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Labor mobility from R&D-intensive multinational companies: implications for knowledge and technology transfer

Abstract

Private sector R&D is largely concentrated in a few multinational companies (MNCs). The mobility of labor between these MNCs and the rest of the economy is therefore an important mechanism for the diffusion of knowledge and technology, but these flows are not without friction. This paper analyses in great detail the flow of labor between firms with specific emphasis on flows to and from R&D intensive MNCs. Using linked employer-employee data for Denmark, we match employees moving from R&D intensive MNCs to other employees switching jobs. We find that employees are more inclined to move between R&D intensive MNCs and their subsidiaries rather than between these firms and other firms in the economy. This is particularly true for high skill employees. Our results suggest that other domestic firms are to a larger extent kept out of the ‘knowledge spillover’ loop, which provides them with fewer opportunities to learn from the R&D intensive MNCs. In other words, R&D intensive MNCs and their subsidiaries form a kind of sub-labor market within the national labor market; employees exhibit higher mobility within this group of firms than between this group and the rest of the labor market.

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Notes

  1. In our analysis, we also run regression analysis on the unrestricted sample and on a sample only including R&D workers, where R&D workers are defined according to Kaiser et al. (2018), i.e. individuals that have a college degree in Science, Technology, Mathematics and Medicine (STEMM) and have an occupation with ISCO level 2 or 3. The findings are robust and details are available upon request.

  2. The full scoreboard is freely accessible at the webpage of the JRC-B3-IRITEC: http://iri.jrc.ec.europa.eu/home.

  3. Information on subsidiaries is obtained directly from Bureau van Dijk using the corporate structure of SB firms in the period 2012–2015. Overall, Scoreboard firms are linked to about 600,000 subsidiaries.

  4. The two numbers do not add up to 1191 as some subsidiaries change parent company from Danish SB to Foreign SB.

  5. The differences between the foreign SB subsidiaries and domestic SB firms and their subsidiaries cannot be attributed to the latter group including the 25 Danish SB firms themselves, as a large share of the SB firms appear very small in the registry data and are not covered by the Community Innovation Survey, cf. earlier. Instead, it indicates a corporate structure among SB firms where activities in the home country are separated into a number of distinct and legally independent firms, e.g. a large domestic SB firm may have a separate R&D subsidiary and not just a R&D department.

  6. To test whether we violate the IIA assumption, we apply a Hausman test especially designed for multinomial logistic analysis with clustered data (Weesie 2000). The Hausman test indicates that the IIA assumption is not violated, which means our specification is correct.

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Acknowledgements

Some of the ideas in this paper were previously explored on less recent data in a European Commission technical report (see Holm et al. 2017). We are grateful to seminar participants at the Fifth International workshop in Inter-Industry Relatedness in The Hague (NL), 2018, and also to the Editor (Al Link) and two anonymous reviewers, for many helpful comments and suggestions. The views expressed are purely those of the authors and may not in any circumstances be regarded as stating an official position of the European Commission. The usual caveat applies.

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Appendix: mobility analysis on movers

Appendix: mobility analysis on movers

  Model A1 Model A2 Model A3
Variables SB firm SB firm Foreign SB firm Domestic SB firm
CEM Sample Sample Sample Sample
Industry and region FE Yes Yes Yes Yes
Scoreboard firm (any) 0.9699***    
(0.079)    
Scoreboard firm (domestic)   1.0598*** 0.4158* 1.2817***
  (0.115) (0.183) (0.148)
Scoreboard firm (foreign)   0.9119*** 1.0084*** 0.8017***
  (0.091) (0.113) (0.125)
Gender 0.0132 0.0122 − 0.0829 0.0959+
(0.040) (0.040) (0.061) (0.051)
Age − 0.0171** − 0.0170** − 0.0082 − 0.0268***
(0.005) (0.005) (0.008) (0.007)
Education (years) 0.0642*** 0.0639*** 0.0133 0.1047***
(0.009) (0.009) (0.012) (0.012)
Experience (years) 0.0091 0.0091 0.0088 0.0114
(0.006) (0.006) (0.009) (0.008)
Tenure (in previous firm) − 0.0143** − 0.0147** − 0.0212*** − 0.0085
(0.005) (0.005) (0.006) (0.006)
ln (hourly_wage) 0.3640*** 0.3680*** 0.4118*** 0.3492***
(0.054) (0.054) (0.074) (0.068)
occuH 0.4118*** 0.4157*** 0.4716*** 0.3777***
(0.058) (0.058) (0.078) (0.079)
occuM 0.1352* 0.1352* 0.2219** 0.0609
(0.064) (0.064) (0.086) (0.092)
ln (employment_size) 0.0796*** 0.0757*** 0.0263+ 0.1165***
(0.014) (0.015) (0.016) (0.022)
Constant − 4.6820*** − 4.6768*** − 5.4780*** − 5.4496***
(0.315) (0.316) (0.446) (0.413)
Observations 55.537 55.537 55.567 55.567
Pseudo-R2 0.143 0.143 0.145 0.145
Log Likelihood − 17,194 − 17,190 − 20,966 − 20,966
  1. Robust standard errors in parentheses. ***p < 0.001. **p < 0.01. *p < 0.05. +p < 0.1

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Holm, J.R., Timmermans, B., Østergaard, C.R. et al. Labor mobility from R&D-intensive multinational companies: implications for knowledge and technology transfer. J Technol Transf 45, 1562–1584 (2020). https://doi.org/10.1007/s10961-020-09776-8

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Keywords

  • Labor mobility
  • Multinational companies
  • Knowledge flows
  • R&D

JEL Classification

  • J21
  • F23
  • O32