Skip to main content

Advertisement

SpringerLink
Log in

The nexus between inventors’ mobility and regional growth across European regions

  • Original Article
  • Published:
Journal of Geographical Systems Aims and scope Submit manuscript

Abstract

The role of the spatial mobility of skilled individuals and knowledge workers on the innovative capacity of the recipient region has largely been highlighted, measured and proved in the literature, by positing a direct link from mobility to innovation. This paper enters this literature by explicitly examining and verifying whether innovation generated by inventors’ mobility is enough to enhance growth and whether such link depends on the innovative context. In fact, areas in which inventors can more easily enter, integrate and complement existing consolidated knowledge bases can be more easily affected by incoming inventors.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

Data availability statement

The data that support the findings of this study are available from the corresponding author upon reasonable request.

Notes

  1. For a recent review, see Lissoni (2018).

  2. For a similar discussion, see Breschi et al. (2010).

  3. A more detailed discussion of the differences and novelties of the regional innovation pattern approach with respect to existing regional innovation frameworks is developed in Capello and Lenzi (2019).

  4. For further details on the variables used in the cluster analysis implemented to detect innovation patterns in European regions and the variables representing the key territorial features of the different groups of regions, see Capello and Lenzi (2013a).

  5. It has largely been proved that each pattern has an efficiency in generating growth and therefore has a reason to exist and be maintained over time (Capello and Lenzi 2013b). Nonetheless, the complexity, in terms of new knowledge produced and innovation developed, is certainly different across patterns and increases from the imitative to the science-based innovation patterns.

  6. IIA is the acronym of Imitative Innovation Area, SCDA of Smart and Creative Diversification Area, STAA of Smart Technological Application Area, ESBA of European Science-based Area.

  7. For a similar approach, see Capello and Lenzi (2016).

  8. Traditionally, patent data have been used to track inventors’ mobility flows across firms and in space (see among others: Agrawal et al. 2006; Breschi et al. 2017). Even if this approach is not free of criticisms (Ge et al. 2016), it remains a valuable choice for detecting the spatial diffusion of knowledge and its potential impact on recipient economies. Additional descriptive evidence on inventors’ mobility by regional innovation patterns and on the top 10 destination regions of inventors’ mobility is available in “Appendix” (Tables 7, 8). Moreover, Fig. 3 displays the inventors’ mobility variable in European NUTS-2 regions, where the zero category refers to the case of no inventors’ mobility detected in a specific region.

  9. Country group dummies are defined as follows: South (Greece, Italy, Portugal and Spain); North: (Denmark, Finland and Sweden); West (France, Ireland and UK); Center (Austria, Belgium, Germany, Luxemburg and the Netherlands); Central and Eastern European Countries, the reference case (Czech Republic, Hungary, Poland, Slovakia and Slovenia); Romania and Bulgaria; Baltic countries (Estonia, Latvia and Lithuania), Malta and Cyprus; Eurozone dummy variable, which takes value 1 for regions in the Eurozone countries: Austria, Belgium, Cyprus, Germany, Greece, Spain, Estonia, Finland, France, Ireland, Italy, Latvia, Luxemburg, Malta, the Netherlands, Portugal, Slovakia, Slovenia (Lithuania was excluded because adoption of euro occurred in 2015, after the period under consideration); European Financial Aid, which takes value 1 for regions in the following countries: Cyprus, Greece, Ireland, Portugal, Spain, which received financial assistance from European institutions during the crisis period.

  10. The rate of convergence in the two models has been computed by using the following formula (Abreu et al. 2005; Le Gallo and Fingleton 2013): s = − ln(1 + Tβ))/T, where s is the average yearly speed of convergence, β the coefficient of the initial GDP per capita level and T the number of years over which the growth rate of GDP per capita is computed. In the present case, T is equal to 9 and β in Models 1 and 2 amounts, respectively, to − 0.0112 and − 0.0117.

  11. This is consistent with Barro and Sala-i-Martin (1995).

  12. Faster convergence, once controlling for inventors’ mobility, signals a negative overall effect of their moves on the convergence process; inventors, in fact, by moving selectively across space, contribute to increase rather than to narrow regional gaps. On the other hand, as commented by Arbia et al. (2005), in a standard converge framework, factor mobility should lead to regional convergence and a decrease in the value of the convergence parameter. We thank an anonymous reviewer for highlighting this point.

