Ann Reg Sci
DOI 10.1007/s00168-007-0146-2
SPECIAL ISSUE PAPER
A forecasting territorial model of regional growth:
the MASST model
Roberta Capello
Received: 15 June 2006 / Accepted: 26 April 2007
© Springer-Verlag 2007
Abstract The profound and unique institutional and economic processes which
characterise the historic period Europe is facing and will face call for appropriate
methodologies to forecast the impact of these processes on Europe and its territory.
Few regional econometric models as the basis of forecasting exercises have been
developed, either replicating national macroeconomic models, or through complex
systems of equations for each region that are linked to both the national aggregate
economy and to the other regional economies through input—output technical coefficients that determine intra- and inter-regional trade and output. This paper presents
a new regional forecasting model, labelled MASST (macroeconomic, sectoral, social
and territorial), built on a modern conceptualization of regional growth. In MASST,
regional growth is conceived as a competitive, endogenous and cumulative process in
which social and a spatial elements play an important role: local resource endowments
and increasing returns in the form of agglomeration economies and spatial growth spillovers perform an important role in the explanation of regional growth differentials.
MASST is generative in nature, since local factors matter, but it is also a model that
considers a second family of development factors, these being macroeconomic and
national. This structure of the model gives rise to the possibility of producing an efficient interactive national—regional approach, combining top-down and bottom-up
approaches.
JEL Classifications R11 · R15
R. Capello (B)
Department of Management, Economics and Industrial Engineering Politecnico di Milano, Milan, Italy
e-mail: [email protected]
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R. Capello
1 Introduction1
The profound and unique institutional and economic processes which characterize
the historic period Europe is facing and will face explain the interest and the need to
forecast the impact of such processes on Europe and its territory.2 Recent institutional
changes deal with the enlargement of the European Union, from one side, and of the
European Monetary Union, from the other. Both institutional changes put the new
integrated Europe under stress in terms of opportunities and threats that have to be
faced in the near future. As mentioned in the official documents of the EU,3 at the
European governmental level the aim is to achieve a “territorial cohesion”; by this
(vague) term, a harmonious, balanced and sustained economic growth is hoped and
longed for, a growth characterised at the same time by economic cohesion and by
reduced and evenly distributed threats on territory, the latter intended in its natural,
geographical and economic meaning.
The European Union of 25 Countries offers various important opportunities, as a
consequence of the enlargement of markets and of the structural change of the institutional context, within the framework of the Lisbon objectives. Opportunities stem
from:
– more diversified needs, constituting market niches of size large enough to allow
product innovations and, consequently, higher consumer welfare and higher
employment opportunities;
– a new division of labour (inter-sectoral reallocation), hence higher productivity
and higher remuneration of factors;
– intra-sectoral economies of scale and hence higher productivity and competitiveness, also with positive employment feedbacks;
– higher mobility of labour (and capital), with decreasing risks of labour mismatch
and, hence, unemployment;
– acceleration of technological progress and of processes of product quality increase;
– acceleration of structural change in the productive sectors and territories which so
far have been beneficiaries of EU funds.
On the other hand, the enlargement process is a source of evident factors of stress
either already present or emerging from the extrapolation of the present model of
economic/territorial growth, like:
– regional disparities in economic growth capability, stronger after the entrance of
the New Member Countries;
1 A first draft of the paper has been presented at the Tinbergen Institute Seminar, held in Amsterdam, 22–23
August 2005, and at the ERSA conference, held in Amsterdam, 23–26 August 2005
2 The MASST model was developed within a research effort, co-ordinated by Roberto Camagni, developed
by the Polythecnic of Milan within the ESPON 3.2 project, entitled “Spatial Scenarios and Orientations in
Relation to the ESDP and Cohesion Policy”. The main project contractor is IGEAT of the Free University of
Brussels. The author is grateful to Barbara Chizzolini of Bocconi University and Ugo Fratesi of Polithecnic
of Milan for their help in data collection and model estimates. ESPON is an EU programme, with the aim
to study the present and future territorial, economic and social conditions of Europe.
3 EU Third and Fourth Cohesion Reports, Brussels.
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A forecasting territorial model of regional growth
– disparities in the level of physical accessibility, and consequently of economic
growth among the European regions;
– physical congestion, and the decreasing returns deriving from it, in the areas where
the development is stronger, and in particular, at micro-territorial level, of the large
European core regions (the so- called “Pentagon”);
– socio-economic desertification of areas which, on the contrary, are penalised in
the development process; today, the main areas at risk are those in where it is
difficult to initiate restructuring processes of productive activities from agricultural
to industrial or tertiary or from industrial to tertiary or, finally, from productive to
residential;
– non-ordered and non-rational use of soil in areas with high availability of soil (such
as the former agricultural areas of the new Countries);
– physical development of urban systems, with too low density of use of soil (with
consequent over-extension of the urban area).
In front of all these radical institutional and economic processes, some important policy
changes have already been planned by the EU for the coming years. A clear example
of these changes is the reform of the structural funds, or the reform of the Community
Agricultural Policy (CAP), both imposed by the entrance of the New Members States
in the EU; these normative changes will also contribute to provide a different picture
of the European territory in the near future.
The above-mentioned opportunities and threats due to the institutional changes are
exacerbated by other important macroeconomic forces at work, which will have an
impact on the way Europe will be in terms of economic and territorial disparities in
the future. The strong competition of emerging Countries (like China, Brazil, India
and Russia) and the loss of competitiveness of the European economy due to the
re-evaluation of the euro/US dollar exchange rate put under severe stress the chances
of economic growth for Europe as a whole. Moreover, the rigid fiscal budget constrains
imposed by the Treaty of Maastricht, coupled with the impossibility of implementing
national devaluation policies within the Monetary Union exacerbate economic growth
possibilities.
The effects of these spontaneous economic tendencies and policy trends will be of
both an economic and a territorial nature but the way in which they will affect the
European territory is still an open question: in economic terms, the evolution of regional disparities and of winners and losers is still unclear. In territorial terms, the
outmigration (or re-population) of peripheral areas and of agricultural areas in Eastern
Countries; the non-ordered and non-rational (vs. ordered and rational) use of soil in
areas with high availability of soil (such as the former agricultural areas of the new
Countries); the physical congestion (vs. decongestion) of urban systems in advanced
Countries are some of the possible (and opposed) results that may stem from the
structural change processes.
This paper presents the MASST (macroeconomic, sectoral, social and territorial)
model, a combination of an econometric model of regional and national economic
growth and a simulation algorithm, whose foremost purpose is to forecast medium
term trends of economic growth and demographic tendencies for the new Europe (the
New 10 + 2, Bulgaria and Romania). The model is built in order to predict alterna-
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R. Capello
tive regional growth rates and new levels of regional per capita income in the EU 27
Countries, and therefore new levels of economic territorial disparities, under different
conditional hypotheses.
The paper is a first step towards the forecasting of territorial scenarios: in particular,
it is dedicated to the presentation of the forecasting model built for this purpose. The
paper describes:
–
–
–
–
–
the theoretical specifications of the forecasting model (Sect. 2);
the structure of the model (Sect. 4);
the technical specification of the model (Sect. 3);
the national and regional datasets built for the model (Sect. 5);
the econometric methodology and the results of the estimations (Sect. 6 ).
Some concluding remarks on future research directions are contained in the last part
of the paper.
2 The theoretical specifications of MASST
The distinguishing feature of econometric models with respect to other operational
models is not an underlying theory (as in the case of, for example, input—output
models or economic base models), but the way a model is specified (i.e., based on an
underlying theoretical framework) and the method used to estimate the coefficients
(Nijkamp et al. 1986; Hewings et al. 2004).
When applied to the study of regional economic growth, econometric model
specifications have always been grounded over time on the main economic growth
theories developed at regional level. Regional econometric models have started as
further elaborations of macroeconomic models dealing with variables such as production, investment, consumption, exports (Nijkamp et al. 1986); in these approaches
important attempts were made to translate econometric models interpreting economic
growth of national systems into regional econometric models (Glickman 1977, 1982;
Cappellin 1975, 1976). These models reflect the Keynesian approach to growth,
based on the theoretical assumption that local development is a demand-driven
process, supported by increases in internal or external consumption of locally produced goods that, via multiplicative effects, generate increases in local employment
and income (Table 1). These models are based on macroeconomic theories of the
fifties; among them, in particular, attention is on the macroeconomic export-base
theory.
The need to put emphasis on supply elements to explain growth has pushed
regional econometric models towards different specifications; interregional flows of
resources (capital and labour) were the main modelling elements, given the primary role they played in neoclassical growth models (Moody and Puffer 1969).
