- Work in Progress -

Inventor mobility and regions' innovation potential

Riccardo Cappelli, U InsubriaDirk Czarnitzki, K.U.Leuven and ZEW MannheimThorsten Doherr, ZEW MannheimFabio 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 StatisticsTab. 1 Descriptive Statistics

Variable Obs Mean Std. Dev Min Max

gdp per capita 240 0.0202 0.0051 0.0116 0.0283population 240 2874971 2278932 117063 9545441Capital/POP 240 0.0044 0.0012 0.0022 0.0081Patent applications/ Total R&D exp. 240 0.2541 0.1998 0.0151 1.2774Total R&D/ POP 240 0.0002 0.0001 0.0000 0.0005

Variable Obs Mean Std. Dev Min Max

Gdp per capita growth 240 0.011 0.016 -0.031 0.057log(gdp/pop) t-1 240 -3.949 0.271 -4.481 -3.544log(pop) t-1 240 14.448 1.058 11.667 16.064log (Capital/POP) t-1 240 -5.490 0.282 -6.158 -4.818(Patent applications/ Total R&D) t-1 240 0.249 0.205 0.006 1.277log(Total R&D/POP) t-1 240 -8.870 0.680 -10.856 -7.655Intra regional t-1 240 0.003 0.012 0 0.145Inter regional Inflow t-1 240 0.003 0.011 0 0.127Inter regional Outflow t-1 240 0.003 0.008 0 0.080Inter regional Net Inflow t-1 240 0.000 0.011 -0.080 0.079

P.S.: the values are expressed in millions of euro.

Regression Results

Tab. 2 Estimation results (OLS, Cluster standard error)        

Variables Model 1   Model 2   Model 3  

log(GDP/POP) t-1 -0.039 ** -0.038 ** -0.038 **

(0.146) (0.013) (0.013)

log(POP) t-1 -0.000 -0.000) -0.000

(0.001) (0.001) (0.001)

log (Capital/POP) t-1 0.011 0.011 0.011

(0.008) (0.009) (0.009)

(Patent applications/ Total R&D exp.) t-1 0.015 *** 0.013 *** 0.013 ***

(0.005) (0.004) (0.004)

log(Total R&D/POP) t-1 0.006 * 0.006 ** 0.005 *

(0.003) (0.003) (0.003)

Mobility            

Intra regional t-1 0.088 0.080

(0.096) (0.089)

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

R-squared 0,499   0,507   0,507  

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 fixed-effects

– 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.