IDEAS home Printed from https://ideas.repec.org/a/ora/journl/v1y2017i1p39-48.html
   My bibliography  Save this article

Measuring Efficiency Of Mongolian Companies By Sfa And Dea Methods

Author

Listed:
  • Batchimeg Bayaraa

    (The University of Debrecen)

Abstract

Efficiency measurement usually adopts one of the following analysis, DEA (Data Envelopment Analysis) or SFA (Stochastic Frontier Analysis), but it is not common to use and compare both models in one research. Especially, there is not any research about performance measurement which used Mongolian companies’ financial data. The aim of this research is to examine the consistency of efficiency scores from DEA and SFA methods on Mongolian public companies. The financial statements of 100 public companies were obtained from the Mongolian Stock Exchange (MSE) website, from 2012 until 2015. Financial statements were chosen which met the requirements of consistency and accuracy, out of 227 public companies. From initially selected 9 output variables, revenue was chosen as an output variable, while cost of goods sold, operating expenses, and cash are used as input variables based on the stepwise regression result. SPSS (Statistical Package for the Social Sciences) software was used for linear regression to choose the variables; Pearson correlation to examine the correlation between variables and the correlation between efficiency scores of DEA, SFA, and COLS (Corrected Ordinary Least Squares); one-way ANOVA was used to determine statistically significant difference among the methods; and unrelated T-test was used for every pair models. In contrary, Data Envelopment Analysis (DEA) and Stochastic Frontier Analysis (SFA) were performed in R- Excel statistical program. The average efficiency results indicated that the SFA model exhibited the highest score of 0.75 (TeMode), followed by DEA-VRS (Variable Return to Scale) 49.1 and DEA-CRS (Constant Return to Scale) 33.8. Due to the low-efficiency score, scale efficiency was adopted, and the result showed only 3 companies work in an optimal efficient scale, while 42 companies work below an efficient scale, and 55 companies work above an efficient scale. Unrelated T-test result showed that there was not statistically significant difference among Tej, TeBC, and COLS; TeMode and CRS; CRS and output efficiency.

Suggested Citation

  • Batchimeg Bayaraa, 2017. "Measuring Efficiency Of Mongolian Companies By Sfa And Dea Methods," Annals of Faculty of Economics, University of Oradea, Faculty of Economics, vol. 1(1), pages 39-48, July.
    • Handle: RePEc:ora:journl:v:1:y:2017:i:1:p:39-48
    as

