Università di Bologna
04 maggio 2012
Facoltà di Scienze Statistiche
Politica Economica corso avanzato
Prof. Cristina Brasili
Polycentric Urban Systems in Europe
and the Lisbon Strategy: Emerging
Territorial Patterns
Francesco Pagliacci
PhD Program in Agrifood Economics and Statistics
University of Bologna - Italy
1
1. Introduction (I)
 Since 2000, the Lisbon Strategy (LS) has played a major role amongst
the European Union (EU) policies. By 2010, most of its goals have not
been achieved.
 A persistent drawback can be observed: LS applies at the EU level.
The regional dimension is ignored.
 Several EU policies are targeted to the regional dimension (CSD,
1999; European Union, 2007). Today, EU’s regional policy holds second
place as a share of EU total expenditures after the CAP.
 In spite of these efforts, socio-economic differences across EU regions
are wide.
 These structural differences can affect the way regions achieve the LS
targets.
2
1. Introduction (II)
Two main hypotheses in explaining these differences in achieving the
LS targets are tested:
1. The relationship between the achievement of the LS targets and
the extent of regional polycentrism
•
EU has considered the polycentric development as a main pre‐requisite
for a more sustainable and balanced development assuring also greater
competitiveness to the whole EU (CSD, 1999).
•
Regional polycentrism can be considered as a specific output of political
local governance. Thus, polycentric regions should be considered as
economic and political actors as well as large metropolitan areas are.
2. The presence of spatial patterns in the performance of the EU
regions according to the LS.
•
An exploratory spatial analysis is performed to test the presence of the
spatial dependence in the achievement of the Lisbon Strategy’s targets.
In particular, the presence of a core-periphery pattern is suggested.
3
Outline and Structure of the presentation
1. Introduction
2. Theoretical background

The Lisbon Strategy

The Polycentric development
3. Analysing Lisbon Strategy’s targets
through PCA

Methodology: multivariate
statistical analysis

Main results: wide differences
in EU regions
3. Hp.1: Effects of regional
polycentrism


Methodology : rank-size
index & issues
Some results
5. Conclusions
4. Hp.2: Spatial patterns in the
achievement of the LS targets

Global and Local Moran’s I

Emerging territorial
patterns
4
Outline and Structure of the presentation
1. Introduction
2. Theoretical background

The Lisbon Strategy

The Polycentric development
3. Analysing Lisbon Strategy’s targets
through PCA

Methodology: multivariate
statistical analysis

Main results: wide differences
in EU regions
3. Hp.1: Effects of regional
polycentrism


Methodology : rank-size
index & issues
Some results
5. Conclusions
4. Hp.2: Spatial patterns in the
achievement of the LS targets

