it  comes  to cyclical  stabilisation 
policies, such as a monetary policy stance and automatic fiscal stabilisers. However, it raises two 
challenges: first, it may increase susceptibility to asymmetric shocks, especially if a large share 
of the economy is concentrated around a few major GVCs; second, it may widen gaps between 
more-integrated and less-integrated regions, raising obvious social and political challenges, even 
if it could occasionally allow for some risk diversification. The two countervailing mechanisms of 
external orientation and domestic buffers should be balanced carefully. 

In  structural  terms,  uneven  specialisation  and  differentiation  along  GVCs  can  challenge 
territorial cohesion and convergence. In addressing regional disparities, there are theoretically 
two extremes: on one side, facilitating labour mobility to help people find the jobs best suited 
to  them,  regardless  of  the  location,  which  might  be  the  most  efficient  solution  in  aggregate 
economic  terms,  but  might  lead  to  a  hollowing  out  of  lagging  regions  given  agglomeration 
effects  and  endogeneities.  On  the  other  side,  policymakers  might  prefer  to  support  lagging 
regions to mitigate against pressure for emigration and geographic concentration.  

Trade integration, growth of manufacturing and high-end services offer avenues to catching up, 
convergence, and not falling behind. The right conditions need to be in place to take advantage, 
which has not been the case everywhere. This highlights the  importance of the right policies 
required to support  possible sustainable regional development. In the absence of supportive 
developments,  regional  disparities  may  well  expand  as  the  effects  of  trade,  value  chains, 
outward  migration  and  other  factors  bear  on  developments.  We  draw  some  tentative 
conclusions from our analysis and suggest some policies that may contribute to a more even 
regional development in Member States.  

The modalities of regional support are crucial to ensure long-term sustainability. The EU, the 
Member  States,  and  the  regions  should  carefully  design  policies  that  attempt  to  avoid  the 
regional  clustering  of  growth  centres,  as  happened  in  the  US.  Policy  intervention  that  aids 
adjustment  and  boosts  productivity  can  set  the  scene  for  a  catching-up  process  and  income 
convergence. But regional transfers that merely conceal the symptoms of divergence can hinder 
adjustment and might eventually become fiscally unsustainable. Policies need to be designed as 
an overall approach where interdependencies are mutually supportive. Policies at the national 
level remain important, but urban policies increasingly determine issues of growth, productivity, 
and high-quality employment.  

Labour markets and labour mobility 

Key  policies  that  mitigate  divergences  address  labour  market  dynamics  such  as  wage 
adjustments, together with labour supply and demand. A region’s economy must  generate a 
virtuous circle between labour supply and demand. Innovative companies representing labour 
demand want to be there because they know they will find workers with the skills they need at 
the right price. And skilled workers that determine the labour supply want to be there because 
they know they will find the jobs they want.79 

Challenges may arise if regional wage differences do not reflect productivity differences. Within 

79 Dauth, W, S. Findeisen, E. Moretti and. J Suedekum (2018), “Matching in Cities”, CEPR Discussion Paper 13347. 

 
                                                           
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a  monetary  union,  disconnected  wage  and  productivity  growth  can  lead  to  competitiveness 
imbalances, both nationally and regionally. Regions where labour productivity grows faster than 
wages  enhance  their  competitiveness.  In  contrast,  if  regional  wages  grow  faster  than 
productivity,  unit  labour  costs  rise  and  the  tradable  sector’s  competitiveness  declines.  This 
effect can be indirect – rising wages in the non-tradable sector, without productivity growth, 
leads  to  higher  tradable  sector  prices.80  Large  differences  in  the  price  and  wage-setting 
mechanism arrangements can also lead to different output and inflation responses to shocks, 
which could exacerbate cyclical heterogeneity across the currency union.81 

Increasing the adaptability of labour and product markets might help reduce regional disparities. 
In particular, flexible labour market regulations could facilitate the alignment of regional wages 
and productivity. For instance, an earlier IMF study (2012) found that low minimum wages and 
low  severance  payments  are  associated  with  faster  convergence  to  the  frontier  regions.82 
However,  centralised  wage-setting  mechanisms,  combined  with  persistent  productivity 
differences, can lead to divergences  in unit labour costs across  regions. Limited geographical 
wage differences in nominal terms undermine the relationship between local productivity and 
local  wages,  and  create  costly  geographic  imbalances;  low  productivity  regions  tend  to  have 
higher  non-employment  rates  than  high  productivity  regions.83  Similarly,  product  market 
regulations in wholesale and retail trades appear to have particularly negative impacts on the 
productivity  growth  of  a  country’s  least-productive  regions.84 For  instance,  previous  research 
shows  European  regions  with  more  sheltered  economies  performed  worse  in  terms  of 
employment change after the financial crisis compared to those with more open economies.85 
However, open and transparent policies can promote trade integration and GVC-led growth.86 

