IDEAS home Printed from https://ideas.repec.org/p/pra/mprapa/14034.html
   My bibliography  Save this paper

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://mpra.ub.uni-muenchen.de/14034/1/MPRA_paper_14034.pdf
    File Function: original version
    Download Restriction: no

    References listed on IDEAS

    as
    1. Tom Broekel, 2008. "From Average to the Frontier: A Nonparametric Performance Approach for Analyzing Externalities and Regions’ Innovativeness," Papers in Evolutionary Economic Geography (PEEG) 0804, Utrecht University, Department of Human Geography and Spatial Planning, Group Economic Geography, revised May 2008.
    2. Tom Broekel & Andreas Meder, 2008. "The Bright and Dark Side of Cooperation for Regional Innovation Performance," DRUID Working Papers 08-12, DRUID, Copenhagen Business School, Department of Industrial Economics and Strategy/Aalborg University, Department of Business Studies.
    3. Kristof DE WITTE & Mika KORTELAINEN, 2008. "Blaming the exogenous environment? Conditional efficiency estimation with continuous and discrete environmental variables," Working Papers Department of Economics ces0833, KU Leuven, Faculty of Economics and Business, Department of Economics.
    4. Emmanuel Thanassoulis & Maria Da Conceicao & A. Silva Portela, 2002. "School Outcomes: Sharing the Responsibility Between Pupil and School1," Education Economics, Taylor & Francis Journals, vol. 10(2), pages 183-207.
    5. Bound, John & Brown, Charles & Mathiowetz, Nancy, 2001. "Measurement error in survey data," Handbook of Econometrics,in: J.J. Heckman & E.E. Leamer (ed.), Handbook of Econometrics, edition 1, volume 5, chapter 59, pages 3705-3843 Elsevier.
    6. Fried, Harold O. & Lovell, C. A. Knox & Schmidt, Shelton S. (ed.), 2008. "The Measurement of Productive Efficiency and Productivity Growth," OUP Catalogue, Oxford University Press, number 9780195183528.
    7. Racine, Jeff & Li, Qi, 2004. "Nonparametric estimation of regression functions with both categorical and continuous data," Journal of Econometrics, Elsevier, vol. 119(1), pages 99-130, March.
    8. Cinzia Daraio & Léopold Simar, 2005. "Introducing Environmental Variables in Nonparametric Frontier Models: a Probabilistic Approach," Journal of Productivity Analysis, Springer, vol. 24(1), pages 93-121, September.
    9. Bonaccorsi, Andrea & Daraio, Cinzia & Räty, Tarmo & Simar, Léopold, 2007. "Efficiency and University Size: Discipline-wise Evidence from European Universities," MPRA Paper 10265, University Library of Munich, Germany.
    10. Cherchye, Laurens & De Witte, Kristof & Ooghe, Erwin & Nicaise, Ides, 2010. "Efficiency and equity in private and public education: A nonparametric comparison," European Journal of Operational Research, Elsevier, vol. 202(2), pages 563-573, April.
    11. Badin, Luiza & Daraio, Cinzia & Simar, Léopold, 2010. "Optimal bandwidth selection for conditional efficiency measures: A data-driven approach," European Journal of Operational Research, Elsevier, vol. 201(2), pages 633-640, March.
    12. Cazals Catherine & Dudley Paul & Florens Jean-Pierre & Patel Shital & Rodriguez Frank, 2008. "Delivery Offices Cost Frontier: A Robust Non Parametric Approach with Exogenous Variables," Review of Network Economics, De Gruyter, vol. 7(2), pages 1-15, June.
    13. Park, B.U. & Simar, L. & Weiner, Ch., 2000. "The Fdh Estimator For Productivity Efficiency Scores," Econometric Theory, Cambridge University Press, vol. 16(06), pages 855-877, December.
    14. Daraio, Cinzia & Simar, Leopold, 2006. "A robust nonparametric approach to evaluate and explain the performance of mutual funds," European Journal of Operational Research, Elsevier, vol. 175(1), pages 516-542, November.
    15. Hayfield, Tristen & Racine, Jeffrey S., 2008. "Nonparametric Econometrics: The np Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 27(i05).