Global and Local Moran’s I

Emerging territorial
patterns
5
2. Theoretical background: the Lisbon Strategy (I)
LS main objective was to make the EU (European Council, 2000):
“the most competitive and dynamic knowledge-based economy in the
world capable of sustainable economic growth with more and better
jobs and greater social cohesion”
LS mainly rests on three main pillars:
Economic
pillar
Social
pillar
Environmental
pillar
6
2. Theoretical background: the Lisbon Strategy (II)
Some specific targets
•
•
•
•
•
Overall employment rate: 70%;
Employment rate for women: 60%;
Employment rate among older workers: 50%;
An annual economic growth around 3%;
More investments in research and innovation: 3% of total GDP in
R&D.
The open method of coordination (OMC)
LS has adopted the open method of coordination (OMC) between
Member States, at different levels of decision-making.
OMC is an intergovernmental method of “soft coordination” by which
Member States are evaluated by one another, with the Commission’s
role being one of surveillance.
OMC was a source of peer pressure and a forum for sharing good
practice.
7
2. Theoretical background: the Lisbon Strategy (III)
The end of the Strategy and its main biases
By 2010, most of LS goals were not fully achieved. LS has been
affected by several drawbacks:
1. LS was ‘a wrong strategy’ for the EU integration: convergence
between different economies and risk for a ‘clash of capitalisms’
(Hopner and Schafer, 2007);
2. A ‘wrong agenda’: extremely liberal mark (Amable, 2009;
Rodriguez, 2002) and shift towards a right-centred approach
(Pochet, 2006);
3. Uneven participation in the LS: weakness and ambiguity of the OMC;
4. LS did not take into account:
 the differences amongst the 27 Member States. According to
Sapir (2006), deep differences exist amongst the social models of
Nordic, Anglo-Saxon, Continental and Mediterranean Countries;
 the existence of regional differences, within each EU Member
States.
8
2. Theoretical background: Polycentrism (I)
Evolution of the concept of polycentrism
 In the 1960s the concept of polycentrism was adopted as a
theoretical tool in the analysis of the spatial organisation of US
metropolitan regions (Ostrom et al., 1961).
 In the 1990s, the concept assumed a normative relevance, especially
at a broader scale of analysis. It played a key role in:
1. the “European Spatial Development Perspective” (CSD, 1999);
2. the “Territorial Agenda of the European Union: Towards a more
Competitive and Sustainable Europe of Diverse Regions” (EU, 2007).
9
2. Theoretical background: Polycentrism (II)
A more polycentric urban development can counterbalance the central
role still played by the so-called “Pentagon”.
Source: CSD (1999)
A polycentric development can improve the promotion of economic
competitiveness, social cohesion and environmental sustainability (CSD,
1999).
10
2. Theoretical background: Polycentrism (III)
Some common features
In spite of the fuzziness of the concept, there is a general consensus
about polycentric regions’ main features.
In polycentric urban regions, cities are (Kloosterman et al., 2001;
Meijers, 2008; Cowell, 2010):
1. located in close proximity (generally within commuting distance);
2. well-connected and interrelated through co-operation flows;
3. historically different;
4. independent political entities;
5. lacking a leading city.
11
2. Theoretical background: Polycentrism (IV)
Polycentric Urban Regions: some examples
Source: OTB in Romein (2004)
Source: Cowell (2010)
12
2. Theoretical background: Polycentrism (V)
Two approaches to the analysis of polycentrism
A polycentric region can be considered a way to manage larger urban
regions that differs from larger single-core metropolitan regions. Both
morphological and functional perspectives are relevant in analysing it
(Nordregio et al., 2004; Meijers, 2008; Veneri et al., 2010):
1. Morphological approaches analyse the way cities differing in size
and population are distributed across a given region (Lambooy,
1998; Parr, 2004; Meijers, 2008);
2. Functional approaches focus on the interactions among urban
centres. Several kinds of flows can be used as a proxy for these
interactions (Van der Laan, 1998; Hall et al., 2006; Limtanakool et
al., 2007):
•
the flows of commuters;
•
the flows of goods;
•
the flows of information/communication.
13
2. Theoretical background: Polycentrism (VI)
Some critical issues
1. Definitions about polycentrism are “vague” (Riguelle et al., 2007).
2. The concept is a typical multiscalar and multidimensional one: a
region may be polycentric at a given spatial scale but monocentric at
a different one.
3. The positive effects of polycentrism (according to EU documents)
often lack a theoretical rationale and they have not been sufficiently
investigated through empirical analysis (Meijers, 2008; Veneri et al.,
2010).
4. The coherence of policies enhancing a polycentric development
across EU with all the other EU policies (e.g., the Lisbon Strategy) is
not straightforward.
14
Outline and Structure of the presentation
1. Introduction
2. Theoretical background

The Lisbon Strategy

The Polycentric development
3. Analysing Lisbon Strategy’s targets
through PCA

Methodology: multivariate
statistical analysis

Main results: wide differences
in EU regions
3. Hp.1: Effects of regional
polycentrism


Methodology : rank-size
index & issues
Some results
5. Conclusions
4. Hp.2: Spatial patterns in the
achievement of the LS targets

