Transcript scenario

Territorial scenarios for Europe
With special regards to Central European Countries
Roberto Camagni
with R. Capello, A. Caragliu, U. Fratesi
Politecnico di Milano, ABC Department
Hungarian Regional Science Association, 11° Meeting
Kaposvàr, 21-22 November, 2013
Objectives of the presentation
To present results of some research works carried out within the
ESPON Programme of the EU, namely:
- ET2050: development scenarios for European Regions (2013)
- KIT: Knowledge, Innovation and Territory (2011-12)
- Span: Spatial Scenarios for the Latin Arc Countries (2008-09)
To produce “quantitative foresights” (not forecasts) based on
conditional scenario assumptions for all European regions up to
2030, with special reference to Central Eastern Countries
To present some early reflections on development policies
2
Introduction
Over the last few years, the world economy has gone through a severe
period of economic downturn, the worst since the end of the second
world war.
If the overall magnitude of the effects generated by the crisis is not yet
fully understood, even less clear is the spatial distribution of these
effects.
This explains the importance of the use of macroeconomic regional
econometric and forecasting models.
3
Introduction
Regions belong to different nations with different exposure to the crisis.
Regions have different industrial specialization, as well as different
capacities to exploit untapped resources, or territorial capital assets.
Macroeconomic demand side effects have different impacts :
- on national economies, according to their different level of public debt
and deficit, and development potential,
-on the different regions, according to their consumption patterns, type
of demand (public vs. private) and productive specializations.
Financial speculation determined a differentiated rise in the spread on
public bonds in different countries, exacerbating the cost of the debt
service, raising public deficits, generating huge effects on public
spending at central and local levels.
4
Introduction
A strong control on public expenditure and on its reduction was imposed
by the EU, especially to “vicious” countries. The effects of such a
reduction are expected to be stronger in those regions with a higher
share of public demand with respect to the private one, being generally
the poorer and less productive regions;
- in “vicious” countries, private investments decreased as a
consequence of the increase in interest rates, penalizing private actors,
and particularly productive regions;
- a credit crunch came as a consequence of the financial intermediaries’
decision to prefer financial investments on public bonds, when
guarantees existed on sovereign default; the real sector and the most
productive regions hosting it were once again penalized more than
others.
In general though, is difficult to predict which regions were hit more.
5
The foresight tool: the MASST3 model
MASST is an econometric forecasting model. Previous versions of
MASST developed for ESPON 3.2; ESPON SPAN; DGRegio
projects.
The ET2050 project is based on a new version of MASST,
considering the economic crisis (two periods of model estimation),
public budget constraints, innovation modes, the role of urban areas
in regional growth.
The model is able to simulate effects on regional growth of:
- the economic crisis;
- macroeconomic elements (public budget constraints, sovereign
debt, spread in interest rates of public bonds, exchange rates);
- territorial capital elements (innovativeness, trust, accessibility);
- cohesion and infastructure policies.
6
The model
The logics of the model is at the same time Top-down & Bottom-up (i.e.
distributive and generative):
- national growth (determined by macro-economic elements: demand
side) is distributed among regions,
- but regions add a “differential” effect (determined by presence of
territorial capital: supply side) able to feed-back on national
performance.
Quantitative foresight is produced for all NUTS2 regions of all 27 EU
countries (270 regions).
7
The MASST3 model:
Submodel 1: National component
 internal
consumption
Submodel 2: Regional differential component
FINAL OUTCOME
Territorial capital
assets
Macroeconomic
elements
Innovativeness:
- product/ process
innovation
in national GDP
Tax rates
 internal
consumption
Localization
economies:
Hirschman
Herfindahl Index
in national GDP
Δ Unit Labour
Costs
National
component
of regional
growth
Δ of FDI stock
National
growth
 investments
Human capital
R&D
 investments
Δ interest rates
Regional
innovation
Final economic
effect
Regional differential
component
Regional growth
as a result of
MAcroeconomic
Social, Sectoral
and Territorial
components
patterns
Differential shift
 imports
Traditional
urban benefits:
- quality of life
- creativity
Innovation
Urbanization
economies:
LUZ population
Sectoral
component:
dynamics of
sectoral structure
Urbanization economies
(LUZ population)
Dynamics of sectoral
structure
Regional
specialization
Traditional
urban costs:
- cost of the city
- social conflict
Nonconvention
al urban
benefits:
- city networks
- high level
urban
functions
Unemployment
growth
Policies:
Structural funds
Accessibility:
- infrastructure
FDIs/Population
Functions
in national GDP
Δ of FDI stock
Functions
 imports
Inter-sectoral
productivity:
- infrastructure
- skills
- energy cons.
