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On research efficiency: A micro-analysis of Dutch university research in Economics and Business Management

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  • Cherchye, L.
  • Abeele, P. Vanden

Abstract

We argue that efficiency assessments of academic research should focus on micro-units of research production rather than on conventionally employed (aggregated) macro-units, and show that such a detailed analysis of research performance provides interesting insights. In addition, we propose a non-parametric methodology that is specially tailored for analyzing the productive efficiency of research: it starts from a specification of the managerial objectives of research activities while imposing minimal structure on the (typically unknown) production technology. We illustrate our points by assessing the productive efficiency of research in Economics and Business Management faculties at Dutch universities. Next to measuring productive efficiency, we look for specific patterns in efficiency distributions over universities, years and areas of specialization. In addition, we investigate the impact of external funding and of the size of research programs on academic research efficiency.
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  • Cherchye, L. & Abeele, P. Vanden, 2005. "On research efficiency: A micro-analysis of Dutch university research in Economics and Business Management," Research Policy, Elsevier, vol. 34(4), pages 495-516, May.
    • Handle: RePEc:eee:respol:v:34:y:2005:i:4:p:495-516
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    1. Léopold Simar & Paul Wilson, 2000. "Statistical Inference in Nonparametric Frontier Models: The State of the Art," Journal of Productivity Analysis, Springer, vol. 13(1), pages 49-78, January.
    2. Kocher, Martin G. & Luptacik, Mikulas & Sutter, Matthias, 2006. "Measuring productivity of research in economics: A cross-country study using DEA," Socio-Economic Planning Sciences, Elsevier, vol. 40(4), pages 314-332, December.
    3. Kalaitzidakis, Pantelis & Mamuneas, Theofanis P. & Savvides, Andreas & Stengos, Thanasis, 2004. "Research spillovers among European and North-American economics departments," Economics of Education Review, Elsevier, vol. 23(2), pages 191-202, April.
    4. Afriat, Sidney N, 1972. "Efficiency Estimation of Production Function," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 13(3), pages 568-598, October.
    5. Varian, Hal R., 1990. "Goodness-of-fit in optimizing models," Journal of Econometrics, Elsevier, vol. 46(1-2), pages 125-140.
    6. Graves, Philip E & Marchand, James R & Thompson, Randal, 1982. "Economics Departmental Rankings: Research Incentives, Constraints, and Efficiency," American Economic Review, American Economic Association, vol. 72(5), pages 1131-1141, December.
    7. Korhonen, Pekka & Tainio, Risto & Wallenius, Jyrki, 2001. "Value efficiency analysis of academic research," European Journal of Operational Research, Elsevier, vol. 130(1), pages 121-132, April.
    8. Cordero, Rene, 1990. "The measurement of innovation performance in the firm: An overview," Research Policy, Elsevier, vol. 19(2), pages 185-192, April.
    9. Jerry G. Thursby, 2000. "What Do We Say about Ourselves and What Does It Mean? Yet Another Look at Economics Department Research," Journal of Economic Literature, American Economic Association, vol. 38(2), pages 383-404, June.
    10. Léopold Simar & Paul W. Wilson, 1998. "Sensitivity Analysis of Efficiency Scores: How to Bootstrap in Nonparametric Frontier Models," Management Science, INFORMS, vol. 44(1), pages 49-61, January.
    11. John R. Hauser, 1998. "Research, Development, and Engineering Metrics," Management Science, INFORMS, vol. 44(12-Part-1), pages 1670-1689, December.
    12. Peter Bogetoft, 1996. "DEA on Relaxed Convexity Assumptions," Management Science, INFORMS, vol. 42(3), pages 457-465, March.
    13. Cazals, Catherine & Florens, Jean-Pierre & Simar, Leopold, 2002. "Nonparametric frontier estimation: a robust approach," Journal of Econometrics, Elsevier, vol. 106(1), pages 1-25, January.
    14. Rajiv D. Banker, 1993. "Maximum Likelihood, Consistency and Data Envelopment Analysis: A Statistical Foundation," Management Science, INFORMS, vol. 39(10), pages 1265-1273, October.
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    More about this item

    JEL classification:

    • A11 - General Economics and Teaching - - General Economics - - - Role of Economics; Role of Economists
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C24 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Truncated and Censored Models; Switching Regression Models; Threshold Regression Models
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • D24 - Microeconomics - - Production and Organizations - - - Production; Cost; Capital; Capital, Total Factor, and Multifactor Productivity; Capacity

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