Global and Local Moran’s I

Emerging territorial
patterns
15
3. Measuring the extent of regional polycentrism:
sample
The analysis focuses on four European Countries: France, Germany,
Italy and Spain. The analysis is performed on a sample of 75 regions:
 NUTS 2 regions for France (Régions), Italy (Regioni) and Spain
(Comunidades Autónomas);
 NUTS 1 regions for Germany (Länder).
When computing the extent
of polycentrism, the total
sample is reduced from 75 to
72 regionsi. In Germany, 3
Länder
are
Stadtstaaten
(city-states): due to this
reason, they are considered
as
belonging
to
the
Flächenländer (area states)
containing them.
Source: personal elaboration
16
3. Analysing Lisbon Strategy’s targets through PCA: a
list of variables
Principal components analysis (PCA) has been applied to a list of 25
variables, focusing on: demography, economy/labour market, innovation,
environment.
Variable
Resident Population
GDP per capita (EU-27 = 100)
GVA agriculture (% on the total)
GVA industrial sect. (% on the total)
Employment in agriculture (% on the total)
Employment in industrial sect. (% on the total)
Total employment rate
Employment rate (55-64 years)
Female employment rate
Unemployment rate
Long-term unemployment rate
Unemployment rate (15-24 years)
Population at risk of poverty after social transfers (% of total popul.)
Early school leavers aged 18-24 (in % on the total same age)
Population aged 25-64 with low education (% on the total)
Population aged 30-34 with tertiary education (% on the total)
Expenditure on R&D (% of GDP)
Patent application to EPO per million inhabitants
Households with broadband connection (% of all households)
Land for artificial uses (% on total)
Railroad accessibility (average value of Nuts 3)
Road accessibility (average value of Nuts 3)
Air accessibility (Nuts 3 with max accessib.)
Passenger cars per 1000 inhabitants
Yearly average concentration of PM10 (μg/m³) (average of Nuts-3)
Source
5
5
5
5
5
5
5
5
Eurostat
Eurostat
Eurostat
Eurostat
Eurostat
Eurostat
Eurostat
Eurostat
Eurostat
Eurostat
Eurostat
report cohesion
report cohesion
report cohesion
report cohesion
report cohesion
Eurostat
report cohesion
report cohesion
Eurostat
Espon
Espon
Espon
Eurostat
report cohesion
Refer. Year
2009
2008
2007
2007
2007
2007
2008
2008
2008
2008
2008
2008
2008
2007-2009
2008
2008
2008
2006-2007
2009
2009
2001
2001
2001
2008
2009
Source: elaboration on Eurostat (2011), © ESPON Database (2006), European Commission
17
(2010b)
3. Analysing Lisbon Strategy’s targets through PCA:
methodology
PCA belongs to multivariate statistics (Hotelling, 1933; Pearson, 1901):
it transforms a group of p indicators into a much smaller group of
variables (k), still explaining a high level of variance. A correlation
matrix was used. The k principal components (where k < p) come from
the following linear combinations, expressed as a matrix:
Y= X A
(1)
where,
 Y is the n-by-k matrix, containing the scores of the n statistical units
in the k components;
 A is the vector matrix p-by-k of the normalized coefficients;
 X is the n-by-p matrix of the standardized data.
18
3. Analysing Lisbon Strategy’s targets through PCA:
model specification
The selection of the “right” number (k) of principal components (where k
< p)
According to these results, 6 PCs are
selected. They account for 81.9% of
total variance;
Test
KMO
=
considered good.
Source: personal elaboration on
Eurostat (2011), © ESPON Database
(2006),
European
Commission
(2010b) [Software: R 2.13.0]
0.7633
can
be
19
3. Analysing Lisbon Strategy’s targets through PCA:
interpretation of results (I)
Factor loadings for the 6 PCs (after VARIMAX rotation)
Resident Population
GDP per capita
GVA of agriculture
GVA of manufacture
Employment in agriculture
Employment in manufacture
Total employment rate
Total employment rate (55-64 y)
Female employment rate
Unemployment rate
Long-term unemployment rate
Unemployment rate (15-24 y)
Population with low education
Population with tertiary education
Early school leavers
R&D expenditures
Patents per million inhabitants
Household with broadband connection
Population at risk-of-poverty (after social transfers)
Concentration of PM10
Land for artificial uses (% on total)
Passenger cars per 1000 inhabitants
Railroad accessibility
Road accessibility
Air accessibility
PC 1
PC 2
PC 3
PC 4
PC 5
PC 6
0.716
0.481 -0.568
0.247
-0.636
0.225
0.213
0.236
0.909
-0.643
0.246 -0.287
0.258
-0.325
0.904
-0.528
0.765
0.381
0.659
-0.430
0.759 -0.346
0.240
0.896
0.266 -0.225
0.892
-0.283
-0.252
0.560 -0.520
0.397 -0.232
-0.386
-0.527
0.700
-0.227
0.920
0.963
0.561
-0.271
0.426
0.687
0.247 -0.297
0.209
0.496
0.501
0.405
0.794 -0.300
0.284
0.666
0.205
0.203
0.599
-0.284 -0.211
-0.582
0.606
0.248 -0.516
0.562
0.266 -0.519
0.905
Source: elaboration on Eurostat (2011), © ESPON Database (2006), European
20
Commission (2010b)
3. Analysing Lisbon Strategy’s targets through PCA:
interpretation of results (II)
A short definition for each PC
PC1 (21.9% of total variance): regional urbanization and
accessibility
PC2 (15.3%): weak economic performance and social exclusion
PC3 (12.2%): well-performing labour market
PC4 (11.6%): low-skilled workers
PC5 (8.2%): role of manufacturing activities
PC6 (7.3%): human capital and innovation
Regional performance according to the LS target is assessed by assigning
a standardized score on each extracted PC to each region.
21
3. Analysing Lisbon Strategy’s targets through PCA:
main results (I)
Regional performance according to the LS: Standardized scores for the 6
PCs
Source: elaboration on Eurostat (2011), © ESPON Database (2006), European
22
Commission (2010b)
3. Analysing Lisbon Strategy’s targets through PCA:
main results (II)
 When considering different pillars of the LS, different patterns emerge
at the EU scale.
 A unique relationship among the three different pillars of the LS does
not exist. A well performing labour market and high investments in
human capital are not always positively linked to the general economic
performance.
 Examples:
1. Eastern German Länder
2. Rural French regions
Therefore, other features could explain these differences
in the regional performance according to the LS…
23
Outline and Structure of the presentation
1. Introduction
2. Theoretical background