Dedicating resources to education might be another way to improve skills and the labour supply. 
Our results demonstrate skilled labour might be an important factor behind a region’s relative 
success.  Given  the  importance  of  human  capital  for  economic  development,  many  local 
governments try to enhance the education of their residents by supporting local colleges and 
universities. Research suggests the presence of a university is associated with a better-educated 
labour force and higher local wages, but that in itself is not enough to form a sustainable cluster 
of innovative companies;87 unless market conditions are attractive, college graduates tend to be 
mobile and do not stay where they went to school. 

This  in  turn  raises  the  question  of  labour  mobility.  ‘Centripetal’  labour  movements  to  more 
productive agglomerations may leave ‘hollowing-out’ regions behind. This may not be desirable 
politically or sustainable economically. A challenge arises for policy-makers: should they invest 
in  areas  in  relative  decline,  to  dampen  the  outflow  of  the  highly  skilled  labour  force  and 
compensate the regions that are ‘left behind’? Or, should they invest in education, housing, and 
transport  in  agglomerations  to  facilitate  the  move  towards  successful  cities?  A  focus  on  the 
agglomerations might lead to larger productivity gains but risks deepening social and political 

80 OECD (2018a), pp. 138. 

81 Cœuré, B. (2017). 

82 Spilimbergo, A. and N. X Che (2012), “Structural Reforms and Regional Convergence,” IMF Working Paper 12/106. 

83 Boeri, T, A. Ichino, E. Moretti and J. Posch (2019), “Wage Equalization and Regional Misallocation,” NBER Working Paper 25612. 

84 OECD (2018a), pp. 138. 

85 Fratesi, U. and A. Rodríguez-Pose (2016), “The crisis and regional employment in Europe: what role for sheltered economies?” 
Cambridge Journal of Regions, Economy and Society 9/1, pp. 33-57. 

86 World Trade Organization (2019), Global Value Chain Development Report “Technological Innovation, Supply Chain Trade, and 
Workers in a Globalized World.” 

87 Moretti, E. (2014), “Are Cities the New Growth Escalator?” World Bank Working Paper 6881. 

 
                                                           
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‘fault-lines.’88 A development strategy focused on the capital city could lead to more volatile and 
potentially lower growth than offered by a more a balanced development approach. 89 

Productivity catch-up 

Job  creation  is  not  the  only  measure  of  success.  A  sustained  rise  in  productivity  is  another 
measure,  as  a  trade-off  may  exist  between  employment  and  productivity  growth.  Structural 
policies  that  address  regional  productivity  differences  would  help  technology  diffusion, 
innovation, and local-economy integration into European and GVCs. 

Regional policy that favours the productivity performance of lagging regions can drive a broader 
growth strategy. Territorial asymmetry means that there is potential to stimulate productivity 
in lagging regions.90 Aggregate productivity growth can be driven by a lagging region’s rebound 
towards that of frontier regions, while frontier regions themselves sustain high productivity. The 
main justification for regional policy is to achieve territorial equity objectives, but it is also a way 
to increase aggregate productivity growth. Hence, regional policy can be considered an integral 
part of a structural policy package aimed at improving a broad growth potential. 

Yet, the productivity convergence of lagging regions will depend on the interaction and linkages 
with frontier regions.91 Links between cities and the surrounding rural can spread agglomeration 
benefits more broadly while reducing congestion costs in cities. Connections include transport 
links, access to finance, and cooperation with the universities and research centres often found 
in  cities.  Areas  where  policy  intervention  can  make  a  difference  encompass  innovation, 
information  and  communications  technology  infrastructure  and  measures  to  support  digital 
growth;92 the digital economy provides opportunities for small- and medium-sized enterprises 
to play a more active role in GVCs, where they have been under-represented.93 

Another widespread example of place-based industrial policies is the use of economic subsidies 
to  attract  large  companies  to  struggling  communities  to  seed  clusters  in  new  locations.94 
However,  subsidies  need  to  be  commensurate  with  the  social  benefit  they  provide,  which 
involves  assessing  the  aggregate  impact  of  place-based  policies  for  the  entire  country.  Even 
when there is economic rationale for a subsidy in a particular region, it may not be beneficial for 
the country as a whole because the competition among regions for a given company can turn 
out to be a zero-sum game.95 Furthermore, place-based policies targeted at lagging regions must 
be carefully calibrated to ensure that they help rather than hinder beneficial adjustments and 
do not shelter or preserve inefficient economic structures.96 