  13. See Mitchell (2012) and Brambor et al. (2006) for a similar discussion.

  14. We thank an anonymous reviewer for this suggestion.

  15. The period 2009–2012 is more volatile presenting both recovery and decline with respect to others (Fig. 1 in “Appendix”). The other periods instead present a clearer trend and are therefore preferable as they enable a clearer interpretation.

  16. See Breschi and Lenzi (2016) for a similar approach.

  17. The test for the endogeneity of the innovation patterns dummy variables allows excluding a relevant concern of endogeneity also for these variables; the C statistic is χ2 = 4.862 with p = 0.30. Being both the inventors’ mobility variable and the innovation patterns dummies exogenous, it is reasonable to conclude that their interactions are not seriously affected by endogeneity concerns and for this reason they are excluded from the first-stage equation.

References

  • Abreu M, de Groot HLF, Florax RJGM (2005) A meta-analysis of β-convergence: the legendary 2%. Journal of Economic Surveys 19(3):389–420

    Article  Google Scholar 

  • Abreu M, Faggian A, McCann P (2015) Migration and inter-industry mobility of UK graduates. J Econ Geogr 15(2):353–385

    Article  Google Scholar 

  • Agrawal A, Cockburn I, McHale J (2006) Gone but not forgotten: labour flows, knowledge spillovers, and enduring social capital. J Econ Geogr 6(5):571–591

    Article  Google Scholar 

  • Agrawal A, Kapur D, McHale J, Oettl A (2011) Brain drain or brain bank? The impact of skilled emigration on poor-country innovation. Journal of Urban Economics 69(1):43–55

    Article  Google Scholar 

  • Arbia G, Basile RG, and Piras G (2005) Using spatial panel data in modelling regional growth and convergence. https://ebiblio.istat.it/digibib/Working_Papers/WP_55_2005_Arbia_Piras_Basile.pdf. Accessed 20 Sept 2018

  • Asheim BT, Isaksen A (2002) Regional innovation systems: the integration of local ‘sticky’ and global ‘ubiquitous’ knowledge. The Journal of Technology Transfer 27(1):77–86

    Article  Google Scholar 

  • Asheim BT, Grillitsch M, Trippl M (2016) Regional innovation systems: past-present-future. In: Shearmur R, Carrincazeaux C, Doloreux D (eds) Handbook on the geographies of innovation. Edward Elgar, Cheltenham, pp 45–62

    Chapter  Google Scholar 

  • Barro RJ, Sala-i-Martin X (1995) Economic growth. McGraw Hill, New York

    Google Scholar 

  • Baum CF (2006) An introduction to modern econometrics using Stata. StataCorp LP, College Station

    Google Scholar 

  • Baum CF, Schaffer ME (2012) ivreg2 h: Stata module to perform instrumental variables estimation using heteroskedasticity-based instruments. http://ideas.repec.org/c/boc/bocode/s457555.html. Accessed 13 June 2019

  • Baum CF, Schaffer ME, Stillman S (2003) Instrumental variables and GMM: estimation and testing. Stata Journal 3(1):1–31

    Article  Google Scholar 

  • Baum CF, Schaffer ME, Stillman S (2007) Enhanced routines for instrumental variables/generalized method of moments estimation and testing. Stata Journal 7(4):465–506

    Article  Google Scholar 

  • Baum CF, Lewbel A, Schaffer ME and Talavera O (2012) Instrumental variables estimation using heteroskedasticity-based instruments. http://repec.org/usug2012/UK12_baum.pdf. Accessed 13 June 2019

  • Beine M, Docquier F, Rapaport H (2008) Brain drain and human capital formation in developing countries: winners and losers. Econ J 118(528):631–652

    Article  Google Scholar 

  • Bellini E, Ottaviano GIP, Pinelli D, Prarolo G (2013) Cultural diversity and economic performance: evidence from European regions. In: Crescenzi R, Percoco M (eds) Geography, institutions and regional economic performance. Springer, Berlin, pp 121–141

    Chapter  Google Scholar 

  • Borjas GJ, Doran KB (2015) Cognitive mobility: labor market responses to supply shocks in the space of ideas. Journal of Labor Economic 33(S1):S109–S145

    Article  Google Scholar 

  • Brambor T, Clarck WR, Golder M (2006) Understanding interaction models: improving empirical analyses. Political Analysis 14(1):63–82

