Growth and the geography of innovation by Attila Varga

1. Growth and the geography of innovation by Attila Varga Center for Research in Economic Policy (GKK) and Department of Economics Faculty of Business and Economics University of Pécs, Hungary

2. I. Introduction • A-spatial mainstream economic theory • K, L and A only? How about their spatial arrangements? • Why should we care about space? - Transport costs(can be integrated relatively easily) - Agglomeration externalities (require a different approach) • Policy relevance (EU)

3. Outline • Introduction • Technological progress, spatial structure and macroeconomic growth: An empirical modeling framework • Integrating agglomeration effects to development policy modeling • Concluding remarks

4. II. Technological progress, spatial structure and macroeconomic growth Complex issue treated in four separate fields of economics: A. EG: “Endogenous economic growth” models: endogenized technological change in growth theory (Romer 1986, 1990, Lucas 1986, Aghion and Howitt 1998) in Romer (1990): • for-profit private R&D • knowledge spillovers are essential in growth • rate of technical change equals rate of per-capita growth on the steady state • Simplistic explanation of technological progress, no geography

5. II. Technological progress, spatial structure and macroeconomic growth B. IS: „Systems of innovation”literature: innovation is an interactive process among actors of the system (Lundval 1992, Nelson 1993) actors of the IS: - innovating firms - suppliers, buyers - industrial research laboratories - public (university) research institutes - business services - “institutions” level of innovation depends on: - the knowledge accumulated in the system - the interactions (knowledge flows) among the actors - codified, non-codified (tacit) knowledge and the potential significance of spatial proximity - geography gets some focus, but IS does not say anything about growth

6. II. Technological progress, spatial structure and macroeconomic growth C. NEG: “New economic geography”models: endogenized spatial economic structure in a general equilibrium model (Krugman 1991, Fujita, Krugman andVenables 1999, Fujita andThisse 2002) - spatially extended Dixit-Stiglitz framework - increasing returns, monopolistic competition - spatial structure depends on some parameter conditions that determine the equilibrium level of centrifugal and centripetal forces - „cumulative causation” - C-P model by Krugman: still the point of departure - models quickly become complex: simulations if analytical solutions are not accessible - Technological change not explained (not even included until very recently), the study of its relation to growth is a recent phenomenon

7. II. Technological progress, spatial structure and macroeconomic growth D. GI: The „Geography of innovation” literature: the study of the spatial extent of knowledge flows in innovation (Jaffe 1989, Jaffe, Trajtenberg and Henderson 1993, Audretsch and Feldman 1996, Anselin, Varga and Acs 1997) • Empirical litarature: US, European, Asian analyses • Common finding: much of knowledge flows in technological change are spatially bounded • Not connected to growth and to the explanation of spatial economic structure

8. II. Technological progress, spatial structure and macroeconomic growth • IS, NEG, EGT, GI: complements to each other in growth explanation, no theoretical integration (Acs-Varga 2002) • IS, NEG, EGT, GI: building blocks of a framework to shape empirical research (Varga 2006) • Theoretical integration: endogenous growth and new economic geography (Baldwin and Forslid 2000, Fujita and Thisse 2002, Baldwin et al. 2003) • EG, IS, NEG, GI: methodological problems in THEORETICAL integration (dramatically diverging initial assumptions, different theoretical structures, research methodologies) • EMPIRICALintegration: very few work (Ciccone and Hall 1996, Varga and Schalk 2004, Acs and Varga 2004)

9. II. Technological progress, spatial structure and macroeconomic growth: An empirical modeling framework • Starting points („stylized facts”): • Technological change is a collective process that depends on accumulated knowledge and interactions (IS) • Technological change is the simple most important determinant of economic growth (EG) • Codified and tacit knowledge: different channels of spillovers (GI) • Centripetal and centrifugal forces shape geographical structure via cumulative processes (NEG) • The resulting geographic structure is a determinant of the rate of growth (NEG)