Inflation
Legend:
Exchange
rates
Δ Unit Labour
Costs
Exogenous variables
 exports
Relations in
estimations
MIX effects
Social
component:
- trust
Territorial
structure:
settlement
Migration Flows
Unemployment
rate
Spatial and
territorial
structure:
- spatial
spillovers
FDIs/Population
Relations in
simulation
Inflation
Sectoral
component:
Dynamics of
sectoral structure
Policies:
Structural funds
Endogenous variables
 exports
Nonconventional
urban costs:
- diffused urban
form
Settlement
structure
Population
growth
Δ Exchange
rates
Spatial and
territorial
structure:
- spatial
spillovers
GDP growth in
USA, Japan and
BRICs
Birth rate
Regional
differential GDP
Mortality rate
Migration flows
Δ public expenditure
in national GDP
Δ public
expenditure
Public
exp'enditure
Interest
payments
Tax revenues
All equations are differentiated between periods of crisis and of no crisis
Permanent income and long-run relationships are assumed and estimated
8
Scenario Assumptions
a) structural breaks brought in by the crisis are considered, due to
emerging global contradictions:
- Stop to demand based on debt in advanced countries,
- Financialization of western economies and related risks,
- China and BRICs supporting western real income (through low-price
exports) and financing the trade deficit of USA : persisting?
By consequence, in the future:
-the balance of the geo-political game will be different;
-winning assets will be different;
- spread in interest rates proportional to sovereign debts;
- necessary (but probably too high) austerity measures imposed on
public deficits by the EU.
9
Scenario Assumptions
b) “Regionalized” globalization, with the large “triad” areas (Europe,
America, East and South Asia) more independent and more internally
integrated
- BRICs enter progressively in the medium and high technology game
- The growth of real income in Europe will be more modest;
- “Regionalized” globalization processes will enable the recovery of
manufacturing activities in Europe (and the US);
- A number of new technologies will develop: nanotech, biotech,
transport technologies, new materials
c) More importantly: a new paradigm will emerge: the “green
economy”, due to increasing energy prices and growing concern about
climate change. Many sectors touched: manufacturing, energy,
transport, building and construction, tourism, agriculture (zero-km)
Provides a new demand source, new jobs and a reduction in
dependency on fossil fuels
It may boost a revival of endogenous growth in Europe
10
Scenario Assumptions
d) Regional disparities are likely to increase (two speed growth)
- Metro regions will host the advanced activities and R&D
- New manufacturing activities, benefiting from technological
progress, will also locate in metro and second rank cities
e) Austerity measures will endanger growth in “vicious” , mainly
southern European countries: for some times, “internal
devaluations”, severe cuts in public spending and difficulties in
financing private investments will determine a cumulative
divergence with respect to stronger countries.
All these elements can be easily accomodated into the MASST model,
thanks to the new inclusion of sectoral regional structures and
constraints coming from national sovereign debt and public budget
disequilibria.
Scenario Assumptions
Concerning Central - Eastern European Countries:
i)
-
-
Catching up in productivity with respect to Old 15 Countries will
continue, but possibly at a lower rate due to:
FDI dependency: they will slowly redirect towards outer eastern
countries and towards trade and commercial functions, in order
to benefit from increasing internal incomes (in CEECs),
Difficulty in keeping low levels of inflation, due to difficulty in
keeping wage increases in line with productivity increases,
Outflows of profits by multinational companies,
Slow taking-off of an endogenous accumulation of capital.
ii) Difficulty in finding a more advanced innovation “pattern” with
respect to the present one (mainly an “imitative innovation
pattern”, driven by FDI, with an internal dualist industrial
structure).
