1. MASST – MAcroeconomic, Sectoral, Social and Territorial model Topics and problems Andrea Caragliu – Politecnico di Milano

2. 2 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.

3. 3 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.

4. 4 The MASST model - Logic scheme

5. 5 Structure of the model where: Z = set of national demand variables K = set of regional structural variables T = set of regional territorial characteristics

6. 6 The starting equation I use the following decomposition of regional growth rates: where: yr = variation in the region’s GDP yn = variation in the nation’s GDP s = shift

7. 7 Estimated equations I – National component 1 – 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

8. 8 3 – Public expenditure growth rate Exogenous 2 – Consumption growth rate Estimated equations I – National component

9. 9 4. Investment growth rate 5. Export growth rate

10. 10 Estimated equations II – Regional component s = f (human and economic resources; structual and sectoral characteristics; spatial spillover effects; integration processes; territorial features)

11. 11 New territorial data DataDefinitionSource 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 regions With a population density 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 regions Regions located within the Pentagon formed by the five European cities of Milan, Munich, Amsterdam, London, Paris ESPON database

12. 12 New socio-economic data DataDefinitionSource 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 level ESPON 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 level ESPON 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 level ESPON database Regional birth rateShare of births on population at NUTS 2 levelESPON database Regional mortality rateShare of deaths on population at NUTS 2 levelESPON database

13. 13 Spatial effects indicators IndicatorsDefinitionSource of raw data Spatial spillovers Sum 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

14. 14 Traditional economic variables National variablesDefintionsSources of the raw data GDP growth rateAnnual % growth rate of real GDP at NUTS 0 in the period 1995-2002 Eurostat Annual change in interest rateAbsolute change in short-term interest rates (3 months) at NUTS 0 in the period 1995-2002 Eurostat Annual change in unit labour costAbsolute 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 rateNominal effective exchange rate at NUTS 0 in the period 1995-2002 Eurostat Inflation rateInflation rate at NUTS 0 in the period 1995-2002Eurostat 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 CountriesAll former Eastern Economies New EU CountriesThe 10 new Member Countries who joined the EU on the 1/5/04

15. 15 Population growth rate

16. 16 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.

17. 17 Results of estimation of shift parameters

18. 18 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?

19. 19 Open questions 1 - Econometrics 2. 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?

20. 20 Open questions 1 - Econometrics 3. 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?

21. 21 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?

22. 22 Open questions 2 - Economics 2. Again from the theoretic point of view, why is σ 2 s so high?

23. 23 Open questions 2 - Economics 3. 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?