10. II. Technological progress, spatial structure and macroeconomic growth: An empirical modeling framework • Y = AKαLβ(EG) • The Romer (1990) equation as in Jones (1995) dA =  HAAφ, - HA: the number of researchers(“person-embodied”, knowledge component of knowledge production) - A: the total stock of technological knowledge (codified knowledge component of knowledge production in books, patent documents etc.) - dA: the change in technological knowledge - : the “research productivity parameter”(0<<1) φ: “codified knowledge spillovers parameter” - reflects spillovers with unlimited spatial accessibility : the “research spillovers parameter” - reflects localized knowledge spillover effects (GI) - regional and urban economics and the new economic geography suggest: changes with geographic concentration of economic activities (depending on the balance between positive and negative agglomeration economies)

11. II. Technological progress, spatial structure and macroeconomic growth: An empirical modeling framework Eq.1 Regional knowledge production: Kr = K (RDr, URDr, Zr) A cumulative process described by Eqs. 2 and 3 (dynamic agglomeration effects: Eq.2 (Static) agglomeration effect in R&D effectiveness: ∂Kr/∂RDr = f (RDr, URDr, Zr) Eq.3 R&D location: dRDr = R(∂Kr/∂RDr) Eq.4 Geography and :  =  (GSTR(HA)) Eq.5 dA =  HA  Aφ Eq.6 dy/y = H(dA, ZN)

12. Empirical research on geography, technology and growth: 1986-2004 1986-2004: 253 papers on the geography of knowledge spillovers journal articles: 175 books, book chapters, working papers: 78

13. Empirical research on geography, technology and growth: 1986-2004 • To test Eq.1: most of the empirical models are based on the „knowledge production framework” log (K) =  +  log(R) +  log(U) + log(Z) + • The KPF framework to study localized knowledge spillovers USA: Jaffe 1989 Acs, Audretsch and Feldman 1991 Anselin, Varga and Acs, 1997 Varga 1998 Feldman and Audretsch 1999 Acs, Anselin and Varga 2002 EU: Moreno-Serrano, Paci, Usai 2005 Italy: Audretsch and Vivarelly 1994, Capello2001 France: Autant-Bernard 1999 Austria: Fischer and Varga 2003 Germany: Fritsch 2002

14. Empirical research on geography, technology and growth: 1986-2004 • Eq.2 and Eq. 3: • empirical studies test the effects SEPARATELY (Jaffe 1989, Bania et al 1992, Anselin, Varga, Acs 1997a,b, Varga 2000, 2001) • The dynamic cumulative process is not modeled empirically • Empirical integration of micro to macro (Eqs. 4-6) is also missing

15. III. Integrating agglomeration effects to development policy modeling • Knowledge-based development policies (R&D promotion, infrastructure investments, education support etc.) • Modeling the effect of geography on policy effectiveness - three steps: 1. modeling static agglomeration effects generated by the spatial distribution of the instruments 2. modeling dynamic agglomeration effects of policy intervention: “cumulative causation” – induced technological change 3. modeling the resulting macroeconomic effects • In most of the current policy analysis models: no geography incorporated

16. III. A key issue in development policy modelling: integrating the spatial dimension of technological change • The GMR Hungary model: - integrates all the above three aspects - developed for ex-ante CSF intervention analysis for the Hungarian government (planning period 2007-13) - result of on international collaboration with German, Dutch and Japanese institutes - both macro and regional aspects are estimated

17. IV. Outline of the GMR model • CSF instruments targeting technology development: • Infrastructure investments • Education/training support • R&D promotion

18. IV. Outline of the GMR model

19. IV. Outline of the GMR model • GMR consists of three sub-models: - the TFP sub-model (static agglomeration effects) - the spatial computable general equilibrium (SCGE) sub-model (dynamic agglomeartion effects) - a complete macroeconomic model (the effects of geography on macroeconomic variables)

20. The function of the TFP sub-model • To generate STATIC TFP changes as a result of CSF interventions (direct short-run CSF-effect) • NOT for forecasting but for impact analysis

21. Main characteristics of the TFP sub-model • TFP equation: - estimates the effects of geographically differently located knowledge sources (local, national, international) - estimates the effects of CSF-instruments (infra, edu) • Time-space data

