Abstract
How is the R&D-productivity link affected by the environment where firms locate? Are companies located with their registered offices in more R&D favorable environments better able to translate their R&D knowledge into productivity gains? Our paper tries to answer these questions analyzing - in the European context - if R&D performing companies cluster themselves in “higher-order R&D regions”, as the Economic Geography theories postulate, inducing a polarisation in terms of labour productivity in comparison with firms located in “lower-order R&D regions”. The proposed microeconometric estimates are based on a unique longitudinal database of publicly-traded companies belonging to manufacturing and service sectors. The final unbalanced sample comprises 626 European companies for a total of 3,431observations, covering the period 1990-2008. Results show that European “higher-order R&D regions” not only invest more in R&D, but also achieve more in terms of productivity gains from their own research activities. Results also show that in the case of “lower-order R&D regions”, physical capital stock is still playing a dominant role.
Keywords
- Gross Domestic Product
- Labour Productivity
- Total Factor Productivity
- Physical Capital
- Firm Productivity
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
This is a preview of subscription content, access via your institution.
Buying options
Notes
- 1.
In this publication, region is used to mean a subunit within a country, rather a supranational grouping of countries.
- 2.
In case of multilocated or multinational corporations, data refer to global activities controlled by mother companies from the region of their registered office. In the estimates, therefore, the NUTS (Nomenclature of Territorial Units for Statistics) codes always refer to the regions from where company activities on the whole are owned and controlled.
- 3.
Romer (1986) and Lucas (1988) defined a model where the main premises where knowledge was considered an input of production and displayed increasing marginal productivity, increasing returns to scale and decreasing returns in production of new knowledge. Lately, Romer (1987, 1990) and Aghion and Howitt (1992) models introduced the assumption of imperfect competition and the fact that technological change aroused by the international decisions from profit-maximising agents. R&D activities reward firms through monopolistic power, and their effect is higher in environments where competition is higher (in specialised clusters of high-tech firms, higher-order R&D regions in our work).
- 4.
Defined as the technological fields in which a particular country exhibits a specialisation index greater than unity.
- 5.
The original data source being Compustat Global data set provided by Standard & Poor’s, for additional information about the data source, consult: http://be.ncue.edu.tw/compustat/manual/MK-CGDC4-02.pdf.
- 6.
In particular, the figure excludes the following: customer- or government-sponsored R&D expenditures engineering expenses such as routinised ongoing engineering efforts to define, enrich or improve the qualities and characteristics of the existing products, inventory royalties, market research and testing.
- 7.
This procedure is consistent with what suggested by the Frascati Manual (OECD 2002) in order to correctly adjust R&D expenditures for differences in price levels over time (i.e. intertemporal differences asking for deflation) and among countries (i.e. interspatial differences asking for a PPP equivalent). In particular, “…the Manual recommends the use of the implicit gross domestic product (GDP) deflator and GDP-PPP (purchasing power parity for GDP), which provide an approximate measure of the average real “opportunity cost” of carrying out the R&D” (ibidem, p. 217). More in detail, nine companies from four countries (Lithuania, Latvia, Malta and Romania) were excluded, due to the unavailability of PPP exchange rates from the OECD. The ten companies reporting in euro but located in non-euro countries (Denmark, Estonia and the UK) were excluded as well, while the 58 companies reporting in US dollars were kept as such.
- 8.
The standard OECD classification was taken (see Hatzichronoglou 1997) and extended it including the entire electrical and electronic sector 36 (considered as a medium-high-tech sector by the OECD). We opted for this extension taking into account that we just compare the high-tech sectors with all the other ones and that we need an adequate number of observations within the subgroup of the high-tech sectors.
- 9.
This means that for firms characterised by breaks in the data, we computed different initial stocks, one for each available time span, consistent with Hall (2007); however, differently from Hall (2007), we consider the different spans as belonging to the same firm and so we will assign – in the following econometric estimates – a single fixed or random effect to all of the spans belonging to the same company history.
