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The effect of holding a research chair on scientists’ productivity

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Abstract

Having combined data on Quebec scientists’ funding and journal publication, this paper tests the effect of holding a research chair on a scientist’s performance. The novelty of this paper is to use a matching technique to understand whether holding a research chair contributes to a better scientific performance. This method compares two different sets of regressions which are conducted on different data sets: one with all observations and another with only the observations of the matched scientists. Two chair and non-chair scientists are deemed matched with each other when they have the closest propensity score in terms of gender, research field, and amount of funding. The results show that holding a research chair is a significant scientific productivity determinant in the complete data set. However, when only matched scientists are kept in data set, holding a Canada research chair has a significant positive effect on scientific performance but other types of chairs do not have a significant effect. In the other words, in the case of two similar scientists in terms of gender, research funding, and research field, only holding a Canada research chair significantly affects scientific performance.

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Notes

  1. http://www.chairs-chaires.gc.ca/about_us-a_notre_sujet/index-eng.aspx.

  2. http://www.chairs-chaires.gc.ca/about_us-a_notre_sujet/publications/ten_year_evaluation_e.pdf.

  3. The paper argues that solving the complex problem provides great benefit for the firms and organizations facing such problems.

  4. When the funding is attributed to more than one recipient researcher, the total amount of funding is divided by the number of researchers in the team within the same university. The SIRU data accounts for all interuniversity transfers and funds are counted where they have been transferred and spent. Unfortunately, we have no means by which to sum the funds from the same grants that are transferred to other universities, as the reporting does not allow a match between the data.

  5. Research funds may serve two purposes: they may be directly used for research cost and researchers’ salary as operational costs (O) or indirectly help research teams in buying instruments or laboratory infrastructure (I). It is therefore possible to generate six research funding variables for each researcher [PublicfundingO, PublicfundingI, PrivatefundingO, PrivatefundingI, NFPfundingO, and NFPfundingI]. In reality, research infrastructure funding stems mainly from public sources and the private and not-for-profit sources (PrivatefundingI, NFPfundingI) are too sporadic, i.e. rarely different from 0, to be used effectively in our models.

  6. We have three fields: ‘engineering and the natural sciences’, ‘health sciences’, and’ humanities, and social sciences’.

  7. The small universities are grouped according to their active disciplines and other institutional similarities. The University of Quebec and Bishop University are in the same group. The second group includes "École de technologie supérieure" (ETS), "Université du Québec à Montréal" (UQAM), and Institut national de la recherche scientifique (INRS).

  8. The “Year 2000”, “McGill University”, and the research division of “Medical science” are selected as reference points and are thus the excluded dummy variables.

  9. It should be noted that variables measuring funding and number of articles are transformed by natural logarithm function to have normal distribution and satisfy the necessary conditions for running regression equations.

  10. The test is reported in the note of each regression table in “Section 3: result and discussion”.

  11. For this test we use all variables that are available for regression, like variables in regression 5 of each table.

  12. Funding from the private sector and funding from the not-for-profit sector are directly put in the regression equation while funding from the public sector is first estimated by the instrumental variables and then inserted into the regression model. The first stage model regressions, reported in Tables 11, 12, 13, 14, 15 and 16 in Appendix, show the significant role of instrumental variables.

  13. The interaction with between the chair dummy variables and not-for-profit funding was tested and was never significant.

References

  • Abramo, G., D’Angelo, C. A., & Caprasecca, A. (2009). Gender differences in research productivity: A bibliometric analysis of the Italian academic system. Scientometrics, 79, 517–539.

    Article  Google Scholar 

  • Baird, L. L. (1986). What characterizes a productive research department? Research in Higher Education, 25, 211–225.

    Article  Google Scholar 

  • Baird, L. L. (1991). Publication productivity in doctoral research departments: Interdisciplinary and intradisciplinary factors. Research in Higher Education, 32, 303–318.

    Article  Google Scholar 

  • Becker, G. S. (1962). Investment in human capital: A theoretical analysis. The Journal of Political Economy, 70, 9–49.

    Article  Google Scholar 

  • Bernier, C., Gill, W., & Hunt, R. (1975). Measures of excellence of engineering and science departments: a chemical engineering example. Chemical Engineering Education, 9, 194–202.

    Google Scholar 

  • Bérubé, C., & Mohnen, P. (2009). Are firms that receive R&D subsidies more innovative? Canadian Journal of Economics/Revue Canadienne d’économique, 42, 206–225.

