the impact of globalisation and increased trade liberalisation on european regions igeat-ulb...
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“The impact of globalisation and increased trade
liberalisation on European regions”
IGEAT-ULB (Bruxelles)Politecnico Milano
UMS-RIATE (Paris)
Political and scientific objectives of the project
- To assess the regional impact of globalization inside EU
- To identify the most vulnerable regions - To imagine policy options
I. The storyline
Globalization andliberalisation of trade
Increased international competition in some
manufacturing sectors
Potential threat onregions specialized in
the most « competitive »Sectors.
Potential economic impact :- Positive reaction;
- Regional crisis
Potential social impact : increased inequalities and
social exclusion
The hypotheses
1 – There is a globalization process and its impact on regions is important (winning and losing regions)
2 – Regional specialization in the sectors which are the most vulnerable to globalization is a potential threat for the regions
3 – This can lead to bad economic performances and social degradation
General methodological approach
Main difficulty: it is impossible to identify directly regional losers in globalization because the flows between the regions and the world are unknown
Consequence: we will use a sectoral approach supposing that sectoral specialization in vulnerable sectors is a potential threat.
The different steps
i. To identify vulnerable sectors at the EU level
ii. To identify regions potentially vulnerable on the base of the economic structure
iii. To cross this potential vulnerability with economic performances
iv. To explain the regional diversity of economic performances by qualitative and quantitative analyses
v. To explore the relationship with social indicators
vi. To develop a prospective analysis
vii. Political conclusions
i.Sectors vulnerable to globalization
Two criteria in a static and dynamic approach:
- Trade balance;
- Openess rate to imports.
Result : four vulnerable sectors1°) Textile, clothing (DB) and footwear and leather (DC)
2°) Manufacturing of basic metals and fabricated metal products (DJ)
3°) Electrical and optical equipment (DL)
4°) miscellaneous manufacturing industries (DN).
Why are these sectors vulnerable to globalization? We interpret this by the concepts of life cycle of the
products and spatial diffusion over time.
A-America B-EurAfricaC-
AsiaPacificaTotal
1-Core 0,29 1,03 0,69 0,802-Semi-Periphery 1,10 1,64 3,86 2,433-BRIC 0,93 0,08 3,28 1,274-Periphery 0,61 0,50 1,89 0,68Total 0,38 1,02 1,76 1,001-Core 0,29 0,69 0,18 0,532-Semi-Periphery 0,63 1,40 0,80 1,013-BRIC 0,64 0,44 2,87 2,324-Periphery 1,14 1,36 5,05 2,35Total 0,45 0,81 1,63 1,00
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Specialization in textile of exports in 1976 and 2006
The specialization in textile exports in the Euro-mediternanean space since 1967
A-America B-EurAfricaC-
AsiaPacificaTotal
1-Core 1,28 1,21 1,13 1,212-Semi-Periphery 0,33 0,63 0,38 0,523-BRIC 0,24 0,34 0,25 0,284-Periphery 0,14 0,12 0,04 0,12Total 1,07 1,03 0,81 1,001-Core 1,44 1,19 1,19 1,242-Semi-Periphery 0,71 0,93 0,66 0,773-BRIC 0,78 0,44 0,60 0,604-Periphery 0,32 0,45 0,24 0,34Total 1,18 1,09 0,75 1,00
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Specialization in mechanic industry in 1976 and 2006
ii. Potentially vulnerable regions
Regions specialized in the vulnerable sectors
500 km
© EuroGeographics Association for the administrative boundaries
Origin of data: EU 25, CC's : Eurostat, National Statistical Offices.
Typology of the performence of vulnerable regions
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Kyiv
Wien
Bern
Oslo
Riga
Roma
Minsk
Praha
Paris
Dublin
BerlinLondon
Sofiya
Tirana
Madrid
Ankara
Zagreb
Skopje
Lisboa
Vilnius
Beograd
Nicosia
Tallinn
Athinai
Warszawa
Budapest
Helsinki
Sarajevo
Kishinev
Valletta
Amsterdam
Bucuresti
København
Reykjavik
Stockholm
Ljubljana
Luxembourg
Bratislava
Bruxelles/Brussel
GVA growth higher than national average
Decrease of VA invulnerable sectors
Increase of VA in vulnerable sectors
GVA growthlower than
national average
Textile (DBDC)
Metal (DJ)
Electr. (DL)
Potentially vulnerable sectors are beyond a treshold in the vulnerable sectors
Remark : NUTS2/NUTS3
iii. Regional vulnerability and economic
performances We explored this link
- At sectoral level
- At global level
- According to GVA or employment.
