“Another roof, another proof” (Paul Erdős).
Quoted in A Tribute to Paul Erdős (1990) edited by Alan Baker, Béla Bollobás, A. Hajnal, Preface, p. ix.
Abstract
The mobility of highly skilled employees is seen as a critical way for organizations to transfer knowledge and to improve organizational performance. Yet, the relationship between mobility and individual performance is still largely a theoretical and empirical puzzle. Integrating human capital mobility research and the economics of science literature, we argue that mobility of academics should have a positive effect on individual productivity. Additionally, we argue that this positive effect is strengthened when academics move towards better-endowed institutions. We find support for our predictions using a unique dataset of 348 academics working in biology department in the United Kingdom supplemented with qualitative evidence from a survey of the focal academic researchers.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Notes
The “0” represents the number of years of lag.
As a robustness check we have specified our models with Scientific Productivity as the yearly number of scientific publications (non-cumulated). Our results do not change. We have opted for cumulated values of scientific publications because we believe they are less sensitive to stochastic yearly fluctuations.
Scopus Elsevier is a bibliographic database containing abstracts and citations for academic journal articles. It covers nearly 22,000 titles from over 5000 publishers, of which 20,000 are peer-reviewed journals in the scientific, technical, medical, and social sciences (including arts and humanities) (Scopus Info 2013). Scopus Elsevier allows legal downloading of data for research purposes.
Using one and two years lag allowed us to model the time necessary for each moving researcher to produce and publish new scientific work. In particular, a one-year lag more realistically reflects the time between production and publication of a new academic article in academic bioscience research.
In accordance with the lagged transformation of Scientific Productivity, we also generated three variables for Previous Productivity: Prev_Prod_0, Prev_Prod_1, and Prev_Prod_2.
The BBSRC was created in 1994, and it is currently the largest UK public funder of non-medical bioscience: in 2012, it disbursed £200 M for bio-scientific research.
For example, if researcher A works at University X in 2001 and moves to University Y in 2002, the instrument we would use is the number of students at University Y in 2001.
In other words, an increase in the number of students enrolled does not entail an increase in teaching hours stated in the employment contract of each individual faculty member.
The results of this analysis are available from the authors upon request.
We also employ an additional instrumental variable, the interaction between Students (our original IV) and Mobility During Education (the number of different countries where each individual was educated up to the first job placement), which we believe is a valid approach to capture both university-level and individual-level variations to instruments Mobility in a Scientific Productivity equation. Results remain the same.
Given the log-transformation of Scientific Productivity, to interpret the correct size effect of Mobility coefficient, we computed the exponential transformation of the estimates. Therefore, 2% = exp (0.0198).
Same results are held also when we introduce Previous Productivity in t-2 either in substitution to or along with Previous Productivity in t − 1.
Roodman (2009) offers an additional test for exogeneity for subsets of instruments. The first subset of instruments is composed of all the instruments for each time period, variable, and lag distance, but not for the set of variables that serve as standard instruments; the test suggests we cannot reject the exogeneity of this subset of instruments (Prob > Chi2 = 0.156). The second subset of instruments is composed by the standard instruments IV model in the specification; the test related to this second subset suggests that we cannot reject the exogeneity of this subset either (Prob > Chi2 = 0.641).
Our results differ from a recent contribution looking at British researchers in physical sciences (Fernandez-Zubieta et al. 2016), which does not find a statistically significant effect of mobility on performance. We believe this discrepancy to be largely due to a different construction of the sample, as Fernandez-Zubieta and colleagues use a group of researchers which have been awarded at least one grant from the EPSRC, while we do not condition our sample on receiving a grant. When we run our model for the subset of the researchers in our sample who obtained at least one grant from the BBSRC (the equivalent of the EPSRC for biological sciences) we are able to replicate the results from Fernandez-Zubieta and colleagues. We believe our sample is better suited to show the effect of mobility on performance for a larger variety of individuals, and not just star performers.
