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DIMETIC Pécs 2009
The geography of innovation and growth:
An introduction and overview
by
Attila Varga
Department of Economics and Regional StudiesFaculty of Business and Economics
University of Pécs, Hungary
I. Introduction
• A-spatial mainstream economic theory
• K, L and A only? How about their spatial arrangements?
• Why should we care about space?
I. Introduction
• Why should we care about space?
- Transport costs (can be integrated relatively easily)
- Agglomeration externalities (require a different approach)
• Policy relevance (EU)
Outline
• Introduction• Technological progress, spatial structure and
macroeconomic growth: An empirical modeling framework
• Integrating agglomeration effects to development policy modeling
• Concluding remarks
II. Technological progress, spatial structure and macroeconomic growth
Complex issue treated in four separate fields of economics:
A. EGT: “Endogenous economic growth” models: endogenized technological change in growth theory (Romer 1986, 1990, Lucas 1986, Aghion and Howitt 1998, 2009)
in Romer (1990):- for-profit private R&D- knowledge spillovers are essential in growth- rate of technical change equals rate of per-capita growth on
the steady state- Simplistic explanation of technological progress, no
geography
II. Technological progress, spatial structure and macroeconomic growth
B. IS: „Systems of innovation” literature: innovation is an interactive process among actors of the system (Lundval 1992, Nelson 1993)
actors of the IS:- innovating firms- suppliers, buyers- industrial research laboratories- public (university) research institutes- business services- “institutions”
level of innovation depends on:- the knowledge accumulated in the system- the interactions (knowledge flows) among the actors
- codified, non-codified (tacit) knowledge and the potential significance of spatial proximity- geography gets some focus, but IS does not say anything about growth
II. Technological progress, spatial structure and macroeconomic growth
C. GE: “Geographical economics” models:
- „New Economic Geography”(NEG): endogenized spatial economic structure in a general equilibrium model (Krugman 1991, Fujita, Krugman and Venables 1999, Fujita and Thisse 2002)- „Evolutionary Economic Geography” (EEG) (Boschma 2007)
II. Technological progress, spatial structure and macroeconomic growth
In the New Economic Geography:
- spatially extended Dixit-Stiglitz framework- increasing returns, monopolistic competition- spatial structure depends on some parameter conditions that determine the equilibrium level of centrifugal and centripetal forces- „cumulative causation”- C-P model by Krugman: still the point of departure- models quickly become complex: simulations if analytical solutions are not accessible
- Technological change not explained (not even included until very recently), the study of its relation to growth is a recent phenomenon
II. Technological progress, spatial structure and macroeconomic growth
D. GI: The „Geography of innovation” literature: the study of the spatial extent of knowledge flows in innovation (Jaffe 1989, Jaffe, Trajtenberg and Henderson 1993, Anselin, Varga and Acs 1997)
- Empirical litarature: US, European, Asian analyses
- Common finding: much of knowledge flows in technological change are spatially bounded (though differences with respect to industry, stage of innovation, institutional proximity exist, related variety important)
- Not connected to growth and to the explanation of spatial economic structure
II. Technological progress, spatial structure and macroeconomic growth
• IS, GE, EGT, GI: complements to each other in growth explanation, no theoretical integration (Acs-Varga 2002)
• IS, GE, EGT, GI: building blocks of a framework to shape empirical research (Varga 2006)
• Theoretical integration: endogenous growth and new economic geography (Baldwin and Forslid 2000, Fujita and Thisse 2002, Baldwin et al. 2003)
• EGT, IS, GE, GI: methodological problems in THEORETICAL integration (dramatically diverging initial assumptions, different theoretical structures, research methodologies)
• EMPIRICAL integration: very few work (Ciccone and Hall 1996, Varga and Schalk 2004)
II. Technological progress, spatial structure and macroeconomic growth: An empirical modeling
framework
• Starting points („stylized facts”):
- Technological change is a collective process that depends on accumulated knowledge and interactions (IS)
- Technological change is the simple most important determinant of economic growth (EG)