    Article  Google Scholar 

  • Breschi S, Lenzi C (2015) The role of external relations and gatekeepers for the expansion and renewal of US cities’ knowledge base, 1990–2004. Reg Stud 49(5):782–797

    Article  Google Scholar 

  • Breschi S, Lenzi C (2016) Co-invention networks and inventive productivity in US cities. Journal of Urban Economics 92(1):66–75

    Article  Google Scholar 

  • Breschi S, Lenzi C, Lissoni F, Vezzulli A (2010) The geography of knowledge spillovers: the role of inventors’ mobility across firms and in space. In: Boschma R, Martin R (eds) Handbook of evolutionary economic geography. Edward Elgar, Cheltenham, pp 353–369

    Google Scholar 

  • Breschi S, Lissoni F, Miguelez E (2017) Foreign-origin inventors in the USA: testing for diaspora and brain gain effects. J Econ Geogr 17(5):1009–1038

    Google Scholar 

  • Cainelli G, Ganau R, Modica M (2019) Industrial relatedness and regional resilience in the European Union. Papers in Regional Science 98(2):755–778

    Article  Google Scholar 

  • Camagni R (2015) Towards creativity-oriented innovation policies based on a hermeneutic approach to the knowledge-space nexus. In: Cusinato A, Philippopoulos-Mihalopoulos A (eds) Knowledge-creating milieus in Europe: firms, cities, territories. Springer, Berlin, pp 341–358

    Google Scholar 

  • Capello R, Lenzi C (2013a) Territorial patterns of innovation in Europe: a taxonomy of innovative regions. Annals of Regional Science 51(1):119–154

    Article  Google Scholar 

  • Capello R, Lenzi C (2013b) Territorial patterns of innovation and economic growth in European regions. Growth and Change 44(2):195–227

    Article  Google Scholar 

  • Capello R, Lenzi C (2016) Innovation modes and entrepreneurial behavioral characteristics in regional growth. Small Bus Econ 47(4):875–893

    Article  Google Scholar 

  • Capello R, Lenzi C (2019) Structural dynamics of regional innovation patterns in Europe: the role of inventors’ mobility. Reg Stud 53(1):30–42

    Article  Google Scholar 

  • Capello R, Caragliu A, Fratesi U (2015) Spatial heterogeneity in the costs of the economic crisis in Europe: are cities sources of regional resilience? J Econ Geogr 15(5):951–972

    Article  Google Scholar 

  • Caragliu A, Del Bo C (2018) The economics of smart city policies. Science Regionali 17(1):81–104

    Google Scholar 

  • Carlino G, Kerr WR (2014) Agglomeration and innovation. Working Paper 14/26, Federal Reserve Bank of Philadelphia, Philadelphia, PA

  • Casi L, Resmini L (2017) Investimenti diretti esteri, identità regionali e crescita. Scienze Regionali 16(2):171–200

    Google Scholar 

  • Crescenzi R, Gagliardi L (2015) Moving people with ideas Innovation, inter-regional mobility and firm heterogeneity, Taubaman Center for State and Local Government Working Paper 2015-01, Harvard Kennedy School. http://isites.harvard.edu/fs/docs/icbtopic1459278files/CRESCENZI-Riccardo_and_Gagliardi_Moving_People_with_Ideas_3-6-15.pdf. Accessed 23 Jul 2019

  • Crescenzi R, Rodríguez-Pose A (2011) Innovation and regional growth in the European Union. Springer, Berlin

    Book  Google Scholar 

  • Elhorst PJ (2010) Applied spatial econometrics: raising the bar. Spatial Economic Analysis 5(1):9–28

    Article  Google Scholar 

  • Faggian A, McCann P (2008) Human capital, graduate migration and innovation in British regions. Camb J Econ 33(2):317–333

    Article  Google Scholar 

  • Fischer MM, Wang JF (2011) Spatial data analysis. Models, methods and techniques. Springer, Berlin

    Book  Google Scholar 

  • Florax RJGM, Folmer H, Rey SJ (2003) Specification searches in spatial econometrics: the relevance of Hendry’s methodology. Regional Science and Urban Economics 33(5):557–579

    Article  Google Scholar 

  • Foray D (2009) Understanding smart specialisation. In: Pontikakis D, Kyriakou D, van Bavel R (eds) The question of R&D specialisation. JRC, European Commission, Directoral General for Research, Brussels, pp 19–28

