Main drivers for local and global R&D sourcing of European Multinational Enterprises

Internationalized R&D and global sourcing of R&D are used interchangeably in this paper and refer to in-house R&D activities performed by the MNE’s subsidiaries in different locations in the world. Since these subsidiaries are owned, operated and managed by the MNE, sourcing activities should be referred to as captive sourcing.

For a comprehensive overview, see Sheehan and Wyckoff (2003).

See: http://​iri.​jrc.​ec.​europa.​eu/​.

See: http://​iri.​jrc.​ec.​europa.​eu/​research/​scoreboard.​htm.

See: http://​iri.​jrc.​ec.​europa.​eu/​research/​survey.​htm.

Most of these companies are European or EU-based Multinational Enterprises (MNEs) European and EU-based companies are used interchangeably in this paper and refer to firms whose Global Ultimate Owner or Headquarter is located in Western Europe, i.e. the EU-15 (except for two companies out of the 130 which are located in the Czech Republic and in Slovenia).

Thus, 50 % of these companies are involved in offshore R&D sourcing (i.e. international) and the other half in near R&D sourcing activities (i.e. local or national).

Ambos and Ambos (2011) also report the US and India as two of the top 10 R&D locations for a sample of German MNEs.

The authors distinguish four distinct types of business activities, i.e. manufacturing, R&D, IT and administrative services and find that the offshoring of R&D typically occurs in locations with substantial knowledge base.

Manning et al. (2008) emphasize the recent trend of offshoring decisions of companies located in advanced economies aimed at sourcing and using science and engineering talent in emerging countries.

See: http://​iri.​jrc.​ec.​europa.​eu/​research/​survey.​htm.

These activities are jointly carried out by the European Commission's Joint Research Centre (JRC)—Institute for Prospective Technological Studies (IPTS) and Directorate General Research—Directorate C, European Research Area: Knowledge-based economy. Their aim is to improve the understanding of industrial R&D and Innovation in the EU and to identify medium and long-term policy implications.

http://​cordis.​europa.​eu/​innovation/​en/​policy/​innobarometer.​htm.

http://​kei.​publicstatistics​.​net.

The Scoreboard and BERD data address industrial R&D in the EU through different concepts and are therefore not directly comparable, but their latest figures were of similar magnitude. According to the latest available data for 2006: Scoreboard €121 bn and BERD (Eurostat) €116 bn.

The sectors were combined into three groups according to their average R&D intensities in the 2007 Scoreboard: High (more than 5 % R&D intensity: biotechnology, health care equipment & services, leisure goods, pharmaceuticals, software, support services, technology hardware & equipment), medium (between 2 and 5 % R&D intensity: aerospace & defence, automobiles & parts, chemicals, commercial vehicles & trucks, computer services, electrical components & equipment, electronic equipment, food producers, general industrials, industrial machinery, personal goods) and low (less than 2 % R&D intensity: banks, construction & materials, electricity, fixed-line telecommunications, food & drug retailers, food producers, forestry & paper, gas, water & multiutilities, general retailers, industrial metals, industrial transportation, oil & gas producers, oil equipment, services & distribution). This grouping has been used in the different EU Scoreboard analyses since 2004, similar to the OECD high-technology classification (Hatzichronoglou 1997), “Revision of the High-Technology Sector and Product Classification” in: OECD Science, Technology and Industry Working Papers, 1997/2).

R&D intensity is the ratio between R&D investment and net sales. An individual company may invest a large overall amount in R&D but have a low R&D intensity if net sales are high (as is the case of many oil & gas producers, for example).

“The Industry Classification Benchmark (ICB) is a definitive system categorizing over 70,000 companies and 75,000 securities worldwide, enabling the comparison of companies across four levels of classification and national boundaries” (http://​www.​icbenchmark.​com). The ICB Database is maintained by FTSE International Limited.

For more details see European Commission (2009).

See: http://​ec.​europa.​eu/​enterprise/​enterprise_​policy/​sme_​definition/​index_​en.​htm.

The CIS uses a stratified sampling for at least three size classes (small, medium and large enterprises) across all EU Member States.

With regard to this, we consider as 'big countries': Germany, France, Italy, Spain and the UK.

It should also be noted that the maximum likelihood functions of these two regression models did not converge when the control variables are included (R&D intensity, size of the firm and share of R&D performed in the EU). Estimating a Heckman two-step model addresses this issue and leads to results that are similar to the ones obtained with the maximum likelihood method.

Yet, we present the results obtained from this more general model.

The countries concerned are China (2 responses); India (5 responses) and Korea; Philippines; Russia and Tunisia (one response).

As pointed out by the authors, "Although the wages of highly qualified Chinese R&D staff is higher compared to Chinese domestic level, it is still 25 or 20 % of that of R&D staff salary in triad regions".