During the 1980s, supply oritented regional econometric growth models developed
in two directions. The first direction was towards a more heterodoxical neoclassical
approach to growth characterised by a specification of a production function containing
other production factors (infrastructure and accessibility) than the mere traditional capital and labour resources, as was put forward by the micro-territorial and behavioural
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A forecasting territorial model of regional growth
Table 1 Theoretical approaches of econometric regional growth models
Theoretical
approaches
of the models:
distinctive
features
Keynesianapproach
Traditional
exogenous
neoclassical
approach
Traditional
endogenous
neoclassical
approach
Heterodoxical
neoclassical
approach
Territorial
approach
Period of
development
1960s and
1970s
1960s and
1970s
1980s and
1990s
1980s and
1990s
1990s and
2000s
Definition of
growth
Increase in
Increase in
Increase in
Increase in
competitivecompetitiveproductivity
productivity
ness
and individual ness
and individual
welfare
welfare
Non-traditional Endogenous
Factor
Endogenous
Demand
territorial
factor
mechanisms
(consumption, endowment
elements
endowment
and productivity of increasing
investments,
(infrafactor
public expenstructure
productivity
diture)
innovation,
accessibility)
MicroInter-regional
Macroeconomic Growth
Export-base
potential
territorial
factor
endogenous
cumulativetheories
endogenous
theories
growth
causation
growth
theories
theories
theories
Growth
determinants
Theories
Increase in
income and
employment
Characteristics National
of econometric account
techniques
system and
input–output
equations
Production
function
estimates
σ -and β-conver- A-spatial quasi- Spatial and tergence
production
ritorial quasi
functions
production
functions
theories of the 1970s and 1980s (Biehl 1986). The second direction witnesses the
attempt to focus on endogenous growth elements, and to estimate regional growth
disparities, as the result of the success obtained by the neoclassical (macroeconomic)
endogenous growth theory of the 1990s (Barro and Sala-i-Martin 1995).
All these specifications seem inappropriate given the new theoretical bases for
regional economic growth and the need to include them in the model specification.
In particular in all these specifications territorial, spatial and non-material factors at
the basis of the most recent theories, both a macro-economic and a micro-territorial
and behavioural nature, are not included. Regional growth is in most recent theories
(territorial endogenous growth theories) the result of:
– a competitive process, based on supply rather than demand elements, such as quality
(and quantity) of local resources, product and process innovation, technological
advances, local knowledge. Purely demand-driven growth models are therefore
inconvenient;
– a socio-relational process, since it is based not only on material production
factors but also on non-material resources endogenously developed thanks to
multiple relations happening inside the local context. Relational elements (like
social capital à la Putman, relational capital à la Camagni, trust à la Becattini,
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R. Capello
leadership à la Stimson and Stough4 ) give rise to local cumulative processes of
knowledge creation, to processes of collective and interactive learning, reinforcing
decision-making processes of local actors.5 These elements have an active and vital role in defining local economic competitiveness and growth. Traditional local
growth models based merely on resource endowment have a limited interpretative
power in this respect;
– a territorial and spatial process, interpreting territory as an autonomous production
factor, rather than the mere geographical place where development occurs; territory
generates increasing returns, cumulative self-reinforcing mechanisms of growth in
the form of dynamic agglomeration economies. Local economic growth is also the
result of interregional interaction processes, rather than the result of inter-regional
resource allocation decisions or of an increase in resources endowment. A-spatial
local growth models are for this reason inappropriate;
– an interactive process of the local economy within the wider national and international economic system. Pure bottom-up models by and large overlook nationalregional linkages, and be avoided;
– an endogenous process, whose explosive or implosive trajectory are determined
by the way the entire local production system reacts to external stimuli and is
able to take advantage of short and long-term trends in the national and global
economy.
The specification of our model attempts to take the above-mentioned theoretical elements into account in a stylized quantitative way. It has to be first of all a territorial
model, where spatial linkages among regions (like proximity and spillover effects)
and the territorial structure of regions (urbanised, agglomerated, rural) find a role
in explaining local growth. At the same time, it has to be a relational and sectoral
model, where the sectoral and relational elements find a place in explaining growth,
but also, more traditionally, a competitive model, in which the dynamics of the local economy are explained by supply elements like quality and quantity of resource
endowment.
Lastly, it is a macroeconomic model, where aggregate macro-economic components
have to find their role (sometimes overlooked in purely regional approaches). Macroeconomic variables in fact play an enormous role in boosting national (and therefore
regional) growth: let us think only to currency de-valuations, movements in exchange
rates, fiscal and monetary policies both at the national and community (i.e., European
Union) levels. Their effects on regional growth follow mainly a demand-driven logical chain that has to be accommodated side-by-side with supply-driven processes if
the model is to fully interpret regional growth patterns. The MASST model therefore
encompasses all these factors and logics even though a full closure of the macroeconomic interrelationships of national accounts is not possible at this stage; most
macro-economic variables concerning state budgets or balance of payments disequilibria remain exogenous.
4 See on this issue Becattini (1990), Camagni (1991), Putnam (1993), Stimson et al. (2005).
5 See on these concepts Lundvall (1992); Keeble and Wilkinson (1999); Camagni and Capello (2002).
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A forecasting territorial model of regional growth
Macroeconomic variables do not cover the full spectrum of possible normative
interventions. At both the European and national level other policy interventions exist:
transport infrastructure policy, energy policy, European cohesion policies, European
institutional decisions are all elements that play a significant role in shaping the future
of regions. Given their crucial role, MASST takes them into consideration.
The next part of the paper is devoted to a presentation of the structure of MASST.
3 The structure of MASST
3.1 The general structure of the econometric model
The MASST model reflects the modern conceptualization of regional growth. The
model specification, in fact, defines regional growth as a competitive, bottom-up,
endogenous and cumulative process. The endowment of local material resources, such
as labour and infrastructure, and of non-material resources like the quality of human
capital and the presence of value added functions, are all elements that in the MASST
model explain the capacity of a region to grow at a rate above (or below) the national
average. Regional competitiveness is therefore closely linked with the presence of
endogenous resources and with the region’s ability to exploit its potentialities.
In MASST the link between the national factors and regional ones concerning
growth is assured by the structure of the model, which interprets regional growth as
the result of a national growth component and a regional differential growth component:
Yr = Y N + s; r ∈ N
(1)
where Yr and Y N denote the GDP growth rate respectively of the region and the
nation, and s represents the regional differential growth with respect to the nation.
Figure 1 presents the logic of the model, in which it is clear that the econometric
model consists of two intertwined blocks of equations, a national block and a regional
one, giving to both the regional and national component a role on local economic
trajectories.
National growth depends on the dynamics of the macroeconomic national elements: private consumption growth, private investment growth, public expenditure
growth and export and import growth. This part of the model is able to capture macroeconomic (national) effects on regional growth generated by interest rates and public
expenditure policies, trends in inflation rates and wages. These policies and trends
differ radically among European Countries (especially between Eastern and Western
members).
In its turn, the regional differential component (the shift component, i.e., the relative regional growth) is dependent on the competitiveness of the local system, based
on the efficiency of local resources: the increase in the quality and quantity of
production factors (like human capital and population), infrastructure endowment,
energy resources, as well as the sectoral and territorial structure of the regions and
the interregional spatial linkages are the main elements that make regions grow more
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R. Capello
Submodel 1: National component
Macroeconomic elements
∆ in national
GDP
∆ efficiency
wage
(inflation and
productivity
increases)
∆ internal
consumption
∆
investments
(including
FDI)
Submodel 2: Regional differential component
National
component
National
growth
- attractiveness
- economic
success
∆ imports
Inflation
Effective
exchange rate
Regional differential
component
∆ exports
Regional
structure
Structural and
sectoral
features:
productive
structure
Infrastructure
endowment
Local
material
inputs and
resources:
human capital
energy
resource
Structural policies
Human capital
policies
Infrastructural
policies
Technological
development
policies
Structural funds
and CAP reforms
Differential shift
∆ public
∆ interest
expenditure
rates
Stock of FDI
Final economic
effect
Macroeconomic
policies
Regional growth
- attractiveness
- economic
success
Spatial and
territorial
structure:
- Spatial
spillovers
-geographical
dummies
- territorial
dummies
Population
growth
Birth rate
Mortality rate
Migration flows
Institutional
elements:
- economic
integration
potentials
- changes in
economic
integration
potentials
Migration Flows
Regional
differential GDP
Regional disparities
Endogenous variables
Unemployment
rate
Exogenous variables
Settlement
structure
Fig. 1 MASST model specification
than the nation they belong to. In this respect, MASST differs substantially from
the regional growth econometric models that exist in the literature, in which a direct
interpretation of absolute regional growth is presented, either replicating national macroeconomic models, or through complex systems of equations for each region that are
linked to both the national aggregate economy and to the other regional economies
through input output technical coefficients that determine intra and inter regional trade
and output.6
6 For examples of replicas of national aggregate models at regional level, see Cappellin (1975, 1976); for
regional growth models based on input–output relationships, see Trey et al. (1992), Guzzi et al. (1999).
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A forecasting territorial model of regional growth
3.2 The national sub-model
The first sub-model is a macroeconomic model applied to each of the 27 European
countries in our sample (the 25 EU countries plus Bulgaria and Romania), very much
similar to the standard macroeconometric models used by national governments and
central banks as programming and policy support tools. MASST differs from these
macroeconomic models in that in MASST only goods and service markets are specified, while the monetary market, the labour market, the public sector budget have no
endogenous treatment. The national sub-model of MASST is therefore a partial equilibrium model, in which prices, wages, interest and exchange rates, public spending
are taken as exogenous variables. If these characteristics of MASST can be interpreted
as a limit, on the other hand they enable the explanation in a fairly simplified way of
real growth as a function of policy tools (interest rates, exchange rate, government
expenditure) or policy targets (inflation, unemployment), influenced by national or
international macroeconomic trends.