References
Abadie, A., Drukker, D., Leber Herr, J., & Imbens, G. W. (2004). Implementing matching estimators for average treatment effects in stata. Stata Journal,4(3), 290–311.
Aghion, P., Dewatripont, M., & Stein, J. C. (2008). Academic freedom, private-sector focus, and the process of innovation. The Rand Journal of Economics,39(3), 617–635. https://doi.org/10.1111/j.1756-2171.2008.00031.x.
Aime, F., Johnson, S., Ridge, J. W., & Hill, A. D. (2010). The routine may be stable but the advantage is not: Competitive implications of key employee mobility. Strategic Management Journal,31(1), 75–87. https://doi.org/10.1002/smj.809.
Aksnes, D. W., Rørstad, K., Piro, F. N., & Sivertsen, G. (2013). Are mobile researchers more productive and cited than non-mobile researchers? A large-scale study of Norwegian scientists. Research Evaluation,22(4), 215–223. https://doi.org/10.1093/reseval/rvt012.
Almeida, P., Hohberger, J., & Parada, P. (2011). Individual scientific collaborations and firm-level innovation. Industrial and Corporate Change, dtr030. https://doi.org/10.1093/icc/dtr030.
Allison, P. D, Long, J. S. (1990). Departmental effects on scientific productivity. American Sociological Review,55(4), 469–478.
Archambault, É., Campbell, D., Gingras, Y., & Larivière, V. (2009). Comparing bibliometric statistics obtained from the Web of Science and Scopus. Journal of the American Society for Information Science and Technology,60(7), 1320–1326. https://doi.org/10.1002/asi.21062.
Arellano, M., & Bond, S. (1991). Some tests of specification for panel data: Monte Carlo evidence and an application to employment equations. The Review of Economic Studies,58(2), 277–297. https://doi.org/10.2307/2297968.
Audretsch, D. B., Bozeman, B., Combs, K. L., Feldman, M., Link, A. N., Siegel, D. S., et al. (2002). The economics of science and technology. The Journal of Technology Transfer,27(2), 155–203. https://doi.org/10.1023/A:1014382532639.
Azoulay, P., Zivin, J. S. G., & Wang, J. (2010). Superstar extinction. The Quarterly Journal of Economics,125(2), 549–589.
Barney, J. (1991). Firm resources and sustained competitive advantage. Journal of Management,17(1), 99–120. https://doi.org/10.1177/014920639101700108.
Borjas, G. J., & Doran, K. B. (2012). The collapse of the soviet union and the productivity of American mathematicians*. The Quarterly Journal of Economics,127(3), 1143–1203. https://doi.org/10.1093/qje/qjs015.
Campbell, B. A., Coff, R., & Kryscynski, D. (2012a). Rethinking sustained competitive advantage from human capital. Academy of Management Review,37(3), 376–395. https://doi.org/10.5465/amr.2010.0276.
Campbell, B. A., Di Lorenzo, F., & Tartari, V. (2018). Cross-organization collaboration and mobility of knowledge workers.
Campbell, B. A., Ganco, M., Franco, A. M., & Agarwal, R. (2012b). Who leaves, where to, and why worry? Employee mobility, entrepreneurship and effects on source firm performance. Strategic Management Journal,33(1), 65–87. https://doi.org/10.1002/smj.943.
Campbell, B. A., Kryscynski, D., & Olson, D. M. (2017). Bridging strategic human capital and employee entrepreneurship research: A labor market frictions approach. Strategic Entrepreneurship Journal,11(3), 344–356. https://doi.org/10.1002/sej.1264.
Cañibano, C., & Bozeman, B. (2009). Curriculum vitae method in science policy and research evaluation: The state-of-the-art. Research Evaluation,18(2), 86–94. https://doi.org/10.3152/095820209X441754.
Cañibano, C., Otamendi, J., & Andújar, I. (2008). Measuring and assessing researcher mobility from CV analysis: The case of the Ramón y Cajal programme in Spain. Research Evaluation,17(1), 17–31. https://doi.org/10.3152/095820208X292797.