A  big  push  might  be  needed  to  generate  growth  centres  far  from  existing  agglomerations, 
shifting a region from a bad equilibrium to a good one. A coordinated policy package can break 
an  impasse  and  simultaneously  attract  skilled  workers,  employers,  and  specialised  business 

88 Odendahl et al. (2019). 

89 Dijkstra et al. (2015). 

90 OECD (2018a), pp. 43-45. 

91 OECD (2018a), p. 152. 

92 Hendrickson, C, M. Muro, and WA Galston (2018), “Countering the geography of discontent,” Brookings. 

93 World Trade Organization (2019). 

94 Shambaugh, J. and R. Nunn eds. (2018), “Place-based policies for shared economic growth,” Brookings. 

95 Moretti (2014). 

96 IMF (2019). 

 
                                                           
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services  to  a  new  location.  Public  authorities  may  be  best  placed  to  initiate  such  ‘big  push’ 
policies,  because  they  can  coordinate  the  individual  actors  —  workers  and  employers  —  to 
trigger a new agglomeration process. For instance, public subsidies provided to first-movers can 
later  be  phased  out  once  the  process  becomes  self-sustaining.97  Support  for  the  innovation 
sector  and  direct  research  and  development  funding  could  be  central  to  such  a  package. 
Workforce  training,  business  financing,  tax  and  regulatory  benefits,  as  well  as  economic 
inclusion  and  infrastructure  development  could  also  provide  significant  inputs,  while  public 
contributions to urban planning and innovation districts could also prove helpful.98 

Other structural reforms might help foster development in lagging regions and accelerate the 
rate of convergence. For instance, an IMF study (2012) found that financial development and 
internal capital mobility enhances the convergence process.99 Therefore, ensuring businesses in 
lagging regions have access to capital might help reduce disparities. Efforts to improve data on 
small  business  performance  can  help  banks  reduce  the  transaction  costs  of  extending  small 
loans,  while  financial  technology  innovations  could  help  create  a  secondary  market  for  such 
loans  and  reduce  risk.  In  addition,  boosting  alternative,  non-bank  sources  of capital,  such  as 
venture capital funding, might help support regional economic growth.100 

The quality of national, regional, and local government and administration plays a large role.101 
Inward investment depends on an absence of corruption, on how fast administrative procedures 
work, and on the predictability of administrative and legal proceedings outcomes. The above 
cited IMF study (2012) also finds that a good institutional environment – involving the rule of 
law,  bureaucratic  quality,  and  low  corruption  –  can  benefit  catching-up  regions,  possibly 
indicating  that  good  institutions  make  resource  reallocation  easier.102  As  an  example,  the 
average duration for insolvency procedures in the EU varies by a factor of four. And, in the slower 
countries,  non-negligible  regional  differences  are  evident.  Long  procedures  mean  less 
investment  and  credit  for  companies,  especially  for  innovative  small-  and  medium-sized 
enterprises,  and  consequently  lower  growth  and  employment.  This  is  something  entirely  for 
Member States to address. 

Among the solutions, the European Commission’s lagging regions report (2017) highlights the 
importance  of  investing  in  the  quality  of  institutions  and  regional  administrative  capacity.  In 
particular, the EU’s cohesion policy supports programmes to strengthen institutional capacity 
and  improve  the  efficiency  of  public  administrations.  These  programmes  can  increase  the 
efficiency, transparency, and accountability of public services, promote e-government, reduce 
regulatory  red  tape,  modernise  public  procurement,  support  anti-corruption  measures  and 
promote judicial reform.103 

Overall, a two-track approach may be desirable to generate a virtuous circle: the first would be 
to  improve  the  labour  supply  through  education,  and  the  second  to  attract  investment  to 
generate labour demand and enhance productivity. These must build upon education calibrated 
in  a  way  that  emphasises  the  retention  of  qualified  people,  while  the  role  of  innovation-
enhancing policies needs to suit the comparative advantages of a region or urban area. EU funds 

97 Moretti (2014). 

98 Atkinson, RD, M. Muro and J. Whiton (2019), “The Case for Growth Centers,” Brookings. 

99 Spilimbergo, A. and N. X Che (2012). 

100 Hendrickson, C, M. Muro, and WA Galston (2018). 

101 Acemoglu, D. and M. Dell (2010). 

102 Spilimbergo, A. and N. X Che (2012). 

103 European Commission (2017), “Competitiveness in low-income and low-growth regions.” 

 
                                                           
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should  focus  on  such  aspects,  for  example  helping  to  develop  high-quality  research  and 
education centres.104 

104 We do not cover the implications for monetary policy here, but for some considerations see “Hauptmeier, S, F. Holm-Hadulla and 
Katerina Nikalexi (2020), “Monetary policy and regional inequality,” ECB Working Paper Series 2385. 