    Article  Google Scholar 

  • Blackburn, R. T., Behymer, C. E., & Hall, D. E. (1978). Research note: Correlates of faculty publications. Sociology of Education, 132–141.

  • Bonaccorsi, A., & Daraio, C. (2003). Age effects in scientific productivity. Scientometrics, 58, 49–90.

    Article  Google Scholar 

  • Buchmueller, T. C., Dominitz, J., & Lee Hansen, W. (1999). Graduate training and the early career productivity of Ph.D. economists. Economics of Education Review, 18, 65–77.

    Article  Google Scholar 

  • Cantu, F. J., Bustani, A., Molina, A., & Moreira, H. (2009). A knowledge-based development model: The research chair strategy. Journal of Knowledge Management, 13, 154–170.

    Article  Google Scholar 

  • Carayol, N., & Matt, M. (2006). Individual and collective determinants of academic scientists’ productivity. Information Economics and Policy, 18, 55–72.

    Article  Google Scholar 

  • Courty, P., & Sim, J. (2012). What is the cost of retaining and attracting exceptional talents?. Evidence from the Canada Research Chair program. Queen’s Economics Department Working Paper.

  • Crespi, G. A., & Geuna, A. (2008). An empirical study of scientific production: A cross country analysis, 1981–2002. Research Policy, 37, 565–579.

    Article  Google Scholar 

  • Diamond, A. M. (1986). The life-cycle research productivity of mathematicians and scientists. Journal of Gerontology, 41, 520.

    Article  Google Scholar 

  • Frey, B. S., & Rost, K. (2010). Do rankings reflect research quality? Journal of Applied Economics, 13, 1–38.

    Article  Google Scholar 

  • Geuna, A., & Nesta, L. (2003). University patenting and its effects on academic research. SEWPS Paper.

  • Golden, J., & Carstensen, F. V. (1992). Academic research productivity, department size and organization: Further results, comment. Economics of Education Review, 11, 153–160.

    Article  Google Scholar 

  • Goldfarb, B. (2008). The effect of government contracting on academic research: Does the source of funding affect scientific output? Research Policy, 37, 41–58.

    Article  Google Scholar 

  • Heinze, T., Shapira, P., Rogers, J. D., & Senker, J. M. (2009). Organizational and institutional influences on creativity in scientific research. Research Policy, 38, 610–623.

    Article  Google Scholar 

  • Henrekson, M., & Waldenström, D. (2007) How should research performance be measured. IFN Working Paper.

  • Jordan, J. M., Meador, M., & Walters, S. J. K. (1988). Effects of department size and organization on the research productivity of academic economists. Economics of Education Review, 7, 251–255.

    Article  Google Scholar 

  • Jordan, J. M., Meador, M., & Walters, S. J. K. (1989). Academic research productivity, department size and organization: Further results. Economics of Education Review, 8, 345–352.

    Article  Google Scholar 

  • Kleinman, D. L., & Vallas, S. P. (2001). Science, capitalism, and the rise of the “knowledge worker”: The changing structure of knowledge production in the United States. Theory and Society, 30, 451–492.

    Article  Google Scholar 

  • Kyvik, S. (1990). Age and scientific productivity. Differences between fields of learning. Higher Education, 19, 37–55.

    Article  Google Scholar 

  • Kyvik, S. (1995). Are big university departments better than small ones? Higher Education, 30, 295–304.

    Article  Google Scholar 

  • Kyvik, S., & Olsen, T. B. (2008). Does the aging of tenured academic staff affect the research performance of universities? Scientometrics, 76, 439–455.

    Article  Google Scholar 

  • Kyvik, S., & Teigen, M. (1996). Child care, research collaboration, and gender differences in scientific productivity. Science, Technology and Human Values, 21, 54.

    Article  Google Scholar 

  • Leahey, E. (2006). Gender differences in productivity. Gender & Society, 20, 754–780.

    Article  Google Scholar 

  • Levin, S. G., & Stephan, P. E. (1991). Research productivity over the life cycle: Evidence for academic scientists. The American Economic Review, 114–132.

  • Long, J. S. (1990). The origins of sex differences in science. Social Forces, 68, 1297–1316.

    Article  Google Scholar 

  • Long, J. S. (1992). Measures of sex differences in scientific productivity. Social Forces, 71, 159–178.

    Article  Google Scholar 

  • Long, J. S., Allison, P. D., & McGinnis, R. (1979). Entrance into the academic career. American Sociological Review, 816–830.

