Skip to main content

Advertisement

SpringerLink
Log in

Specialization, R&D and productivity growth: evidence from EU regions

  • Original Paper
  • Published:
The Annals of Regional Science Aims and scope Submit manuscript

Abstract

The present paper analyzes the effect of regional specialization and R&D expenditures on labor productivity growth. Following Fingleton [Environ Plan 32:1481–1498 2000], we assume positive externalities in labor productivity growth and technological spillovers depend on interregional distances and economy size. Regional specialization and R&D expenditures are assumed to enhance growth by affecting the level of technology. Although it may seem natural that specialization and R&D expenditures can convey great advantages on economic growth, evidence varies across sectors. We conduct an empirical analysis for two economic sectors and the economy as a whole. Recently developed spatial econometric methods are adopted to control for potential heteroscedasticity in the growth equation.

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

References

  • Alexiadis S, Tsagdis D (2006) Reassessing the validity of verdoorn’s law under conditions of spatial dependence: a case of study of the greek regions. J Post Keyn Econ 29(1): 149–175

    Article  Google Scholar 

  • Andrews D (1991) Heteroscedastcity and autocorrelation consistent covariance matrix estimation. Econometrica 59: 817–858

    Article  Google Scholar 

  • Andrews D, Monahan J (1992) An improved heteroscedastcity and autocorrelation consistent covariance matrix estimation. Econometrica 60: 953–966

    Article  Google Scholar 

  • Anselin L (1988) Spatial Econometrics: Methods and Models. Kluwer Academic Publisher, Dordrecht

    Google Scholar 

  • Anselin L, Lozano-Gracia N (2008) Errors in variables and spatial effects in hedonic house price models of ambient air quality. Emp Econ 34(1): 5–34

    Article  Google Scholar 

  • Arraiz I, Drukker D, Kelejian H, Prucha I (2010) A spatial cliff-ord-type model with heteroscedastic innovations: Small and large sample results. J Reg Sci 50: 592–614

    Article  Google Scholar 

  • Atesoglu H (1993) Manufacturing and economic growth in the united states. Appl Econ 25(1): 67–69

    Article  Google Scholar 

  • Biehl D (1991) The role of infrastructure in regional development. In: Vickerman R (ed.) Infrastructure and Regional Development, European Research in Regional Science 1. Pion, London, pp 9–35

    Google Scholar 

  • Canova F (2004) Testing for convergence clubs in income per capita: a predictive density approach. Int Econ Rev 45(1): 49–77

    Article  Google Scholar 

  • Case A (1991) Spatial patterns in household demand. Econometrica 59: 953–965

    Article  Google Scholar 

  • Case A (1993) Neighborhood influence and technological change. Reg Sci Urban Econ 22: 491–508

    Article  Google Scholar 

  • Case A, Rosen H, Hines J (1993) Budget spillovers and fiscal policy interdependence: Evidence from the states. J Pub Econ 52: 285–307

    Article  Google Scholar 

  • Ciccone A, Hall R (1996) Productivity and the density of economic activity. Am Econ Rev 86(1): 54–70

    Google Scholar 

  • Conley T, Ligon E (2002) Economic distance, spillovers and cross country comparison. J Econ Growth 7: 157–187

    Article  Google Scholar 

  • Conley T, Topa G (2002) Socio-economic distance and spatial patterns in unemployment. J Appl Econ 17: 303–327

    Article  Google Scholar 

  • Corrado L, Martin R, Weeks M (2005) Identifying and interpreting regional convergence clusters across europe. Econ J 115: 133–160

    Article  Google Scholar 

  • Dall’erba S, Percoco M, Piras G (2008) The european regional growth process revisited. Spat Econ Anal 3(1): 7–25

    Article  Google Scholar 

  • Dall’erba S, Percoco M, Piras G (2009) Service industry and cumulative growth in the regions of europe. Ent Reg Develop 21(4): 333–349

    Article  Google Scholar 

  • Durlauf S, Johnson P (1995) Multiple regimes and cross-country growth behavior. J Appl Econo 10: 365–384

    Article  Google Scholar 

  • Durlauf S, Quah D (1999) The new empirics of economic growth. In: Taylor J, Woodford M (eds) Handbooks of macroeconomics. Elsevier Sciences, Amsterdam, pp 163–198

    Google Scholar 

  • Fingleton B (2000) Spatial econometrics, economic geography, dynamics and equilibrium: a third way?. Environ Plan 32: 1481–1498

    Article  Google Scholar 

  • Fingleton B (2001) Equilibrium and economic growth: spatial econometric models and simulations. J Reg Sci 41: 117–147

    Article  Google Scholar 

  • Fingleton B (2004) Regional economic growth and convergence: insights from a spatial econometric perspective. In: Anselin L, Florax R, Rey S (eds) Advances in spatial econometrics, Springer, pp 397– 432

  • Kaldor N (1957) A model of economic growth. Econ J 67: 591–624

    Article  Google Scholar 

  • Kelejian H, Prucha I (1998) A generalized spatial two stages least square procedure for estimating a spatial autoregressive model with autoregressive disturbances. J Real Estate Financ Econo 17(1): 99–121

    Article  Google Scholar 

  • Kelejian H, Prucha I (1999) A generalized moments estimator for the autoregressive parameter in a spatial model. Int Econ Rev 40(2): 509–533