12
Past trends in productivity
140.0
Productivity per employee (2005=100)
130.0
120.0
European Union (27 countries)
Bulgaria
110.0
Czech Republic
Estonia
100.0
Croatia
Cyprus
Latvia
90.0
Lithuania
Hungary
80.0
Malta
Poland
70.0
Romania
Slovenia
Slovakia
60.0
50.0
40.0
1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012
13
Past trends in FDI inflows (absolute values)
9000000
8000000
Total FDI inflows in current USD (millions)
7000000
6000000
5000000
European Union
Old15
NMS
4000000
3000000
2000000
1000000
0
1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012
14
Past trends in FDI (% on GDP)
140.00%
NMS
120.00%
FDI inflows as a % of GDP
100.00%
80.00%
European Union
Old15
NMS
60.00%
EU27
40.00%
Old15
20.00%
0.00%
1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012
15
Trends in competitiveness
Bulga ria
Czech Republic
Estonia
Croatia
La tvia
Lithuania
Hunga ry
Pola nd
Roma nia
Slovenia
Slova kia
Equa l rela tive competitiveness a s in 2004
160.00
140.00
Slovakia
Czech Republic
120.00
Hungary
100.00
Poland
80.00
60.00
40.00
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
Real Effective Exchange rates for the NMS, 1994-2012
16
Assumptions of the Baseline Scenario
- the socio-economic and demographic trends of the past will continue,
-
-
and no major change will come to alter the EU economy;
economic policies will remain the present ones;
a general slow economic recovery will start in 2016;
a slight increase in competitiveness of European countries is assumed
in 2030;
By 2030 interest rates on bonds will return back to lower, pre-crisis
values, thanks to the end of strong speculation;
the stability pact remains the same, still imposing highly
restrictive public budget policies.
17
RESULTS FOR THE
BASELINE SCENARIO
(2030)
Aggregate results of the Baseline scenario
Average
Average annual
annual GDP
population
growth rate
growth rate
Average annual
employment
growth rate
Average annual
manufacturing
employment
growth rate
Average annual
service
employment
growth rate
EU27
1.89
0.31
1.58
1.38
1.63
Old 15
1.88
0.47
1.53
1.48
1.54
New 12
1.93
-0.38
1.90
0.98
2.33
1. The New12 countries grow a little more than the Western countries.
2. New12 countries increase employment in services more than in
manufacturing, entering a new stage of development.
3. Western countries have a balanced growth between manufacturing and
services.
19
Baseline: annual average GDP growth rate
Two speed Europe; Southern peripheral
countries grow less than Northern
countries.
Reykjavik
!
Canarias
Baseline March 18 2013
Average regional GDP growth rate
< 0.00
0.01 - 0.90
0.91 - 1.24
1.25 - 1.49
1.50 - 1.78
1.79 - 2.09
2.10 - 2.47
2.48 - 2.94
> 2.95
Guadeloupe
Réunion
Martinique
Southern European countries discount
the difficult present conditions on their
future evolutionary trajectories.
Helsinki
!
Oslo
!
Guyane
Tallinn
!
Stockholm
!
Madeira
Riga
!
København
!
Dublin
!
Vilnius
!
Minsk
!
Acores
Amsterdam
!
London
!
Berlin
!
Eastern European countries still grow
more than the EU 15, but this is not
enough to catch up the GDP per capita
levels of the Western countries in 2030.
Warszawa
!
Kyiv
!
Bruxelles/Brussel
!
Praha
!
Luxembourg
!
Paris
!
Bratislava
Wien
!
!
Bern
!
Kishinev
!
Budapest
!
Vaduz
!
Ljubljana
!
Zagreb
!
Bucuresti
!
Beograd
!
Sarajevo
!
Sofiya
!
Podgorica
!
Madrid
!
Roma
!
Lisboa
!
Skopje
!
Ankara
!
Tirana
!
Overall intra-national regional disparities
increase.
Athinai
!
El-Jazair
!
Ar Ribat
!
Nicosia
!
Tounis
!
Valletta
!
© Politecnico di Milano, Project ET2050, 2013
0
250
500
km
Regional level: NUTS2
Source: Politecnico di Milano, 2013
Origin of data: - MASST3 model
© EuroGeographics Association for administrative boundaries
20
.15
Theil index in the Baseline scenario
Total regional disparities
.1
Inter-national disparities
.05
Intra-national disparities
2010
2015
2020
Year
Total Theil index
Within Country Theil index
2025
2030
Between Country Theil index
21
EXPLORATORY SCENARIOS
(2030)
Summary of assumptions for the exploratory scenarios
“Megas” scenario
Market driven scenario; welfare system fully privatized; financial debt repaid in
2030; budget reduced for cohesion policies; concentration of investments in
European large cities.
