Introduction

1. - 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

2. 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?

3. „Technology gap models“ Technology gapmodelsattempttoexplaingrowth (or „catching-up) in income per capita in economiesorregionsby • changes in knowledgestocksorinnovation(see e.g. Fagerberg, 1994 in JEL for an overview) • and othercommoncontrols, e.g. • Laggedincome per capita • investmentintophysicalassets (change in stock ofphysicalassets) • Size oftheregionoreconomy (usuallymeasuredbypopulation)

4. Technology gap models Howtomeasureknowledgeorinnovation? • Scholarshaveused R&D expendituretoproxythechange in knowledgestocksofregions • e.g. Verspagen and Fagerberg, 2002, Research Policy • Latersubstitutedoraugmentedby patent applications • Patents measureinventions but not innovations • Patents couldgenerate a premium astheyapproximate „successful R&D“ or „valuableknowledge“ to a certainextent • As thevaluedistributionofpatentsisveryskewed, scholarshave also usedforwardcitationsasproxyfor patent value • Trajtenberg1990, Hall et al., 2005

5. 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)

6. 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

7. Our approach • Knowledgeisembedeed in people • Thus, inventormobilityis a moredirectmeasureofknowledgeflows • Challenge: howtomeasureinventormobility(see e.g. Trajtenberg‘s NBER WP „The namegame“) • Name homonyms • Spellingvariations and so forth  Ourapproach:inventormobilityindexthathas just beenpresentedby Thorsten.

8. 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)

9. Data Controls: • GDP/Capita in previousperiod • Total R&D expenditure (public and private) per capita change in „knowledge stock“ • Patent applications per capitaasproxyfor „successful R&D“ change in stock ofsuccessful R&D • Investment intophysicalcapital per capita in previousperiod (change in asset stock)  both variables measured in million EUR in real terms(GDP deflator)

10. Descriptive Statistics

11. Regression Results

12. 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.