The specification of the national sub-model consists of five equations. The first equation specifies the private consumption growth rate (Cnt ) in a traditional
Keynesian approach, depending on the growth of income Ynt−1 in a positive way:
Cnt = a + cYnt−1
(2)
where a is the part of the consumption growth which is independent from income
growth, while c is the marginal propensity to consume, measuring the increase in
consumption growth due to an increase in income growth (temporally lagged).
The private investment growth (Int ) equation has also a traditional structure,
making the investment growth rate be dependent on the growth of output (Ynt−1 )
(as accelerator theory suggests), positively, on interest rates (i nt−1 ), negatively, on a
measure of the country competitiveness (in this case, unit labour costs growth rate
(U LCnt−1 ), the inverse of productivity growth, and therefore negatively linked to
the investment growth rate) and on the share of FDI on domestic investments made
in the country, given the domino effect that a flow of FDI may generate on domestic
investment growth:
Int = Ynt−1 − i nt−1 − U LCnt−1 + F D Int−1
(3)
The import growth (Mnt ) equation is made dependent on changes in domestic
demand (Ynt−1 ), positively, on the nominal exchange rate (E nt−1 ) directly; on the
internal inflation rate (nt−1 ), and on the share of FDI flows on domestic investments made in the country, linked positively to import growth because of the domino
effect that FDI may generate not only on internal investment growth but also on import
growth:
Mnt = Ynt−1 − E nt−1 + nt−1 + F D Int−1
(4)
Lastly, the export growth (X nt ) equation is expected to depend on changes in unit
labour cost (U LCnt−1 ) and on the nominal exchange rate (E nt−1 ); for both of these
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R. Capello
variables, the relationship is expected to be negative. The export growth equation also
depends on the changes in world demand which are not explicitly mentioned as a
control variable, but are captured in the constant term of the equation:7
X nt = U LCnt−1 + E nt−1
(5)
The government expenditure growth rate is an exogenous independent variable of the
model.
The national growth rate is determined by a “pseudo” identity equation derived
from the national accounts identity: aggregate income plus imports (Y+M) must equal
the sum of consumption, investments, public expenditures and exports, (C+I+G+X).
By applying the total differential formula to the identity and by doing some simple
algebraic manipulations we obtain:
Y =C+I +G+X −M
∂Y
∂Y
∂Y
∂Y
∂Y
C +
I +
G +
X −
M
Y =
∂C
∂I
∂G
∂X
∂M
Y
∂Y C C
∂Y I I
∂Y G G
∂Y X X
∂Y M M
=
+
+
+
−
Y
∂C Y C
∂I Y I
∂G Y G
∂X Y X
∂M Y M
C
I
G
X
M
Y
= ηY C
+ ηY I
+ ηY G
+ ηY X
− ηY M
Y
C
I
G
X
M
(6)
Equation (6) states that the income growth rate is equal to the weighted sum of the
aggregate demand components and the weights are the elasticities of income with
respect to each component, (ηY j , j = C, I, G, X, M).
3.3 The regional sub-model
The regional part of the MASST model aims to explain relative regional growth with
respect to national growth, and represents the novelty compared to traditional regional
econometric models of the 1970s and 1980s.
In the logic of MASST, the higher/lower relative capacity of a region to grow
depends on its structural elements: its productive structure, its relative position, its
accessibility, its settlement structure, its degree of economic and social integration—
all elements that identify a particular economic trajectory of a local economy which
might differ from the national one.
The regional sub-model is identified by three blocks of equations. The first equation
is the regional shift equation represented as a quasi-production function in a reduced
form function of factors that are thought to determine regional production capacity.
7 The nominal effective series measure changes in the value of a currency against a trade-weighted basket
of currencies. A rise in the index means a strengthening of the currency. A rise in the index means a loss of
competitiveness.
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A forecasting territorial model of regional growth
These factors, stemming from the modern theories of regional growth, without denying
the importance of traditional growth theories are the following:8
– local material inputs and resources, like infrastructure endowment, share of selfemployees, external resources like CAP (Community Agricultural Policy) funds,
share of tertiary activity;
– structural and sectoral resources: quantity and quality of human capital, availability of energy resources;
– the institutional elements, like economic integration processes which provide a
larger market potential for regions;
– the spatial and territorial structure, the former captured through the relative geographical position, which emphasises growth opportunities of a region dependent
on its neighbouring regions’ dynamics (spatial spillovers of growth). The latter
captured through the settlement structure of region, a good proxy to capture the role
of agglomeration and urbanisation economies on regional performance, enabling
parameters of the different explicative variables to vary across different settlement structures present in space, again emphasising the strategic elements, like
agglomeration economies.
The differential shift equation is therefore:
sr = f (local material inputs and resources; structural and sectoral characteristics;
institutional elements, spatial and territorial structure)
(7)
Not all the explicative variables are exogenous in the model; three of them are endogenous and allow for cumulative processes, namely:
– self-employment is in part dependent on structural funds expenditures, as the
creation of new firms is viewed as one of the most productive effects of structural funds expenditures (SF):
selfemployeesr t = λ0 + λ1 SFr t
(8)
– demographic changes (population growth rate Pr t ) are dependent on birth (fr)
and death rates (mr) and on in-migration (im):
Pr t = λ0 + λ1 frr t−1 + λ2 mrt−1 + λ3 imr t−1
(9)
– that part of regional growth dependent on the other regions’ dynamics (spatial
spillovers) is dependent on the regional growth of neighbouring regions in the
8 Recent new textbooks have been published which provide a new approach to regional growth, namely,
Capello (2007), Pyke et al. (2006).
123
R. Capello
previous year:9
SPr t =
n
Y jt
j=1
dr j
(10)
Y jt = income growth
j = all neighbouring regions of region r
dr j = physical distance between region r and j
n = number of neighbouring regions
– a removal of institutional barrier is a dependent variable, since it is obtained as
the difference between the indicator of the growth differential with neighbouring
regions and the same indicator calculated by squaring the distance for those regions
at the border between Eastern and Western Countries, as follows:
n n Y jt − Yr t Y jt − Yr t −
; r = j
IPr t =
dr j
dr2j
j=1
(11)
j=1
where all symbols have already been defined. This indicator was built for border
regions between the new and old member countries up to 2007 and for border regions
between member countries and Bulgaria and Romania after 2007. In fact, it was built
with the aim to measure the effects of a barrier reduction on the regional GDP growth
rate; in particular, it was used to measure the effects of the integration of Bulgaria and
Romania in 2007, after the entrance of the two countries in the EU.
In its turn, in-migration is made dependent on regional income differentials (wet−1 −
wr t−1 ), unemployment rate (u), and on the different settlement structures of regions:
imr t = η0 + η1 u r t−1 + η2 (wet−1 − wr t−1 )
(12)
where:
we = European average wage
wr = regional average wage
9 An indicator weighting each regional growth rate for the share of each regional economy (GDP) on
the European total GDP was calculated in addition to the non-weighted one. A high statistical correlation
emerged between the two, (Pearson’s correlation coefficient = 0.93). Moreover, the difference between the
two standardised indices showed a low spatial autocorrelation, with Moran’s I index of 0.30. By removing a
few outliers (mainly a few Nordic and Spanish regions), the resulting Moran’s I index equaled 0.18. On the
basis of this correlation, the decision to use the non-weighted spillover indicator was made given its higher
similarity with the classic spatially-lagged models of spatial econometrics. This indicator is an economic
potential measure, which is generally calculated as the accessibility to total income at any location allowing
for distance, following Clark et al. (1969). Here the concept is attributed to accessibility to the income
growth rate.
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A forecasting territorial model of regional growth
Table 2 Logic of the simulation procedure
Forecasts
Year t
Year t+1 (and following)
Estimated national
growth
(At) Calculation of actual
national growth with the
national sub-model
(output of MASST in time t)
(At+1) Calculation of actual
national growth with the national
model, as a function of lagged
potential growth
(output of MASST in t+1)
(Bt ) Calculation of regional
differential shift with the regional
sub-model
(Bt+1) Calculation of regional
differential shift with the regional
model
(Ct) Actual regional growth is
calculated as the sum of A and
B, where B is rescaled to have 0
mean within each country
(output of MASST in time t)
(Ct+1) Regional growth is
calculated as the sum of A and B,
where B is rescaled to have 0 mean
within each country
(output of MASST in t+1)
(Dt) Potential regional growth is
equal to the sum of A and B
(non rescaled).
Potential national growth is
equal to the increase in the sum
of potential regional income
levels in Dt
(Dt+1) Potential regional growth is
equal to the sum of A and B (non
rescaled).
Potential national growth is equal
to the increase in the sum of
potential regional income levels in
Dt+1
Estimated regional
growth
The last year available by official statistics at the beginning of the estimations was 2002
3.4 The simulation algorithm
The way in which the recursive mechanism works over time in a forecasting model is
of great importance for the full understanding of the logic hidden behind the simulation
procedure.