“Cities” scenario
Public policies mostly at national level; actual welfare system reinforced through
increased taxation; financial debt not fully repaid in 2050; budget maintained
for cohesion policies; concentration of investments in second rank cities.
“Regions” scenario
Social policies; strong public welfare system; financial debt repaid in 2050;
budget significantly increased for cohesion policies; concentration of
investments in rural and cohesion areas.
23
Aggregate GDP growth results for the exploratory scenarios
Aggregates Baseline Megas Cities Regions Megas vs. baseline Cities vs. Baseline Regions vs. Baseline
EU27
1.89
2.22
2.31
1.82
0.33
0.42
-0.06
old15
1.88
2.22
2.31
1.81
0.34
0.43
-0.07
new12
1.93
2.22
2.23
1.98
0.29
0.30
0.05
1. The “Cities scenario” is the most expansionary: territorial capital and
the urban system are better exploited than in the other scenarios.
2. This holds also for New 12 countries.
3. New 12 countries are those that gain in the regions scenario with
respect to the baseline.
24
Theil Index by Scenario: Total Regional Disparities
0.155
0.150
Baseline scenario
Megas scenario
Cities scenario
0.145
Regions scenario
0.140
0.135
2012
2013
2014
2015
2016
2017
2018
2019
2020
2021
2022
2023
2024
2025
2026
2027
2028
2029
2030
Theil Index by Scenario: Between Countries Disparities
0.098
0.096
0.094
0.092
Baseline scenario
Megas scenario
Cities scenario
0.090
Regions Scenario
0.088
0.086
0.084
2012
2013
2014
2015
2016
2017
2018
2019
2020
2021
2022
2023
2024
2025
2026
2027
2028
2029
2030
Theil Index by Scenario: Inside Countries Disparities
0.065
0.060
0.055
Baseline scenario
Megas scenario
Cities scenario
Regions scenario
0.050
0.045
0.040
2012
2013
2014
2015
2016
2017
2018
2019
2020
2021
2022
2023
2024
2025
2026
2027
2028
2029
2030
GDP growth rates in the Megas scenario:
differences with respect to the baseline
Reykjavik
!
Canarias
Megas March 18 2013
Average regional GDP growth rate, difference w.r.t. baseline
< 0.00
0.01 - 0.12
0.13 - 0.20
0.21 - 0.26
0.27 - 0.31
0.32 - 0.37
0.38 - 0.42
0.43 - 0.49
> 0.50
Guadeloupe
Réunion
Martinique
Helsinki
!
Oslo
!
Guyane
Tallinn
!
Stockholm
!
Madeira
Riga
!
København
!
Dublin
!
Vilnius
!
Minsk
!
Acores
Amsterdam
!
London
!
Berlin
!
Warszawa
!
Kyiv
!
Bruxelles/Brussel
!
Praha
!
Luxembourg
!
Paris
!
WienBratislava
!
!
Bern
!
Kishinev
!
Budapest
!
Vaduz
!
In Western countries:
- strong advantages to rich
and central regions;
- rural areas of rich regions
gain relatively less;
- relatively poor countries
(like Greece) take
advantage of a general
increase in demand.
Ljubljana
!
Zagreb
!
Bucuresti
!
Beograd
!
Sarajevo
!
Sofiya
!
Podgorica
!
Madrid
!
Roma
!
Lisboa
!
Skopje
!
Ankara
!
Tirana
!
Athinai
!
El-Jazair
!
Ar Ribat
!
Nicosia
!
Tounis
!
Valletta
!
© Politecnico di Milano, Project ET2050, 2013
0
250
500
km
Regional level: NUTS2
Source: Politecnico di Milano, 2013
Origin of data: - MASST3 model
© EuroGeographics Association for administrative boundaries
In Eastern countries:
- relatively more diffused
growth, thanks to a general
recovery of the EU
economy.
GDP growth rates in the Cities scenario:
differences with respect to the baseline
Reykjavik
!
Canarias
Cities March 18 2013
Average regional GDP growth rate, difference w.r.t. baseline
< 0.00
0.01 - 0.20
0.21 - 0.26
0.27 - 0.33
0.34 - 0.39
0.40 - 0.45
0.46 - 0.51
0.52 - 0.59
> 0.60
Guadeloupe
Réunion
Martinique
Helsinki
!