- 10.
Options for the choice of g – different from the standard one – have been implemented by other authors, as well. For instance, Parisi et al. (2006) assume that the rate of growth in R&D investment at the firm level in the years before the first positive observation equals the average growth rate of industry of R&D between 1980 and 1991 (the time span antecedent to the longitudinal microdata used in their econometric estimates). In general terms, the choice of a feasible g does not significantly affect the final econometric results of the studies. As clearly stated by Hall and Mairesse (1995, p.270, footnote 9): “In any case, the precise choice of growth rate affects only the initial stock, and declines in importance as time passes”.
- 11.
The occurrence of negative stocks happens when g turns out to be negative and larger – in absolute value – than δ.
- 12.
The default number of iterations is 16,000.
- 13.
The Grubbs test is defined under the null hypothesis (H0) that there are no outliers in the data set; the test statistic is \( G=\frac{\underset{i=1,\mathrm{..},N}{\mathrm{max}}\left|{Y}_{i}-\overline{Y}\right|}{s}\)with \( \overline{Y}\)and s denoting the sample mean and standard deviation, respectively. Therefore, the Grubbs test detects the largest absolute deviation from the sample mean in units of the sample standard deviation. With a two-sided test, the null hypothesis of no outliers is rejected if \( G gt;\frac{\left(N-1\right)}{\sqrt{N}}\sqrt{\frac{{t}^{2}{}_{(a/(2N),N-2)}}{N-2+{t}^{2}{}_{(a/(2N),N-2)}}}\)with \( {t}^{2}{}_{(a/(2N),N-2)}\)denoting the critical value of the t-distribution with (N-2) degrees of freedom and a significance level of α/(2 N).
- 14.
As clearly stated and demonstrated in Hall and Mairesse (1995), the direct production function approach to measure returns to R&D capital is preferred on other possible alternative specifications.
- 15.
Final sample (number of firms and observations) by country is reported in Table 12.8 in the Appendix.
References
Aghion P, Howitt P (1992) A model of growth through creative destruction. Econometrica 60(2):232–251
Antonelli C (2010) Pecuniary externalities and the localized generation of technological knowledge. In: Boschma R, Martin R (eds) The handbook of evolutionary economic geography. Edward Elgar, Cheltenham/Northampton
Audia PG, Freeman JH, Reynolds P (2006) Organizational foundings in community context: instruments manufacturers and their interrelationship with other organizations. Admin Sci Quart 51:381–419
Baldwin R, Forslid R (2000) The core-periphery model and endogenous growth: stabilizing and destabilizing integration. Economica 67:307–324
Boshma R, Frenken K (2009) Technological relatedness and regional branching. In: Bathelt H, Feldman M, Kloger D (eds) Dynamic geographies of knowledge creation and innovation. Routledge/Taylor and Francis, London
Bronzini R, Piselli R (2009) Determinants of long-run regional productivity with geographical spillovers: the role of R&D, human capital and public infrastructure. Reg Sci Urban Econ 39:187–199
Cantwell J, Iammarino S (1998) MNCs, technological innovation and regional systems in the EU: some evidence in the Italian case. Int J Econ Bus 5:383–408
Cantwell J, Iammarino S (2000) Multinational corporations and the location of technological innovation in the UK regions. Reg Stud 34:317–322
Cantwell J, Iammarino S (2001) EU regions and multinational corporations: change, stability and strengthening of technological comparative advantages. Ind Corp Change 10:1007–1039
Carroll GR, Hannan MT (2000) The demography of corporations and industries. Princeton University Press, Princeton
Cohen CW, Levinthal DA (1990) Absorptive capacity: a new perspective on learning and innovation. Admin Sci Quart 35:128–152
Cooke P, Gómez Uranga M, Etxebarria G (1997) Regional innovation systems: institutional and organisational dimensions. Res Policy 26:475–491
