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Blaming the exogenous environment? Conditional efficiency estimation with continuous and discrete exogenous variables

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  • De Witte, Kristof
  • Mika, Kortelainen

Abstract

This paper proposes a fully nonparametric framework to estimate relative efficiency of entities while accounting for a mixed set of continuous and discrete (both ordered and unordered) exogenous variables. Using robust partial frontier techniques, the probabilistic and conditional characterization of the production process, as well as insights from the recent developments in nonparametric econometrics, we present a generalized approach for conditional efficiency measurement. To do so, we utilize a tailored mixed kernel function with a data-driven bandwidth selection. So far only descriptive analysis for studying the effect of heterogeneity in conditional efficiency estimation has been suggested. We show how to use and interpret nonparametric bootstrap-based significance tests in a generalized conditional efficiency framework. This allows us to study statistical significance of continuous and discrete exogenous variables on production process. The proposed approach is illustrated using simulated examples as well as a sample of British pupils from the OECD Pisa data set. The results of the empirical application show that several exogenous discrete factors have a statistically significant effect on the educational process.

Suggested Citation

  • De Witte, Kristof & Mika, Kortelainen, 2009. "Blaming the exogenous environment? Conditional efficiency estimation with continuous and discrete exogenous variables," MPRA Paper 14034, University Library of Munich, Germany.
  • Handle: RePEc:pra:mprapa:14034
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    References listed on IDEAS

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    Cited by:

    1. Li, Zhi & Ouyang, Xiaoling & Du, Kerui & Zhao, Yang, 2017. "Does government transparency contribute to improved eco-efficiency performance? An empirical study of 262 cities in China," Energy Policy, Elsevier, vol. 110(C), pages 79-89.
    2. Weihua Su & Zhen Wang & Chonghui Zhang & Tomas Balezentis, 2023. "Determinants of the innovation efficiency of strategic emerging enterprises: evidence from the robust frontiers," Economic Change and Restructuring, Springer, vol. 56(3), pages 1433-1465, June.
    3. Michael Zschille, 2014. "Nonparametric measures of returns to scale: an application to German water supply," Empirical Economics, Springer, vol. 47(3), pages 1029-1053, November.
    4. Tajudeen, Ibrahim A., 2021. "The underlying drivers of economy-wide energy efficiency and asymmetric energy price responses," Energy Economics, Elsevier, vol. 98(C).
    5. A. Ruijs & M. Kortelainen & A. Wossink & C.J.E. Schulp & R. Alkemade & Paul Madden, 2012. "Opportunity cost estimation of ecosystem services," Economics Discussion Paper Series 1222, Economics, The University of Manchester.
    6. Vidoli, Francesco, 2011. "Evaluating the water sector in Italy through a two stage method using the conditional robust nonparametric frontier and multivariate adaptive regression splines," European Journal of Operational Research, Elsevier, vol. 212(3), pages 583-595, August.
    7. Kristof De Witte & Chris Van Klaveren, 2014. "How are teachers teaching? A nonparametric approach," Education Economics, Taylor & Francis Journals, vol. 22(1), pages 3-23, February.
    8. Nieswand, Maria & Baake, Pio & Wagner, Lilo, 2015. "Voting for Inefficiency," VfS Annual Conference 2015 (Muenster): Economic Development - Theory and Policy 113068, Verein für Socialpolitik / German Economic Association.
    9. Yao, Xin & Huang, Ruting & Du, Kerui, 2019. "The impacts of market power on power grid efficiency: Evidence from China," China Economic Review, Elsevier, vol. 55(C), pages 99-110.

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    More about this item

    Keywords

    Nonparametric estimation; Conditional efficiency measures; Exogenous factors; Generalized kernel function; Education;
    All these keywords.

    JEL classification:

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

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