 
 
 
                                                           
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5.  Conclusion 

 
 
 
 
 
 
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The European integration process and the role of trade  relationships can appear in different 
forms  as  driving  forces.  GVCs  play  an  important  role  in  international  trade  and  trade 
specialisation, as do agglomeration effects in increasingly knowledge-based economies, which 
may well determine just where highly productive firms locate. 

Convergence  is  indeed  at  work,  and  has  helped  new  Member  States  greatly.  Across  the  EU 
between 2000 and 2016 poorer regions grew faster than more advanced regions on average 
because most of the poorest regions in new Member States seem to be catching up, suggesting 
that convergence is at work. The single market and EMU both had a positive impact through 
facilitating  trade  and  strengthening  integration.  Tradables,  particularly  manufacturing,  are  a 
source  of  growth  that  can  help  catching-up  regions,  favouring  those  with  a  comparative 
advantage in certain traded goods.  

Trade and integration into GVCs is a source of growth, but participation by regions is uneven. 
The  share  of  GVC  value-added  in  the  total  regional  value-added  has  a  significantly  positive 
association with change in GDP per capita, suggesting that stronger integration into GVCs is a 
growth-enhancing factor that can support catch-up. In particular, any integration of regions into 
the GVCs can produce innovation and productivity benefits. However, some regions are lagging 
in GVC integration, so the chains might also lead to regional differentiation. And not all types of 
GVC integration yield the same benefits. Labour intensive, low-skilled manufacturing can bring 
important jobs to regions with high unemployment, but the value-added may be limited. 

Agglomeration  effects  do  matter,  particularly  in  old  Member  States.  Our  results  identify 
agglomeration effects very much at work, especially in the euro area. Fixed capital has become 
more flexible, less tied to certain geographies, and so might have contributed to a widening of 
disparities;  de  Larosiere’s  thinking  has  proven  to  be  quite  prescient  and  there  may  be  an 
inherent advantage in being close to an economic centre of gravity. 

Hence, a lack of careful policy attention might risk deepening regional disparities. Our findings 
suggest regional disparity is a serious concern in Europe and deserves more policy attention. 
Regional  disparities  appear  to  be  rising,  with  hysteresis  after  the  financial  crisis  possibly  an 
aggravating factor. The magnitude of regional disparities in Europe is comparable to the US, and 
larger by some measures. We also see symptoms of a regional ‘middle income trap,’ because 
most regions suffering from an increasing negative distance to the average income are in old 
Member States, which has a bearing on labour mobility; a clear pattern of outward migration 
from lagging regions has emerged, and inward migration to leading ones has increased. 

Regional disparities can raise both cyclical and structural challenges. In cyclical terms, regional 
disparities may increase susceptibility to asymmetric shocks, especially if a large share of the 
economy concentrates around a few major GVCs. In structural terms, uneven specialisation and 
differentiation  along  GVCs  can  challenge  territorial  cohesion  and  hinder  convergence,  with 
competitiveness and unit labour costs divergence giving rise to regional-level imbalances similar 
to those seen previously between Member States.  

Therefore,  Member  States  must  adopt  the  right  policy  framework  to  facilitate  broad-based 
integration and avoid exacerbating regional disparities. Such regional disparities may well grow 
without supportive developments when the effects of trade, value chains, outward migration 
and other factors on influence change. Regional support modalities are crucial to ensure long-
term sustainability, and policy intervention that helps adjustment and boosts productivity can 
set the scene for a catch-up process and income convergence. However, regional transfers that 
merely  conceal  the  divergence  symptoms  can  only  hinder  adjustment  –  and  may  eventually 
become fiscally unsustainable.  

A two-track approach may be desirable to generate a virtuous circle: the first approach would 
improve the labour supply through education and training and the second would attract the 

 
R E G I O N A L   D I S P A R I T I E S   I N   E U R O P E   |   4 1  

investment needed to generate labour demand and enhance productivity. In parallel, structural 
policies  that  reduce  distortions and encourage more  flexible and open markets can facilitate 
regional adjustment to adverse shocks. In particular, enhancing the adaptability of labour and 
product markets could help prevent the re-emergence of imbalances. 

EU and national economic policies need to address how to contribute to reversing, or at least 
mitigating,  the  process  by  which  regions  slowly  wither,  an  economically  and  politically 
unacceptable trend. The underlying processes by which some regions profit more than others 
from  integration  and  trade  linkages  will  not  disappear.  Policymakers  should  incorporate  an 
understanding of these mechanisms into their policy response during the coronavirus shock and 
afterwards,  because  only  such  an  appropriate  commitment  can  help  support  recovery  and 
sustain convergence.  