In the case of MASST model, the simulation algorithm has a particular role, that
of creating a “generative” process of regional growth. In other words, our intention
was to create a model in which regional dynamics play an active role in explaining
national growth, and do not derive only from distributive mechanisms of allocation of
national growth.
A conceptual difference between ex-post and ex-ante national growth can be useful,
and finds an operational treatment in MASST. Ex-post national growth rates can be
nothing else than the weighted sum of regional growth rates. If an ex-post, competitive approach to growth is chosen, the regional blocks of equations play only the role
of distributing national growth among the regions of the country. On the contrary, if
an ex-ante, generative, approach is chosen, the possibility exists that national growth
could be obtained thanks to the performance of the single regions; in this case, regional
growth plays an active role in defining national growth.
Our conceptual and operational approach follows the second definition: in MASST,
the regional sub-model explains in part the national performance. Operationally,
MASST treats ex-ante and ex-post growth rates as follows:
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R. Capello
National shocks
Nation
I
II
Region i
II
III
IV
Region j
III
Regional shocks
Fig. 2 National-regional linkages in MASST: the numbers near the arrows refer to Table 3, where the
mechanisms of national-regional linkages are explained
– ex-post national (and regional) growth rates are obtained through the national submodel, and distributed to the regions through the results of the regional differential
submodel, rescaled in order to match the aggregate result (point C in Table 2);
these results are considered as the actual outcome of the model in time t;
– ex-ante regional growth rates are obtained when the regional differential growth is
not rescaled; they are interpreted as “potential” growth rates (point D), from which
potential regional GDP levels is obtained. The sum of the increase of GDP levels
determine the “potential” national GDP growth rate in the following year (point
At+1 ), through its influence on aggregate consumption, investment and imports.
Thanks to this simulation algorithm, MASST can be interpreted as a “generative”
model: ex-ante regional growth rates play an active role in defining national growth.
Ex-post, the national account identity is fulfilled.
4 Technical specifications of the model
Technical specifications of the model emerge by the structure just described. The model
is first of all an interactive national-regional model. It combines top-down and bottom-up approaches, so that an interdependent system of national and regional effects
is built (Fig. 2). Such a structure allows vertical and horizontal feedbacks between
the regional and the national economy to be taken into account. In fact, thanks to its
structure, the MASST model is able to register the effects of a shock at the national
level (a change in macroeconomic trends or a policy choice), on both the national and
regional growth rates; moreover, it is able to interpret the effects of a shock at the
regional level on both national and regional performance.
The model allows for endogenous differentiated regional feedbacks of national
policies and trends; it captures the vertical feedbacks of a national policy on regional
growth, and distributes them differently among regions, according to their capacity
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Table 3 Measurement methods of interactive national-regional linkages
Effects
Shocks
National
Regional
National
Regional
I
II
National effects measured through
dynamic national income growth
present in the estimation procedure
Regional effects measured through
the national component in
regional growth compounded
by regional growth spillovers and
territorial dummies present in the
estimation procedure
IV
III
National effects measured through
the national income growth
obtained as an increase in regional income levels in the
simulation procedure
Regional effects measured through
the presence of regional control
variables and spillovers in the estimation procedure
to capture national growth potentialities (regional growth spillovers, settlement structure). Table 3 presents the way in which these linkages take place. National shocks
are registered on national GDP growth rates through the national GDP growth present
in the consumption and import growth equations. National shocks propagate to the
regional level since regional GDP growth is obtained as the sum of the national GDP
growth and the regional differential GDP growth. The latter is distributed differently
among regions via spillover effects and territorial dummies.
Regional shocks, and regional feedbacks, propagate on regional GDP growth thanks
to the shift equation: regional shocks differ among regions thanks to spillovers, dummy
variables and different levels of the control variables. Regional shocks propagate to
the national level through the sum of the regional GDP levels which defines the annual
national GDP growth. This feedback is the only one which takes place in the simulation
and not in the estimation procedure.
Moreover, the MASST model is an integrated model. In its structure, the model finds
a specific place for both socio-economic and spatial (horizontal) feedbacks among
regional economies. While the former are captured by the socio-economic conditions
generating interregional migration flows, the latter are measured by spatial spillover
effects, the growth rate of a region being also dependent on the growth rate of its
neighbouring regions.
MASST does not confine its explanation of regional growth to economic material
resources alone: two elements of a different nature play an important role in determining regional growth in the model, relational and spatial elements. In MASST,
regional growth is in fact also conceived as a relational and a spatial process: demographic (population growth and migration flows) and territorial tendencies perform
an important role in the explanation of regional growth differentials. In the case of
relational elements one has to admit that the data unavailability hampers the full empirical analysis of this dimension, at present replaced by socio-demographic phenomena,
like migration; it is nevertheless important to stress theoretically its importance and
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suggest that data be collected in this area at regional level in the future. The spatial
and territorial dimensions have a role in the explanation of regional growth in two
ways. First of all, the model directly captures proximity effects through the measurement of spatial spillovers; moreover, with the introduction of variables interpreting
the territorial (agglomerated, urbanised, rural) structure, the model indirectly measures
the agglomeration economy (diseconomy) effects that influence growth (decline) in
a cumulative way. Spillover variables enter the differential growth equation on their
own and crossed with territorial variables, in order to capture spillovers that may occur
between complimentary territorial structures that are not proximal.
Another important feature of the model is that it is an endogenous, local competitiveness driven model in the explanation of regional growth, as we expected it to be.
Regional growth is explained by local factors and interregional competitiveness stems
from specific locational advantages and resource endowment.
MASST is a macroeconomic (multinational) model. Short-term (macroeconomic)
effects are dealt with at the national level, and their feedbacks on national and regional
economies are taken into consideration in explaining local dynamic patterns.
MASST is a dynamic model. The outcome of one period of time at both national
and regional level enters the definition of the output of the following period, in a
cumulative and self-reinforcing development pattern.
As already noted above, MASST is a generative regional growth model, in which
regional performance influences national growth patterns. This feature is what distinguishes the model from the ones present in the literature.
Given the above characteristics, the model is a multi-layer, policy impact assessment model. The structure of the model allows it, in fact, to measure the impact of
national (and supranational) policy instruments on both regional and national growth,
and the impact of regional policies on national and regional growth.
5 The national and regional datasets
5.1 Description of data
No major problems exist in the collection of national level data. The national data
used in our model come from the Eurostat database, which presents a rich collection of
national data including macroeconomic variables, in time series for all European countries. Table 4 presents the indicators built for our model, available annually between
1995 and 2002 for the 27 countries. The series do not go further back in time because
data for Eastern countries are in fact available in a consistent way only since 1995.
A set of dummy variables identifying new member countries has easily been built and
added to the dataset.
Most of the national data come from the Eurostat database—as most economic data
included in the Espon database—and taken at ESA95.
At the regional level, the availability of data is very different. As is often the case,
data availability represents one of the main constraints for regional econometric model
applications. Our case is not an exception in this respect. The main novelty concerning data have been the existence of the ESPON database, containing interesting and
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Table 4 Variables used by the MASST at national level
National variables
(NUTS0 level)
Defintions
Period covered
Source of
raw data
GDP growth rate
Annual % growth rate of real GDP
1995–2002
Eurostat
Annual change in interest rate
Absolute change in short-term interest rates (3 months)
Absolute change in unit labour cost
(calculated as unit salary × number of employees / GDP)
Flow of FDI / gross fixed capital
formation
Nominal effective exchange rate
calculated on 41 countries
(NEER41)
Inflation rate (% change of CPI)
1995–2002
Eurostat
1995–2002
Eurostat
1995–2002
OECD
1995–2002
Eurostat
Annual change in unit labour cost
Share of FDI on total internal
investments
Exchange rate
Inflation rate
Consumption growth
1995–2002
Eurostat
1995–2002
Eurostat
1995–2002
Eurostat
1995–2002
Eurostat
Import growth
% annual real consumption growth
rate
% annual real gross fixed capital formation growth rate
% annual real import growth
Eastern countries
All former Eastern Economies
Dummy
New EU countries
The 10 new Member Countries who
joined the EU on the 1/5/04
Dummy
Investment growth
unusual data at NUTS2 and NUTS3 regions, collected by research partners within the
different ESPON projects.
The originality of our database consists in: (a) specific and so far unavailable territorial and socio-economic data; (b) specific spatial effects indicators, built in order
to capture proximity effects, in keeping with the large and accepted literature on this
issue;10 (c) a merged Eurostat and ESPON economic data base, which enabled filling
gaps and checking for data consistency.
(a) Specific and so far unavailable territorial and socio-economic data
The new and original territorial variables are (Table 5):
– a typology of regions according to their settlement structure. Regions are in fact
divided into agglomerated, urban and rural regions, on the basis of the type of
urban system (dimension and density of cities) present in the region (Fig. 3);
– a typology of best performing regions, defined MEGAs (Metropolitan European
Growth Areas), selected on the basis of five functional specialisation and performance indicators: population, accessibility, manufacturing specialisation, degree
of knowledge and distribution of headquarters of top European firms. All of these
variables were collected at the FUA (Functional Urban Area) level and combined
10 See among others Cheshire (1995), Cheshire and Carbonaro (1996), and the wide literature on spatial
econometrics. On the latter, see, among others, Anselin (1988), Anselin and Florax (1995).