- Codified and tacit knowledge: different channels of spillovers (GI)
- Centripetal and centrifugal forces shape geographical structure via cumulative processes (GE)
- The resulting geographic structure is a determinant of the rate of growth (NEG)
• Y = AKαLβ
• The Romer (1990) equation as in Jones (1995)
dA = HA Aφ,
- HA: the number of researchers (“person-embodied”, knowledge component of knowledge production)
- A: the total stock of technological knowledge (codified knowledge component of knowledge production in books, patent documents etc.)- dA: the change in technological knowledge
φ: “codified knowledge spillovers parameter” - reflects spillovers with unlimited spatial accessibility
: the “research productivity parameter”- reflects localized knowledge spillover effects (GI)- regional and urban economics and the new economic geography suggest: changes with geographic concentration of economic activities (depending on the balance between positive and negative agglomeration economies)
II. Technological progress, spatial structure and macroeconomic growth: An empirical modeling
framework
II. Technological progress, spatial structure and macroeconomic growth: An empirical modeling framework
Eq.1 Regional knowledge production:Kr = K (RDr, URDr, Zr)
A cumulative process described by Eqs. 2 and 3 (dynamic agglomeration effects):
Eq.2 (Static) agglomeration effect in R&D effectiveness: ∂Kr/∂RDr = f (RDr, URDr, Zr)
Eq.3 R&D location: dRDr = R(∂Kr/∂RDr)
Eq.4 Geography and : = (GSTR(HA))
Eq.5 dA = HA Aφ
Eq.6 dy/y = H(dA, ZN)
II. Technological progress, spatial structure and macroeconomic growth: An empirical modeling framework
• To test Eq.1: most of the empirical models are based on the
„knowledge production framework”
log (K) = + log(R) + log(U) + log(Z) +
• The KPF framework to study localized knowledge spillovers
USA: Jaffe 1989 Acs, Audretsch and Feldman 1991
Anselin, Varga and Acs, 1997 Varga 1998
Feldman and Audretsch 1999 Acs, Anselin and Varga 2002
EU: Moreno-Serrano, Paci, Usai 2005Italy: Audretsch and Vivarelly 1994, Capello 2001
France: Autant-Bernard 1999 Austria: Fischer and Varga 2003 Germany: Fritsch 2002
II. Technological progress, spatial structure and macroeconomic growth: An empirical modeling framework
To test Eq.2 and Eq. 3:
– Some of the empirical studies test the effects SEPARATELY (Jaffe 1989, Bania et al 1992, Anselin, Varga, Acs 1997a,b, Varga 2000, 2001)
– The dynamic cumulative process modeled econometrically (Varga, Pontikakis, Chorafakis 2009)
Table 1. Regression Results for Log (Patents) for 189 EU regions, 2000-2002 (N=567)
Model (1) (2) (3) (4) (5) (6) Estimation OLS OLS OLS OLS OLS 2SLS- Spatial
Lag (INV2) Constant W_Log(PAT) Log(GRD(-2)) Log(GRD(-2))*Log(δ(-2)) Log(GRD(-2))*Log(NETRD(-2)) Log(PSTCK(-2)) PAHTCORE
-1.6421*** (0.1776)
1.0822*** (0.0308)
-0.3107 (0.2316)
0.8453*** (0.0407)
0.3242*** (0.0389)
-0.5391* (0.2806)
0.9585*** (0.0886)
0.3222*** (0.0389)
-8.675E-05 (6.03E-05)
-1.7864*** (0.2381)
0.7142*** (0.0377)
0.2443*** (0.0351)
0.2502*** (0.0203)
-1.7227*** (0.2372)
0.6879*** (0.0384)
0.2136*** (0.0363)
0.2536*** (0.0202)
0.4814*** (0.1568)
-1.3881*** (0.2665) 0.0027** (0.0012)
0.6067*** (0.0483)
0.2429*** (0.0393)
0.2547*** (0.0214)
0.5007*** (0.1612)
R2-adj Log Likelihood
0.69 -885.30
0.72 -852.36
0.72 -851.32
0.78 -784.69
0.78 -779.98
0.78
Multicollinearity Condition Number F on pooling (time) F on slope homogeneity White test for heteroscedasticity LM-Err Neighb INV1 INV2 LM-Lag Neighb INV1 INV2
7
0.9071 0.4815
0.7529
111.78*** 252.17*** 215.12***
142.53*** 247.03*** 237.99***
10
0.6777 0.7613
1.0462
69.36*** 129.64*** 121.59***
100.88*** 159.07*** 148.93***
24
0.5644 0.5836
12.8409
66.85*** 117.26*** 114.45***
99.03*** 153.47*** 145.48***
13
0.8143 0.6485
3.6634
26.95*** 29.87*** 32.40***
24.99*** 28.16*** 31.42***
13
0.6425 0.4645
12.1852
23.46*** 26.13*** 29.24***
25.89*** 27.96*** 30.95***
Notes: Estimated standard errors are in parentheses; spatial weights matrices are row-standardized: Neigh is neighborhood contiguity matrix; INV1 is inverse distance matrix; INV2 is inverse distance squared matrix; W_Log(PAT) is the spatially lagged dependent variable where W stands for the weights matrix INV2. *** indicates significance at p < 0.01; ** indicates significance at p < 0.05; * indicates p < 0.1 . Endogenous variables in Model 6: W_Log(PAT) and Log(GRD(-2); Instruments: W_Log(PUB), Log(PUB), W_Log(GRD(-2)), PUBCORE. The Durbin-Wu_Hausman test for Log(GRD(-2))* Log(δ(-2)) does not reject exogeneity.
