Advertisement

The Journal of Technology Transfer

, Volume 41, Issue 3, pp 469–505 | Cite as

How much does it cost to be a scientist?

  • Benjamin Balsmeier
  • Maikel Pellens
Article

Abstract

We examine the academe–industry wage gap. Once self-selection and different personal characteristics of academic and industrial scientists have been taken into account the wage gap narrows from 28 to 13 %. The counterfactual wage faced by an academic scientist increases with time spent on development and decreases with time spent on research. This finding challenges the idea of a solely negative relationship between science and wages. We further find that preferences for science augment the relationship between research orientation and wages. Overall, the results have implications for policy makers that aim to increase development oriented research activities at universities, individual scientists thinking about whether to pursue a career in industry or academe, and managers trying to hire academic scientists.

Keywords

Academe–industry wage gap Economics of science Matching 

JEL Classification

G24 O31 O38 

Notes

Acknowledgments

Balsmeier gratefully acknowledges financial support from the Flemish Science Foundation. Pellens gratefully acknowledges financial support from the National Bank of Belgium. Both authors thank participants in the LEI & BRICK workshop on the ‘Organization, Economics, and Policy of Scientific Research’, the Technology Transfer Society Conference, the DRUID Society Conference, a lunch seminar at KU Leuven, as well as Dirk Czarnitzki, Lee Fleming, Christoph Grimpe, Reinhilde Veugelers, Stijn Kelchtermans, Henry Sauermann, Toby Stuart, and Scott Stern for their insightful comments.