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R. Capello
Table 5 Territorial and social and economic data so far unavailable
Data
Definition
Source of raw data
Agglomerated regions
With a centre of >300,000 inhabitants
and a population density >300 inhabitants / km sq. or a population density
150–300 inhabitants / km sq.
With a centre between 150,000 and
300,000 inhabitants and a population
density 150–300 inhabitants / km sq.
(or a smaller population density—
100–150 inh. /km with a bigger centre (>300.000) or a population density
between 100 and 150 inh./km sq.
With a population density <100/km sq.
and a centre >125,000 inh. or a population density < 100/km sq. with a
centre < 125,000
Regions with the location of at least one
of the 76 “Megas”—FUAs with the
highest score in a combined indicator of transport, population, manufacturing, knowledge, decision-making in
the private sectors
Regions located within the Pentagon
formed by the five European cities of
London, Paris, Milan, Munich, Hamburg
Average net immigration flows of people
between 17 and 27 years in the period
1/1/95–1/1/00 at NUTS 2
Average net immigration flows of people
between 32 and 42 years in the period
1/1/95–1/1/00 at NUTS 2
Average net immigration flows of people
between 52 and 67 years in the period
1/1/95–1/1/00 at NUTS 2
Share of births on population at NUTS 2
in the years 1995–2001. In estimations
used 1999
Share of deaths on population at NUTS 2
in the years 1995–2001. In estimations
used 1999
Share of energy toe (tons oil
equivalent) on 1,000 inhabitants at
NUTS 0 1990–2002. Estimations at
Nuts 2 made as report in notea .
% change in GDP due to 10% change in
energy price
Espon database
Urban regions
Rural regions
Megas regions
Pentagon regions
Net immigration flows (people
between 17 and 27 years)
Net immigration flows (people
between 32 and 42 years)
Net immigration flows (people
between 52 and 67 years)
Regional birth rate
Regional mortality rate
Energy consumption
Energy price elasticityb
Espon database
Espon database
Espon database
Espon database
Espon database
Espon database
Espon database
Espon database
Espon database
Our estimation from
national data of
ESPON 2.1.4
Espon 2.1.4 project
a Regional energy consumption has been estimated by distributing total national consumption to regions
on the basis of a weighted sum of regional km made by car (weight = 0.15), by train (weight = 0.35) and
by plane (weight = 0.5) in 2001 and of the share of population
b The energy price elasticity is an estimated data. The estimation procedure can be found in the final report
of ESPON project 2.1.4 available on the Espon website (http://www.espon.eu), pp. 135–145
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A forecasting territorial model of regional growth
Settlement types
Rural
Urban
Agglomerated
Fig. 3 Settlement structure of European regions
to give an overall ranking of FUAs; the 76 FUAs with the highest average score
have been labelled MEGAs (Fig. 4). 11 MEGA regions are the NUTS2 level administrative areas with at least one of the 76 FUAs located in it;
– a definition of Pentagon regions, indicating the regions located within the Pentagon area delineated by the five European cities of London, Paris, Milan, Munich,
Hamburg.
The socio-economic variables collected by the ESPON projects, which would be
otherwise unavailable at NUTS 2 level, are found in (Table 5):
– total energy consumption, obtained by summing the different sources of energy
consumption (travel, industrial and domestic use), once estimated at national level
through an input–output model, and distributed among regions according to the
weighted sum of regional km traveled by car, by train and by plane in 2001 and to
the share of population;
11 See ESPON project 1.1.1. for technical details, available on the Espon web-site http://www.espon.lu.
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Regions with Megas
Fig. 4 Mega regions in Europe
– energy price elasticity, as percentage change in GDP due to 10% change in energy
price;
– interregional and international migration flows, for different population age;
– birth and death rates;
– structural funds expenses;
– agricultural support funds, divided into Pillar 1 and 2 of CAP.
(b) Specific spatial effects indicators
Specific indicators for spatial effects concern:
– a spatial spillover indicator for a generic region r , capturing an economic potential
(Clark et al. 1969) as the sum of the annual absolute difference between income
growth rates of all other regions j divided by the distance between each region r
and region j, defined in Eq. (10):
– a European integration potential indicator for a generic region r , obtained as
described in Eq. (11).
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A forecasting territorial model of regional growth
Table 6 Traditional economic variables
Indicators
Definition
Source of raw data
Regional share of human resources
in S&T
Share of people working in S&T on
population at NUTS 2 in the year
2000
Average annual population growth
rate at NUTS 2 in the period 1995–
2002
Share of un-employed people, available for the period 1995–2002. In
estimations used 1999
Km of roads/regional surface on km
of roads/national surface in the year
2001
Total funds devoted to Pillar 1 and 2/
number of agricultural working
units in the period 1994–1999
Total structural funds expenditure/
population in the period 1994–
1999
Employment in service/total
regional employment in 2001
Annual average regional GDP
growth rate less national GDP
growth rate in the period 1999–
2002
Absolute difference between
per capita regional GDP level and
per capita GDP at EU level in the
period 1995–2002
Eurostat
Regional average annual population
growth rate
Regional unemployment
Relative regional density of
infrastructure endowment
Expenses per agricultural working
unit (awu)
Per capita structural funds
Regional share of tertiary activity
Regional average annual differential
GDP growth rate
Regional differential per
capita GDP
Eurostat
Eurostat
Espon database
Espon database
Espon database
Espon database
Eurostat
Eurostat
(c) Traditional variables available in both Eurostat and Espon databases
Traditional variables, available in both Eurostat and Espon databases, were useful in
the estimation of the MASST model; the relative indicators are (Table 6):
– share of human resources in S&T;
– regional average annual population growth rate;
– relative regional infrastructure density (km of roads on surface at NUTS 2 with
respect to the national average);
– regional share of tertiary activity;
– regional average annual differential GDP growth rate (the shift component);
– regional differential per capita GDP.
For the above mentioned indicators, the availability is for all 259 NUTS 2 European
regions of the 27 European member countries. Data availability is instead rather poor
once a time series database at the NUTS 2 level is required. Most of the Espon data
is available only for one year (for example energy consumption and migration flows);
for other data, the spatial coverage is unacceptable when extending the database to
time series. As we will see in the next section, our choice has been to have the maximum spatial coverage of the database, by restricting the estimations at NUTS 2 level
to a cross-section database. This choice is justified by: (a) the structural nature of
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Table 7 National estimates
Independent variables
(lagged for 1 year)
Dependent variables
Consumption
growth model
Investment
growth model
Export
growth model
8.45*** (16.8)
Constant
0.90** (2.06)
0.597 (0.66)
Real GDP growth rate
0.529*** (4.84)
1.01*** (4.06)
New EU Countries
1.89** (2.53)
Real GDP growth rate −0.29** (−1.80)
in new EU Countries
Change in interest rates
14.05*** (4.15)
0.56** (2.47)
−0.52*** (−12.31)
−0.43* (−1.87)
Change in unit labour
cost
Share of FDI on total
internal investments
Change in nominal
effective exchange
rate
Inflation rate
Import
growth model
−0.64*** (−3.58)
0.048 (0.76)
0.034 (0.66)
−0.57*** (−6.23)
0.13** (2.47)
0.34*** (2.87)
Type of estimation
OLS common
constant (robust
estimations)
OLS common
constant (robust
estimations)
OLS common
constant (robust
estimations)
OLS common
constant (robust
estimations)
Number of
observations
R-square
183
132
150
178
0.30
0.24
0.23
0.11
Pseudo identity
%Y = 0.739%C + 0.104%I + 0.015%G + 0.266%X − 0.186%M
* Significant at 10%; ** significant at 5%; *** significant at 1%; t student in brackets
the characteristics expressed in the indicators; (b) our main goal to cover most of the
European territory.
6 The econometric methodology
6.1 The estimates of the national sub-model
As shown in the previous section, data on most relevant variables at Nuts2 level
are only available for 1 year, while data were collected at the national level for the
European countries in the sample over the period 1995–2002. Due to the different
characteristics of the two data sets, different techniques were required to estimate the
national and regional models. The estimate procedure and the results obtained for the
national block of equations, shown in Table 7, will be discussed first.
The estimation of the parameters of the equations of the national sub-model were
obtained using a panel of data on 27 European countries for the years 1995–2002. The
actual number of observations varies across equations due to data availability issues
and none of the equations has been estimated on a balanced panel. All specifications
are in fact reduced form equations where only exogenous and predetermined variables
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A forecasting territorial model of regional growth
enter as regressors. In particular, the lagged income variable is considered to be a
predetermined variable that proxies current income growth, so that we are able to
avoid dealing with simultaneity issues that would arise if we used the current income
as a regressor in the consumption, investment and imports equations. After testing
for the presence of individual effects, for the presence of serial correlation within
each individual (country) and for heteroskedasticity, all equations ended up being
estimated by robust OLS that ensure consistent but not necessarily efficient estimates
of the parameters. Comments on each equation follow.