  • Mirnezami, S. R., & Beaudry, C. (2015). The effect of having a research chair on scientists’ productivity. In The 15th International Society of Scientometrics and Informetrics Conference, Istanbul, Turkey.

  • Nakhaie, M. R. (2002). Gender differences in publication among University Professors in Canada*. Canadian Review of Sociology/Revue canadienne de sociologie, 39, 151–179.

    Article  Google Scholar 

  • Niosi, J. (2002). Regional systems of innovation: Market pull and government push. In J.-A. Holbrook & D. Wolfe (Eds.), Knowledge, clusters and regional innovation (pp. 39–55). Montréal & Kingston: McGill-Queen’s University Press.

    Google Scholar 

  • Partha, D., & David, P. A. (1994). Toward a new economics of science. Research Policy, 23, 487–521.

    Article  Google Scholar 

  • Pavitt, K. (2000). Why European Union funding of academic research should be increased: a radical proposal. Science and Public Policy, 27, 455–460.

    Article  Google Scholar 

  • Pavitt, K. (2001). Public policies to support basic research: What can the rest of the world learn from US theory and practice? (And what they should not learn). Industrial and Corporate Change, 10, 761–779.

    Article  Google Scholar 

  • Salazar, M., & Holbrook, A. (2007). Canadian science, technology and innovation policy: The product of regional networking? Regional Studies, 41, 1129–1141.

    Article  Google Scholar 

  • Salter, A. J., & Martin, B. R. (2001). The economic benefits of publicly funded basic research: A critical review. Research Policy, 30, 509–532.

    Article  Google Scholar 

  • Schimank, U. (2005). ‘New Public Management’and the academic profession: Reflections on the German situation. Minerva, 43, 361–376.

    Article  Google Scholar 

  • Van Raan, A. F. J. (2005). Fatal attraction: Conceptual and methodological problems in the ranking of universities by bibliometric methods. Scientometrics, 62, 133–143.

    Article  Google Scholar 

  • West, M. A., Smith, H., Feng, W. L., & Lawthom, R. (1998). Research excellence and departmental climate in British universities. Journal of Occupational and Organizational Psychology, 71, 261–281.

    Article  Google Scholar 

  • Winter, M., Smith, C., Morris, P., & Cicmil, S. (2006). Directions for future research in project management: The main findings of a UK government-funded research network. International Journal of Project Management, 24, 638–649.

    Article  Google Scholar 

  • Xie, Y., & Shauman, K. A. (1998). Sex differences in research productivity: New evidence about an old puzzle. American Sociological Review, 847–870.

  • Zhou, Y. B., Lü, L., & Li, M. (2012). Quantifying the influence of scientists and their publications: distinguishing between prestige and popularity. New Journal of Physics, 14, 033033.

    Article  Google Scholar 

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Acknowledgments

We acknowledge funding from the Canada research chair program. We are grateful for the help of Carl St-Pierre for his advice on statistics and of Vincent Larivière for extracting the data from the OST (Observatoire des sciences et des technologies) database. All remaining errors are our own.

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Authors and Affiliations

  1. Polytechnique Montreal, P.O. Box. 6079, Montreal, QC, H3C 3A7, Canada

    Seyed Reza Mirnezami & Catherine Beaudry

Authors
  1. Seyed Reza Mirnezami
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  2. Catherine Beaudry
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Correspondence to Catherine Beaudry.

Appendix

Appendix

See Tables 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21 and 22.

Table 8 Variable description
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Table 9 Summary statistics
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Table 10 Correlation table
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Table 11 First stage of the regressions reported in Table 2
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Table 12 First stage of the regressions reported in Table 3
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Table 13 First stage of the regressions reported in Table 4
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Table 14 First stage of the regressions reported in Table 5
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Table 15 First stage of the regressions reported in Table 6
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Table 16 First stage of the regressions reported in Table 7
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Table 17 Regression results over the entire sample and using dChair (the second stage of 2SLS)
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Table 18 Regression results over the entire sample and using dIndGCChair (the second stage of 2SLS)
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Table 19 Regression results over the entire sample and using dChair (the second stage of 2SLS)
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Table 20 Regression results over matched scientists over the dCRC sample (the second stage of 2SLS)
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Table 21 Regression results over matched scientists over the dIndGCChair sample (the second stage of 2SLS)
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Table 22 Regression results over matched scientists over the dChair sample (the second stage of 2SLS)
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Mirnezami, S.R., Beaudry, C. The effect of holding a research chair on scientists’ productivity. Scientometrics 107, 399–454 (2016). https://doi.org/10.1007/s11192-016-1848-y

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