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  1. Geographic Macro and Regional (GMR) Model for EU Policy Impact Analysis of Intangible Assets and GrowthAttila VargaPéter JárosiTamás SebestyénPTE KTK KRTI

  2. Development policy instruments • Knowledge-based development policy • Policy instruments: • Promoting firms’ technological potential (start-up and investment supports, tax credits, low interest rate loans or venture capital) • Local technological environment support (R&D promotion: universities and private firms, human capital improvement, support of public-private interactions in innovation, financing physical infrastructure building)

  3. GMR: Geographic Macro and Regional Modelling

  4. Why should geography be incorporated into development policy impact modeling? • Geography and policy effectiveness: 1. Interventions happen at a certain point in space and the impacts appear there / spill over toproximate locations to a considerable extent. 2. The initialimpacts could significantly be amplified/reduced by short runagglomeration effects. 3. Cumulative long run process resulting from migration of K and L: - furtheramplification/reduction of the initial impacts in the region - the spatial structure of the economy (K, L, Y, w) might eventuallychangein a significant manner. 4. Different spatial patterns of interventions might result in significantly different growth and convergence/divergence patterns.

  5. Why „regional”

  6. Why „macro”?

  7. GMR-Eurozone • The particular model developed for the Eurozone NUTS 2 regions includes: • a KPF model (to model: 1 and 2) • an SCGE model (for 3) • a macro DSGE model (for 4)

  8. Introduction • Antecedents: • Empirical modeling framework (Varga 2006) • EcoRet model (Schalk, Varga 2004, Varga, Schalk 2004) • GMR-Hungary model (Varga, Schalk, Koike, Járosi, Tavasszy 2008) • Dynamic KPF model for EU regions (Varga, Pontikakis, Chorafakis, 2009)

  9. Outline • Model structure • The KPF model • The SCGE model • Dynamism and macro effects: macro DSGE model (QUEST III) • Policy simulations

  10. The role of the KPF model • To generate initial TFP changes as a result of technology policy interventions • NOT for forecasting but for impact analysis

  11. Equations in the TFP block

  12. The TFP equation

  13. Require the integration of TFP with the SCGE and MACRO models • BUT: • How strong these processes are? • What are the economic impacts on the regions? • What are the macro (EU level) economic impacts?

  14. The role of the SCGE model • To generate dynamic TFP changes that incorporate the effects of agglomeration externalities on labor-capital migration • Agglomeration effects depend on: - centripetal forces: local knowledge (TFP) - centrifugal forces: transport cost, congestion • To calculate the spatial distribution of L, I, Y, w for the period of simulation

  15. The SCGE model • C-D production function, cost minimization, utility maximization, interregional trade, migration • Equilibrium: - short run (regional equilibrium) - long run (interregional equilibrium)

  16. Main characteristics of the SCGE model • NOT for historical forecasting • The aim: to study the spatial effects of shocks (technology policy intervention) • Without interventions: it represents full spatial equilibrium - regional and interregional (no migration) • Shock: interrupts the state of equilibrium, the model describes the gradual process towards full spatial equilibrium

  17. The role of the MACRO model • Regional technology policy impacts depend to a large extent on macro level variables (fiscal/monetary policy shocks, exchange rates, international trade etc.) • Dynamising the (static) SCGE model

  18. The MACRO model • The QUEST III Dynamic stochastic general equilibrium (DSGE)model for the EURO area • A-spatial model • Macro effects of exogenous TFP shocks • Baseline: TFP growth without interventions • Policy simulations: describe the effects of TFP changes on macro variables

  19. Regional and national level short run and long run effects of TFP changes induced by regional technologypolicy interventions 1. Intervention in any region changes regional TFP level 2. „Short run” effect: - price of the good decreases - decreasing demand for both L and K (substitution effect - SE) - increasing regional and interregional demand for the good increases demand for L and K (output effect - OE) - if OE>SE: increased regional demand increases wages and utility levels of consumers inthe region 3. „Long run” effects: increasing utility levels induces labor migration into the region (until congestion does not prevail) followed by capital migration - resulting in a further increase in TFP - and finally a changed spatial economic structure 4. Macroeconomic variables reflect the long run equilibrium TFP level resulting from dynamic agglomeration effects

  20. Policy Models, Procedures State of Equilibrium MACRO model Dynamic supply and demand side effects Dynamic impact on macroeconomic variables B C Regional SCGE model Agglomeration effects on regional and interregional variables Dynamic impact on regional economic variables A Regional KPF model Regional TFP effects Policy intervention

  21. Data, software environment • The model is build for the NUTS 2 regions of the EURO zone • Regional KPF model estimated in SpaceStat • The complex model is programmed and run in MATLAB • Easy to run/make simulation changes with an Excel interface • The regional model is large considering that equilibriums have to be found for 144 interconnected (interregional trade and migration) regions • A simulation with 20 periods needs the computer time of about 20 minutes

  22. Regional R&D policy impact assessment: The EU FP6 program • EURO zone 144 NUTS 2 regions (QUEST constraint) • Interventions: 2003-2007

  23. Policy implications

  24. Policy implications (cont.)