(a) The consumption equation (see Table 7, column 2)
Theory tells us that consumption depends primarily on income. Which among past,
current or expected income is the “right” measure to use in a consumption equation
is both a theoretical and empirical issue, and there has been a considerable amount
written on this topic. In MASST, we choose to specify and estimate the aggregate
growth rate of consumption as a linear function of the lagged aggregate income growth
rate, allowing for different coefficients between the old and the new EU members,12
as well as a dummy that changes the intercept of the equation for the new EU member countries. We estimate the consumption equation by ordinary least squares (OLS)
on a panel of data from 1996 through 2002 for the 27 European countries in the
sample.13
Neither fixed nor random individual effects were found statistically significant, and
this allowed us to use OLS rather than panel specific estimation techniques. We tested
for serial correlation within each country in the OLS residuals and we accepted the
null hypothesis of no serial correlation, which we took as support of taking the lagged
income growth variable as a predetermined variable.
We did not test for spatial correlation in the residuals of the consumption equation
but we decided to estimate robust standard errors of the parameters of the equation
to ensure that we were able to make correct statistical inferences on the parameters
themselves even if the error terms were heteroskedastic or somehow correlated across
observations.14
The estimation process shows that consumption follows significantly different patterns of behaviour between old and new EU member countries. In particular the growth
rate of consumption is on average greater in the new countries, while the marginal rate
of consumption relative to income is larger for the old countries: the latter save less
and they have already reached an almost steady state consumption growth rate.
12 In this equation Bulgaria and Romania are added to the set of the 10 new EU members, despite their
entrance into the EU took place only, January 1, 2007.
13 For some of those countries data are only available from 1998.
14 See the “robust” option in the STATA command “regress”. Such an option produces the so called “sand-
wich coefficient covariance matrix” which is a consistent estimate of the coefficient covariance matrix even
when there exists heteroskedasticity or correlation among residuals.
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R. Capello
(b) The investment equation (see Table 7, column 3)
From a standard keynesian approach, aggregate investment depends positively on
aggregate demand, i.e., income, (both current and expected), and negatively on interest rates, a proxy for cost of capital. The literature on investment equations is at least
as wide as the literature on consumption,15 but once again in MASST a simple specification was chosen: aggregate investment growth is a traditional linear function of the
lagged income growth and of the nominal (three months) interest rate, but also of the
lagged unit labor costs growth and of the amount of FDI’s received by Eastern European countries. The latter variable is not significant, but it has the expected positive
sign: foreign direct investments push domestic investments.
On the other hand, the negative and significant coefficient of the unit labour cost
variable shows that as labour costs grow investments decline, which means that labour
and capital are complementary rather than substitutes. These estimations capture the
fact that the countries where labour costs are low and slow-rising are the countries
where investments are growing at a faster rate in the years 1996–2002. The coefficients
attached to lagged income and interest rates are highly significant and of the expected
sign. Interest rates are lagged one year and are considered a predetermined variable; they will be treated as an economic policy instrument when devising simulation
scenarios.
The same tests were performed on the estimated residuals of the investment
equation as on the consumption equation, with the same results. In particular no individual effects were found significant and robust pooled OLS were used to estimate
the coefficients and their standard errors.
(c) The exports equation (see Table 7, column 4)
In standard Keynesian demand driven macro models, exports are taken as exogenous,
that is they are only determined by the demand of the rest of the world. We prefer to
model exports as a function of supply as well as of demand factors, in particular as
a function of internal competitiveness as measured by both unit labour costs and the
exchange rate. A decrease in competitiveness (i.e., an increase in unit labour costs)
will slow down exports and so will an increase in the nominal effective exchange
rates. The constant in the equation of aggregate export growth may be interpreted as
the effect of the average demand growth of the rest of the world.
The estimation was executed using robust pooled OLS on a sample that excluded
some outlier observations, mainly relative to Romania before 1999, where and when
inflation rose to three digit values.
(d) The imports equation (see Table 7, column 5)
Aggregate import growth depends positively and significantly on lagged income
growth, on inflation, and on changes in the exchange rate: imports increase if internal
15 See Jorgenson (1971), and more recently Galeotti (1996), for a review of the literature on investment
equations.
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A forecasting territorial model of regional growth
demand grows, if domestic prices increase (and domestic goods are substituted for by
imported goods), and if the nominal effective exchange rate increases.16 There is modest evidence that FDI’s finance imports in Eastern European countries (the coefficient
attached to FDI is positive but not significant).
In MASST inflation and exchange rates are not endogenously modelled and are
actually taken as exogenous variables under the assumption that they are economic
policy instruments controlled by national or supranational authorities.
The parameters of this equation were estimated using robust pooled OLS.
(e) The “pseudo” identity (see Table 7, last raw)
The last equation in the national model is the national accounts identity expressed
in the growth rates of both GDP and aggregate demand components. The estimated
coefficients measure the average elasticity of GDP to each aggregate demand component, over all countries in the sample for years 1996 through 2002. As expected the
elasticity is close to 80% for consumption, while it is approximately 18% though with
opposite signs, for exports and imports. The elasticity of GDP to public expenditure is
very small: this is a result that probably depends on the tight fiscal policies that most
of the countries in the sample followed during the time period when the data were
collected, so that public expenditure contributed little to the economic growth of each
country.
6.2 Regional estimates
The approach followed to estimate the parameters in the regional block of equations
was mostly determined by data availability problems. Some of the variables needed
to estimate the differential regional growth equation and the population and migration
equations are available for almost all 259 regions in our sample for years 1995 through
2002. However, some other relevant variables, such as human capital or accessibility
and infrastructure measured through the kilometers of available roads in regions, are
available only for 1 year, 2000 in most cases. Lastly, some territorial variables are
constant through time because of their nature (Table 8). Thus, it was not possible to
use panel techniques. We chose to estimate all equations in the regional block in one
cros-section, on 259 regions in one year. As it will be clearer later in the paper, the
information along the time dimension, whenever available, was not omitted, but it
has been used to solve some specification and strictly econometric problems relating
to the possible correlation between some of the regressors and the error term of the
equations and to the likely presence of spatial correlation in the estimated residuals.17
16 Nominal effective exchange rates measure changes in the value of a currency against a trade-weighted
basket of currencies. A rise in the index means a strengthening of the currency, hence a loss in competitiveness. The index is calculated as a weighted geometric average of the bilateral exchange rates against the
currencies of 41 competing countries. The weights use information on both exports and imports throughout.
The import weights are the simple shares of each partner country in total euro-zone imports from partner
countries. Exports are double-weighted in order to account for the so-called “third market effects”.
17 See Anselin (1988).
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Table 8 List of variables in the regional differential shift equation
Classification
Type
Definition
Share of human resources in S&T
Predetermined
% of people working in S&T on
population at NUTS 2 in the year
2000
Average population growth rate
(1995–2002)
Predetermined
Average annual population growth
rate at NUTS 2 in the period 1995–
2002
Energy consumption by
population in 2002
Predetermined
Total energy consumption on
population at NUTS 2 in the year
2002
Regional economic resources
Regional structural and sectoral characteristics
Relative density of infrastructure
endowment in 2001
Predetermined (intermediate policy target)
Km of roads on surface at NUTS 2
on km of roads on surface at NUTS
0 in the year 2001
Share of self-employment
Predetermined
Share of self-employment on total
employment
Share of tertiary activity in 2001
Predetermined (intermediate policy target)
Employment in services in 2001 in
percentage of the total at NUTS 2
Territorial specificities
Dummy variables
Rural, urban, agglomerated, megas
Pillar 2 expenses of CAP
Policy instrument
Total funds devoted to Pillar 2 on
agricultural working units (awu)
Structural funds expenditures
Policy instrument
Total structural funds expenditures
in the period 1994–1999 on population
Predetermined
Sum of the relative annual regional
growth rates of all regions j other
than region r divided by the
distance between each other
region and region r (see Eq. 10)
Predetermined
A European integration potential
indicator for a generic region r ,
obtained as the difference between
the indicator of growth differential with neighbouring regions
described above and the same
indicator calculated by squaring
distance for those regions at the
border between Eastern and Western Countries (see Eq. 11)
Spatial processes
Spatial spillovers (1997–1998)
European integration process
Regional integration potentials
(1998–1999)
All equations have been tested for spatial dependence using the spatial regression
and testing modules in STATA,18 and a distance matrix consisting of the distances in
kilometres between all couples of regions in the sample.
