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Time substitution and network effects with an application to nanobiotechnology policy for US universities

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  • Fukuyama, Hirofumi
  • Weber, William L.
  • Xia, Yin

Abstract

We present a dynamic network model of the knowledge production process for nanobiotechnology research at 25 US universities during 1990–2005. Universities produce knowledge outputs in nanobiotechnology consisting of Ph.D. graduates, research publications, and patents. Inputs include the university’s spending on R&D in engineering and the life sciences, and the university’s own stock of knowledge measured by past publications in nanobiotechnology. In addition, universities take advantage of the stock of knowledge produced by other universities in previous periods. We simulate the effect of the National Science Foundation being able to optimally allocate research funds for nanobiotechnology research between universities and across time so as to maximize the aggregate amounts of the three knowledge outputs produced by the universities.

Suggested Citation

  • Fukuyama, Hirofumi & Weber, William L. & Xia, Yin, 2016. "Time substitution and network effects with an application to nanobiotechnology policy for US universities," Omega, Elsevier, vol. 60(C), pages 34-44.
    • Handle: RePEc:eee:jomega:v:60:y:2016:i:c:p:34-44
    DOI: 10.1016/j.omega.2015.04.020
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    5. Moriah B. Bostian & Cinzia Daraio & Rolf Fare & Shawna Grosskopf & Maria Grazia Izzo & Luca Leuzzi & Giancarlo Ruocco & William L. Weber, 2018. "Inference for Nonparametric Productivity Networks: A Pseudo-likelihood Approach," DIAG Technical Reports 2018-06, Department of Computer, Control and Management Engineering, Universita' degli Studi di Roma "La Sapienza".
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    8. Yu Zhu & Feng Yang & Bengang Gong & Wei Zeng, 2023. "RETRACTED ARTICLE: Assessing the efficiency of innovation entities in China: evidence from a nonhomogeneous data envelopment analysis and Tobit," Electronic Commerce Research, Springer, vol. 23(1), pages 175-205, March.

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