    Google Scholar 

  • Ganguli I (2015) Immigration & ideas: what did Russian scientists ‘bring’ to the US? J Labor Econ 33(S1):S257–S288

    Article  Google Scholar 

  • Ge C, Huang KW, Png IP (2016) Engineer/scientist careers: patents, online profiles, and misclassification bias. Strateg Manag J 37(1):232–253

    Article  Google Scholar 

  • Grillitsch M, Trippl M (2014) Combining knowledge from different sources, channels and geographical scales. Eur Plan Stud 22(11):2305–2325

    Article  Google Scholar 

  • Hansen LP (1982) Large sample properties of generalized method of moments estimators. Econometrica 50:1029–1054

    Article  Google Scholar 

  • Hunt J, Gauthier-Lauselle M (2010) How much does immigration boost innovation? American Economic Journal: Macroeconomics 2(2):31–56

    Google Scholar 

  • Jensen MB, Johnson B, Lorenz E, Lundvall BA (2007) Forms of knowledge and modes of innovation. Res Policy 36(5):680–693

    Article  Google Scholar 

  • Kerr WR (2008) Ethnic scientific communities and international technology diffusion. Rev Econ Stat 90(3):518–537

    Article  Google Scholar 

  • Kerr WR (2010) Breakthrough inventions and migrating clusters of innovations. Journal of Urban Economics 67(1):46–60

    Article  Google Scholar 

  • Kerr SP, Kerr WR (2017) Global collaborative patents. Economic Journal 128(612):F235–F272

    Article  Google Scholar 

  • Kerr SP, Kerr WR, Özden Ç, Parsons C (2017) Global talent flows. Journal of Economic Perspectives 30(4):83–106

    Article  Google Scholar 

  • Kleibergen F, Paap R (2006) Generalized reduced rank tests using the singular value decomposition. Journal of Econometrics 133:97–126

    Article  Google Scholar 

  • Le Gallo J, Fingleton B (2013) Regional growth convergence and empirics. In: Fischer MM, Njikamp P (eds) Handbook of regional science, vol 1. Springer, Berlin, pp 291–315

    Google Scholar 

  • LeSage JP, Pace RK (2009) Introduction to spatial econometrics. Taylor and Francis, Boca Raton

    Book  Google Scholar 

  • LeSage JP, Pace RK (2014) Interpreting spatial econometric models. In: Fischer MM, Nijkamp P (eds) Handbook of regional science. Springer, Berlin, pp 1535–1552

    Chapter  Google Scholar 

  • Lewbel A (2012) Using heteroscedasticity to identify and estimate mismeasured and endogenous regressor models. Journal of Business and Economic Statistics 30(1):67–80

    Article  Google Scholar 

  • Licht G (2009) How to better diffuse technologies in Europe. Knowledge Economy Policy Brief 7:1–5

    Google Scholar 

  • Lincoln W, Kerr W (2010) The supply side of innovation: H-1B visa reforms and US ethnic invention. J Labor Econ 28(3):473–508

    Article  Google Scholar 

  • Lissoni F (2018) International migration and innovation diffusion: an eclectic survey. Reg Stud 52(5):702–714

    Article  Google Scholar 

  • Lucas RE (1988) On the mechanics of economic development. Journal of Monetary Economics 22(1):3–42

    Article  Google Scholar 

  • Mack E (2014) Broadband and knowledge intensive firm clusters: essential link or auxiliary connection? Papers in Regional Science 93(1):3–29

    Article  Google Scholar 

  • Maré DC, Fabling R, Stillman S (2014) Innovation and the local workforce. Papers in Regional Science 93(1):183–201

    Article  Google Scholar 

  • Marrocu E, Paci R, Usai S (2013) Productivity growth in the old and new Europe: the role of agglomeration externalities. Journal of Regional Science 53(3):418–442

    Article  Google Scholar 

  • Marzucchi A, Antonioli D, Montresor S (2015) Industry-research cooperation within and across regional boundaries. What does innovation policy add? Papers in Regional Science 94(3):499–525

    Article  Google Scholar 

  • Miguélez E, Moreno R (2013) Research networks and inventors’ mobility as drivers of innovation: evidence from Europe. Reg Stud 47(10):1668–1685

    Article  Google Scholar 

  • Miguélez E, Moreno R (2014) What attracts knowledge workers? The role of space and social networks. Journal of Regional Science 54(1):33–60