Crépon B, Duguet E, Mairesse J (1998) Research, innovation, and productivity: an econometric analysis at firm level. Econ Innov New Tech 7:115–158
Cuneo P, Mairesse J (1983) Productivity and R&D at the firm level in French manufacturing. NBER working paper no. 1068
Dettori B, Marrocu E, Paci R (2008) Total factor productivity, intangible assets and spatial dependence in the European regions. CRENOS working paper, 2008/23
Feldman MP (1994) The geography of innovation. Economics of science, technology and innovation. Kluwer, Amsterdam
Fischer M, Scherngell T, Reismann M (2008) Knowledge spillovers and total factor productivity. Mimeo, paper presented at the ERSA conference, Liverpool
Forslid R, Wooton I (2003) Comparative advantage and the location of production. Rev Int Econ 11:588–603
Frenken K, van Oort F, Verburg T (2007) Related variety, unrelated variety and regional economic growth. Reg Stud 41:685–697
Griliches Z (1979) Issues in assessing the contribution of research and development to productivity growth. Bell J Econ 10:92–116
Griliches Z, Mairesse J (1982) Comparing productivity growth: an exploration of French and US industrial and firm data. NBER working paper no. 961
Grubbs F (1969) Procedures for detecting outlying observations in samples. Technometrics 11:1–21
Gumbau-Albert M, Maudos J (2006) Technological activity and productivity in the Spanish regions. Ann Regional Sci 40:55–80
Hall BH (2007) Measuring the returns to R&D: the depreciation problem. NBER working paper no. 13473
Hall BH, Mairesse J (1995) Exploring the relationship between R&D and productivity in French manufacturing firms. J Econometrics 65:263–293
Hannan MT, Carroll GR, Dutton EA, Torres JC (1995) Organizational evolution in a multinational context: entries of automobiles manufacturers in Belgium, Britain, France, Germany and Italy. Am Sociol Rev 60:509–528
Harhoff D (1998) R&D and productivity in German manufacturing firms. Econ Innov New Tech 6:29–49
Hatzichronoglou T (1997) Revision of the high-technology sector and product classification. OECD, Paris
Hulten CR (1990) The measurement of capital. In: Berndt E, Triplett J (eds) Fifty years of economic management. University of Chicago Press, Chicago
Iammarino S, McCann P (2010) The relationship between multinational firms and innovative clusters. In: Boschma B, Martin R (eds) The handbook of evolutionary economic geography. Edward Elgar, Cheltenham
Jorgenson DW (1990) Productivity and economic growth. In: Berndt E, Triplett J (eds) Fifty years of economic management. University of Chicago Press, Chicago
Klepper S (2007) Disagreements, spinoffs, and the evolution of Detroit as the capital of the U.S. automobile industry. Manage Sci 53:616–631
Kline SJ, Rosenberg N (1986) An overview of innovation. In: Landau R, Rosenberg N (eds) The positive sum strategy: harnessing technology for economic growth. National Academy Press, Washington, DC
Krugman P (1991) Increasing returns and economic geography. J Polit Econ 99:483–499
Krugman P, Venables AJ (1996) Integration, specialization, and adjustment. Eur Econ Rev 40:959–967
Kwon HU, Inui T (2003) R&D and productivity growth in Japanese manufacturing firms. ESRI Discussion Paper Series No. 44, Tokyo
Le Bas C, Sierra C (2002) Location versus home country advantages in R&D activities: some further results on multinationals’ location strategies. Res Policy 31:589–609
Lucas R (1988) On the mechanics of economic development. J Monetary Econ 22:3–42
Lundvall BA (1988) Innovation as an interactive process: from user-producer interaction to the national system of innovation. In: Dosi G, Freeman C, Nelson R, Silverberg G, Soete L (eds) Technical change and economic theory. Pinter, New York
Malmberg A, Maskell P (1997) Towards an explanation of industry agglomeration and regional specialization. Eur Plann Stud 5:25–41