The Lisbon Strategy

The Polycentric development
3. Analysing Lisbon Strategy’s targets
through PCA

Methodology: multivariate
statistical analysis

Main results: wide differences
in EU regions
3. Hp.1: Effects of regional
polycentrism


Methodology : rank-size
index & issues
Some results
5. Conclusions
4. Hp.2: Spatial patterns in the
achievement of the LS targets

Global and Local Moran’s I

Emerging territorial
patterns
24
4. LS and regional polycentrism: methodology (I)
 Morphological extent of regional polycentrism.
 The rank-size index can be a crude but useful tool to measure
regional polycentrism (Haggett, 1965; Nordregio et al., 2004; Meijers,
2008).
Methodological aspects
1. Within each region, cities
are ranked according to their
population.
2. The logarithms of both rank
and population are taken.
3. An example: the EmiliaRomagna region (Italy)
Log
Log
Rank# City
Populat. (Rank#) (Pop.)
1 Bologna 371,217
0.00 12.80
2 Modena
175,502
0.69 12.10
3 Parma
163,457
1.10 12.00
Reggio
4 Emilia
141,877
1.39 11.90
5 Ravenna 134,631
1.61 11.80
6 Ferrara
130,992
1.79 11.80
7 Rimini
128,656
1.95 11.80
8 Forl•
ì
108,335
2.08 11.60
9 Piacenza
95,594
2.20 11.50
10 Cesena
90,948
2.30 11.40
25
4. LS and regional polycentrism : methodology (II)
 Rank-size equation of cities is estimated (OLS method):
Ln (pop) = a + β Ln (rank)
(2)
 The equation is expressed in the Lotka form (Parr, 1985), a special
application of the Zipf’s Law (Zipf, 1935; 1949).
 When cities are arrayed by their size on double-log graph paper, the
‘log-normal’ distribution takes the form of a straight line, whose slope is
close to -1.
 The law holds for big countries (e.g., India, China, the USA) as well as
for the EU, but explanations about it pose some difficulties. The
framework is similar to that proposed by the Gibrat’s Law for firms’ size
distribution (Gabaix, 1999).
26
4. LS and regional polycentrism : methodology (III)
 Estimations for the coefficient β in (2) provide a proxy for the level of
polycentrism within a given region. The slope of the OLS regression line
is:
o greater than -1 (regression line is flatter) in polycentric regions.
o smaller than -1 (regression line is steeper) in monocentric regions.
Rank-size distribution: a polycentric region and in a monocentric one
Aragón (ES)
14
14
Nordrhein-Westfalen (DE)
y= 13.840 - 0.548x
y=12.161 - 1.266x
Koln
13
12
9
10
11
log(population)
12
11
10
9
log(population)
13
Zaragoza
-1
0
1
2
log(rank)
3
4
-1
0
1
2
3
4
log(rank)
Source: personal elaboration on Istat (2001) and Insee (1999)
27
4. LS and regional polycentrism: some issues (I)
i.
Which definition of city should be used to provide international
comparison? The concept of Functional Urban Region (FUR) should be more
appropriate in this identification problem. Due to the lack of comparable data,
administrative units are used (Italian comuni; Spanish municipios; German
gemeinden; French communes/ communautés d’agglomeration)
ii. National Census are the main sources for data about population
iii. Estimations are affected by the number of cities included in the OLS
analysis (Meijers, 2008)
Different methods:
•
A fixed number of towns per region?
•
A fixed size threshold of inhabitants?
•
A size above which the sample accounts for some given proportion of
regional population?
Following Meijers (2008), a fixed number of towns per region is chosen
(regions are largely heterogeneous): the 5, 8, 10, 12 and 15 largest cities
within each region are used in the OLS models.
28
4. LS and regional polycentrism: the sample of cities
Source: personal elaboration on Insee (1999), Istat (2001), Ine (2001), Destatis (2008)
29
4. LS and regional polycentrism: main results (I)
The extent of mono-/ polycentrism, estimated for samples of the 5, 8,
10, 12 and 15 largest cities per region
30
Source: personal elaboration on Insee (1999), Istat (2001), Ine (2001), Destatis (2008)
4. LS and regional polycentrism: some correlations (I)
Can the sharp differences emerging from PCA be explained through a
different extent of the polycentrism at the regional level?
Correlations amongst extracted PCs and polycentricity indexes
Polyc.
Index 5
cities
-0.215
PC1: urbanization and
accessibility
(0.064)
0.101
PC2: weak economic
performance/ social exclusion (0.3897)
-0.128
PC3: performance of the
labour market
(0.2729)
0.007
PC4: presence of low-skilled
workers
(0.9526)
0.409
PC5: extent of manufacture
(0.00027)
-0.187
PC6: human capital and
innovation
(0.1072)
Polyc.
Polyc.
Polyc.
Polyc.
Index 8 Index 10 Index 12 Index 15
cities
cities
Cities
cities
-0.16
-0.101
-0.049
0.020
(0.1695)
(0.3893)
(0.6745)
(0.8664)
0.086
0.091
0.116
0.142
(0.4613)
(0.4361)
(0.3232)
(0.2256)
-0.109
-0.118
-0.112
-0.090
(0.3515)
(0.3114)
(0.3379)
(0.4447)
0.091
0.159
0.216
0.265
(0.4393)
(0.1726) (0.06219) (0.02151)
0.428
0.430
0.436
0.430
(0.00013) (0.00012) (0.00009) (0.00012)
-0.288
-0.351
-0.417
-0.484
(0.01219) (0.00199) (0.0002) (0.00001)
Source: elaboration on Insee (1999), Istat (2001), Ine (2001), Destatis (2008) and on
Eurostat (2011), © ESPON Database (2006), European Commission (2010b)
31
4. LS and regional polycentrism: some correlations (II)
A positive correlation is found between “Polycentricity Index” and PC5; a
negative one between “Policentricity Index” and PC6
Source: elaboration on Insee (1999), Istat (2001), Ine (2001), Destatis (2008) and on
Eurostat (2011), © ESPON Database (2006), European Commission (2010b)
32
Outline and Structure of the presentation
1. Introduction
2. Theoretical background

The Lisbon Strategy

The Polycentric development
3. Analysing Lisbon Strategy’s targets
through PCA

Methodology: multivariate
statistical analysis

Main results: wide differences
in EU regions
3. Hp.1: Effects of regional
polycentrism


Methodology : rank-size
index & issues
Some results
5. Conclusions
4. Hp.2: Spatial patterns in the
achievement of the LS targets