Table 2. Regression Results for Log (Publications) for 189 EU regions, 2000-2002 (N=567)
Model (1) (2) (3) (4) (5) (6) Estimation OLS OLS OLS OLS OLS 2SLS Spatial Lag
(Neigh) Heteroscedasticity
robust Constant W_Log(PUB) Log(GRD(-2)) Log(GRD(-2))*Log(δ(-2)) Log(GRD(-2))*Log(NETRD(-2)) Log(PSTCK(-2)) PUBCORE
1.4026*** (0.1298)
0.942*** (0.0225)
2.3886*** (0.1645)
0.445*** (0.0597)
0.0004*** (4.40E-05 )
2.196*** (0.202)
0.480*** (0.633) -0.0462 (0.0282) 0.0004**
* (4.40E-
05)
2.3395*** (0.1711)
0.4158*** (0.066)
0.0004*** (4.56E-05)
0.01758 (0.01689)
2.4568*** (0.1697)
0.4523*** (0.0602)
0.0004*** (4.68E-05)
0.2247** (0.1032)
2.6237*** (0.2127)
-0.0313*** (0.0021)
0.7083*** (0.1283)
0.0002*** (7.3225E-5)
0.1964* (0.1106)
R2-adj Log Likelihood
0.76 -707.30
0.79 -670.05
0.79 -668.70
0.79 -669.51
0.79 -667.89
0.79
Multicollinearity Condition Number F on pooling (time) F on slope homogeneity White test for heteroscedasticity LM-Err Neighb INV1 INV2 LM-Lag Neighb INV1 INV2
7
0.6694 0.2059
44.575***
0.7199 3.3586* 0.3687
12.214*** 1.6479
5.2928**
22
0.9269 0.357
77.378***
0.7727 2.5407 0.9367
3.0067* 0.0642 0.6649
23
0.6712 0.2752
84.013***
0.7518 1.8767 0.8782
2.4689 0.4640 0.1242
27
0.7141 0.2683
92.231***
0.9808 3.4006* 1.2604
4.2311** 0.061 1.9522
24
0.7055 0.2501
86.884***
0.5749 2.6595 1.020
3.7861* 0.0069 1.1352
Notes: Estimated standard errors are in parentheses; spatial weights matrices are row-standardized: Neigh is neighborhood contiguity matrix; INV1 is inverse distance matrix; INV2 is inverse distance squared matrix; *** indicates significance at p < 0.01; ** indicates significance at p < 0.05; * indicates p < 0.1. Endogenous variable in Model 6: W_Log(PUB). Instruments: W_Log(PAT), Log(PAT), W_Log(GRD(-2)), PATHCORE. The Durbin-Wu_Hausman test for Log(GRD(-2)) and Log(GRD(-2))* Log(NETRD(-2)) does not reject exogeneity.
Regional R&D productivity in competitive research
BETAPAT< -3 Std. Dev.-3.0 - -2.5 Std. Dev.-2.5 - -2.0 Std. Dev.-2.0 - -1.5 Std. Dev.-1.5 - -1.0 Std. Dev.-1.0 - -0.5 Std. Dev.-0.5 - 0.0 Std. Dev.Mean0.0 - 0.5 Std. Dev.0.5 - 1.0 Std. Dev.1.0 - 1.5 Std. Dev.1.5 - 2.0 Std. Dev.2.0 - 2.5 Std. Dev.
Regional R&D productivity in pre-competitive research
BETAPUB< -3 Std. Dev.-3.0 - -2.5 Std. Dev.-2.5 - -2.0 Std. Dev.-2.0 - -1.5 Std. Dev.-1.5 - -1.0 Std. Dev.-1.0 - -0.5 Std. Dev.-0.5 - 0.0 Std. Dev.Mean0.0 - 0.5 Std. Dev.0.5 - 1.0 Std. Dev.1.0 - 1.5 Std. Dev.
