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Geographic Macro and Regional (GMR)
Model for EU Policy Impact Analysis of
Intangible Assets and Growth
Attila Varga
Péter Járosi
Tamás Sebestyén
PTE KTK KRTI
Development policy instruments
• Knowledge-based development policy
• Policy instruments:
– Promoting firms’ technological potential (start-up and
investment supports, tax credits, low interest rate
loans or venture capital)
– Local technological environment support (R&D
promotion: universities and private firms, human
capital improvement, support of public-private
interactions in innovation, financing physical
infrastructure building)
• GMR: Geographic Macro and Regional
Modelling
Why should geography be incorporated into
development policy impact modeling?
•
Geography and policy effectiveness:
1. Interventions happen at a certain point in space and the impacts appear
there / spill over to proximate locations to a considerable extent.
2. The initial impacts could significantly be amplified/reduced by short run
agglomeration effects.
3. Cumulative long run process resulting from migration of K and L:
- further amplification/reduction of the initial impacts in the region
- the spatial structure of the economy (K, L, Y, w) might eventually
change in a significant manner.
4. Different spatial patterns of interventions might result in significantly
different growth and convergence/divergence patterns.
Why „regional”
Why „macro”?
GMR-Eurozone
• The particular model developed for the
Eurozone NUTS 2 regions includes:
– a KPF model (to model: 1 and 2)
– an SCGE model (for 3)
– a macro DSGE model (for 4)
Introduction
• Antecedents:
– Empirical modeling framework (Varga 2006)
– EcoRet model (Schalk, Varga 2004, Varga,
Schalk 2004)
– GMR-Hungary model (Varga, Schalk, Koike,
Járosi, Tavasszy 2008)
– Dynamic KPF model for EU regions (Varga,
Pontikakis, Chorafakis, 2009)
Outline
• Model structure
– The KPF model
– The SCGE model
– Dynamism and macro effects: macro DSGE
model (QUEST III)
• Policy simulations
The role of the KPF model
• To generate initial TFP changes as a result
of technology policy interventions
• NOT for forecasting but for impact analysis
Equations in the TFP block
1. Log(PATENTS) = 1.325381*(-2.3006 + BETAPAT*Log(GRD(-2)) + 0.1804* Log(PSTCKN(-2)) + 0.4614* PAHTCORE) + U1 .....Knowledge Production
[2. Log (PUBLICATIONS) = 2.6137 + BETAPUB*Log(NETRD(-2))]* Log(GRD(-2)) + 0.3293* PUBCORE+ U2] ............................ Publication Production
3. BETAPAT = [(0.7088 + 0.1439*Log(δ(-2))] .................................................................................................................................RD Productivity (patents)
4. BETAPUB = [0.4317 + 0.0003* WFP5_Log(RD(-2))] .......................................................................................................... RD Productivity (publications)
5. (GRD-GRD(-3)) = -391.369+ 352.437*BETAPAT(-3) + 325.33*BETAPUB(-3) + 266.917*RDHCORE-280.882*NL5REG+U3 .Endogeneous RD Growth
6. (HTEMP-HTEMP(-3)) = 11168.3 + [(0.0262 + 5.624E-06* GRD(-3))]* HTEMP(-3) + 21321.1*RDCORE+ U4
Growth
Endogeneous High-Tech Employment
7. PSTCK = PSTCK(-1) + PATENTS ................................................................................................................................................................... Patentstock
8. PSTCKN = SUM(PSTCK) .................................................................................................................................................................... National Patentstock
9. HTEMP = HTEMP(-3) + (HTEMP – HTEMP(-3)).......................................................................................................................... High Tech Employment
10. HTEMPEU = SUM(HTEMP)............................................................................................................................................ National High Tech Employment
11. TOTEMP = FROM SCGE.............................................................................................................................................................................. Employment
12. TOTEMPEU = SUM(TOTEMP) ........................................................................................................................................................... Global Employment
concentration
13. δ..................................................................................................................................................................................................Knowledge
i = [(EMPKIi / EMPKIEU) / (EMP i / EMP EU)] / [(1 - ∑ j (EMPKIi,j / EMPKIj,EU)][1 – (EMP i / EMP EU)]
14. TFP = 57.42*(HUMCAP(-2)) 0.0004*SOCKAP(-2) (PATSTCK(-2)) 0.0056*ln(DENS(-2))
equation
TFP
The TFP equation
Table 1. Regression Results for Log (TFP) for 135 Eurozone regions, 2004
Model
Estimation
(1)
OLS
(2)
OLS
(3)
OLS
(4)
OLS
Constant
3.6425***
(0.2105)
4.0850***
(0.0460)
3.9331***
(0.0425)
3.9832***
(0.0385)
(5)
IV (2SLS)
Spatial Lag
(INV1)
3.9309***
(0.0414)
Log(HUMCAP)
0.0722***
(0.0175)
0.0008***
(7.9577E-5)
0.0003***
(8.7574E-05)
0.0004***
(7.5823E-5)
0.0004***
(7.4023E-5)
0.0073***
(0.0008)
0.0054***
(0.0010)
Log(HUMCAP)*SOCKAP
Log(PATSTCK)
0.0623***
(0.0078)
Log(PATSTCK)*Log(DENS)
W_Log(TFP)
R2-adj
Sq. Corr.
Multicollinearity condition
number
0.0015***
(0.0005)
0.11
0.41
0.60
0.63
0.65
22
6
9
7
White test for
heteroskedasticity
8.8335**
11.1798***
10.5357*
7.7393
LM-Err
INV1
INV2
154.48***
19.56***
57.35***
9.00***
1.57
0.61
3.27*
0.02
LM-Lag
INV1
INV2
52.47***
29.31***
38.11***
22.03
