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The role of public funding in nanotechnology scientific production: Where Canada stands in comparison to the United States

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Abstract

This paper presents cross-country comparisons between Canada and the United States in terms of the impact of public grants and scientific collaborations on subsequent nanotechnology-related publications. In this study we present the varying involvement of academic researchers and government funding to capture the influence of funded research in order to help government agencies evaluate their efficiency in financing nanotechnology research. We analyze the measures of quantity and quality of research output using time-related econometric models and compare the results between nanotechnology scientists in Canada and the United States. The results reveal that both research grants and the position of researchers in co-publication networks have a positive influence on scientific output. Our findings demonstrate that research funding yields a significantly positive linear impact in Canada and a positive non-linear impact in the United States on the number of papers and in terms of the number of citations we observe a positive impact only in the US. Our research shows that the position of scientists in past scientific networks plays an important role in the quantity and quality of papers published by nanotechnology scientists.

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Notes

  1. We consider higher betweenness centrality and higher cliquishness in terms of better network position.

  2. Even though the regressions are estimated on a sample starting in 1996, we extracted data from 1985 onwards to build the ‘career age’ variable described below.

  3. Geodesic distance is a shortest path between any particular pair of researchers in a scientific network.

  4. Egocentric density is the density among a researcher’s direct connections and indicates the fraction of possible links present in the network (Koput 2010).

  5. We also constructed 5-year sub-networks, but 3-year sub-networks gave us more consistent results.

  6. We estimated more than 15 models as we considered and neglected potential endogeneity using panel data via xtnbreg and xtpoisson. We also performed non-paneled regressions using the clustering method of nbreg and Poisson to account for repeated measures of the same individual scientist. Note that we tried zero-inflated negative binomial model as well, but it does not work on our data for the number of papers and the results for the number of citations are similar to zero-inflated Poisson model.

  7. We do not believe that this relationship between funding and scientific production (and of its quality) is infinite. We examined a cubic term in the regressions but it turned out non significant. Considering the wisdom of the granting councils and of the peer review process, however, we very much doubt that an embarrassment of riches in academia is likely to appear.

  8. The full results are available from the authors in an unpublished appendix.

  9. In this model, marginal effects are computed to measure a change in one of explanatory variables when the values of other explanatory variables are set at their means.

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Acknowledgments

Beaudry acknowledges financial support from the Social Science and Humanities Research Council of Canada (Grants # 430-2012-0849 and # 435-2013-1220). We are grateful to Carl St-Pierre for his advice on statistical analysis, to Ahmad Barirani for the data extraction program used in the data collection, to Maxime Clerk-Lamalice for the construction of the data consolidation program and for the exploratory statistical work, and to Sedki Allaoui and Ricard-Olivier Moreau for their contribution to the long and painful database matching process. We would like to thank the editors of this journal and two anonymous referees for their helpful comments. None of these, however, are responsible for any remaining errors.

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Authors and Affiliations

  1. Department of Mathematics and Industrial Engineering, Polytechnique Montreal, Downtown Office, P.O. Box. 6079, Montreal, QC, H3C 3A7, Canada

    Leila Tahmooresnejad & Catherine Beaudry

  2. Faculty of Engineering and Computer Science, Concordia Institute for Information Systems Engineering, Concordia University, 1455 Maisonneuve West, Montreal, QC, H3G 1M8, Canada

    Andrea Schiffauerova

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  1. Leila Tahmooresnejad
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Correspondence to Catherine Beaudry.

Appendices

Appendix 1

See Tables 7 and 8.

Table 7 First stage regressions results—the US (standard errors in parentheses and *** p < 0.01; ** p < 0.05; * p < 0.1)
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Table 8 First stage regressions results—Canada (standard errors in parentheses and *** p < 0.01; ** p < 0.05; * p < 0.1)
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Appendix 2

See Table 9.

Table 9 Descriptive statistics
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Table 10 Correlation matrix—Canada
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Appendix 3

See Tables 10 and 11.

Table 11 Correlation matrix—the US
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Appendix 4

See Tables 12 and 13.

Table 12 Second stage of regression results of poisson model—impact of public funding on the number of papers and the number of citations in Canada and the US (standard errors in parentheses and *** p < 0.01; ** p < 0.05; * p < 0.1)
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Table 13 Second stage of regression results of xtpoisson model—impact of public funding on the number of papers and the number of citations in Canada and the US (Standard errors in parentheses and *** p < 0.01; ** p < 0.05; * p < 0.1)
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Appendix 5

See Fig. 2.

Fig. 2
figure 2

A quadratic effect of past individual cliquishness of scientists, Cliquishness, on (E.1) the number of papers in the US and (E.2) the number of citations in Canada

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Tahmooresnejad, L., Beaudry, C. & Schiffauerova, A. The role of public funding in nanotechnology scientific production: Where Canada stands in comparison to the United States. Scientometrics 102, 753–787 (2015). https://doi.org/10.1007/s11192-014-1432-2

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