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Efficiency and technological change at US research universities

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  • Jeremy Foltz
  • 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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  • 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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    Cited by:

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    2. Fu, Tsu-Tan & See, Kok Fong, 2022. "An integrated analysis of quality and productivity growth in China’s and Taiwan’s higher education institutions," Economic Analysis and Policy, Elsevier, vol. 74(C), pages 234-249.
    3. Zhou, Yi & Zhou, Wenji & Wei, Chu, 2023. "Environmental performance of the Chinese cement enterprise: An empirical analysis using a text-based directional vector," Energy Economics, Elsevier, vol. 125(C).
    4. 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.
    5. David J. Mayston, 2012. "Analysing the effectiveness of public service producers with endogenous resourcing," Discussion Papers 12/30, Department of Economics, University of York.
    6. Berbegal-Mirabent, Jasmina & Gil-Doménech, Dolors & de la Torre, Rocío, 2019. "Dealing with heterogeneity: An analysis of Spanish universities," TEC Empresarial, School of Business, Costa Rica Institute of Technology (ITCR), vol. 13(3), pages 58-77.
    7. 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.
    8. Cao, Yihui & Sickles, Robin C. & Triebs, Thomas P. & Tumlinson, Justin, 2024. "Linguistic distance to English impedes research performance," Research Policy, Elsevier, vol. 53(4).
    9. Meng, Qiaoyu & Li, Yi & Cao, Qinwei, 2024. "The paradox analysis and functional mechanism between R&D efficiency and transformation effect: Evidence from key universities in China," Technovation, Elsevier, vol. 130(C).
    10. Shamohammadi, Mehdi & Oh, Dong-hyun, 2019. "Measuring the efficiency changes of private universities of Korea: A two-stage network data envelopment analysis," Technological Forecasting and Social Change, Elsevier, vol. 148(C).
    11. 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.
    12. 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.
    13. 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.
    14. 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.

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    More about this item

    Keywords

    Efficiency; Technical change; Distance function; University R&D; O3; O31; O33; C6; L31;
    All these keywords.

    JEL classification:

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

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