References

  1. Agarwal, R., & Ohyama, A. (2013). Industry or academia, basic or applied? Career choices and earnings trajectories of scientists. Management Science, 59, 950–970.CrossRefGoogle Scholar
  2. Aghion, P., Dewatripont, M., & Stein, J. (2008). Academic freedom, private-sector focus, and the process of innovation. Rand Journal of Economics, 39(3), 617–635.CrossRefGoogle Scholar
  3. Altbach, P., Reisberg, L., Yudkevich, M., Androushcak, G., & Pacheco, I. (2012). Paying the professoriate: A global comparison of compensation and contracts. London: Routledge.Google Scholar
  4. Angrist, J. (1998). Estimating the labor market impact of voluntary military service using social security data on military applicants. Econometrica, 66(2), 249–288.CrossRefGoogle Scholar
  5. Arora, A., & Gambardella, A. (1994). The changing technology and technological change: General and abstract knowledge and the division of innovative labour. Research Policy, 23(5), 523–532.CrossRefGoogle Scholar
  6. Auriol, L. (2007). Labour market characteristics and international mobility of doctorate holders: Results for seven countries. OECD Science, Technology and Industry Working Papers 2007/02. doi: 10.1787/310254328811.
  7. Auriol, L. (2010). Careers of doctorate holders: Employment and mobility patterns. OECD Science, Technology and Industry Working Papers 2010/04. doi: 10.1787/5kmh8phxvvf5-en.
  8. Auriol, L., Schaaper, M., & Felix, B. (2012). Mapping careers and mobility of doctorate holders: Draft guidelines, model questionnaire and indicators. OECD Science, Technology and Industry Working papers 2007/6. doi: 10.1787/5k4dnq2h4n5c-en.
  9. Balsmeier, B., & Pellens, M. (2014). Who makes, who breaks: Which scientists stay in academe? Economics Letters, 122(2), 229–232.CrossRefGoogle Scholar
  10. Barbezat, D. (1987). Salary differentials by sex in the academic labour market. Journal of Human Resources, 22, 422–428.CrossRefGoogle Scholar
  11. Bayer, A., & Astin, H. (1968). Sex differences in academic rank and salary among science doctorates in teaching. Journal of Human Resources, 2, 191–200.CrossRefGoogle Scholar
  12. Blundell, R., & Costa Dias, M. (2009). Alternative approaches to evaluation in empirical microeconometrics. Journal of Human Resources, 44, 565–640.CrossRefGoogle Scholar
  13. Card, D., & Sullivan, D. (1988). Measuring the effect of subsidized training-programs on movements in and out of employment. Econometrica, 56, 497–530.CrossRefGoogle Scholar
  14. Cohen, W. M., & Levinthal, D. A. (1989). Innovation and learning: The two faces of R&D. The Economic Journal, 99(397), 569–596.Google Scholar
  15. Cohen, W., & Levinthal, D. (1990). Absorptive capacity: A new perspective on learning and innovation. Administrative Science Quarterly, 35, 128–152.CrossRefGoogle Scholar
  16. Creedy, J. (1988). Cohort and cross-sectional earnings profiles: Scientists in Britain and Australia. Journal of Economic Studies, 15(1), 44–52.CrossRefGoogle Scholar
  17. Dasgupta, P., & David, P. (1994). Towards a new economics of science. Research Policy, 23, 487–521.CrossRefGoogle Scholar
  18. Diamond, A. (1986). The life-cycle research productivity of mathematicians and scientists. Journal of Gerontology, 41, 520–525.CrossRefGoogle Scholar
  19. Eurostat. (2012). Careers of doctorate holders. Retrieved 05 March, 2012, from http://epp.eurostat.ec.europa.eu/statistics_explained/index.php/Careers_of_doctorate_holders.
  20. Fini, R., & Lacetera, N. (2010). Different yokes for different folks: Individual preference, institutional logics, and the commercialization of academic research. In G. Libecap (Ed.), Spanning boundaries and disciplines: University technology commercialization in the idea age (Vol. 21, pp. 1–25). Bingley: Emerald Group Publishing.CrossRefGoogle Scholar
  21. Frolich, M. (2007). Propensity score matching without conditional independence assumption—With an application to the gender wage gap in the United Kingdom. Econometric Journal, 10, 359–407.CrossRefGoogle Scholar
  22. FWO. (2013). Ph.D. Fellowship. Retrieved 27 Jan, 2013, from www.fwo.be/Aspirant.aspx.
  23. Gerfin, M., & Lechner, M. (2002). A microeconometric evaluation of the active labour market policy in Switzerland. Economic Journal, 112, 854–893.CrossRefGoogle Scholar
  24. Heckman, J. (1976). The common structure of statistical models for truncation, sample selection and limited dependent variables and a simple estimator for such models. Annals of Economic and Social Measurement, 5(4), 475–492.Google Scholar
  25. Heckman, J. J., Ichimura, H., & Todd, P. (1998). Matching as an econometric evaluation estimator. Review of Economic Studies, 65(2), 261–294.Google Scholar
  26. Imbens, G., & Wooldridge, J. (2009). Recent developments in the econometrics of program evaluation. Journal of Economic Literature, 47, 5–86.CrossRefGoogle Scholar
  27. Konrad, A., & Pfeffer, J. (1990). Do you get what you deserve? Factors affecting the relationship between productivity and pay. Administrative Science Quarterly, 35, 258–285.CrossRefGoogle Scholar