Carnahan, S., Agarwal, R., & Campbell, B. A. (2012). Heterogeneity in turnover: The effect of relative compensation dispersion of firms on the mobility and entrepreneurship of extreme performers. Strategic Management Journal,33(12), 1411–1430. https://doi.org/10.1002/smj.1991.
Chiang, S.-H., & Chiang, S.-C. (1990). General human capital as a shared investment under asymmetric information. The Canadian Journal of Economics/Revue canadienne d’Economique,23(1), 175–188. https://doi.org/10.2307/135526.
Coff, R. W. (1997). Human assets and management dilemmas: Coping with hazards on the road to resource-based theory. Academy of Management Review,22(2), 374–402. https://doi.org/10.5465/AMR.1997.9707154063.
Cohen, W. M., Nelson, R. R., & Walsh, J. P. (2000). Protecting Their Intellectual Assets: Appropriability Conditions and Why U.S. Manufacturing Firms Patent (or Not) (Working Paper No. 7552). National Bureau of Economic Research. http://www.nber.org/papers/w7552. Accessed 8 September 2016
Corredoira, R. A., & Rosenkopf, L. (2010). Should auld acquaintance be forgot? The reverse transfer of knowledge through mobility ties. Strategic Management Journal,31(2), 159–181. https://doi.org/10.1002/smj.803.
Crane, D. (1972). Invisible colleges, diffusion of knowledge in scientific communities. Chicago: University of Chicago Press.
Cruz-Castro, L., & Sanz-Menéndez, L. (2010). Mobility versus job stability: Assessing tenure and productivity outcomes. Research Policy,39(1), 27–38. https://doi.org/10.1016/j.respol.2009.11.008.
De Filippo, D., Casado, E. S., & Gomez. (2009). Quantitative and qualitative approaches to the study of mobility and scientific performance: a case study of a Spanish university, 18(3), 191–200.
De Solla Price, D. (1986). Little science, big science…and beyond. New York: Columbia University Press.
Deloitte. (2012). University Staff Academic Salaries and Remuneration. http://www.universitiesnz.ac.nz/node/276.
Di Lorenzo, F., & Almeida, P. (2017). The role of relative performance in inter-firm mobility of inventors. Research Policy,46, 1162–1174. https://doi.org/10.1016/j.respol.2017.05.002.
Dokko, G., & Rosenkopf, L. (2009). Social capital for hire? Mobility of technical professionals and firm influence in wireless standards committees. Organization Science,21(3), 677–695. https://doi.org/10.1287/orsc.1090.0470.
Drucker, P. F. (1998). Peter Drucker on the profession of management. Boston: Harvard Business Press.
Ernst, H., & Vitt, J. (2000). The influence of corporate acquisitions on the behaviour of key inventors. R&D Management,30(2), 105–120. https://doi.org/10.1111/1467-9310.00162.
Feldman, M. P., Link, A. N., & Siegel, D. S. (2012). The economics of science and technology: An overview of initiatives to foster innovation, entrepreneurship, and economic growth. Berlin: Springer Science & Business Media.
Fernandez-Zubieta, A., Geuna, A., & Lawson, C. (2015). What do we know of the mobility of research scientists and of its impact on scientific production (SSRN Scholarly Paper No. ID 2611203). Rochester, NY: Social Science Research Network. http://papers.ssrn.com/abstract=2611203. Accessed 26 August 2015.
Fernandez-Zubieta, A., Geuna, A., & Lawson, C. (2016). Productivity pay-offs from academic mobility: should I stay or should I go? Industrial and Corporate Change,25(1), 91–114. https://doi.org/10.1093/icc/dtv034.
Franzoni, C., Scellato, G., & Stephan, P. (2014). The mover’s advantage: The superior performance of migrant scientists. Economics Letters,122(1), 89–93. https://doi.org/10.1016/j.econlet.2013.10.040.