    16. Simar, Leopold & Wilson, Paul W., 2007. "Estimation and inference in two-stage, semi-parametric models of production processes," Journal of Econometrics, Elsevier, vol. 136(1), pages 31-64, January.
    17. Jeffery Racine & Jeffrey Hart & Qi Li, 2006. "Testing the Significance of Categorical Predictor Variables in Nonparametric Regression Models," Econometric Reviews, Taylor & Francis Journals, vol. 25(4), pages 523-544.
    18. Cinzia Daraio & Léopold Simar, 2007. "Conditional nonparametric frontier models for convex and nonconvex technologies: a unifying approach," Journal of Productivity Analysis, Springer, vol. 28(1), pages 13-32, October.
    19. Peter Hall & Jeff Racine & Qi Li, 2004. "Cross-Validation and the Estimation of Conditional Probability Densities," Journal of the American Statistical Association, American Statistical Association, vol. 99, pages 1015-1026, December.
    20. Kristof Witte & Rui Marques, 2011. "Big and beautiful? On non-parametrically measuring scale economies in non-convex technologies," Journal of Productivity Analysis, Springer, vol. 35(3), pages 213-226, June.
    21. Li, Qi & Racine, Jeffrey S, 2008. "Nonparametric Estimation of Conditional CDF and Quantile Functions With Mixed Categorical and Continuous Data," Journal of Business & Economic Statistics, American Statistical Association, vol. 26, pages 423-434.
    22. Roberta Blass Staub & Geraldo da Silva e Souza, 2007. "A Probabilistic Approach for Assessing the Significance of Contextual Variables in Nonparametric Frontier Models: an Application for Brazilian Banks," Working Papers Series 150, Central Bank of Brazil, Research Department.
    23. Laurens Cherchye & Kristof De Witte & Erwin Ooghe, 2008. "Equity and efficiency in private and public education: a nonparametric comparison," Working Papers Department of Economics ces0725, KU Leuven, Faculty of Economics and Business, Department of Economics.
    24. Racine, Jeff, 1997. "Consistent Significance Testing for Nonparametric Regression," Journal of Business & Economic Statistics, American Statistical Association, vol. 15(3), pages 369-378, July.
    25. Qi Li & Jeffrey Scott Racine, 2006. "Nonparametric Econometrics: Theory and Practice," Economics Books, Princeton University Press, edition 1, number 8355.
    26. repec:cor:louvrp:-571 is not listed on IDEAS
    27. Daouia, Abdelaati & Simar, Leopold, 2007. "Nonparametric efficiency analysis: A multivariate conditional quantile approach," Journal of Econometrics, Elsevier, vol. 140(2), pages 375-400, October.
    28. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. A. Ruijs & M. Kortelainen & A. Wossink & C.J.E. Schulp & R. Alkemade & Paul Madden, 2012. "Opportunity cost estimation of ecosystem services," The School of Economics Discussion Paper Series 1222, Economics, The University of Manchester.
    2. 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.
    3. repec:eee:enepol:v:110:y:2017:i:c:p:79-89 is not listed on IDEAS
    4. 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.
    5. 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.
    6. Nieswand, Maria & Baake, Pio & Wagner, Lilo, 2015. "Voting for Inefficiency," Annual Conference 2015 (Muenster): Economic Development - Theory and Policy 113068, Verein für Socialpolitik / German Economic Association.

    More about this item

    Keywords

    Nonparametric estimation; Conditional efficiency measures; Exogenous factors; Generalized kernel function; Education;

    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

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:pra:mprapa:14034. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Joachim Winter) or (Rebekah McClure). General contact details of provider: http://edirc.repec.org/data/vfmunde.html .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.