  28. Lacetera, N. (2009). Different missions and commitment power in RD organizations: Theory and evidence on industry–university alliances. Organization Science, 20, 565–582.CrossRefGoogle Scholar
  29. Laitner, J., & Stafford, F. (1985). The academic labor market: Has compensation diverged from other professions?. Washington, DC: Econometric Society Meetings.Google Scholar
  30. Lechner, M. (2001). Identification and estimation of causal effects of multiple treatments under the conditional independence assumption. In M. Lechner & F. Pfeiffer (Eds.), Econometric evaluation of active labor market policies (pp. 43–58). Heidelberg: Physica-Verlag.Google Scholar
  31. Lillard, L., & Weiss, Y. (1979). Components of variation in panel earnings data: American scientists. Econometrica, 47, 437–454.CrossRefGoogle Scholar
  32. McNabb, R., & Wass, V. (1997). Male–female salary differentials in British universities. Oxford Economic Papers, 49, 328–343.CrossRefGoogle Scholar
  33. Merton, R. (1973). The sociology of science: Theoretical and empirical investigations. Chicago: University of Chicago Press.Google Scholar
  34. Moore, W., Newman, R., & Turnball, G. (1998). Do academic wages decrease with seniority? Journal of Labour Economics, 16, 352–366.CrossRefGoogle Scholar
  35. Moortgat, P., & Van Mellaert, G. (2011). CDH (Careers of Doctorate Holders). Onderzoek, ontwikkeling en innovatie in België studiereeks 12. http://www.belspo.be/belspo/organisation/Publ/pub_ostc/ind/ind12_nl.pdf. Brussels: Belgian Science Policy Office.
  36. National Science Board. (2012). Science and engineering indicators 2012. Arlington VA: National Science Foundation.Google Scholar
  37. Nopo, H. (2008). Matching as a tool to decompose wage gaps. Review of Economics and Statistics, 90, 290–299.CrossRefGoogle Scholar
  38. OECD. (2013). OECD/UNESCO Institute for Statistics/Eurostat Careers of Doctorate Holders (CDH) project. Retrieved June 08, 2013, from http://www.oecd.org/innovation/inno/oecdunescoinstituteforstatisticseurostatcareersofdoctorateholderscdhproject.htm.
  39. Ong, L., & Mitchell, J. (2000). Professors and hamburgers: An international comparison of relative academic salaries. Applied Economics, 32, 869–876.CrossRefGoogle Scholar
  40. Roach, M., & Sauermann, H. (2010). A taste for science? PhD scientists’ academic orientation and self-selection into research careers in industry. Research Policy, 39, 422–434.CrossRefGoogle Scholar
  41. Rosenbaum, P., & Rubin, D. (1983). The central role of the propensity score in observational studies for causal effects. Biometrika, 70, 41–55.CrossRefGoogle Scholar
  42. Rosenberg, N. (1990). Why do firms do basic research (with their own money)? Research Policy, 19, 165–174.CrossRefGoogle Scholar
  43. Rubin, D. (1977). Assignment to a treatment group on the basis of a covariate. Journal of Educational Statistics, 2, 1–26.Google Scholar
  44. Sauermann, H., Roach, M. (2012). Taste for science, taste for commercialization, and hybrid scientists. Paper presented at DRUID conference, June 19–21. Denmark: Copenhagen Business School.Google Scholar
  45. Sauermann, H., & Roach, M. (2014). Not all scientists pay to be scientists: Heterogeneous preferences for publishing in industrial employment. Research Policy, 43(1), 32–47.CrossRefGoogle Scholar
  46. Sauermann, H., & Stephan, P. (2013). Conflicting logics? A multidimensional view of industrial and academic science. Organization Science, 24(3), 889–909.CrossRefGoogle Scholar
  47. Stephan, P. E. (1996). The economics of science. Journal of Economic Literature, 34, 1199–1235.Google Scholar
  48. Stephan, P., & Levin, S. (1992). Striking the mother lode in science: The importance of age, place, and time. Oxford: Oxford University Press.Google Scholar
  49. Stern, S. (2004). Do scientists pay to be scientists? Management Science, 50, 835–853.CrossRefGoogle Scholar
  50. Stevens, P. (2004). Academic salaries in the UK and US. National Institute Economic Review, 190, 104–113.CrossRefGoogle Scholar
  51. Uebersax, J. (2000). Estimating a latent trait model by factor analysis of tetrachoric correlations. Retrieved March 16, 2011, from http://john-uebersax.com/stat/irt.htm.
  52. Unesco. (2012). Tracking the careers of doctorate holders. Retrieved May 07, 2012, from http://www.uis.unesco.org/ScienceTechnology/Pages/doctorate-degree-holders.aspx.
  53. Walker, J., Vignoles, A., & Collins, M. (2010). Higher education academic salaries in the UK. Oxford Economic Papers, 62, 13–35.CrossRefGoogle Scholar
  54. Weiss, Y., & Lillard, L. (1978). Experience, vintage, and time effects in the growth of earnings: American scientists. Journal of Political Economy, 86, 427–474.CrossRefGoogle Scholar
  55. Wetenschapsbeleid, Federaal. (2006). Careers of Doctorate Holders Survey [Database]. Gent: ECOOM UGent.Google Scholar

Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  1. 1.Department of Managerial Economics, Strategy and InnovationKU LeuvenLeuvenBelgium
  2. 2.Institute for Organisational EconomicsUniversity of MünsterMünsterGermany
  3. 3.Centre for European Economic Research (ZEW)MannheimGermany

Personalised recommendations