Oslo
!
Guyane
Tallinn
!
Stockholm
!
Madeira
Riga
!
København
!
Dublin
!
In Western countries:
- more widespread and diffused
growth at intranational level;
- strong countries like Germany,
the Netherlands, Austria, increase
less than Southern countries
(catching-up).
Vilnius
!
Minsk
!
Acores
Amsterdam
!
London
!
Berlin
!
In Eastern countries:
- diffused advantages, relatively
less pronounced than in Western;
- similar increase in growth than in
the megas scenario.
Warszawa
!
Kyiv
!
Bruxelles/Brussel
!
Praha
!
Luxembourg
!
Paris
!
Bratislava
Wien
!
!
Bern
!
Kishinev
!
Budapest
!
Vaduz
!
Ljubljana
!
Zagreb
!
Bucuresti
!
Beograd
!
Sarajevo
!
Sofiya
!
Podgorica
!
Madrid
!
Roma
!
Lisboa
!
Skopje
!
Ankara
!
Tirana
!
Athinai
!
El-Jazair
!
Ar Ribat
!
Nicosia
!
Tounis
!
Valletta
!
© Politecnico di Milano, Project ET2050, 2013
0
250
500
km
Regional level: NUTS2
Source: Politecnico di Milano, 2013
Origin of data: - MASST3 model
© EuroGeographics Association for administrative boundaries
GDP growth rates in the Regions scenario:
differences with respect to the baseline
Central/core regions grow less
than in the baseline scenario.
Reykjavik
!
Canarias
Regions March 18 2013
Average regional GDP growth rate, difference w.r.t. baseline
< -0.22
-0.21 - -0.17
-0.16 - -0.11
-0.10 - -0.06
-0.05 - 0.00
0.01 - 0.11
0.12 - 0.22
0.23 - 0.42
> 0.43
Guadeloupe
Réunion
Martinique
Helsinki
!
Oslo
!
Guyane
Tallinn
!
Stockholm
!
Rural or peripheral areas gain
relatively more than in the baseline
scenario.
Madeira
Riga
!
København
!
Dublin
!
Vilnius
!
This holds for both Western and
Eastern countries.
Minsk
!
Acores
Amsterdam
!
London
!
Berlin
!
Warszawa
!
Kyiv
!
Bruxelles/Brussel
!
Praha
!
Luxembourg
!
Paris
!
Bratislava
Wien
!
!
Bern
!
Kishinev
!
Budapest
!
Vaduz
!
Ljubljana
!
Zagreb
!
Bucuresti
!
Beograd
!
Sarajevo
!
Sofiya
!
Podgorica
!
Madrid
!
Roma
!
Lisboa
!
Skopje
!
Ankara
!
Tirana
!
Athinai
!
El-Jazair
!
Ar Ribat
!
Nicosia
!
Tounis
!
Valletta
!
© Politecnico di Milano, Project ET2050, 2013
0
250
500
km
Regional level: NUTS2
Source: Politecnico di Milano, 2013
Origin of data: - MASST3 model
© EuroGeographics Association for administrative boundaries
A sensitivity analysis on disparities
Several analyses on sensitivity of total disparities were to macro-economic
assumptions in the baseline scenario were run.
The most relevant and interesting:
Removing the assumption of a persistence of higher inflation rates in
CEECs with respect to western countries, a higher GDP performance
appears in CEECs (mainly due to higher exports and lower imports)
and by consequence a much lower increase in total regional disparities
in 2030.
31
Conclusions
1. Difficult times ahead for inter-regional disparities
(due to centralization trends, macroeconomic constraints),
confirmed by the recent DG Regio “webminar”
2. A scenario supporting “cities” (first, second and possibly third
rank cities) looks the most desirable: in terms of growth potential
(best use of “concentrated diffusion” of territorial capital) and in
terms of territorial cohesion
3. Relevant policy tasks for CEECs:
- keep wage and price increases in line with productivity growth
- launch a wave of endogenous capital accumulation
- Find a new “innovation pattern”, based on a “smart”
specialization and on selected inter-regional cooperations.
32
THANK YOU VERY MUCH
FOR YOUR ATTENTION!
33