Gambardella, A., Giarratana, M. S., & Panico, C. (2010). How and when should companies retain their human capital? Contracts, incentives and human resource implications. Industrial and Corporate Change,19(1), 1–24.
Geuna, A., Kataishi, R., Toselli, M., Guzmán, E., Lawson, C., Fernandez-Zubieta, A., et al. (2015). SiSOB data extraction and codification: A tool to analyze scientific careers. Research Policy,44(9), 1645–1658. https://doi.org/10.1016/j.respol.2015.01.017.
Groysberg, B., Lee, L.-E., & Nanda, A. (2008). Can they take it with them? The portability of star knowledge workers’ performance. Management Science,54(7), 1213–1230. https://doi.org/10.1287/mnsc.1070.0809.
Hansen, L. P. (1982). Large sample properties of generalized method of moments estimators. Econometrica,50(4), 1029–1054. https://doi.org/10.2307/1912775.
Hagstrom, W. O. (1997). Inputs, outputs, and the prestige of university science departments. Sociology of Education,44(4), 375–397.
Heckman, J. J., Ichimura, H., & Todd, P. (1998). Matching as an econometric evaluation estimator. The Review of Economic Studies,65(2), 261–294. https://doi.org/10.1111/1467-937X.00044.
HEFCE. (2011). Higher education-business and community interaction survey 2009–2010. Higher Education Council Funding for England.
Hoffman, P. (1999). The man who loved only numbers: The story of Paul Erdos and the search for mathematical truth. Hyperion Books.
Hoisl, K. (2007). Tracing mobile inventors—The causality between inventor mobility and inventor productivity. Research Policy,36(5), 619–636. https://doi.org/10.1016/j.respol.2007.01.009.
Jovanovic, B. (1979). Job matching and the theory of turnover. Journal of Political Economy,87(5), 972–990.
Kogut, B., & Zander, U. (1992). Knowledge of the firm, combinative capabilities, and the replication of technology. Organization Science,3(3), 383–397. https://doi.org/10.1287/orsc.3.3.383.
Levin, S. G., & Stephan, P. E. (1997). The critical importance of careers in collaborative scientific research. Revue d’économie industrielle,79(1), 45–61. https://doi.org/10.3406/rei.1997.1652.
Lissoni, F., Montobbio, F., & Zirulia, L. (2013). Inventorship and authorship as attribution rights: An enquiry into the economics of scientific credit. Journal of Economic Behavior and Organization,95, 49–69. https://doi.org/10.1016/j.jebo.2013.08.016.
Long, J. S. (1978). Productivity and academic position in the scientific career. American Sociological Review,43(6), 889–908.
Lotka, A. (1926). The frequency distribution of scientific productivity. Journal of Washington Academy Sciences,16, 317–323.
Mackey, A., Molloy, J. C., & Morris, S. S. (2013). Scarce human capital in managerial labor markets. Journal of Management. https://doi.org/10.1177/0149206313517265.
Mahoney, J. T., & Qian, L. (2013). Market frictions as building blocks of an organizational economics approach to strategic management. Strategic Management Journal,34(9), 1019–1041. https://doi.org/10.1002/smj.2056.
Mawdsley, J. K., & Somaya, D. (2016). Employee mobility and organizational outcomes an integrative conceptual framework and research Agenda. Journal of Management,42(1), 85–113. https://doi.org/10.1177/0149206315616459.
Merton, R. K. (1957). Priorities in scientific discovery: A chapter in the sociology of science. American Sociological Review,22(6), 635–659. https://doi.org/10.2307/2089193.
Merton, R. K. (1968). The matthew effect in science: The reward and communication systems of science are considered. Science,159(3810), 56–63.
Merton, R. K. (1973). The sociology of science. Theoretical and empirical investigations. Chicago: University of Chicago Press
Moser, P., Voena, A., & Waldinger, F. (2014). German-Jewish Emigres and US invention (Working Paper No. 19962). National Bureau of Economic Research. http://www.nber.org/papers/w19962. Accessed 26 August 2015.
