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III. Integrating agglomeration effects to development policy modeling

Integrating the geography of innovation to policy modeling by Attila Varga Department of Economics and Regional Studies a nd Center for Research in Economic Policy (GKK) Faculty of Business and Economics University of Pécs, Hungary.

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III. Integrating agglomeration effects to development policy modeling

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  1. Integrating the geography of innovation to policy modeling byAttila VargaDepartment of Economics and Regional StudiesandCenter for Research in Economic Policy (GKK)Faculty of Business and EconomicsUniversity of Pécs, Hungary

  2. 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

  3. 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

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

  5. IV. Outline of the GMR model

  6. 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)

  7. 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

  8. 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

  9. 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

  10. 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

  11. 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)

  12. 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

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

  14. 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

  15. 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

  16. Effects on spatial economic structure Macroeconomic effects 7 6 SCGE sub-model (regional model) MACRO sub-model (demand, supply, income distribution) TFP sub-model (regional model) 5 4 Long run effects 3 1 Short run effects 2 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

  17. Does geography matter in public policy?

  18. Allocation of CSF support in Mill.1995 HUF

  19. Core-periphery structure of Hungarian counties with respect to Gross Value Added per employee

  20. The effects of policy scenarios on the GDP growth rate

  21. The policy effects on convergence measured by standard deviation of regional value added

  22. Measuring the cost of growth promotion

  23. Elasticity of the standard deviation of regional GVA with respect to GDP (relative to baseline)

  24. 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.

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