18 See Pisati (2001), pp. 277–298.
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A forecasting territorial model of regional growth
Table 9 Estimates of the regional differential shift
Independent variables
Dependent variable : regional average
annual differential GDP growth rate
1999–2002
Constant
−5.25 (−3.93)
Economic resources
Regional share of human resources in S&T in urban areas
in 2001
Regional share of human resources in S&T in Eastern
Countries in 2001
Regional average population growth rate (1995–2002)
−0.012 (−0.5)
Regional energy consumption by population in 2002
0.0067 (2.48)**
Regional energy consumption by population in tertiary
regions in 2002
Structural and sectoral characteristics
0.075 (4.51)***
0.646 (3.29)***
−0.000111 (−4.1)***
Relative regional density of infrastructure endowment in 2001
−0.13 (−0.90)
Relative regional density of infrastructure endowment
in mega areas in 2001
Regional share of self-employees
0.12 (0.8)
Regional share of tertiary activity in 2001
0.058 (4.40)***
0.045 (2.75)***
Dummy for mega regions
0.52 (2.24)**
Dummy for rural regions
−0.56 (−1.37)
Pillar 2 expenses per agricultural working unit (awu)
0.03 (3.18)***
Spatial processes
Spatial spillovers
(1997–1998)
Spatial spillovers in the agglomerated regions (1997–1998)
91.23 (1.72)*
Spatial spillovers in urban areas in Eastern Countries
(1997–1998)
European integration process:
−98.69 (−2.27)**
−88.37 (−2.42)**
Regional integration potentials in Western Countries
(1998–1999)
Regional integration potentials in Eastern Countries
(1998–1999)
Number of observations
14.2 (0.47)
R-square
0.30
−29.02 (−0.65)
227
Spatial error test: robust Lagrange multiplier (P value)
0.168 (0.68)
Spatial lag test: robust Lagrange multiplier (P value)
0.36 (0.54)
* Significant at 10%; ** significant at 5%; *** significant at 1%
In only one equation, the young workers net immigration equation (see later on),
were the residuals characterized by spatial dependence, and the appropriate maximum
likelihood estimation technique needed to be used. For all other equations robust OLS
estimates were performed.
6.2.1 The regional growth differential equation (see Table 9)
As explained in the overall description of MASST, this equation is a quasi production
function where potential regional output is determined by factors such as economic
123
R. Capello
and human resources, structural and sectoral characteristics, spatial processes, integration processes and territorial specificities. More precisely, the dependent variable
in this equation is regional “shift” sr , presented in Eq. (1).
In Table 7 all the relevant explanatory variables are presented and classified. Table 8
presents the estimated specification, discussed in more detail in the following points:
– regional GDP at constant prices is available for years 1995 through 2002 for most
regions, and GDP growth rates and regional shift (sr ) from 1996 on. Yearly growth
rates measure, by definition, only short term fluctuations, while our intention is in
fact to explain the structural part of regional growth, due to structural elements,
like human capital, infrastructure endowment, population growth, the settlement
structures and agglomeration economies. Therefore, the choice of the average
regional-national differential GDP growth between 1999 and 2002 was chosen as
the dependent variable, so as to smooth out any abnormal short-term fluctuations
in regional income;
– while dealing with production functions, we must acknowledge that output and
production factors are actually jointly determined: output is a function of production factors, but the latter are demanded by firms as a function of (planned)
output (as well as of factor prices). In econometric terms, production factors used
as regressors in a production function will be correlated with the error term of
the equation, and induce inconsistent parameter estimates. For this reason, in the
regional shift equation the lagged in time proxies of the production factors were
introduced whenever possible. Labour growth rate, for instance, is proxied by the
average population growth rate between 1995 and 1998; this regressor may be
defined as a predetermined variable and will not be correlated with the error term
in the equation, that we assume to be serially independent;19
– unfortunately, for other production factors and sectoral characteristics (share of
tertiary employment, for example) only data on year 2000 are available (one of the
years used to compute the dependent variable). In this case, the assumption is made
that their volume, although measured for year 2000, was actually determined by
previous years’ incomes and activity levels. Thus, also these variables are treated
as predetermined variables and are assumed to be uncorrelated with the error term
in the equation;
– the growth spillover and integration potential variables are computed for each
region/observation as weighted averages of the income growth rates of the other
regions in the sample, using as weights the distances between each couple of
regions (Formulas 10 and 11). Spatial econometrics proves that regional growth
rates are jointly determined and these spatially lagged regressors will be contemporaneously correlated with the error term. To avoid this simultaneity problem,
and given the availability of data on regional income for years before 1999, the
growth spillover and potential integration variables were computed on lagged in
time income growths. In econometrics terms, this operation allows the use of OLS
to estimate the parameters of this equation, once again relying on the property of
19 It is not possible to test for serial correlation within each region, given that, with the available data, we
can only estimate one cros-section in time.
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A forecasting territorial model of regional growth
consistency of OLS estimators that holds when regressors and error term are not
contemporaneously correlated and error terms are not serially correlated. From an
economist’s perspective, using lagged in time spillover and integration potential
variables as regressors introduces a dynamic component into the specification that
may yield useful information on the speed of adjustment of each region’s growth to
neighbouring regions’ growth, and on how such speed may be affected by territorial
characteristics. In fact these lagged in space and lagged in time spillover variables
enter the differential growth equation on their own and crossed with territorial variables (see Table 9). Both these elements, the introduction of time dynamics into
the specification and the possibility to estimate the effects of spillovers crossed
with territorial variables, were the reason we chose to compute our own spillover
and integration potential variables instead of using the available spatial regression
packages that automatically compute the spatially lagged variable and estimate by
maximum likelihood the spatial lag model in one point in time;20
– tests for spatial dependence were run and the null hypothesis of no spatial correlation in the error terms was not rejected. The model was estimated with robust
OLS.
Table 9 shows the estimation results. R 2 of the equation is 0.3, not large indeed in
absolute terms, but more than acceptable given the growth rate specification of the
explanandum: it is worth remembering that the dependent variable in this equation is
a difference in growth rates, almost a random variable itself. The coefficients of most
of the relevant variables are however statistically significant.
From the economic point of view, the model suggests that regional competitiveness finds its roots in the presence of: (a) structural and sectoral features; (b), local
economic resources; (c) spatial and territorial structure and (d) institutional elements
present in the “border regions” effect on the integration potential.
(a) Structural and sectoral features
The relative infrastructure endowment coefficient has a negative and non-significant
sign. The conclusion of this result is that in general better infrastructure endowment
with respect to the national average does not necessarily result in a greater competitiveness of the local economy. Interestingly enough, results change in terms of sign
when infrastructure endowment is related to the mega regions. In these regions a rich
infrastructure endowment helps to keep the decreasing returns and the inefficiency of
a highly congested infrastructure context under control.
In terms of sectoral structure, the model proves that the share of tertiary activity
in a region explains its differential growth; the coefficient of this variable shows a
positive and significant sign. This result supports the empirical evidence that higher
competitive gains stem from tertiary rather than industrial activities.
Another interesting result is the role played by agricultural support funds, and in
particular by Pillar 2 expenses in agriculture, i.e., those directly supporting production
and productivity, while, as easily explainable, Pillar 1 agricultural expenses, devoted
20 See fore example the module “spatreg” in STATA, with the spatial lag option.
123
R. Capello
to the support of farmer income (rather than production), proved to have no impact on
regional growth of GDP.
(b) Local economic resources
The presence of a high quality of human capital proved once again to be an important
element explaining local competitiveness in areas where the initial level of qualified human capital is low, like in Eastern Countries; the non-rival and partially non-excludable
nature of a part of the outcome of knowledge creation, as theorised in Romer’s endogenous growth model (Romer 1986), induces more knowledge generation, and creates
dynamic increasing returns in the form of a faster growth rate. Our results prove however that the knowledge spillover effects are accompanied by decreasing returns, in
that Eastern Country regions, characterised by a lower level of knowledge, experience
greater relative positive effects from knowledge spillovers than highly and technologically advanced urban regions.
The results witness also that human resource growth, measured in terms of population growth, plays an important role in explaining local competitiveness. By the
same token, energy resource consumption has a positive impact on local growth. Interestingly enough, in tertiary activity regions the energy consumption coefficient has a
lower value, witnessing a more limited (though positive) effect of energy consumption on growth, due both to the specialisation patterns and increasing returns in energy
consumption characterising the tertiary—more concentrated—areas.
Lastly, the share of self-employment has a rather important role in explaining
regional differential growth. It has a significant and positive sign, indicating that
entrepreneurship is playing a strategic role in regional growth patterns. Note that the
model specifies and estimates (see column 1 in Table 10) self-employment as affected
by structural funds decided upon jointly by European and national policy makers; thus
MASST is able to analyse the effects of another relevant policy instrument on regional
growth.
(c) Spatial and territorial structure
The most recent literature on spatial growth gives great emphasis to spatial processes, in
the form of increasing returns, agglomeration economies, cumulative self-reinforcing
processes and spatial spillovers. The results of the model witness that these processes
do have an effect on regional differential growth, and are in general positive; a faster
growing area spreads around its positive effects, and stimulates local production and
growth of its neighbouring regions.
A striking effect takes place when spatial growth spillovers are estimated for specific
territorial areas.