22. The TFP equation The estimated regional model of technological change TFPGR = α0 + α1KNAT + α2RD+ α3KIMP + α4INFRAINV + α5HUMCAPINV + ε, TFPGR: the annual rate of growth of Total Factor Productivity (TFP), KNAT: domestically available technological knowledge accessible with no geographical restrictions (measured by stock of patents), RD: private and public regional R&D, KIMP: imported technologies (measured by FDI), INFRAINV: investment in physical infrastructure, HUMCAPINV: investment in human capital, region i and time t α1 estimates domestic knowledge effects α2 estimates localized (regional) knowledge effects α3 estimates international knowledge effects

24. The function of the SCGE sub-model • To generate DYNAMIC TFP changes that incorporate the effects of agglomeration externalities on labor-capital migration (induced long-run CSF effect) • Agglomeration effects depends on: - centripetal forces: local knowledge (TFP) - centrifugal forces: transport cost, congestion • To calculate the spatial distribution of L, I, Y, w by sectors for the period of simulation

25. The SCGE sub-model • Adaptation of RAEM-Light (Koike, Thissen 2005) • C-D production function, cost minimization, utility maximization, interregional trade, migration • Equilibrium: - short run (regional equilibrium) - long run (interregional equilibrium)

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

27. The function of the MACRO sub-model • Based on dynamic TFP values: the resulting effects on macro variables

28. The characteristics of the MACRO sub-model • Complete macro model (supply, demand, income distribution) – the EcoRET model (Schalk, Varga 2004) • C-D production technology, cost minimization • Supply and demand side effects of CSF • A-spatial model • Describes the effects of exogenous technological change • Baseline: TFP growth without CSF interventions • Policy simulations: describe the effects of CSF-induced TFP changes on macro variables

30. Regional and national level short run and long run effects of TFP changes induced by TFP-related CSF interventions 1. Intervention in any region increases regional TFP level in the mth sector (static agglomeration effect) 2. Short run effect: - price of the good decreases - decreasing demand for both L and K (assuming output unchanged) - increasing regional and interregional demand for the good that increases demand for L and K - increased regional demand increases utility levels of consumers in the region 3. Long run effects: increasing utility levels induces labor migration into the region followed by capital migration - resulting in a further increase in TFP (dynamic agglomeration effect) - and finally a changed spatial economic structure 4. Macroeconomic variables reflect the long run equilibrium TFP level resulting from dynamic agglomeration effects

31. Effects on spatial structure Macroeconomic effects SCGE sub-model (regional model) Macroeconomic sub-model (demand, supply, income distribution) TFP sub-model (Regional model) Dynamic TFP changes Static TFP changes Economic policy instruments: infrastructure, R&D and education Regional and national level short run and long run effects of TFP changes induced by TFP-related CSF interventions

32. Does geography matter in public policy?

33. Allocation of CSF support in Mill. 2004 HUF

34. Elasticity of GDP growth rate changes with respect to CSF spending by geographic concentration of funds (Budapest)

35. Elasticity of GDP growth rate changes with respect to CSF spending by geographic concentration of funds (Veszprém, Komárom, Győr-Sopron, Vas, Fejér)

36. Elasticity of GDP growth rate changes with respect to CSF spending by geographic concentration of funds (East of Danube)

37. Elasticity of GDP growth rate changes with respect to spatial concentration of CSF spending by geographic concentration of funds (Budapest)

38. Elasticity of GDP growth rate changes with respect to spatial concentration of CSF spending by geographic concentration of funds (Veszprém, Komárom, Győr-Sopron, Vas, Fejér)

39. Elasticity of GDP growth rate changes with respect to spatial concentration of CSF spending by geographic concentration of funds (East of Danube)

40. Conluding remarks • Growth and the geography of innovation: theoretical versus empirical integration • Geographic effects in policy modelling: the GMR model • Results show that agglomeration effects are important factors in macroeconomic performance and neglecting them in development policy analyses could result in misleading expectations as to how a particular mixture of policies affect the economy.