No clear correlation within the different vulnerable sectors
Growth of value addedin the sum of vulnerablesectors 1995-2002
sk04
sk02
si02
si01
ro41
ro32ro31
pt11
mt00
itf4itf1
ite3ite2 ite1
itd5
itd4itd3
itc4itc1
hu23
hu22
hu21
gr24
fr71
fr43
fr41
fi1a
fi19
es23es21
dea5
dea4
de72
de25
de24de23
de14de13
de11
cz08
cz07
cz06 cz05
cz03
be25
at34
at31
at22
-25
0
25
0 1 2 3
Regional Location Quotientsin the sum of vulnerable sectors
Regionsgloballyvulnerable
Otherregions
a
Growth of value added in sector dbcd 1995-2002
ukf2
sk02
si02
si01
ro42
ro41ro32
ro31
ro21
ro12
ro11
pt16
pt11 (-0.47;9.04)
pl11
mt00
lv00
lt00
itf4
itf2
itf1
ite3 (-3.35;6.23)
ite2
ite1 (-0.73;6.21)
itd5
itd3
itc4
itc1
hu32
hu22
gr13
gr12gr11
fr30
es52es23
ee0
de24
cz05
bg42
bg41
bg34
bg32
bg31
be25
be23
at34 (2.54;5.76)
-25
-15
-5
5
15
25
35
0 1 2 3 4 5
Regional Location Quotientsin sector dbcdClothing and textile industries
Regions vulnerable in DBDC
Other regions
b
Growth of value addedin sector dj 1995-2002
at12
at22at31
at34
be22
be23
be33
cz06
cz07
cz08 (-4.28;4.20)
de11
de13de14
de27
de72 (5.77;3.23)
dea1
dea4
dea5 (-2.53;3.89)
dec0
ded1
es12
es21 (3.49;3.74)
es22
es23
fi1a
fr21fr22
fr23
fr30
fr41
fr43 (8.47;3.19)
fr71
fr72
gr24
hu21itc1
itc4
itd3itd4
itd5
ite2
ite3
itf1
ro12
se12
si01
si02
sk03
sk04
uke3
ukg3
-20
0
20
0 1 2 3
Regional LocationQuotients in sector djBasic metals and fabricatedmetal industries
Regions vulnerable in sector DJ
Other regions
c
Growth of value addedin sector dl 1995-2002
at22
cz03
cz05
cz07
de11
de12
de13
de14
de21de22
de23
de24
de25
de26
de27
de30
de71
de72
dea4
dea5
ded2
deg0
fi18
fi19
fi1a (13.23;4.95)
fr43
hu21
hu22 (14.73;3.51)
hu23
hu31
ie01
ie02
itc4
itd3
mt00
se12
sk02
-20
0
20
0 1 2 3
Regional Location Quotientsin sector dlElectric and optical equipments
Regions vulnerable in sector DL
Other regions
d
No correlation with global economic performances neither!
All European rgions
y = -0,1414x + 1,6006
R2 = 0,0666
-6
-4
-2
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2
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0,00 5,00 10,00 15,00 20,00 25,00
Share of vulnerable sectors
Em
ploy
men
t gro
wth
95-
04
Differentiating by different European macro-regions, we found no simple relationship between the share of vulnerable sectors and global
regional performances. Generally, the impact is negative but very
unstable according to sectors and regions
Impact of vulnerable sectors on GDP growth, with control for national trends (1995-2004)
IV. How to explain this diversity of performances?
First conclusion : inside the vulnerable regions, some are able to deal with the threat of globalization and others not
Two approaches to explain this diversity :
- Quantitative (econometric)
- Qualitative (case-study)
Qualitative analysis
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Kyiv
Wien
Bern
Oslo
Riga
Roma
Minsk
Praha
Paris
Dublin
BerlinLondon
Sofiya
Tirana
Madrid
Ankara
Zagreb
Skopje
Lisboa
Vilnius
Beograd
Nicosia
Tallinn
Athinai
Warszawa
Budapest
Helsinki
Sarajevo
Kishinev
Valletta
Amsterdam
Bucuresti
København
Reykjavik
Stockholm
Ljubljana
Luxembourg
Bratislava
Bruxelles/Brussel
TextileMetalElectr.