    Article  Google Scholar 

  • Kelejian H, Prucha I (2004) Estimation of systems of spatially interrelated cross sectional equations. J Econo 118: 27–50

    Article  Google Scholar 

  • Kelejian H, Prucha I (2007) Hac estimation in a spatial framework. J Econ 140: 131–154

    Google Scholar 

  • Kelejian H, Prucha I (2010) Specification and estimation of spatial autoregressive models with autoregressive and heteroscedastic disturbances. J Econ 157(1): 53–67

    Google Scholar 

  • Kelejian H, Prucha I, Yuzefovich Y (2004) Instrumental variable estimation of a spatial autoregressive model with autoregressive disturbances: Large and small sample results. In: LeSage JP, Pace R (eds) Advances in econometrics: spatial and spatio-temporal econometrics. Elsevier Sciences Ltd., Oxford pp 163–198

  • Lee L (2002) Consistency and efficiency of least squares estimation for mixed regressive, spatial autoregressive models. Econ Theory 18: 252–277

    Article  Google Scholar 

  • Lee L (2003) Best spatial two-stage least square estimators for a spatial autoregressive model with autoregressive disturbances. Econ Rev 22: 307–335

    Article  Google Scholar 

  • Lee L (2004) Asymptotic distributions of quasi-maximum likelihood estimators for spatial autoregressive models. Econometrica 72: 1899–1926

    Article  Google Scholar 

  • Leon-Ledesma M (2000) Economic growth and Verdoorn’s law in the spanish regions, 1962–1991. Int Rev Appl Econ 14(1): 55–69

    Article  Google Scholar 

  • Lucas R (1988) On the mechanics of economic development. J Monet Econ 22: 3–42

    Article  Google Scholar 

  • Lutter H, Pütz T, Spangenberg M (1992) Accessibility and peripherality of community regions: the role of road, long-distance railways and airport networks. Report to the European Commission, DG XVI, Bonn

  • McDonald J, d’Ouville E, Liu L (1999) Economics of Urban Highway Congestion and Pricing. Kluwer Academic Publisher. (Transportation research, economics and policy, vol 9)

  • Myrdal G (1957) Economic theory and underdeveloped regions. Duckworth, London

    Google Scholar 

  • Newey W, West K (1984) A simple, positive semi-definite, heteroskedastic and autocorrelated consistent covariance matrix. Econometrica 55: 703–708

    Article  Google Scholar 

  • Pinkse J, Slade M, Brett C (2002) Spatial price competition: a semiparametric approach. Econometrica 70: 1111–1153

    Article  Google Scholar 

  • Piras G (2010) sphet: Spatial models with heteroskedastic innovations in R. J Stat Softw 35(1):1–21. http://www.jstatsoft.org/v35/i01/

    Google Scholar 

  • Pons-Novell J, Viladecans-Marsal E (1999) Kaldor’s laws and spatial dependence: evidence for the european regions. Reg Stud 33(5): 443–451

    Article  Google Scholar 

  • Postiglione P, Benedetti R, Lafratta G (2009) A regression tree algorithm for the identification of convergence clubs. Computational Statistics and Data Analysis

  • R Development Core Team (2010) R: A language and environment for statistical computing. R Foundation for statistical computing, Vienna, Austria. http://www.R-project.org. ISBN 3-900051-07-0

  • Rey S, Le Gallo J (2009) Spatial analysis of economic convergence. In: Mills T, Patterson K (eds) Palgrave handbook of econometrics: Vol 2, forthcoming

  • Romer P (1986) Increasing returns and long-run growth. J Polit Econ 94:1002–1037

    Google Scholar 

  • Romer P (1990) Endogenous technological change. J Polit Econ 98: 71–102

    Article  Google Scholar 

  • Solow R (1956) A contribution to the theory of economic growth. Quart J Econ 70: 75–94

    Google Scholar 

  • Stoneman P (1979) Kaldor’s law and british economic growth: 1800–1970. Appl Econ 11(3): 309–319

    Article  Google Scholar 

  • Verdoorn P (1949) Fattori che regolano lo sviluppo della produttività del lavoro. L’Industria 1: 3–10

    Google Scholar 

  • Wulwick N (1991) Did the verdoorn law hang on japan?. East Econ J 17(1): 15–20

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Regional Research Institute, West Virginia University, 886 Chestnut Ridge Road, P. O. Box 6825, Morgantown, WV, 26506-6825, USA

    Gianfranco Piras

  2. DASTA, University “G. d’Annunzio” of Chieti-Pescara, Viale Pineta 4, 65127, Pescara, Italy

    Paolo Postiglione

  3. IDEAR, Universidad Catolica del Norte, 0610 Av. Angamos, Antofagasta, Chile

    Gianfranco Piras & Patricio Aroca

Authors
  1. Gianfranco Piras
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Paolo Postiglione
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Patricio Aroca
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Gianfranco Piras.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Piras, G., Postiglione, P. & Aroca, P. Specialization, R&D and productivity growth: evidence from EU regions. Ann Reg Sci 49, 35–51 (2012). https://doi.org/10.1007/s00168-010-0424-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00168-010-0424-2

JEL Classification

Search

Navigation