 
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Annex 

Table 5 
Sample for the descriptive section 
(Number of regions, share of the sub-sample %) 

Initial EA12 

-NUTS2 

-NUTS3 

Pulling 
away 

Catching 
down 

Catching 
up 

Lagging 

(Benchmarked to EA12 average) 

26 (20%) 

27 (21%) 

34 (26%) 

44 (34%) 

151 (17%) 

188 (21%) 

285 (32%) 

257 (29%) 

New EA members (7) 

(Benchmarked to EA19 average) 

-NUTS2 

-NUTS3 

EA-19 total 

-NUTS2 

-NUTS3 

1 (10%) 

2 (5%) 

0 (0%) 

0 (0%) 

8 (80%) 

37 (86%) 

1 (10%) 

4 (9%) 

(Benchmarked to EA19 average) 

27 (19%) 

27 (19%) 

42 (30%) 

45 (32%) 

153 (17%) 

188 (20%) 

322 (35%) 

261 (28%) 

Old non-EA members (3)  

(Benchmarked to EU28 average) 

-NUTS2 

-NUTS3 

4 (16%) 

27 (14%) 

16 (64%) 

96 (48%) 

0 (0%) 

9 (5%) 

New non-EA members (6) 

(Benchmarked to EU28 average) 

-NUTS2 

-NUTS3 

EU-28 total 

-NUTS2 

-NUTS3 

2 (4%) 

2 (1%) 

0 (0%) 

0 (0%) 

45 (96%) 

179 (93%) 

(Benchmarked to EU28 average) 

33 (15%) 

43 (20%) 

87 (41%) 

50 (23%) 

182 (14%) 

284 (22%) 

510 (39%) 

341 (26%) 

Note: We  distinguish  four  groups of  regions  based on how  their  GDP  per  capita  evolved between  2000  and 2016: pulling  away  (initially  above 
average and rising faster than average), catching down (above average but rising slower than average), catching up (below average rising faster 
than average), and lagging (below average rising slower than average).  The GDP per capita is based on USD per head, current prices and PPP, and 
we compare euro area member states to EA average and non-euro countries to EU average respectively. 
Source: OECD Regional Database 

5 (20%) 

68 (34%) 

0 (0%) 

12 (6%) 

 
 
 
 
R E G I O N A L   D I S P A R I T I E S   I N   E U R O P E   |   4 5  

Figure 11 
Share of small regions in each country by relative change in GDP per capita between 2000 and 2016 
Compared to full-sample cross-country average, GDP weighted 

Compared to full-sample cross-country average, employment weighted 

Source: ESM based on Eurostat and OECD. Luxembourg is omitted due to the limited number of regions 

 
 
 
 
4 6   |   D I S C U S S I O N   P A P E R   S E R I E S   |   J u l y   2 0 2 0    

Figure 12 
GDP per capita relative to EU28 mean change 2000-16 by NUTS3 regions 

Source: ESM based on Eurostat and OECD; geographic data from Mapbox and OpenStreetMap 

 
 
R E G I O N A L   D I S P A R I T I E S   I N   E U R O P E   |   4 7  

Figure 13 
Gravity index by NUTS3 regions 

Source:  ESM  calculation  using  methodology  by  Odendahl  et  al.  (2019),  based  on  Eurostat  and  OECD  data;  geographic  data  from  Mapbox  and 
OpenStreetMap 

 
 
 
 
4 8   |   D I S C U S S I O N   P A P E R   S E R I E S   |   J u l y   2 0 2 0    

Summary statistics 

Table 6a 
Summary statistics of variables used in cross sectional regressions 

EU28 

EA19 

EA12 

count 

mean 

sd 

mean 

sd 

mean 

sd 

GDP per capita relative to mean change 

210 

2.17 

20.16 

0.26 

17.82 

-0.00 

14.87 

GVC value-added share in regional total 

212 

17.77 

5.48 

16.41 

5.60 

16.13 

5.53 

Gravity index (ln) 

215 

14.98 

2.48 

15.19 

2.67 

15.38 

2.68 

Labour force share with tertiary education 

222 

0.25 

0.08 

0.25 

0.08 

0.25 

0.08 

Patent applications per million inhabitants 

181 

106.19 

139.38 

126.72 

157.98 

127.08 

161.80 

Employment Protection Legislation index 

201 

2.61 

0.35 

2.75 

0.26 

2.76 

0.26 

Product Market Regulation index 

217 

1.59 

0.26 

1.57 

0.24 

1.56 

0.24 

EU Fund allocations 

208 

0.02 

0.02 

0.02 

0.02 

0.02 

0.02 

European Quality of Government index 

152 

56.31 

21.40 

62.08 

18.41 

63.03 

18.23 

Federal state dummy 

202 

0.23 

0.42 

0.34 

0.47 

0.36 

0.48 

Distance to capital (ln) 

