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Efficiency and technological change at US research universities

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  • Jeremy Foltz

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  • Bradford Barham
  • Jean-Paul Chavas
  • Kwansoo Kim

Abstract

This paper investigates the determinants of efficiency and technological progress at US research universities. It relies on a unique panel data set of multiple outputs and inputs from 92 universities covering the period 1981-1998. Over that time span, US universities experienced large increases in industry funding and in academic patenting activity. In this context, the directional distance function and a nonparametric representation of the underlying production technology are combined to obtain estimates of productivity growth and technical efficiency. A pooled-Tobit estimator is used to examine the determinants of technical efficiency and the rate of technological progress. The results show how changes in funding sources for U.S. research universities affects research performance.
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Suggested Citation

  • Jeremy Foltz & Bradford Barham & Jean-Paul Chavas & Kwansoo Kim, 2012. "Efficiency and technological change at US research universities," Journal of Productivity Analysis, Springer, vol. 37(2), pages 171-186, April.
  • Handle: RePEc:kap:jproda:v:37:y:2012:i:2:p:171-186
    DOI: 10.1007/s11123-011-0249-8
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    References listed on IDEAS

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    Cited by:

    1. Lee, Boon L. & Worthington, Andrew C., 2016. "A network DEA quantity and quality-orientated production model: An application to Australian university research services," Omega, Elsevier, vol. 60(C), pages 26-33.
    2. 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.
    3. Kim, Kwansoo & Barham, Bradford L. & Chavas, Jean-Paul & Foltz, Jeremy D., 2005. "Research and Development at U.S. Research Universities: An Analysis of Scope Economies," 2005 Annual meeting, July 24-27, Providence, RI 19147, American Agricultural Economics Association (New Name 2008: Agricultural and Applied 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. Berbegal-Mirabent, Jasmina & Lafuente, Esteban & Solé, Francesc, 2013. "The pursuit of knowledge transfer activities: An efficiency analysis of Spanish universities," Journal of Business Research, Elsevier, vol. 66(10), pages 2051-2059.
    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.

    More about this item

    Keywords

    Efficiency; Technical change; Distance function; University R&D; O3; O31; O33; C6; L31;

    JEL classification:

    • C6 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling

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