    Article  Google Scholar 

  • Miguélez E, Moreno R (2015) Knowledge flows and the absorptive capacity of regions. Res Policy 44(4):833–848

    Article  Google Scholar 

  • Miguélez E, Moreno R, Surinach J (2013) Knowledge flows and regional knowledge creation. In: Capello R, Lenzi C (eds) Territorial patterns of innovation. An inquiry on the knowledge economy in European Regions. Routledge, Oxford, pp 210–229

    Google Scholar 

  • Mitchell MM (2012) Interpreting and visualizing regression models using Stata. Stata Press, College Station

    Google Scholar 

  • Moser P, Voena A, Waldinger F (2014) German Jewish Émigrés and US Invention. Am Econ Rev 104(10):3222–3255

    Article  Google Scholar 

  • Nathan M (2015) Same difference? Minority ethnic inventors, diversity and innovation in the UK. J Econ Geogr 15(1):129–168

    Article  Google Scholar 

  • Ott H, Rondé P (2019) Inside the regional innovation system black box: evidence from French data. Papers in Regional Science. https://doi.org/10.1111/pirs.12446

    Article  Google Scholar 

  • Ozgen C, Nijkamp P, Poot J (2010) The effect of migration on income growth and convergence: meta-analytic evidence. Papers in Regional Science 89(3):537–561

    Article  Google Scholar 

  • Ozgen C, Peters C, Niebuhr A, Nijkamp P, Poot J (2014) Does cultural diversity of migrant employees affect innovation? Int Migrat Rev 48(s1):S377–S416

    Article  Google Scholar 

  • Pavlínek P (2002) Transformation of central and east European passenger car industry: selective peripheral integration through foreign direct investment. Environment and Planning A 34(9):1685–1709

    Article  Google Scholar 

  • Peri G, Shih K, Sparber C (2015) STEM workers, H-1B visas, and productivity in US cities. J Labor Econ 33(S1):S225–S255

    Article  Google Scholar 

  • Shea J (1997) Instrument relevance in multivariate linear models: a simple measure. Rev Econ Stat 49:348–352

    Article  Google Scholar 

  • Singh J, Agrawal AK (2011) Recruiting for ideas: how firms exploit the prior inventions of new hires. Manage Sci 57(1):129–150

    Article  Google Scholar 

  • Solow RM (1956) A contribution to the theory of economic growth. Quart J Econ 70(1):65–94

    Article  Google Scholar 

  • Stock JH, Yogo M (2005) Testing for weak instruments in linear IV regression. In: Andrews DWK, Stock JH (eds) Identification and inference for econometric models: essays in honor of Thomas Rothenberg. Cambridge University Press, Cambridge, pp 80–108

    Chapter  Google Scholar 

  • Trippl M, Grillitsch M, Isaksen A (2018) Exogenous sources of regional industrial change: attraction and absorption of non-local knowledge for new path development. Prog Hum Geogr 42(5):687–705

    Article  Google Scholar 

  • Varga A, Schalk H (2004) Knowledge spillovers, agglomeration and macroeconomic growth: an empirical approach. Reg Stud 38(8):977–989

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Architecture, Built Environment and Construction Engineering, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133, Milan, Italy

    Roberta Capello & Camilla Lenzi

Authors
  1. Roberta Capello
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Camilla Lenzi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Camilla Lenzi.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Appendix

Appendix

See Figs. 1, 2, 3 and Tables 7, 8, 9.

Fig. 1
figure 1

Source: EUROSTAT 2005–2014

Regional real GDP per capita and GDP growth rate, 2005–2014 (% change on the previous year)

Full size image
Fig. 2
figure 2

Source: Capello and Lenzi (2013a)

Regional patterns of innovation in Europe

Full size image
Fig. 3
figure 3

Inventors’ mobility (per 1000 inhabitants) in European NUTS2 regions 1998–2004

Full size image
Table 7 Inventors’ mobility by regional innovation pattern and in the EU–ANOVA test
Full size table
Table 8 Top 10 destination regions of inventors’ mobility
Full size table
Table 9 Correlation matrix
Full size table

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Capello, R., Lenzi, C. The nexus between inventors’ mobility and regional growth across European regions. J Geogr Syst 21, 457–486 (2019). https://doi.org/10.1007/s10109-019-00308-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10109-019-00308-z

Keywords

JEL Classification

Search

Navigation