14.98***
11.33***
7.67***
3.09*
1.38
• BUT:
– How strong these processes are?
– What are the economic impacts on the
regions?
– What are the macro (EU level) economic
impacts?
Require the integration of TFP with the
SCGE and MACRO models
The role of the SCGE model
• To generate dynamic TFP changes that
incorporate the effects of agglomeration
externalities on labor-capital migration
• Agglomeration effects depend on:
- centripetal forces: local knowledge (TFP)
- centrifugal forces: transport cost,
congestion
• To calculate the spatial distribution of L, I,
Y, w for the period of simulation
The SCGE model
• 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 model
• NOT for historical forecasting
• The aim: to study the spatial effects of
shocks (technology policy 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 role of the MACRO model
• Regional technology policy impacts
depend to a large extent on macro level
variables (fiscal/monetary policy shocks,
exchange rates, international trade etc.)
• Dynamising the (static) SCGE model
The MACRO model
• The QUEST III Dynamic stochastic
general equilibrium (DSGE) model for the
EURO area
• A-spatial model
• Macro effects of exogenous TFP shocks
• Baseline: TFP growth without interventions
• Policy simulations: describe the effects of
TFP changes on macro variables
Regional and national level short run and long run
effects of TFP changes induced by regional technology
policy interventions
1. Intervention in any region changes regional TFP level
2. „Short run” effect:
- price of the good decreases
- decreasing demand for both L and K (substitution effect - SE)
- increasing regional and interregional demand for the good
increases demand for L and K (output effect - OE)
- if OE>SE: increased regional demand increases wages and utility
levels of consumers in the region
3. „Long run” effects: increasing utility levels induces labor migration into the
region (until congestion does not prevail) followed by capital migration
- resulting in a further increase in TFP
- and finally a changed spatial economic structure
4. Macroeconomic variables reflect the long run equilibrium TFP level resulting
from dynamic agglomeration effects
Policy
Models, Procedures
State of Equilibrium
MACRO model
Dynamic
supply and demand side effects
B
Regional SCGE model
Agglomeration effects on
regional and interregional
variables
A
Regional KPF model
Policy
intervention
Regional TFP effects
Dynamic impact on
macroeconomic variables
C
Dynamic impact on
regional economic
variables
Data, software environment
• The model is build for the NUTS 2 regions of the EURO
zone
• Regional KPF model estimated in SpaceStat
• The complex model is programmed and run in MATLAB
• Easy to run/make simulation changes with an Excel
interface
• The regional model is large considering that equilibriums
have to be found for 144 interconnected (interregional
trade and migration) regions
• A simulation with 20 periods needs the computer time of
about 20 minutes
Regional R&D policy impact
assessment: The EU FP6 program
• EURO zone 144 NUTS 2 regions (QUEST
constraint)
• Interventions: 2003-2007
Regional shares of FP6 funds
0 - 0.003
0.003 - 0.009
0.009 - 0.017
0.017 - 0.037
0.037 - 0.153
Figure 2. Regional distribution of FP6 funds in the Euro-zone, 2003-2007
1,00%
0,80%
0,60%
0,40%
0,20%
0,00%
2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022
-0,20%
-0,40%
TIER1
Figure 3.
TIER2
TIER3
TIER4
EU
Average FP6 impacts on GDP in regions belonging to different agglomeration
tiers: percentage differences between scenario and baseline values
Y2022
-0.007 - -0.003
-0.003 - 0.001
0.001 - 0.006
0.006 - 0.018
0.018 - 0.029
Figure 4. Regional impacts of FP6 funds on GDP of Euro-zone regions, year 2022:
percentage differences between scenario and baseline values
0,45%
0,40%
0,35%
0,30%
0,25%
0,20%
0,15%
0,10%
0,05%
0,00%
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
2021
2022
SCEN_Y/BASELINE_Y
Figure 5. Impacts of FP6 funds on EU GDP, Euro-zone, period 2003-2022: percentage
differences between scenario and baseline values
0,030%
0,025%
0,020%
0,015%
0,010%
0,005%
0,000%
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
2021
2022
SCEN_Y-BASELINE_y_growth
Figure 6. The impact of FP6 funds on EU-level GDP growth rates, Euro-zone, 2003-2022:
percentage point differences between scenario and baseline values
1,20%
1,00%
0,80%
0,60%
0,40%
0,20%
0,00%
2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022
-0,20%
-0,40%
-0,60%
TIER1
TIER2
TIER3
TIER4
EU
Figure 7. The effect of EU FP6 research support augmented with an annual 1 percent
quality-oriented redistribution of national R&D expenditures, Euro-zone, 20032022: percentage differences between scenario and baseline values
1,00%
0,80%
0,60%
0,40%
0,20%
0,00%
2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022
-0,20%
-0,40%
TIER1
TIER2
TIER3
TIER4
EU
Figure 8. The effect of a 0.5 percent annual increase of human capital in Tier 2, 3 and 4
regions to compensate for the impact of the quality-oriented redistribution of
national R&D expenditures, Euro-zone, 2003-2022: percentage differences
between scenario and baseline values
1,00%
0,80%
0,60%
0,40%
0,20%
0,00%
2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022
-0,20%
-0,40%
TIER1
TIER2
TIER3
TIER4
EU
Figure 9. The effect of a 0.05 percent annual increase of social capital in Tier 2, 3 and 4
regions to compensate for the impact of the quality-oriented redistribution of
national R&D expenditures, Euro-zone, 2003-2022: percentage differences
between scenario and baseline values
Policy implications