215 

4.67 

1.99 

4.73 

2.06 

4.99 

1.82 

GDP per capita in 2000 (ln) 

210 

9.88 

0.48 

10.03 

0.33 

10.08 

0.29 

Note: sd refers to standard deviation. Ln refers to the natural logarithm. 
Source: ESM calucations based on European Commission, OECD, and University of Gothenburg data 

 
 
 
 
 
R E G I O N A L   D I S P A R I T I E S   I N   E U R O P E   |   4 9  

Table 6b 
Summary statistics of variables used in panel regressions 

EU28 

EA19 

EA12 

count 

mean 

sd 

mean 

sd 

mean 

sd 

GDP per capita relative to mean change 

3708 

0.16 

3.58 

0.02 

3.59 

-0.00 

3.53 

Productivity growth overall (%) 

3782 

3.85 

4.31 

3.29 

3.65 

3.08 

3.45 

Productivity growth manufacturing (%) 

3601 

4.98 

10.38 

4.32 

10.45 

4.15 

10.50 

Productivity growth services (%) 

3585 

3.54 

46.10 

3.28 

56.17 

3.14 

58.66 

Productivity overall 

4004 

58.45 

20.60 

64.59 

17.87 

66.14 

17.16 

Productivity manufacturing 

3823 

66.65 

36.81 

73.27 

37.89 

75.76 

38.16 

Productivity services 

3807 

47.70 

16.79 

49.70 

16.12 

50.17 

16.07 

Gravity index (ln) 

3870 

15.02 

2.51 

15.22 

2.69 

15.41 

2.70 

Labour force share with tertiary education 

4130 

0.25 

0.09 

0.25 

0.09 

0.25 

0.09 

Employment Protection Legislation index 

3118 

2.70 

0.43 

2.85 

0.36 

2.85 

0.36 

EU Fund allocations 

4925 

0.01 

0.02 

0.01 

0.02 

0.01 

0.02 

Balassa-Hoover Index manufacturing 

3835 

0.99 

0.35 

0.96 

0.37 

0.96 

0.38 

Balassa-Hoover Index services 

3835 

0.91 

0.32 

0.90 

0.25 

0.89 

0.23 

GDP per capita (ln) 

3930 

10.20 

0.46 

10.33 

0.35 

10.36 

0.33 

Net migration rate (%) 

3087 

-0.01 

0.34 

0.00 

0.38 

-0.00 

0.39 

Entrants from same country (ln) 

3095 

9.54 

1.18 

9.64 

1.28 

9.78 

1.23 

Leavers to same country (ln) 

3179 

9.60 

1.16 

9.69 

1.30 

9.81 

1.27 

Employment (ln) 

4004 

13.39 

0.98 

13.30 

1.09 

13.32 

1.13 

Unemployment rate (%) 

4105 

9.76 

5.91 

10.10 

6.06 

10.07 

6.12 

EA19 dummy 

BMS dummy 

6438 

0.42 

0.49 

0.65 

0.48 

0.68 

0.47 

6438 

0.05 

0.22 

0.08 

0.27 

0.09 

0.28 

Note: sd refers to standard deviation. Ln refers to the natural logarithm. 
Source: ESM calucations based on European Commission and OECD data 

 
 
 
 
 
5 0   |   D I S C U S S I O N   P A P E R   S E R I E S   |   J u l y   2 0 2 0    

GDP per capita refers to OECD regional GDP statistics in USD per head in  current purchasing 
power parity (PPP) adjusted prices. 

GDP per capita relative to mean change, refers to the percentage point change in the ratio of 
per capita output relative to the mean per capita output of the period sample. For the cross 
sectional regressions this refers to the difference between 2000 and 2016, while for the panel 
regressions we compare ratios to the previous year. 

GDP per capita relative to mean changer,t = (

yr,t
y̅t

−

yr,t−1
y̅t−1

) ∗ 100 

GVC  value-added  share  in  regional  total,  refers  to  the  share  of  regional value-added  that  is 
created  within  GVCs  throughout  the  year  2010.  The  data  is  based  on  regional  input-output 
tables, as published in the context of OECD 2018a, Figures 3.3 and 3.5 on pp. 101 and 116. 