© EuroGeographics Association for the administrative boundaries
500 km
16 case studies all over Europe and in the three types of regions with winning and losing regions
Textile regions: Kortrijk area and Northern Portugal
Kortrijk area*
Northern Portugal
Population 2005 1100000 3732550
GDP/inhab.according to European average (and national average)
1995 132 (102) 63 (84)
2000 131 (104) 64 (81)
2005 123 (101) 60 (79)
Unemployment rate 2005, (and according to national average)
2005 6,2 (73) 8,8 (115)
Textile employment
Total 1995 17059 227964
Total 2005 14053 180043
share in 2005 7,2 16,6
Share of manufacturing industries 2005 27,5 (128) 25,1 (118)
Share of high-level services 2005 24,2 (93) 17,6 (103)
Development paths
- Historical inheritage: long textile tradition
- Development in the last decadesKortrijk area becomes a typical marshallian district after WWII
Northern Portugal has developed exogenously from the 80’s onwards
- The limits of development Regions are hit by the textile crisis at the end of the 90’s (liberalization)
Kortrijk area has been able to adapt, notably through a specialization in carpet production and some technological segments
Economic performances are declining because of insufficient R&D, too specialized workforce…
In Northern Portugal, the competition of cheaper regions is decisive and we observe a structural crisis (weakening of Porto).
Strengths and weaknesses
In Portugal Exogenous control, limited technologies, limited qualification of the workforce,
weakness of the metropolitan areas.
But signs of diversification
In Kortrijk (Wesetrn Flanders)Limits in technological upgrading
Limitation in the qualification of the workforce
Strong entrepreneurship within a socially coherent tissue
High diversfication of the industrial tissue with endogenous control.
« Electronic » regions: Northern Finland and West Hungary
Oulu*
Western Hungary
GDP/inhab.according to European average (and national average)
1995 96 (89) 48 (92)
2000 100 (85) 53 (95)
2005 99 (86) 54 (84)
Unemployment rate 2005, (and according to national average)
2005 11,1 (132) 7,2 (100)
Employment in the electric/electronic sector
Total 1995 6235 29378
Total 2005 9894 53578
share (%) in
200510,4 6,2
Share of other manufacturing industries 2005 29,2 (104) 30,3 (99)
Share of high-level services 2005 17,1 (96) 16,6 (107)
Two different development trajectories
- Firm size (the role of the big firms) and the question of endogeneity;
- Embededness: why is Nokia embedded in Finland and not in Hungary? The dependance on a big firm has to be qualified by the embededness of the firm and the local know-how which has been favored/created by its presence.
The strengths and weaknesses: 1. Workforce: “A core explanation to the resilience of the
vulnerable sector is its deep knowledge specialization, generated through co-evolution of institutions providing poly-technical education, and corporate actors supporting on-the-job-training and life-long education”
In Hungary, the cheap, but qualified workforce has been a major asset but certainly more vulnerable than in the Finnish case
2. Public policy : the example of Triple helix in Oulu: “the Triple Helix cooperation between the university, business and public sector in electronics, ICT and electric-related industries has played a fundamental role” Based on a long tradition (since the 50’s) and the decisive support of the central state.
In Hungary, policies focused on tax exemptions.
Conclusions of the case studies
• Factors related to firms: – Size of firms– Embeddedness (local networks; endogeneity…)– Innovation
• Contextual factors: - Sectoral specialization– Quality of the workforce– entrepreneurship
Synthesis : a typology of risk according to globalization : geographical synthesis of comparative advantages in
vulnerable regions
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Types
© EuroGeographics Association for the administrative boundaries
500 km
Non vulnerable regions
Type 1. Average typeType 2. Risky Eastern type benefitting from delocalizationType 3. Non risky type because of their technological advanceType 4. Limited risk despite structural weaknessesType 5. Mediterranean type with limited risk. Strong entrepreneurship.Type 6. Mediterranean risky type.Type 7. Slovak type.
