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

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

Does geography matter in public policy?

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.