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