Global and Local Moran’s I

Emerging territorial
patterns
33
5. Spatial patterns in achieving the LS targets:
methodology (I)
According to the distribution of the scores obtained by regions in the 6
PCs, some spatial patterns seem to emerge. Thus, an exploratory
spatial data analysis is performed.
Both Global and Local Moran’s I statistics are computed.
Weight matrix
A row standardized spatial weights matrix W, defined as:
The generic element can take different values:
w*ij= 0
if
i=j
w*ij = 0
if
j  N(i)
w*ij = 1
if
j  N(i)
where: N(i) is the list of neighbours of the region i, according to a first
order queen contiguity matrix.
34
5. Spatial patterns in achieving the LS targets:
methodology (II)
The chosen first order contiguity matrix: map and main features
First Order Contiguity
Number of nonzero links: 312
Share of nonzero weights: 5.55
Average number of links: 4.16
Source: personal elaboration – Software GeoDa and Software R (package: spdep)
35
5. Spatial patterns in achieving the LS targets: Global
Moran’s I statistics
Each PC shows high values for the Global Moran’s I statistics: the test is
well above the null hypothesis of no spatial correlation.
Global Moran’s I statistics and p-value for the 6 extracted PCs
PC1:
PC2:
PC3:
PC4:
PC5:
PC6:
urbanization and accessibility
weak economic performance and social exclusion
performance of labour market
low-skilled workers
extent of manufacture
human capital and innovation
Moran's I
p-value
0.3500
6.02E-06
0.6333
3.65E-15
0.7355
<2.2E-16
0.7053
<2.2E-16
0.1880
0.0078
0.5738
8.26E-13
Source: personal elaboration
36
5. Spatial patterns in achieving the LS targets: Local
Moran’s I statistics
In order to detect geographic patterns, local Moran’s I tests are
performed.
Local Moran’s I cluster maps for the 6 extracted PCs
Source: personal elaboration
37
5. Spatial patterns in achieving the LS targets: some
results
The spatial analysis suggests the existence of some territorial patterns:
1. EU-level: a core-periphery pattern. More ‘central’ regions perform
better than peripheral ones. According to the economic/labour
market performance, poor performing regions are spatially clustered
in peripheral regions. The differences pointed out by Sapir (2006)
between the Continental social model and Mediterranean one also
hold at the regional level.
2. National level: strong differences are observed within each
Country (especially across Italy and Germany, where the divide
between central regions and lagging behind ones has stronger
historical roots).
38
Outline and Structure of the presentation
1. Introduction
2. Theoretical background

The Lisbon Strategy

The Polycentric development
3. Analysing Lisbon Strategy’s targets
through PCA

Methodology: multivariate
statistical analysis

Main results: wide differences
in EU regions
3. Hp.1: Effects of regional
polycentrism


Methodology : rank-size
index & issues
Some results
5. Conclusions
4. Hp.2: Spatial patterns in the
achievement of the LS targets

Global and Local Moran’s I

Emerging territorial
patterns
39
6. Conclusions (I)
 In spite of the strong criticism against the Lisbon Strategy, it has
played a key role among EU policies, since 2000. A major bias deals
with the absence of any regional approach in the strategy.
 Polycentrism plays a key role within EU planning policies.
Polycentrism should foster inclusion, economic competitiveness and
environmental sustainability across EU regions. It should counterbalance
the key role which is still played by more central regions.
 Unfortunately the analysis does not support this hypothesis. As
polycentrism is deeply related to manufacturing activities, more
polycentric regions perform worse than monocentric ones according to
the investments in R&D and innovation.
 Spatial and geographical patterns seem to play a more important role
in describing regional performances according to the LS.
40
6. Conclusions (II)
 The lack of any regional approach within the LS remain the most
important bias. It has been unrealistic to consider the whole EU as a
homogeneous area, able to tackle the same challenges in a similar way.
 This lack has hindered the fully achievement of the LS’s targets by
2010. Therefore, there is now a stronger need for a general re-framing
of the policy agenda of the EU: regions should be treated separately
and Europe 2020 Strategy should take into account these regionspecific features and issues.
 Europe should become a stronger global economy thanks to its
heterogeneity and not in spite of it.
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Thanks for your attention!
Dott. Francesco Pagliacci
Università di Bologna
Dipartimento di Scienze Statistiche
e-mail: [email protected]
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