Malmberg A, Maskell P (2002) The elusive concept of localization economies - towards a knowledge-based theory of spatial clustering. Environ Plann A 34:429–449
Marshall A (1890) Principles of economics. Macmillian, London
Martin P, Ottaviano G (2001) Growth and agglomeration. Int Econ Rev 42:947–968
Maskell P (2001) The firm in economic geography. Econ Geogr 77(4):329–344
Nadiri M, Prucha I (1996) Estimation of the depreciation rate of physical and R&D capital in the US total manufacturing sector. Econ Inq 34:43–56
OECD (2002) Frascati manual - proposed standard practice for surveys on research and experimental development. OECD, Paris
OECD (2009a) Regions matter. Economic recovery, innovation and sustainable growth. OECD, Paris
OECD (2009b) How regions grow. Trends and analysis. OECD, Paris
Ortega-Argilés R, Piva M, Potters L, Vivarelli M (2010) Is corporate R&D investment in high-tech sectors more effective? Contemp Econ Pol 28:353–365
Ortega-Argilés R, Potters L, and M. Vivarelli (2011) R&D and Productivity: Testing Sectoral Peculiarities Using Micro Data. Empirical Economics 41(3):817–839
Parisi M, Schiantarelli F, Sembenelli A (2006) Productivity, innovation creation and absorption, and R&D. Microevidence for Italy. Eur Econ Rev 50:2037–2061
Patel P, Vega M (1999) Patterns of internationalization of corporate technology: location versus home country advantages. Res Policy 28:145–155
Porter M (1990) The competitive advantage of nations. Free Press, New York
Porter M (1998) Clusters and the new economics of competition. Harv Bus Rev 76:77–90
Porter M (2000) Location, competition, and economic development: local clusters in a global economy. Econ Dev Q 14:15–34
Rincon A, Vecchi M (2003) Productivity performance at the company level. In: O’Mahony M, Van Ark B (eds) EU productivity and competitiveness: an industry perspective. Can Europe Resume the Catching-up Process? European Commission, Luxembourg
Romer P (1986) Increasing returns and long-run growth. J Polit Econ 94:1002–1037
Romer P (1987) Growth based on increasing returns due to specialization. Am Econ Rev 77:56–62
Romer P (1990) Endogenous technical change. J Polit Econ 99:72–102
Segarra A (2010) Innovation and productivity in manufacturing and service firms in Catalonia: a regional approach. Econ Innov New Tech 19:233–258
Stefansky W (1972) Rejecting outliers in factorial designs. Technometrics 14:469–479
Verspagen B (1995) R&D and productivity: a broad cross-section cross-country look. J Prod Anal 6:117–135
von Hippe E (1988) The sources of innovation. Oxford University Press, New York
Wakelin K (2001) Productivity growth and R&D expenditure in UK manufacturing firms. Res Policy 30:1079–1090
Acknowledgement
Financial and data support from the “Corporate R&D and Productivity: Econometric Tests Based on Microdata” JRC-IPTS project is gratefully acknowledged. Part of the work done in this chapter was carried out, while some authors were staff at the European Commission, Joint Research Centre (JRC), Institute for Prospective Technological Studies (IPTS), Seville, Spain.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Appendix
Appendix
Rights and permissions
Copyright information
© 2012 Springer Science+Business Media New York
About this chapter
Cite this chapter
Cozza, C., Ortega-Argilés, R., Piva, M., Baptista, R. (2012). Productivity Gaps Among European Regions. In: Audretsch, D., Lehmann, E., Link, A., Starnecker, A. (eds) Technology Transfer in a Global Economy. International Studies in Entrepreneurship, vol 28. Springer, Boston, MA. https://doi.org/10.1007/978-1-4614-6102-9_12
Download citation
DOI: https://doi.org/10.1007/978-1-4614-6102-9_12
Published:
Publisher Name: Springer, Boston, MA
Print ISBN: 978-1-4614-6101-2
Online ISBN: 978-1-4614-6102-9
eBook Packages: Business and EconomicsBusiness and Management (R0)