Integrating%20the%20geography%20of%20innovation%20to%20policy%20modeling%20%20by%20Attila%20Varga%20Department%20of%20Economics%20and%20Regional%20Studies%20and%20Center%20for%20Research%20in%20Economic%20Policy%20(GKK)%20Faculty%20of%20Business%20and%20Economics%20University%20of%20P

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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
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III. Integrating agglomeration effects to
development policy modeling

• Knowledge-based development policies (RD
  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

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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

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IV. Outline of the GMR model

• CSF instruments targeting technology development
• Infrastructure investments
• Education/training support
• RD promotion

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IV. Outline of the GMR model
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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)

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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

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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

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The TFP equation

• The estimated regional model of technological
  change
• TFPGR a0 a1KNAT a2RD a3 KIMP a4INFRAINV
  a5HUMCAPINV e,
• 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 RD,
• KIMP imported technologies (measured by FDI),
• INFRAINV investment in physical infrastructure,
• HUMCAPINV investment in human capital,
• region i and time t
• a1 estimates domestic knowledge effects
• a2 estimates localized (regional) knowledge
  effects
• a3 estimates international knowledge effects

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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

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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)

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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

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The function of the MACRO sub-model

• Based on dynamic TFP values the resulting
  effects on macro variables

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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

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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

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Regional and national level short run and long
run effects of TFP changes induced by TFP-related
CSF interventions
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Does geography matter in public policy?
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Allocation of CSF support in Mill. 1995 HUF
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Core-periphery structure of Hungarian counties
with respect to Gross Value Added per employee
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The effects of policy scenarios on the GDP
growth rate
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The policy effects on convergence measured by
standard deviation of regional value added
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Measuring the cost of growth promotion
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Elasticity of the standard deviation of regional
GVA with respect to GDP (relative to baseline)
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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.