In those agglomerated areas showing a greater density of productive activities and
population, and therefore expected to be characterised by greater dynamic agglomeration economies, spatial spillovers are much lower, though still positive. Highly
negative spatial spillovers—i.e., backwash effects—occur instead in highly urbanised
regions in Eastern countries: faster growing regions tend to attract resources (more
productive and highly paid workers, efficient and dynamic firms, more financial invest-
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A forecasting territorial model of regional growth
Table 10 Estimation of regional share of self employment and regional annual population growth equations
Independent
variables
Dependent variables
Regional share of
self-employment
Regional annual average
population growth rate
1996–2002
Constant
11.06 (19.5)***
−0.084 (−0.25)
Structural funds expenditures in the period
1994–1999 on population
Birth rate (lagged 1 year)
0.010 (7.31)***
0.10 (6.66)***
Death rate (lagged 1 year)
−0.079 (−2.98)***
Net immigration flows (people between 17
and 27 years). Average value in the period
1995–2000
Net immigration flows (people between 32
and 42). Average value in the period 1995–
2000
Net immigration flows (people between 52
and 67 years). Average value in the period
1995–2000
New EU countries
0.012 (2.91)***
Agglomerated regions
−0.18 (−3.24)**
0.0461 (4.95)***
−0.014 (−0.57)
−0.33 (−4.95)**
Number of observations
191
R-square
0.42
0.36
Type of estimate
OLS (robust estimates)
OLS (robust estimates)
Spatial error test: robust Lagrange multiplier
(P value)
Spatial lag test: robust Lagrange multiplier
(P value)
257
0.85 (0.36)
2.59 (0.108)
* Significant at 10%; ** significant at 5%; *** significant at 1%
ments) from its neighbouring regions, increasing the differential in economic growth
rate with respect to them. This result is in line with other empirical studies on spatial effects on regional differential growth.21 Urban regions in Eastern countries grow
at the expenses of neighbouring regions characterised by medium cities and a rural
environment.
Coefficients for dummy variables concerning the mega regions and the rural regions
prove first of all to be very significant in explaining regional growth; secondly they
prove to differ substantially, in terms of sign: the sign is in fact positive for mega regions
and negative for rural areas. Through their econometric significance, these results
confirm the conceptual importance of territorial elements in regional growth: agglomeration economies, present in the mega regions in the form of cumulative knowledge
and collective learning processes, play a fundamental role in a cumulative and selfreinforcing economic growth process; their absence in rural areas explain a large part
of the limited economic dynamics of these territories. These results also witness the
21 See Cheshire (1995); Cheshire and Carbonaro (1996).
123
R. Capello
powerful capacity of dummy variables on the settlement structure in interpreting the
role of space in regional growth.
(d) Economic integration processes
The last group of variables contained in the model measures the effects of a more integrated Europe. As mentioned before (Sect. 3.3), the concept of integration potential
is measured on the basis of the change taking place in the economic potential of a
region once integration and disappearance of previous political-institutional barriers
takes place.
The results are interesting but unfortunately not significant from a statistical point
of view. The signs of the coefficient suggest that the enlargement process generates
in the first stages of its implementation negative effects to the weakest regions, which
offer a large market potential to more advanced and competitive areas; at the same
time, it generates advantages to advanced regions, able to grasp new market opportunities. The integration potential coefficient has in fact a negative sign for Eastern
country regions and a positive one for Western country areas.
6.2.2 The population growth equation (See Table 10, column 2)
The last equation in the demographic block, population growth is specified as a function
of net immigration, birth and death rates and territorial variables. Note that population growth also depends, via the contemporaneous migration variables, on the lagged
regional GDP. In fact, the demographic block of equations has been built along a recursive pattern: we assume that population depends on migration but migration does not
depend simultaneously on population growth. This very plausible assumption enables
treating the migration variables as non contemporaneously correlated with the error
term of the population equation and to avoid simultaneity issues.
The dependent variable has been computed as the average population growth
between 1999 and 2002 for reasons similar to those given for the choice of dependent
variable in the growth differential equation. As expected, the birth rate and death rate
(lagged 1 year) have a positive and negative sign, respectively. Immigration flows of
people also increase the population growth rate, with the exception of the 52–67-year
age group. This has a negative impact on population growth, which finds a double
explanation: (a) the low fertility rate of this group; (b) the general average negative sign
of the variable (measuring therefore an out-migration process on average). Tendencies to lose population are typical of new EU Countries and of agglomerated regions;
economic difficulties, physical congestion and in general agglomeration diseconomies
(high land prices) explain these tendencies (Table 10).
The tests accepted the null hypotheses of no spatial dependence or spatial lag and
robust OLS were used to estimate the parameters of the equation. The R 2 , measure of
goodness of fit is 65%.
6.2.3 The migration equations (See Table 11)
The model contains three equations that track the pattern of net immigration by age
group: young, middle aged and older workers. Each of the three variables depends on
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A forecasting territorial model of regional growth
Table 11 Estimation results of the regional immigration equations
Independent variables
Dependent variables
Net immigration flows Net immigration flows Net immigration flows
(people between 17
(people between 32
(people between 52
and 27 years)
and 42)
and 67 years)
Constant
1.59 (0.51)
Regional differential GDP
(with the EU) (lagged 1 year)
Unemployment rate
(lagged 1 year)
Megas Regions
1.14 (8.07)***
Agglomerated Regions
5.41 (3.27)***
Eastern countries
15.29 (4.41)***
−0.73 (−4.00)***
7.61 (8.53)***
1.79 (1.33)
−0.92 (−5.83)***
−0.57 (−4.72)***
−2.26 (−3.34)***
−2.87 (−5.07)***
−0.34 (−5.56)***
−0.359 (−7.08)***
3.59 (2.01)**
Regional share of tertiary activity
Regional differential GDP
(with the EU) (lagged 1 year)
in Western countries
Regional differential GDP
(with the EU) (lagged 1 year)
in Eastern countries
λ-spatial correlation coefficient
in errors
Type of estimation
Number of observations
R-square
Spatial error test robust
Lagrange multiplier (P value)
Spatial error test robust
Lagrange multiplier (P value)
0.053 (2.15)**
0.19 (5.28)***
− 1.4E-05
(−5.63)***
Spatial Error
Maximum
Likelihood
244
OLS (robust
estimates)
250
OLS (robust
estimates)
250
0.27 [Squared corre- 0.30
lation]
38.43 (0.00)
0.021 (0.88)
0.097 (0.75)
22.92 (0.00)
2.25 (0.13)
0.77 (0.38)
0.32
* Significant at 10%; ** significant at 5%; *** significant at 1%. t-student in brackets
time lagged per capita GDP differentials between the region and the average EU
per capita GDP and on unemployment rates in the region. Territorial and socioeconomic variables characterize each equation. After estimation, the results show
that the younger work force migrates towards agglomerated and fast growing regions.
Middle aged and especially older workers, instead, migrate away from highly concentrated and industrialized regions towards regions with a larger share of tertiary
activities. All categories move away from areas with large unemployment rates.
Statistical evidence of spatial dependence in the error term of the young workers
net immigration equation was found, but not for the other two migration equations.
Accordingly, the parameters of the young workers equation and the variation coefficient of spatial dependence in the first equation were jointly estimated22 by Maximum
Likelihood, while robust OLS regressions were used to estimate the parameters of the
other two equations.
22 See Anselin (1999, p. 16); see also the spatreg procedure in STATA, Pisati (2001).
123
R. Capello
7 Conclusions and future research directions
The paper had the aim to present the conceptual logic of MASST as a forecasting
regional growth and a policy simulation model. The characteristics of the model were
to be based on a modern interpretation of regional growth. The model is based in fact
on a concept of endogenous growth, highly dependent on internal structural characteristics of the local economy; local competitiveness drives regional growth, being
supply side effects of growth determined at the local level. Demand effects of growth
are captured at the national level, and linked to the regional effects enabling development of a combination of top-down and bottom-up approach, so that an interdependent
system of national and regional effects is built, and vertical linkages between national
and regional systems are taken into account.
Moreover, in its structure, the model finds a specific place for both socio-economic
and spatial (horizontal) feedbacks among regional economies. While the former are
captured by the socio-economic conditions generating interregional migration flows,
the latter are measured by spatial spillover effects that are measured by the growth
rate of a region that also depends on the growth rate of neighbouring regions.
It is a spatial-territorial model. The spatial and territorial dimensions have a role
in the explanation of regional growth in two ways. First, the model directly captures
proximity effects through the measurement of spatial spillovers; moreover, with the
introduction of variables interpreting the territorial (agglomerated, urbanised, rural)
structure, the model indirectly measures the agglomeration economy (diseconomy)
effects that influence growth (decline) in a cumulative way. In this sense, it represents
a valid and modern conceptual interpretation of regional growth, which finds very
good empirical support in the estimations presented.
The model is built in order to predict new regional growth rates and new levels of
regional per capita income in the EU 27 Countries, and therefore new levels of economic territorial disparities, under different conditional hypotheses. Given the way
in which it is built, the MASST model enables forecasting future regional growth
rates on the basis of a variety of driving forces of change. Macroeconomic driving
forces (like trend in the euro/$ exchange rate; trend in fiscal morality; trend in interest
rates; trend in inflation rate; geo-political orientation of FDI; rebalancing of external
accounts of large emerging countries; increase in energy price), institutional driving
forces (i.e., the future enlargement strategies such as deepening or widening of the EU;
restructuring of Community Agricultural Policy; reorientation and size of structural
funds; attitude towards East-West and North-South migration) and socio-economic
and demographic driving forces (trend in fertility rate, in population aging; in saving
ratio; in migration patterns) can all easily be taken into consideration to provide a map
of European regional growth rates in the years to come.
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