Compared to the relatively small share of EU Framework Program research support in
Member States’ R&D budgets regional and EU level economic impacts of FP6
expenditures are considerable. It suggests that this policy instrument is an effective tool
not only for promoting scientific publication activities but also for supporting regional and
macro level productivity and economic development.

Redistributing R&D funds to regions where research productivity is the highest is a
promising economic policy instrument in the hands of Member States. This instrument
increases regional GDP in the most agglomerated regions as well as at the level of the
European Union. However, as expected there is a small negative effect on regions with
average development and a more adverse effect on lagging regions.

There are policy instruments to compensate for the negative effects of specialization in the
form of a spatial quality redistribution of R&D resources. Continuous regional human
capital development can successfully overcompensate the adverse effects in regions where
technological knowledge is about medium developed. There is also a considerable impact
of regional human capital development on GDP at the macro level.
Policy implications (cont.)

Compensating for R&D specialization in the form of persistent social capital development
is also a powerful tool for Member States to improve economic positions of regions with
medium-level agglomeration of technological knowledge. This policy option results in a
significant macro level GDP impact as well.

It is clear from the policy analyses that EU regions where agglomeration of technological
knowledge shows the lowest levels are not responsive to compensations in forms of either
human capital or social capital development. These regions should be considered
separately when local development policies are formed. They are not (yet) able to be the
sites of future knowledge-based development. Instead, specific sectoral policies aiming at
leisure or tourism would be more effective for those regions.