Z Textile (DB & DC)Ú Metal. (DJ)Y Electronic and optical equipment (DL)
Main specialization in vulnerable sectors
Methodology:
- From qualitative and quantitative analysis, we identify factors of success (economic structure, workforce, entrepreneurship…)
- on this basis, we distinguish amongst potentially vulnerable regions those which are indeed vulnerable and those who are not
Share of the total EU-27
active population
Number of regions
Number of Textile regions (DB-DC)
Number of Metal regions
(DJ)
Number of Electric and
optical equipment
regions (DL)
Economic structure-
component 1*
Economic structure - component
2**
Share of low graduated
Share of high
graduated
Technological level
Share of independant
s with employees
Standardized size of
entreprises
Type 1 11,2 33 12 22 8 0,94 0,29 22,0 25,1 -0,093 4,0 1,39Type 2 7,5 20 14 4 5 1,78 -0,82 18,5 15,3 -1,158 3,0 1,05Type 3 7,6 14 0 5 13 -0,33 1,27 18,6 32,1 1,780 4,6 2,00Type 4 4,3 10 0 8 8 0,38 1,52 20,3 19,7 0,569 4,9 2,73Type 5 7,8 14 12 10 3 0,70 0,22 40,2 17,9 -0,435 7,6 0,72Type 6 2,4 7 6 1 0 1,16 -1,31 65,1 12,5 -1,276 6,8 0,71Type 7 1,0 3 1 2 1 1,26 -0,42 7,9 13,3 -1,260 3,4 4,62All Vulnerale regions 41,9 101 45 52 38 0,50 0,61 26,1 21,7 -0,034 4,8 1,16Non vulnerable regions 58,1 153 - - - -0,32 -0,39 25,7 26,8 0,025 4,4 0,85EU-27 100,0 254 0 0 0 0 0 25,9 24,6 0,000 4,6 1,00
Synthesis : a typology of risk according to globalization : geographical synthesis of comparative advantages in
vulnerable regions
V. Social performances among vulnerable regions
Are vulnerable regions most affected in social terms?
• The first hypothesis is that vulnerable regions have seen the labour market situation worsen because they have to face global competition more than other regions.
• The second hypothesis is that the vulnerability to globalization will first hit the least qualified persons, and increase the gap between low and high skilled on the labour market.
- Vulnerable regions do not significantly differ from the others in terms precariousness in the labour market.
- Vulnerable sectors are ejecting more low qualified workers than the rest of the sector.
- We observe significant differences between the types of vulnerable regions according to their capacity to integrate low qualified workers: the unemployment gap between low qualified and highly qualified is strongly reduced in textile regions, but higher than average in electr(on)ic regions.
Conclusions
Prospective analysis for policy
• Statistical analysis on past evolutions• Policy targets the future• Need for foresight on possible evolutions
and their consequences• No prediction !• Raising awareness
– Driving forces – Possible levers for policy
Methodological choices• Future cannot be quantitatively predicted• Quantitative foresight offers
– Explicit formalisation of often implicit assumptions
– Focus on some selected cause-effect relationships
– General directions and orders of magnitude
• Quali-quantitatif scenarios• MASST – MAcroeconomic, Sectoral, Social
and Territorial (MASST) model
MASST ModelPolitecnico di Milano
NrsYY Nr ;
MASST ModelPolitecnico di Milano
Imagining strategies: BRIC
PRICE COMPETITIVE BRICs
Competitiveness strategy of BRICs strongly oriented to the control of production costs. The present trend is reinforced.
Focus on low price low quality products. Low wages and consequent low purchasing power of
BRICs consumers. Actual vulnerable sectors will be more strongly
affected by BRICs’ competition.
MODERNIZING BRICs Significant modernization of the economies of the
BRIC countries. Global customized production and competition based
on quality. Significant increase in wages resulting in an increase
of purchasing power of BRICs consumers; New sectors, at present very marginally affected by
globalisation patterns, will be highly affected by
2.Globalisation patterns
Imagining strategies: Member states
A DEFENSIVE STRATEGY
- protectionism of European economies; - attraction of FDI for New Member States countries; - international competition on production costs; - protectionism especially in vulnerable sectors.
