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Integrating the geography of innovation to policy modeling
by
Attila Varga
Department of Economics and Regional Studiesand
Center for Research in Economic Policy (GKK)Faculty of Business and Economics
University of Pécs, Hungary
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 instruments2. modeling dynamic agglomeration effects of policy intervention: “cumulative causation” – induced technological change3. modeling the resulting macroeconomic effects
• In most of the current policy analysis models: no geography incorporated
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
IV. Outline of the GMR model
• CSF instruments targeting technology development:
– Infrastructure investments– Education/training support– R&D promotion
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)
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
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
The TFP equationThe estimated regional model of technological change
TFPGR = α0 + α1KNAT + α2RD+ α3 KIMP + 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
Table 1: Pooled FGLS estimation results for TFP growth rates (TFPGR) and for 20 Hungarian counties, 1996 – 2003
Note: estimated standard errors are in parentheses; Neighb is first order neighborhood standardized weights matrix; *** is significance at 0.01, ** is significance at 0.05, * is significance at 0.1.
Model 1
Model 2
Model 3
Model 4
Model 5
Model 6
Final
Model
C -2.5434 -2.4740 -2.4797 -2.4965 -2.2423 -1.8243 -1.0389
(0.2989) (0.2910) (0.2919) (0.2735) (0.2728) (0.2372) (0.3408)
TFPGR(-2) -0.2587
(0.0749)
0.0002 0.0002 0.0002 0.0002 0.0002 0.0002 8.84E-5 KNAT (-2)
(2.68E-05) (2.59E-05) (2.60E-05) (2.45E-05) (2.44E-05) (2.10E-05) (3.04E-05)
0.1582 0.1526 0.1455 0.0892 0.1219 0.0826 KIMP (-3)
(0.0449) (0.0456) (0.043) (0.0430) (0.0393) (0.0392)
1.29E-06 RD (-2)
(1.77E-06)
3.79E-06 1.46E-06 1.56E-06 2.11E-06 d(INFRA(-1))
(9.60E-07) (1.34E-06) (9.41E-07) (8.44E-07)
6.95E-06 4.74E-06 5.63E-06 d(HUMRES(-2))
(2.84E-06) (2.47E-06) (2.41E-06)
-0.0601 -0.0610
(0.0081) (0.0080)
DUM99
Weighted Statistics
R2-adj 0.31 0.37 0.37 0.42 0.42 0.59 0.62
F-statistic 54.02 35.71 23.83 31.15 18.44 29.27 28.36
Prob (F-statistic) 0.00 0.00 0.00 0.00 0.00 0.00 0.00
Durbin-Watson stat 1.90 2.06 2.07 2.02 1.68 2.22 2.42
N
Unweighted Statistics
120 120 120 120 100 100 100
R2-adj 0.14 0.19 0.20 0.21 0.23 0.35 0.42
ML Spatial error Neighb
21.3***
16.18***
18.55***
14.79***
1.25
ML Spatial lag Neighb
21.3***
19.23***
20.64***
18.12***
3.78*
0,8600
0,8800
0,9000
0,9200
0,9400
0,9600
0,9800
1,0000
1,0200
1,0400
1999 2000 2001 2002 2003
TFP level as in GMR observ TFP level as in GMR forecasted
Figure 1: Observed and predicted levels of national TFP
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
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)
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
The function of the MACRO sub-model
• Based on dynamic TFP values: the resulting effects on macro variables
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
Regional and national level short run and long run effects of TFP changes induced by TFP-related CSF
interventions1. 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
Regional and national level short run and long run effects of TFP
changes induced by TFP-related CSF interventions
1
Effects on spatial economic structure
Macroeconomic effects
2
3
4
67
SCGEsub-model
(regional model)
MACROsub-model (demand,
supply, income distribution)
TFPsub-model
(regional model)
Economic policy instruments: infrastructure, R&D and education
Short run effects
Long run effects
5
Allocation of CSF support in Mill. 1995 HUF
0
50 000
100 000
150 000
200 000
250 000
300 000
350 000
400 000
2007 2008 2009 2010 2011 2012 2013 2014 2015
Year
Ex
pe
nd
itu
res
in M
ill. H
UF
Infrastructure Education R&D Investment Demand side only
Core-periphery structure of Hungarian counties with respect to Gross Value Added per employee
Core-periiphery structure of Hungary
CorePeriphery
The effects of policy scenarios
on the GDP growth rate
-0,50
0,00
0,50
1,00
1,50
2,00
2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017
Core Periphery Equal
The policy effects on convergence measured by standard deviation of regional value added
0,00
0,50
1,00
1,50
2,00
2,50
2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017
Core Periphery Equal
εc,g = [(σRGVA, scen - σRGVA, bline)/ σRGVA, bline]/[(GDP scen - GDP bline)/ GDPbline]; where εc,g is the elasticity of the change in the standard deviation of regional GVA relative to the baseline with respect to the change in GDP relative to the baseline, σRGVA, scen and σRGVA, bline are standard deviations of regional GVA in the scenario and the baseline, GDP scen and GDP bline are GDP at the national level in the scenario and the baseline.
Measuring the cost of growth promotion
Elasticity of the standard deviation
of regional GVA with respect to GDP (relative to baseline)
0,000
0,020
0,040
0,060
0,080
0,100
0,120
2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017
Core Periphery Equal
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.