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.
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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
Andrews D (1991) Heteroscedastcity and autocorrelation consistent covariance matrix estimation. Econometrica 59: 817–858
Andrews D, Monahan J (1992) An improved heteroscedastcity and autocorrelation consistent covariance matrix estimation. Econometrica 60: 953–966
Anselin L (1988) Spatial Econometrics: Methods and Models. Kluwer Academic Publisher, Dordrecht
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
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
Atesoglu H (1993) Manufacturing and economic growth in the united states. Appl Econ 25(1): 67–69
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
Canova F (2004) Testing for convergence clubs in income per capita: a predictive density approach. Int Econ Rev 45(1): 49–77
Case A (1991) Spatial patterns in household demand. Econometrica 59: 953–965
Case A (1993) Neighborhood influence and technological change. Reg Sci Urban Econ 22: 491–508
Case A, Rosen H, Hines J (1993) Budget spillovers and fiscal policy interdependence: Evidence from the states. J Pub Econ 52: 285–307
Ciccone A, Hall R (1996) Productivity and the density of economic activity. Am Econ Rev 86(1): 54–70
Conley T, Ligon E (2002) Economic distance, spillovers and cross country comparison. J Econ Growth 7: 157–187
Conley T, Topa G (2002) Socio-economic distance and spatial patterns in unemployment. J Appl Econ 17: 303–327
Corrado L, Martin R, Weeks M (2005) Identifying and interpreting regional convergence clusters across europe. Econ J 115: 133–160
Dall’erba S, Percoco M, Piras G (2008) The european regional growth process revisited. Spat Econ Anal 3(1): 7–25
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
Durlauf S, Johnson P (1995) Multiple regimes and cross-country growth behavior. J Appl Econo 10: 365–384
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
Fingleton B (2000) Spatial econometrics, economic geography, dynamics and equilibrium: a third way?. Environ Plan 32: 1481–1498
Fingleton B (2001) Equilibrium and economic growth: spatial econometric models and simulations. J Reg Sci 41: 117–147
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
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
Kelejian H, Prucha I (1999) A generalized moments estimator for the autoregressive parameter in a spatial model. Int Econ Rev 40(2): 509–533
Kelejian H, Prucha I (2004) Estimation of systems of spatially interrelated cross sectional equations. J Econo 118: 27–50
Kelejian H, Prucha I (2007) Hac estimation in a spatial framework. J Econ 140: 131–154
Kelejian H, Prucha I (2010) Specification and estimation of spatial autoregressive models with autoregressive and heteroscedastic disturbances. J Econ 157(1): 53–67
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
Lee L (2003) Best spatial two-stage least square estimators for a spatial autoregressive model with autoregressive disturbances. Econ Rev 22: 307–335
Lee L (2004) Asymptotic distributions of quasi-maximum likelihood estimators for spatial autoregressive models. Econometrica 72: 1899–1926
Leon-Ledesma M (2000) Economic growth and Verdoorn’s law in the spanish regions, 1962–1991. Int Rev Appl Econ 14(1): 55–69
Lucas R (1988) On the mechanics of economic development. J Monet Econ 22: 3–42
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
Newey W, West K (1984) A simple, positive semi-definite, heteroskedastic and autocorrelated consistent covariance matrix. Econometrica 55: 703–708
Pinkse J, Slade M, Brett C (2002) Spatial price competition: a semiparametric approach. Econometrica 70: 1111–1153
Piras G (2010) sphet: Spatial models with heteroskedastic innovations in R. J Stat Softw 35(1):1–21. http://www.jstatsoft.org/v35/i01/
Pons-Novell J, Viladecans-Marsal E (1999) Kaldor’s laws and spatial dependence: evidence for the european regions. Reg Stud 33(5): 443–451
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
Romer P (1990) Endogenous technological change. J Polit Econ 98: 71–102
Solow R (1956) A contribution to the theory of economic growth. Quart J Econ 70: 75–94
Stoneman P (1979) Kaldor’s law and british economic growth: 1800–1970. Appl Econ 11(3): 309–319
Verdoorn P (1949) Fattori che regolano lo sviluppo della produttività del lavoro. L’Industria 1: 3–10
Wulwick N (1991) Did the verdoorn law hang on japan?. East Econ J 17(1): 15–20
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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
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DOI: https://doi.org/10.1007/s00168-010-0424-2
JEL Classification
- C21
- C14
- R11
- R12