Table 3. Regression Results for (GRD2001-GRD1998) for EU regions (N=189)
Model (1) (2) (3) (4) Estimation OLS OLS OLS OLS-Heteroscedasticity
Robust (White) Constant BETAPAT1998 BETAPUB1998 PSTCK2001
-1423.44*** (196.344)
2345.18*** (301.971)
-1385.92*** (193.372)
1863.47*** (345.535)
364.843*** (131.183)
-550.111*** (149.521)
720.125*** (256.492) 190.336** (93.496)
360.982*** (26.322)
-550.111*** (143.413)
720.125*** (241.882)
190.336*** (69.895)
360.982*** (47.416)
R2-adj 0.24 0.27 0.63 0.63 White test for heteroscedasticity LM-Err Neighb INV1 INV2 LM-Lag Neighb INV1 INV2
52.316***
0.113 0.009 0.089
0.096 2.696 0.595
57.884***
0.023 0.198 0.963
0.043 1.820 0.531
42.236***
0.068 1.148 0.942
0.103 1.996 1.989
Table 4. Regression Results for (HTEMP2001-HTEMP1998) for EU regions
(N=189) Model (1) (2) (3) (4) Estimation OLS OLS OLS ML – Spatial Error (INV2)
with Heteroscedasticity weights
Constant HTEMP1998 HTEMP1998*GRD1998 RDCORE LAMBDA
5399.78* (3032.61) 0.071*** (0.006)
8821.36*** (3314.62) 0.054*** (0.009)
3.788E-06** (1.582E-06)
9955.96*** (3267.78) 0.032*** (0.012)
5.043E-06*** (1.604E-06) 19896.5*** (6614.64)
11168.3*** (2879.48) 0.0262** (0.011)
5.624E-06*** (1.604E-06) 21321.1*** (6366.96) -0.0181**
(0.009) R2-adj 0.41 0.42 0.45 0.45 Multicollinearity Condition Number White test for heteroscedasticity LM-Err Neighb INV1 INV2 LM-Lag Neighb INV1 INV2
2
27.37***
0.922 0.052 1.008
2.181 0.479 4.000*
4
28.182***
0.164 0.023 3.263*
1.846 0.043
4.574**
6
34.522***
0.042 0.28
5.878**
1.916 0.645
4.316**
II. Technological progress, spatial structure and macroeconomic growth: An empirical modeling framework
• Empirical integration of micro to macro (Eqs. 4-6): a real research challenge: needs an integrated macro-regional approach (back to this on Friday afternoon)
Research questions related to the empircal model: The structure of the week
– The geography of innovation: knowledge interactions in space• Stefano USAI• Francesco LISSONI
– How to explain the geographical structure of innovation and the resulting growth?
• EEG: Ron BOSCHMA, Giulio BOTTAZZI• NEG: Mark THISSEN
– How new product varieties emerge and how they are related to economic growth?
• Pier Paolo SAVIOTTI
– Empirical research methodology:• Frank van OORT• Attila VARGA
The structure of the weekMonday, June 29th 9.00 – 9.30: Welcome, practical and organizational issues – Ron BOSCHMA and Attila VARGA 9.30 – 10.30: The geography of innovation and growth: An introduction and overview – Attila VARGA 11.00 – 12.30: Knowledge spillovers in space – revisiting the issue with a novel OECD data set – Stefano USAI 14.00 – 16.00: PhD presentation n°1: Senior discussant: Attila VARGA 16.15 – 17.45: PhD presentation n°2: Senior discussant: Stefano USAI Tuesday, June 30th 9.00 - 10.30: Geography and growth: The perspective of the evolutionary economic geography – Ron BOSCHMA 11.00 - 12.30: Geography and growth: The perspective of the new economic geography – Harry GARRETSEN 14.00 - 16.00: PhD Presentation n° 3: Senior discussant: Ron BOSCHMA 16.15 - 17.45: PhD Presentation n° 4: Senior discussant: Harry GARRETSEN Wednesday, July 1st 9.00 – 10.30: New variety and economic growth - Pier Paolo SAVIOTTI 11.00- 12.30: Evolutionary modeling of location – Giulio BOTTAZZI 14.00-16.00: PhD Presentation n° 5: Senior discussant: Pier Paolo SAVIOTTI 16.15 - 17.45: PhD Presentation n° 6: Senior discussant: Giulio BOTTAZZI Thursday, July 2nd 9.00 – 10.30: Spatial spillovers or markets? – Francesco LISSONI 11.00- 12.30: Empirical methodologies to research agglomeration externalities – Frank van Oort 14.00-16.00: PhD Presentation n° 5: Senior discussant: Pier Paolo SAVIOTTI 16.15 - 17.45: PhD Presentation n° 6: Senior discussant: Giulio BOTTAZZI Friday, July 3rd 9.00 - 10.30: Applied Spatial Econometric Analysis I. – Julie Le Gallo 11.00 - 12.30: Applied Spatial Econometric Analysis II. - Julie Le Gallo 14.00-16.00: Spatial econometrics in practice – application in the field of the geography of innovation – Julie Le Gallo 16.15 – 16:45: Integrating the geography of innovation to policy modeling – Attila VARGA 17:00 – 17:45: Concluding session. A summary discussion – Ron BOSCHMA and Attila VARGA