Abstract
The aim of this paper is to investigate the extent to which the environmental technological spillover effects on firms’ productivity are affected by the spatial dimension. To this end, we introduce a spatial Durbin model with additional endogenous variables for the energy production efficiency activity of large R&D-intensive firms located in three economic areas: the USA, Japan and Europe. To identify the technological proximity between the firms, we construct an original Mahalanobis environmental industry weight matrix, based on the construction of technological vectors for each firm, with European environmental patents distributed across more technology classes. The findings show a statistically negative impact of spatially distributed environmental spillovers on firms’ productivity in all the economic areas.
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Notes
See Maraut et al. (2008) for the methodology used for the construction of REGPAT. Please contact Helene.DERNIS@oecd.org to download the REGPAT database.
We use the exchange rates from Eurostat for the year 2007.
Eurostat GDP deflators.
References
Aldieri, L.: Knowledge technological proximity: evidence from US and European patents. Econ. Innov. New Technol. 22, 807–819 (2013)
Aldieri, L., Cincera, M.: Geographic and technological R&D spillovers within the Triad: micro evidence from US patents. J. Technol. Transf. 34(2), 196–211 (2009)
Aldieri, L., Kotsemir, M., Vinci, C.P.: Jacobian spillovers in environmental technological proximity: the role of Mahalanobis index on European patents within the Triad. MPRA Working Paper N. 77274 (2017)
Ambec, S., Cohen, M.A., Elgie, S., Lanoie, P.: The Porter hypothesis at 20: Can environmental regulation enhance innovation and competitiveness? Rev. Environ. Econ. Policy 7(1), 2–22 (2013)
Anselin, L.: Local indicators of spatial association—LISA. Geograph. Anal. 27, 93–115 (1995)
Capello, R.: Spatial spillovers and regional growth: a cognitive approach. Eur. Plan. Stud. 17(5), 639–658 (2009)
Cincera, M., de la Potterie, B.V.P.: International R&D spillovers: a survey. Cahiers Economiques de Bruxelles 169, 3–32 (2001)
Crow, K.: SHP2DTA: Stata module to convert shape boundary files to Stata datasets. https://ideas.repec.org/c/boc/bocode/s456718.html (2015)
European Commission: The 2013 EU Industrial R&D Investment Scoreboard, JRC Scientific and Technical Research Series. http://iri.jrc.ec.europa.eu/scoreboard.html (2013)
Gans, J.S.: Innovation and climate change policy. Am. Econ. J. Econ. Policy 4(4), 125–145 (2012)
Greaker, M.: Strategic environmental policy: Eco-dumping or a green strategy? J. Environ. Econ. Manag. 45(3), 692–707 (2003)
Griliches, Z.: Issues in assessing the contribution of R&D to productivity growth. Bell J. Econ. 10, 92–116 (1979)
Griliches, Z.: The search for R&D spillovers. Scand. J. Econ. 94, 29–48 (1992)
Hoppmann, J.: The role of interfirm knowledge spillovers for innovation in mass-produced environmental technologies. Organ. Environ. https://doi.org/10.1177/1086026616680683 (2016)
Jaffe, A.B.: Technological opportunity and spillovers of R&D: evidence from firms’ patents, profits and market value. Am. Econ. Rev. 76(5), 984–1001 (1986)
Kelejian, H.H., Robinson, D.P.: A suggested method of estimation for spatial interdependent models with autocorrelated errors, and an application to a county expenditure model. Pap. Reg. Sci. 72(3), 297–312 (1993)
Kelejian, H.H., Prucha, I.R.: A generalized spatial two-stage least squares procedure for estimating a spatial autoregressive model with autoregressive disturbances. J. Real Estate Finan. Econ. 17(1), 99–121 (1998)
Kelejian, H.H., Prucha, I.R.: A generalized moments estimator for the autoregressive parameter in a spatial model. Int. Econ. Rev. 40(2), 509–533 (1999)
Kondo, K.: SPGEN: Stata module to generate spatially lagged variables. http://econpapers.repec.org/software/boc/bocode/S458105.html (2015)
Kondo, K.: Hot and cold spot analysis using Stata. Stata J. 16, 613–631 (2016)
Maraut, S., Dernis, H., Webb, C., Spiezia, V., Guellec, D.: The OECD REGPAT database: a presentation. STI Working Paper 2008/2, OECD, Paris (2008)
Marin, G., Lotti, F.: Productivity effects of eco-innovations using data on eco-patents. Ind. Corp. Chang. (2016). https://doi.org/10.193/icc/dtw014
Mohnen, P.: R&D externalities and productivity growth. STI Rev. 18, 39–66 (1996)
Mohr, R.D.: Technical change, external economies and the Porter hypothesis. J. Environ. Econ. Manag. 43(1), 158–168 (2002)
Moran, P.A.P.: Notes on continuous stochastic phenomena. Biometrika 37 (1/2): 17–23. OECD. REGPAT database, February 2016 (1950)
Pisati, M.: SPMAP: Stata module to visualize spatial data. https://ideas.repec.org/c/boc/bocode/s456812.html (2008)
Porter, M.: America’s green strategy. Sci Am 264(4), 168 (1991)
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Aldieri, L., Vinci, C.P. An assessment of energy production efficiency activity: a spatial analysis. Lett Spat Resour Sci 11, 233–243 (2018). https://doi.org/10.1007/s12076-017-0196-8
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DOI: https://doi.org/10.1007/s12076-017-0196-8