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The Productivity of Nanobiotechnology Research and Education in U.S. Universities

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  • William L. Weber
  • Yin Xia

Abstract

The knowledge production process of thirty universities that participated in nanobiotechnology research during 1990--2005 is estimated by a stochastic directional technology distance function. Knowledge outputs include journal articles, patents, and Ph.D students. The dual elasticity of transformation between knowledge outputs is estimated. An increase in patents reduces the revenue shares of publications and Ph.D students for a majority of the thirty universities. Aggregate university inefficiency has a U shape with a minimum in 1997. Evidence of technological regress is found which indicates that the increased production of patents, publications, and Ph.D students is due to bigger research budgets. Copyright 2011, Oxford University Press.

Suggested Citation

  • William L. Weber & Yin Xia, 2011. "The Productivity of Nanobiotechnology Research and Education in U.S. Universities," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 93(4), pages 1151-1167.
    • Handle: RePEc:oup:ajagec:v:93:y:2011:i:4:p:1151-1167
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    File URL: http://hdl.handle.net/10.1093/ajae/aar032
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    Cited by:

    1. Yaisawarng, Suthathip & Ng, Ying Chu, 2014. "The impact of higher education reform on research performance of Chinese universities," China Economic Review, Elsevier, vol. 31(C), pages 94-105.
    2. Badau, Flavius & Färe, Rolf & Gopinath, Munisamy, 2016. "Global resilience to climate change: Examining global economic and environmental performance resulting from a global carbon dioxide market," Resource and Energy Economics, Elsevier, vol. 45(C), pages 46-64.
    3. Silva, Felipe & Fulginiti, Lilyan E. & Perrin, Richard K., 2016. "Did technical change in agricultural production decrease the emission of pollutants on the Amazon Forest during 1990-2009?," 2016 Annual Meeting, February 6-9, 2016, San Antonio, Texas 230092, Southern Agricultural Economics Association.
    4. 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.
    5. Jinyang Cai & Weiqiong Chen & Jikun Huang & Ruifa Hu & Carl E. Pray, 2020. "The Evolving Structure of Chinese R&D Funding and its Implications for the Productivity of Agricultural Biotechnology Research," Journal of Agricultural Economics, Wiley Blackwell, vol. 71(2), pages 287-304, June.
    6. Pedro Macedo & Elvira Silva, 2017. "Sensitivity of directional technical inefficiency measures to the choice of the direction vector: a simulation study," Economics Bulletin, AccessEcon, vol. 37(1), pages 52-62.
    7. Rolf Färe & Shawna Grosskopf & William L. Weber, 2016. "Pricing Nonmarketed Outputs with an Application to Community Colleges," Public Finance Review, , vol. 44(2), pages 197-219, March.

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