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MASST – MAcroeconomic, Sectoral, Social and Territorial model
Topics and problems
Andrea Caragliu – Politecnico di Milano
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Aims of the project
The final goal of the project is forecasting future socio-economic trends for European regions over a period of 15 years from now.
However, currently my commitment is to the estimation stage.
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Research steps
1. Drawing up of a sound theoretical model and definition of the appropriate econometric counterpart;
2. Estimation of the model;3. Forecast of main relationships and
definition of possible scenarios.
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The MASST model - Logic scheme
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Structure of the model
nrnr ddd ),()( rrnr TKfZfd
where:
Z = set of national demand variables
K = set of regional structural variables
T = set of regional territorial characteristics
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The starting equation
I use the following decomposition of regional growth rates:
where:yr = variation in the region’s GDPyn = variation in the nation’s GDPs = shift
syy nr
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Estimated equationsI – National component1 – GDP variation
where α = Parameters to be estimated ΔC = Consumption growth rate ΔI = Investment growth rate ΔG = Public expenditure growth rate ΔX = Exports growth rate ΔM = Imports growth rate
tttttnt MXGICY 543210
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3 – Public expenditure growth rate
1 tt YcC
Exogenous
2 – Consumption growth rate
Estimated equationsI – National component
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Estimated equationsI – National component
4. Investment growth rate
1111 ntntntntnt FDIULCiYI
5. Export growth rate
121 nt1tnt Eγ+ΔULCγ=ΔX
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Estimated equationsII – Regional component
s = f (human and economic resources; structual and sectoral characteristics; spatial spillover effects; integration processes; territorial features)
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New territorial data
Data DefinitionSource of raw
data
Agglomerated regions
With a center of > 300.000 inhabitants and a population density > 300 inh./sq. Km. or a
population density between 150 and 300 inh. /sq. Km.
ESPON database
Urban regions
With a center between 150.000 and 300.000 inh. And a population density of 150-300 inh./sq. Km. (or a smaller pop. density, 100-150 inh./sq. Km. with a
bigger centre (> 300.000 inh.) or a population density between 100 and 150 inh./sq. Km.)
ESPON database
Rural regionsWith a population density < 100 inh./sq. Km. and
centre > 125.000 inh. or a population density < 100 inh./sq. Km. with a centre < 125.000 inh.
ESPON database
Megas regions
Regions with the location of at least one of the 76 FUAs with the highest average score in a combined indicator of transport, population, manufacturing, knowledge, decision-making in the private sector
ESPON database
Pentagon regionsRegions located within the Pentagon formed by the five European cities of Milan, Munich, Amsterdam,
London, ParisESPON database
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New socio-economic data
Data Definition Source of raw data
Regional energy consumption by
population
Total energy consumption on population at NUTS 2 in the year 2002
ESPON database
Net immigration flows (people between 17 and
27 years)
Average immigration flows of people between 17 and 27 years in the period 1/1/95 - 1/1/00
at NUTS 2 levelESPON database
Net immigration flows (people between 32 and
42 years)
Average immigration flows of people between 32 and 42 years in the period 1/1/95 - 1/1/00
at NUTS 2 levelESPON database
Net immigration flows (people between 52 and
67 years)
Average immigration flows of people between 52 and 67 years in the period 1/1/95 - 1/1/00
at NUTS 2 levelESPON database
Regional birth rate Share of births on population at NUTS 2 level ESPON database
Regional mortality rate Share of deaths on population at NUTS 2 level ESPON database
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Spatial effects indicators
Indicators DefinitionSource of raw
data
Spatial spilloversSum of the relative annual growth rates of all regions other than region i divided by the distance between
each other region and region i.Eurostat
Economic potential
Sum of the annual absolute difference between income growth rates of region j and region i divided by the distance between region i and all other regions j.
Eurostat
Integration potential
Change in the sum of the annual absolute difference between income growth rates of regions j and region i divided by the distance between region i and all other regions j, when in the second term distance is squared for those regions at the border between Eastern and
Western Countries.
Eurostat
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Traditional economic variablesNational variables Defintions Sources of the raw
data
GDP growth rate Annual % growth rate of real GDP at NUTS 0 in the period 1995-2002
Eurostat
Annual change in interest rate Absolute change in short-term interest rates (3 months) at NUTS 0 in the period 1995-2002
Eurostat
Annual change in unit labour cost Absolute change in unit labour cost (calculated as unit salary * number of employees / GDP) at NUTS 0 in the period 1995-2002
Eurostat
Share of FDI on total internal investments
% Flow of FDI / Gross Fixed Capital Formation at NUTS 0 in the period 1995-2002
Eurostat
Nominal exchange rate Nominal effective exchange rate at NUTS 0 in the period 1995-2002
Eurostat
Inflation rate Inflation rate at NUTS 0 in the period 1995-2002
Eurostat
Consumption growth % annual real consumption growth rate at NUTS 0 in the period 1995-2002
Eurostat
Investment growth % annual real gross fixed capital formation growth rate at NUTS 0 in the period 1995-2002
Eurostat
Import growth % annual real import growth at NUTS 0 in the period 1995-2002
Eurostat
Eastern Countries All former Eastern Economies
New EU Countries The 10 new Member Countries who joined the EU on the 1/5/04
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Population growth rate
immrfrPt 32101
where fr = fertility rate - exogenous mr = mortality rate - exogenous im = interregional migration )(210 re wwuim
where u = unemployment
ew = European average wage
rw = regional average wage
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Database and indicators The database is built for 27 Countries (all EU25
countries plus Bulgaria and Romania) and 259 regions (NUTS2). The national database is in panel form (1995-2002).
The database’s originality is due to:
- The use of territorial and socio-economic data at NUTS2 level (so far inexistent), coming from other ESPON projects;
- The use of other spillover indicators created for 259 regions;
- Building up a database which is consistent with Eurostat and ESPON sources for which missing values were filled and consistency was checked.
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Results of estimation of shift parameters
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Open questions 1 - Econometrics
1. As I am estimating spatial spillover effects, most of the spatial autocorrelation should be already wiped out. Which kind of spcification test, in the shape of the Moran’s I, might I use in this case?
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Open questions 1 - Econometrics2. The spillover equation can be written as
Therefore, I am already using income in the equation. Am I running into endogeneity of the regressors problem?
r
i ji
j jiD
y
1 ,
,
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Open questions 1 - Econometrics3. Regional shift effects do not
automatically sum up to 0 (as we would wish for); instead, given the fact that the describing equation is filled with positive explanatory variables, they tend to be distorted towards positive values. Summing up to 0 is imposed in the estimation process; is there any alternative solution?
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Open questions 2 - Economics
1. Calculated shift s, plotted for each year and each region, is characterized by high variane. That’s why its average over the period 1999-2002 is chosen. This choice should be econometrically correct, bu how do I motivate it from the theoretical point of view?
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Open questions 2 - Economics
2. Again from the theoretic point of view, why is σ2
s so high?
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Open questions 2 - Economics3. In the national equations subgroup,
consumption growth rate was described by the following expression:
It is in reduced form, which is a technique used in all the equations. Given its econometrically accetable use, how do I justify it from the economic perspective?
1 tt YcC