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Estimating and explaining efficiency in a multilevel setting: A robust two-stage approach

Author

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  • K. DE WITTE
  • M. VERSCHELDE

Abstract

Various applications require multilevel settings (e.g., for estimating fixed and random effects). However, due to the curse of dimensionality, the literature on non-parametric efficiency analysis did not yet explore the estimation of performance drivers in highly multilevel settings. As such, it lacks models which are particularly designed for multilevel estimations. This paper suggests a semi-parametric two-stage framework in which, in a first stage, non-parametric efficiency estimators are determined. As such, we do not require any a priori information on the production possibility set. In a second stage, a semiparametric Generalized Additive Mixed Model (GAMM) examines the sign and significance of both discrete and continuous background characteristics. The proper working of the procedure is illustrated by simulated data. Finally, the model is applied on real life data. In particular, using the proposed robust two-stage approach, we examine a claim by the Dutch Ministry of Education in that three out of the twelve Dutch provinces would provide lower quality education. When properly controlled for abilities, background variables, peer group and ability track effects, we do not observe differences among the provinces in educational attainments

Suggested Citation

  • K. De Witte & M. Verschelde, 2010. "Estimating and explaining efficiency in a multilevel setting: A robust two-stage approach," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 10/657, Ghent University, Faculty of Economics and Business Administration.
    • Handle: RePEc:rug:rugwps:10/657
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    File URL: http://wps-feb.ugent.be/Papers/wp_10_657.pdf
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    References listed on IDEAS

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    1. Kristof DE WITTE & Mika KORTELAINEN, 2008. "Blaming the exogenous environment? Conditional efficiency estimation with continuous and discrete environmental variables," Working Papers of Department of Economics, Leuven ces0833, KU Leuven, Faculty of Economics and Business (FEB), Department of Economics, Leuven.
    2. De Witte, K., 2009. "Dropout from secondary education: All's well that begins well," Working Papers 14, Top Institute for Evidence Based Education Research.
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    Cited by:

    1. Halkos, George E. & Tzeremes, Nickolaos G., 2013. "A conditional directional distance function approach for measuring regional environmental efficiency: Evidence from UK regions," European Journal of Operational Research, Elsevier, vol. 227(1), pages 182-189.
    2. Halkos, George & Tzeremes, Nickolaos, 2011. "A conditional full frontier modelling for analyzing environmental efficiency and economic growth," MPRA Paper 32839, University Library of Munich, Germany.
    3. Michael L. Lahr & João Pedro Ferreira & Johannes R. Többen, 2020. "Intraregional trade shares for goods‐producing industries: RPC estimates using EU data," Papers in Regional Science, Wiley Blackwell, vol. 99(6), pages 1583-1605, December.
    4. Halkos, George & Tzeremes, Nickolaos, 2011. "A conditional full frontier approach for investigating the Averch-Johnson effect," MPRA Paper 35491, University Library of Munich, Germany.
    5. George Halkos & Nickolaos Tzeremes, 2014. "Measuring the effect of Kyoto protocol agreement on countries’ environmental efficiency in CO 2 emissions: an application of conditional full frontiers," Journal of Productivity Analysis, Springer, vol. 41(3), pages 367-382, June.

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    More about this item

    Keywords

    Productivity estimation; Multilevel setting; Generalized Additive Mixed Model; Education; Social segregation;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities
    • I21 - Health, Education, and Welfare - - Education - - - Analysis of Education

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