- Work in Progress Inventor mobility and
regions' innovation potential
Riccardo Cappelli, U Insubria
Dirk Czarnitzki, K.U.Leuven and ZEW Mannheim
Thorsten Doherr, ZEW Mannheim
Fabio Montobbio, U Insubria and Bocconi
Introduction
• In knowledge-based economies, human capital
and innovation are usually seen as key driver of
wealth and growth
– „new growth theory“, see e.g. Aghion and co-authors
• How to measure „knowledge“ that is present in an
economy or region?
• To what extent does knowledge contribute to growth?
„Technology gap models“
Technology gap models attempt to explain growth (or
„catching-up) in income per capita in economies or
regions by
• changes in knowledge stocks or innovation
(see e.g. Fagerberg, 1994 in JEL for an overview)
• and other common controls, e.g.
– Lagged income per capita
– investment into physical assets (change in stock of physical
assets)
– Size of the region or economy (usually measured by
population)
Technology gap models
How to measure knowledge or innovation?
• Scholars have used R&D expenditure to proxy the
change in knowledge stocks of regions
– e.g. Verspagen and Fagerberg, 2002, Research Policy
• Later substituted or augmented by patent applications
– Patents measure inventions but not innovations
– Patents could generate a premium as they approximate
„successful R&D“ or „valuable knowledge“ to a certain extent
• As the value distribution of patents is very skewed,
scholars have also used forward citations as proxy for
patent value
– Trajtenberg 1990, Hall et al., 2005
Measuring knowledge continued
• Knowledge spillovers at both macro and micro level are
important to explain the relative growth performance
– Grossman and Helpman, 1991; Griliches, 1992
• Knowledge Spillovers are geographically localized
– Jaffe et al., 1993; Bottazzi and Peri, 2003; Maruseth and
Verspagen, 2002; Peri, 2005
• There are some factors that can explain the
geographically localized diffusion of knowledge:
– importance of face-to-face contacts to spread tacit knowledge
– labor market (Almeida and Kogut, 1999)
– inventor mobility and co-invention networks (Breschi and
Lissoni, 2009)
Measuring knowledge spillovers
• Frequently, scholars have tried to control for knowledge
spillovers“ using patent citations
• Justified in US studies, as USPTO applies „duty of
candor“
– Patentees have to cite all relevant prior art in the patent
applications
• At EPO, however, most citations are added by
examiners
– Citations as measure of knowledge flows and thus value of
knowledge are questionable
– Patentee might not have been aware of existing knowledge
during the inventive process
Our approach
• Knowledge is embedeed in people
• Thus, inventor mobility is a more direct measure of
knowledge flows
• Challenge: how to measure inventor mobility
(see e.g. Trajtenberg‘s NBER WP „The name game“)
– Name homonyms
– Spelling variations and so forth
 Our approach:
inventor mobility index that has just been
presented by Thorsten.
Data
• 20 Italian regions from 1995 to 2007
• Dependent variable: %-growth of GDP per capita
• Variables based on the inventor mobility index:
– Intra-regional: inventor that „change jobs“ (switch applicants)
within the same region.
– Inter-regional inflow: incomnig inventors that change jobs and
move to region i from a different region.
– Inter-regional outflow: inventors formerly employed in region i
that now move to a new job in a different region.
– Inter-regional net inflow: difference between inflow and outflow.
all mobility figures enter regions as ratio: mobility relative to
stock of inventors in t-1 (derived by the perpetual inventory
method with 15% of obsolescence rate)
•
(Stock is corrected for double counting of inventors)
Data
Controls:
• GDP/Capita in previous period
• Total R&D expenditure (public and private) per capita
 change in „knowledge stock“
• Patent applications per capita as proxy for „successful
R&D“
 change in stock of successful R&D
• Investment into physical capital per capita in previous
period (change in asset stock)
 both variables measured in million EUR in real terms (GDP
deflator)
Descriptive Statistics
Tab. 1 Descriptive Statistics
Variable
gdp per capita
population
Capital/POP
Patent applications/ Total R&D exp.
Total R&D/ POP
Obs Mean Std. Dev Min
Max
240 0.0202
0.0051 0.0116
0.0283
240 2874971 2278932 117063 9545441
240 0.0044
0.0012 0.0022
0.0081
240 0.2541
0.1998 0.0151
1.2774
240 0.0002
0.0001 0.0000
0.0005
Variable
Gdp per capita growth
log(gdp/pop) t-1
log(pop) t-1
log (Capital/POP) t-1
(Patent applications/ Total R&D) t-1
log(Total R&D/POP) t-1
Intra regional t-1
Inter regional Inflow t-1
Inter regional Outflow t-1
Inter regional Net Inflow t-1
Obs Mean Std. Dev Min
240
0.011
0.016 -0.031
240 -3.949
0.271 -4.481
240 14.448
1.058 11.667
240 -5.490
0.282 -6.158
240
0.249
0.205
0.006
240 -8.870
0.680 -10.856
240
0.003
0.012
0
240
0.003
0.011
0
240
0.003
0.008
0
240
0.000
0.011 -0.080
P.S.: the values are expressed in millions of euro.
Max
0.057
-3.544
16.064
-4.818
1.277
-7.655
0.145
0.127
0.080
0.079
Regression Results
Tab. 2 Estimation results (OLS, Cluster standard error)
Variables
Model 1
log(GDP/POP) t-1
-0.039
log(POP) t-1
log (Capital/POP) t-1
(Patent applications/ Total R&D exp.) t-1
Model 2
**
**
-0.038
(0.146)
(0.013)
(0.013)
-0.000
-0.000)
-0.000
(0.001)
(0.001)
(0.001)
0.011
0.011
0.011
(0.008)
(0.009)
(0.009)
0.015
***
(0.005)
log(Total R&D/POP) t-1
-0.038
Model 3
0.006
(0.003)
0.013
***
(0.004)
*
0.006
0.013
**
***
(0.004)
**
0.005
(0.003)
(0.003)
0.088
0.080
(0.096)
(0.089)
*
Mobility
Intra regional t-1
Inter regional Inflow t-1
0.072
(0.062)
Inter regional Outflow t-1
-0.138
**
(0.064)
Inter regional Net Inflow t-1
0.097
(0.044)
Time year dummies
Yes
Yes
Yes
Number of observations
240
240
240
0,499
0,507
0,507
R-squared
Notes: Year 1996-2007, 20 Italian regions; * < 0.1, ** <0.05, *** <0.01
**
Very preliminary conclusions….
• Inventor mobility appears to explain a change in GDP
growth among Italian regions
• To-Do:
– Employ a revised version of the inventor mobility index
• According to the new version of the algorithm there is more mobility among
regions
– Try to collect more data to enable controlling for region fixedeffects
– Generate patent forward citations to control for heterogeneity in
value of patents
• More recent patent data required
– Try to handle potential endogeneity of measures such as R&D,
patenting and inventor mobility.