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2012 | OriginalPaper | Chapter

12. Productivity Gaps Among European Regions

Authors : Claudio Cozza, Raquel Ortega-Argilés, Mariacristina Piva, Rui Baptista

Published in: Technology Transfer in a Global Economy

Publisher: Springer US

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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.

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previous chapter The Enrollment in an R&D Subsidy Program for SMEs: Evidence from Southwest Germany

next chapter The Effect of Regional Characteristics on the Relationship Between University Resources and Knowledge-Based Startup’s Performance

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In this publication, region is used to mean a subunit within a country, rather a supranational grouping of countries.

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.

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).

Defined as the technological fields in which a particular country exhibits a specialisation index greater than unity.

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.

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.

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.

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.

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.

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”.

The occurrence of negative stocks happens when g turns out to be negative and larger – in absolute value – than δ.

The default number of iterations is 16,000.

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).

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.

Final sample (number of firms and observations) by country is reported in Table 12.8 in the Appendix.

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Title: Productivity Gaps Among European Regions
Authors: Claudio Cozza
Raquel Ortega-Argilés
Mariacristina Piva
Rui Baptista
Publisher: Springer US
Book: Technology Transfer in a Global Economy
Print ISBN: 978-1-4614-6101-2

Electronic ISBN: 978-1-4614-6102-9

Copyright Year: 2012
DOI: https://doi.org/10.1007/978-1-4614-6102-9_12

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