Gravity index is based on the gravity equation from physics: the product of the masses of two 
objects, divided by the square of the distance between them determines gravity. Translated to 
our setting, we calculate this measure based on OECD published regional GVA in PPP-adjusted 
current USD for all possible region-pairs, and then sum it up for each region. The closer a region 
is to other economically large regions, the higher the gravity index. Regional distances refer to 
data  published  under  Eurostat’s  Territorial  Classifications  and  Typologies  initiative.  The 
calculation  methodology  follows  Odendahl  et  al.  (2019)  Chart  14  pp.  14-15.  For  the  cross 
sectional regressions this refers to the average across time. 

gravityr,t = ln (GVAr,t ∑

a∈regions,a≠r

GVAa,t
2
distancea,r

) 

Labour force share with tertiary education refers to Eurostat table “lfst_r_lfp2acedu”. The age 
range for economic active population is 15 to 74 years. Tertiary education refers to ISCED 2011 
levels five and higher. This includes Short-cycle tertiary education, Bachelor, Master, Doctoral 
or equivalent. For the cross sectional regressions this refers to the average across time. 

Patent  applications  per  million  inhabitants  refers  to  Eurostat  table  ‘ipr_ta_popr’,  European 
Union  trademark  (EUTM)  applications.  For  the  cross  sectional  regressions  this  refers  to  the 
average across time. 

Employment  Protection  Legislation  Index  refers  to  the  synthetic  composite  of  the  OECD 
indicators of employment protection legislation of regular employment. For the cross sectional 
regressions this refers to the average across time. 

Product Market Regulation Index refers to the OECD published composite of a comprehensive 
and  internationally-comparable  set  of  indicators  that  measure  the  degree  to  which  policies 
promote or inhibit competition in areas of the product market where competition is viable. For 
the cross sectional regressions this refers to the average across time. 

EU fund allocations refer to data provided by the European Commission’s Directorate‑General 
for Regional Policy. In our regressions we refer to billion euros of the estimated expenditure of 
funds, and not to the annual EU payments made. For the cross sectional regressions this refers 
to the average across time. 

European Quality of Government Index developed by the Quality of Government Institute of 
Gothenburg University, is the only measure of institutional quality available at the regional level 
in the European Union. Institutional quality is defined as a multi-dimensional concept consisting 
of high impartiality and quality of public service delivery, along with low corruption. For the cross 
sectional regressions this refers to the average across time. 

Federal state dummy follows OECD definition and is 1 for federal states (AT, BE, DE, ES), and 0 

 
R E G I O N A L   D I S P A R I T I E S   I N   E U R O P E   |   5 1  

for unitary countries (CZ, DK, EE, GR, FI, FR, HU, IE, IT, LU, LV, NL, PL, PT, SE, SI, SK, UK). 

Distance to capital refers to the natural logarithm of (distance in km + 1) between the NUTS 
region and the NUTS region of the countries capital. Regional distances refer to data published 
under Eurostat’s Territorial Classifications and Typologies initiative. 

Productivity  growth  refers  to  the  year-on-year  growth  rate  of  gross  value  added  per 
employment in current USD PPP adjusted prices as reported by the OECD, for regional total and 
by sector for Manufacturing (sector C) and Services (sectors M and N). 

Balassa-Hoover Index is calculated based on OECD sectoral and regional employment data for 
Manufacturing (sector C) and Services (sectors M and N). It refers to the employment share of a 
particular sector in a region, relative to the countrywide ratio. In the formula below, s refers to 
the sector and c refers to the country of region r. 

BHIs,r,t =

empls,r,t
emplr,t

⁄

empls,c,t
emplc,t

Net migration rate refers to OECD database on migration, the difference between the number 
of persons entering and leaving a country during the year, per 1,000 persons (based on midyear 
population). The net migration rate indicates the contribution of migration to the overall level 
of population change. The net migration rate does not distinguish between economic migrants, 
refugees,  and  other  types  of  migrants  nor  does  it  distinguish  between  lawful  migrants  and 
unlawful migrants. 

Entrants from & leavers to same country  refer to the  logarithm of OECD regional migration 
data. 

Employment refers to the logarithm of OECD regional labour market data. 

Unemployment rate refers to OECD regional labour market data. 

EA19 dummy is defined 1 from the year a country joined the euro area onwards and 0 otherwise. 

BMS dummy  is defined 1 for European Stability Financial Facility (EFSF)  and ESM  beneficiary 
Member States from 2008 until the end of the financial assistance programme. That is 2013 for 
Ireland and Spain, 2014 for Portugal, 2016 for Cyprus, and 2018 for Greece. 