Mustar, P., & Wright, M. (2009). Convergence or path dependency in policies to foster the creation of university spin-off firms? A comparison of France and the United Kingdom. The Journal of Technology Transfer,35(1), 42–65. https://doi.org/10.1007/s10961-009-9113-7.
Nakajima, R., Tamura, R., & Hanaki, N. (2010). The effect of collaboration network on inventors’ job match, productivity and tenure. Labour Economics,17(4), 723–734. https://doi.org/10.1016/j.labeco.2009.11.006.
OECD. (2010). Science, technology and industry outlook.
Perkmann, M., Tartari, V., McKelvey, M., Autio, E., Broström, A., D’Este, P., et al. (2013). Academic engagement and commercialisation: A review of the literature on university–industry relations. Research Policy,42(2), 423–442. https://doi.org/10.1016/j.respol.2012.09.007.
Peteraf, M. A. (1993). The cornerstones of competitive advantage: A resource-based view. Strategic Management Journal,14(3), 179–191. https://doi.org/10.1002/smj.4250140303.
Roodman, D. (2009). How to do xtabond2: An introduction to difference and system GMM in Stata. The Stata Journal,9(1), 86–136.
Rosenbaum, P. R., & Rubin, D. B. (1983). The central role of the propensity score in observational studies for causal effects. Biometrika,70(1), 41–55. https://doi.org/10.1093/biomet/70.1.41.
Rosenkopf, L., & Almeida, P. (2003). Overcoming local search through alliances and mobility. Management Science,49(6), 751–766. https://doi.org/10.1287/mnsc.49.6.751.16026.
Scopus Info. (2013). Scopus content overview. Elsevier. http://www.elsevier.com/online-tools/scopus/content-overview.
Singh, J., & Agrawal, A. (2011). Recruiting for ideas: How firms exploit the prior inventions of new hires. Management Science,57(1), 129–150. https://doi.org/10.1287/mnsc.1100.1253.
Slavova, K., Fosfuri, A., & De Castro, J. O. (2015). Learning by hiring: The effects of scientists’ inbound mobility on research performance in Academia. Organization Science,27(1), 72–89. https://doi.org/10.1287/orsc.2015.1026.
Somaya, D., Williamson, I. O., & Lorinkova, N. (2008). Gone but not lost: The different performance impacts of employee mobility between cooperators versus competitors. Academy of Management Journal,51(5), 936–953. https://doi.org/10.5465/AMJ.2008.34789660.
Stephan, P. E. (1996). The economics of science. Journal of Economic Literature,34(3), 1199–1235.
Stern, S. (2004). Do scientists pay to be scientists? Management Science,50(6), 835–853. https://doi.org/10.1287/mnsc.1040.0241.
Van Heeringen, A., & Dijkwel, P. (1987). The relationships between age, mobility and scientific productivity. Part I. Scientometrics,11(5–6), 267–280. https://doi.org/10.1007/BF02279349.
Waldinger, F. (2012). Peer effects in science: Evidence from the dismissal of scientists in Nazi Germany. The Review of Economic Studies,79(2), 838–861. https://doi.org/10.1093/restud/rdr029.
Wezel, F. C., Cattani, G., & Pennings, J. M. (2006). Competitive implications of interfirm mobility. Organization Science,17(6), 691–709. https://doi.org/10.1287/orsc.1060.0219.
Wuchty, S., Jones, B. F., & Uzzi, B. (2007). The increasing dominance of teams in production of knowledge. Science,316(5827), 1036–1039. https://doi.org/10.1126/science.1136099.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Tartari, V., Di Lorenzo, F. & Campbell, B.A. “Another roof, another proof”: the impact of mobility on individual productivity in science. J Technol Transf 45, 276–303 (2020). https://doi.org/10.1007/s10961-018-9681-5
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10961-018-9681-5