D evelopment polic y instruments

1. Geographic Macro and Regional (GMR) Model for DevelopmentPolicy Impact AnalysisAttila VargaUniversity of Pécs

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. Introduction • Antecedents: • Empirical modeling framework (Varga 2006) • The EcoRet model (Schalk, Varga 2004, Varga, Schalk 2004) • The GMR-Hungary model (Varga, Schalk, Koike, Járosi, Tavasszy 2008) • Dynamic KPF model for EU regions (Varga, Pontikakis, Chorafakis, 2009) • The current version (developed within the frame of the IAREG project): • GMR-EU (Varga, Járosi, Sebestyén 2009; Varga,Törma 2011)

4. GMR: Geographic Macro and Regional Modeling

5. 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.

6. Why „regional”

7. Why „macro”?

8. The particular GMR model developed for EU NUTS 2 regions includes: • a regional Knowledge Production Function (KPF)sub-model • a regional Spatial Computable General Equilibrium (SCGE)sub-model • a macro Dynamic Stochastic General Equilibrium (DSGE)sub-model (Quest III)

9. 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

10. Starting point: Romer-Jones KPF dA/dt =  HAAφ - HA: research input - A: the total stock of technological knowledge (codified knowledge component of knowledge production in books, patent documents etc.) - dA: the change in technological knowledge - φ: the „codified knowledge spillover parameter” - : scaling factor - : the “research productivity parameter”

11. Empirical model, data and estimation • Data sources: • EUROSTAT New Cronos database (PAT, RD, δ, PATSTOCK) • EC DG-Research FP5 database (NET) • Regional Key Figures Publications database (PUB) • Estimation: • Pre-competitive and competitive research productivity effects are tested • Panel with temporally lagged dependent variables (1998-2002) • spatial econometrics methodology

12. The system of equations in the KPF sub-model

13. The regional TFP equation

14. Modelling regional R&D impact on TFP in the KPF sub-model

15. Require the integration of the KPF sub-model with the SCGE and MACRO sub-models After having regional TFP impact modeled… • What are the economic impacts on the regions? • What are the macro (EU level) economic impacts?

16. 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

17. Main characteristics of the SCGE model • NOT for historical forecasting • The aim: to study the spatial effects of shocks (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

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

19. 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

20. 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

21. 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

22. Data, software environment • The model is build for the NUTS 2 regions of the EURO zone and 3 CEE countries (Czech Republic, Hungary, Slovakia) • 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 45 minutes

23. The „Europe 2020” scenario: Dynamic hard and soft impacts

24. The „Donut” scenario

25. The „Agglomeration and concentration” scenario Figure 5: The “Agglomeration and concentration” scenario. Percentage differences between scenario and baseline GDP values. Note: The extended GMR model system was run for the analysis