A PROACTIVE STRATEGY - open trade; - increased productivity in traditional sectors; - customised production and quality competition; - increased competition in new sectors, at present
influenced in a limited way by globalisation.
2.Competitive strategies of Member States countries
Imagining strategies: European Commission
A COHESIVE POLICY
- Flexibility in pursuing the Lisbon agenda objectives; - Infrastructure projects selected on the basis of a
rebalancing of territorial infrastructure endowment; - 30% budget less than 2007-2013 - Structural funds only to convergence regions.
AN EXCELLENCE BASED COMPETITIVE POLICY
- Rigidity in the accompliance of the Lisbon agenda
objectives; - Infrastructure projects selected on the basis of
profitability aims; - Structural funds to all regions; - 20% budget more than the 2007-2013.
3. European Commission strategies
Choice of scenario hypotheses
Defensive EU Member State Countries
a) An aggressive Europe in a high-quality competitive world (scenario A)
b) A defensive Europe in a price-competitive world (scenario B)
An excellence based competitive policy
Price-competitive BRIC
Price-competitive BRIC
Reactive EU Member State Countries
Reactive EU Member State Countries
A cohesive EU policy Modernising BRIC
Modernising BRIC
Defensive EU Member State Countries
A cohesive policy
An excellence based EU competitive policy
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Annual average GDP growth rate 2005-2020 - Baseline scenario
500 km
No data
Textile regions
#Y
Metal regionsElectric and optical equipment regions
ÊÚ
Remote_areas_non_espon_space_03.shp
0.03 - 0.860.86 - 1.481.48 - 1.921.92 - 2.372.37 - 2.872.87 - 3.473.47 - 4.414.41 - 5.65
MASST2 Model - 2008
© EuroGeographics Association for the administrative boundaries
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Baseline scenario for comparison
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© EuroGeographics Association for the administrative boundaries
MASST2 Model - 2008
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Electric and optical equipment regionsMetal regions
#Y
Textile regions
No data
500 km
Annual average GDP growth rate 2005-2020 - Difference between scenario A and Baseline
500 kmMASST2 Model - 2008
0.33 - 0.420.42 - 0.490.49 - 0.550.55 - 0.60.6 - 0.650.65 - 0.720.72 - 0.820.82 - 0.99
Annual average GDP growth rate 2005-2020 - Difference between scenario B and Baseline
No data
Textile regions
#Y
Metal regionsElectric and optical equipment regions
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-2.4 - -2.1-2.1 - -0.78-0.78 - -0.3-0.3 - -0.15-0.15 - 00.00 - 0.400.40 - 1.250.42 - 0.82
Comparing scenariosScenario A
• Almost all regions grow more than in the baseline
• Rural vulnerable regions gain less
• Vulnerable regions in Western Europe profit of their endowment in urban and tertiary structure
• Vulnerable regions in the East generally outperformed by the – non-vulnerable – capital regions
• In spite of shift in sectors, vulnerable regions lose more industrial employment => non-vulnerable regions seem to benefit from more tertiary and less specialized structure
• vulnerable regions seem unable to replace industry with tertiary activities.
Scenario B• Eastern vulnerable
regions higher variance in terms of GDP growth than Western
• Decisive loss in industrial employment growth in vulnerable regions;
• Relative lower loss of service employment growth in vulnerable regions with respect to the others.
VII. Conclusions
Scientific conclusions• Sectoral structure not sufficient to detect
vulnerability• No clear information about impact of
globalisation and of globalised sectors on regions
• Generic approach of regional development more efficient
• Segments of production probably more decisive, but very difficult to measure
• Situation quite different between GDP and employment
Political conclusions
• Need to ensure regional “embeddedness” of firms to increase and prolong impact of their presence
• Need to enhance region's capacities of profiting of the presence of large exogenous firms:– Policies to increase intensity and speed of
spill-overs– Decisive investments in knowledge transfer,
education, etc, often in a very short period of time
Political conclusions
• Difficulty to politically create cluster structures
• Need to support existing (SME) clusters in the development of more technological innovation
• Importance of education, notably basic secondary eduction, for the capacity of a region to profit of opportunities
• Need to maintain territorial capital in regions in decline