Regression model 

For the model in Table 1 we regress in a time and region fixed effects  ordinary least squares 
(OLS)  panel  regression  productivity  growth  on  lagged  productivity,  gravity,  lagged  GDP  per 
capita,  as  well  as  the  EA19  and  BMS  dummies  for  the  sample  period  2000  to  2016.  This 
regression is subsampled 27 times. We sampled by region group (EU28, EA19, and EA12), by 
productivity  in  the  year  2000  (first  quintile,  fifth  quintile,  full  sample),  and  by  sector  (full 
economy,  manufacturing  sector,  services  sector).  We  assume  standard  errors  clustered  by 
region. 
pr,t
pr,t−1

− 1 = β1 pr,t−1 + β2 gravr,t + β3 gdppcr,t−1 + β4EA19r,t + β5BMSr,t + γr + δt + ϵr,t 

For  the  model  in  Table  2  we  regress  in  a  simple  cross  section  OLS  regression  the  change  in 
regional GDP per capita relative to the period sample average on the GVC, gravity, education, 
patent applications, employment protection legislation, product market regulation, EU funds, 
governance index, GDP per capita in 2000 and the EA19 and BMS dummies. In specification 5, 
instead of referring to 2000-2016 period averages for explanatory variables, we  consider the 
values for gravity and education in the year 2000. In specifications 6 and 8, we present results 

 
 
5 2   |   D I S C U S S I O N   P A P E R   S E R I E S   |   J u l y   2 0 2 0    

interactions  between  employment  protection  and  EA19  dummy. 

In 
also  considering 
specifications 7 and 8 we additionally control for distance to capital, the federal state dummy 
and  the  interaction  of  the  two.  The  sample  refers  to  EA19  for  specification  2,  EA12  for 
specification 3 and EU28 otherwise. Heteroskedasticity robust standard errors are reported. 

yr,2016
(
y̅2016

−

yr,2000
y̅2000

) ∗ 100 = β1yr,2000 + β Xr + c + ϵr 

For the model in Table 3 we regress in a time and region fixed effects OLS panel regression the 
change  in regional GDP per capita relative  to the period sample average  on gravity, Balassa-
Hoover  Index,  EU  funds,  education,  lagged  GDP  per  capita,  as  well  as  the  EA19  and  BMS 
dummies  for  the  sample  period 2000  to 2016.  In  specifications  4  and  5  we  replace  the  BMS 
dummy  by  year  dummies  interacted  with  1  for  EFSF  and  ESM  programme  countries.  In 
specifications 6, 7, and 8, we also control for productivity growth and its interaction with gravity. 
We assume standard errors clustered by region. 

yr,t
(
y̅t

−

yr,t−1
y̅t−1

) ∗ 100 = β1yr,t−1 + β Xr,t + γr + δt + ϵr,t 

For specifications 4 and 5 the time varying crisis dummies are modelled: 

yr,t
(
y̅t

−

yr,t−1
y̅t−1

) ∗ 100 = β1yr,t−1 + β Xr,t + γr + δt + ρt BMSr + ϵr,t 

For the model in Table 4, we estimate a time and region fixed effects OLS panel regression for 
the sample period 2000 to 2016. The migration proxy explanatory variables are net migration 
rate, entrants from same country, and leavers to same country. All control variables are lagged. 
We consider employment or unemployment rate, productivity growth, gravity index, EU funds, 
employment  protection  legislation,  education,  as  well  as  the  EA19  and  BMS  dummies.  We 
assume standard errors clustered by region. 

migrr,t+1 = β Xr,t + γr + δt + ϵr,t 

 
R E G I O N A L   D I S P A R I T I E S   I N   E U R O P E   |   5 3  

Acronyms 

BIS  

BMS 

CEPS 

CompNet 

DG ECFIN 

EA12 

EA19 

ECB 

Ecofin 

EFSF 

EIB 

EMU 

ESM 

EU 

EU28 

GDP 

GVA 

GVC 

IMF 

NBER 

NUTS 

OECD 

OLS 

PPP 

UK  

US 

USD 

WEO 

Bank for International Settlements 

European Stability Mechanism beneficiary Member States 

Centre for European Policy Studies 

Competitiveness Research Network  

European Commission’s Directorate‑General for Economic and Financial Affairs 

12 countries that joined the euro area up until 2001 

19 euro area member states 

European Central Bank 

Economic and Financial Affairs Council 

European Financial Stability Facility 

European Investment Bank 

Economic and Monetary Union 

European Stability Mechanism 

European Union 

27 European Union Member States and United Kingdom 

Gross domestic product  

Gross value added  

Global value chain 

International Monetary Fund 

National Bureau of Economic Research 

Nomenclature of Territorial Units for Statistics 

Organisation for Economic Co-operation and Development 

Ordinary least squares 

Purchasing power parity 

United Kingdom 

United States 

US dollars 

IMF’s World Economic Outlook