    Download full text from publisher

    File URL: http://anale.steconomiceuoradea.ro/volume/2017/n1/3.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Robert Lensink & Aljar Meesters, 2014. "Institutions and Bank Performance: A Stochastic Frontier Analysis," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 76(1), pages 67-92, February.
    2. James I. Price & Steven Renzetti & Diane Dupont & Wiktor Adamowicz & Monica B. Emelko, 2017. "Production Costs, Inefficiency, and Source Water Quality: A Stochastic Cost Frontier Analysis of Canadian Water Utilities," Land Economics, University of Wisconsin Press, vol. 93(1), pages 1-11.
    3. Banker, Rajiv D. & Cooper, William W. & Seiford, Lawrence M. & Thrall, Robert M. & Zhu, Joe, 2004. "Returns to scale in different DEA models," European Journal of Operational Research, Elsevier, vol. 154(2), pages 345-362, April.
    4. Peter Bogetoft & Lars Otto, 2011. "Benchmarking with DEA, SFA, and R," International Series in Operations Research and Management Science, Springer, number 978-1-4419-7961-2, September.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Costa, Marcelo Azevedo & Lopes, Ana Lúcia Miranda & de Pinho Matos, Giordano Bruno Braz, 2015. "Statistical evaluation of Data Envelopment Analysis versus COLS Cobb–Douglas benchmarking models for the 2011 Brazilian tariff revision," Socio-Economic Planning Sciences, Elsevier, vol. 49(C), pages 47-60.
    2. Peter Fernandes Wanke & Rebecca de Mattos, 2014. "Capacity Issues and Efficiency Drivers in Brazilian Bulk Terminals," Brazilian Business Review, Fucape Business School, vol. 11(5), pages 72-98, October.
    3. Vaneet Bhatia & Sankarshan Basu & Subrata Kumar Mitra & Pradyumna Dash, 2018. "A review of bank efficiency and productivity," OPSEARCH, Springer;Operational Research Society of India, vol. 55(3), pages 557-600, November.
    4. Ahn, Heinz & Clermont, Marcel & Langner, Julia, 2023. "Comparative performance analysis of frontier-based efficiency measurement methods – A Monte Carlo simulation," European Journal of Operational Research, Elsevier, vol. 307(1), pages 294-312.
    5. Amar Oukil & Slim Zekri, 2021. "Investigating farming efficiency through a two stage analytical approach: Application to the agricultural sector in Northern Oman," Papers 2104.10943, arXiv.org.
    6. Forsund, Finn R. & Sarafoglou, Nikias, 2005. "The tale of two research communities: The diffusion of research on productive efficiency," International Journal of Production Economics, Elsevier, vol. 98(1), pages 17-40, October.
    7. Hyeri Choi & Min Jae Park, 2019. "Evaluating the Efficiency of Governmental Excellence for Social Progress: Focusing on Low- and Lower-Middle-Income Countries," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 141(1), pages 111-130, January.
    8. 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.
    9. Gómez-Calvet, Roberto & Conesa, David & Gómez-Calvet, Ana Rosa & Tortosa-Ausina, Emili, 2014. "Energy efficiency in the European Union: What can be learned from the joint application of directional distance functions and slacks-based measures?," Applied Energy, Elsevier, vol. 132(C), pages 137-154.
    10. Beverelli, Cosimo & Fiorini, Matteo & Hoekman, Bernard, 2017. "Services trade policy and manufacturing productivity: The role of institutions," Journal of International Economics, Elsevier, vol. 104(C), pages 166-182.
    11. Annika Maren Schneider & Eva-Maria Oppel & Jonas Schreyögg, 2020. "Investigating the link between medical urgency and hospital efficiency – Insights from the German hospital market," Health Care Management Science, Springer, vol. 23(4), pages 649-660, December.
    12. Mousavi, Mohammad M. & Ouenniche, Jamal & Xu, Bing, 2015. "Performance evaluation of bankruptcy prediction models: An orientation-free super-efficiency DEA-based framework," International Review of Financial Analysis, Elsevier, vol. 42(C), pages 64-75.
    13. Vittadini, Giorgio & Sturaro, Caterina & Folloni, Giuseppe, 2022. "Non-Cognitive Skills and Cognitive Skills to measure school efficiency," Socio-Economic Planning Sciences, Elsevier, vol. 81(C).
    14. Cesaroni, Giovanni & Kerstens, Kristiaan & Van de Woestyne, Ignace, 2017. "Global and local scale characteristics in convex and nonconvex nonparametric technologies: A first empirical exploration," European Journal of Operational Research, Elsevier, vol. 259(2), pages 576-586.
    15. Michael Zschille, 2012. "Consolidating the Water Industry: An Analysis of the Potential Gains from Horizontal Integration in a Conditional Efficiency Framework," Discussion Papers of DIW Berlin 1187, DIW Berlin, German Institute for Economic Research.
    16. Julia Schaefer & Marcel Clermont, 2018. "Stochastic non-smooth envelopment of data for multi-dimensional output," Journal of Productivity Analysis, Springer, vol. 50(3), pages 139-154, December.
    17. Heinz Ahn & Peter Bogetoft & Ana Lopes, 2019. "Measuring potential sub-unit efficiency to counter the aggregation bias in benchmarking," Journal of Business Economics, Springer, vol. 89(1), pages 53-77, February.
    18. Andor, Mark A. & Parmeter, Christopher & Sommer, Stephan, 2019. "Combining uncertainty with uncertainty to get certainty? Efficiency analysis for regulation purposes," European Journal of Operational Research, Elsevier, vol. 274(1), pages 240-252.
    19. Chang, Hsihui & Choy, Hiu Lam & Cooper, William W. & Ruefli, Timothy W., 2009. "Using Malmquist Indexes to measure changes in the productivity and efficiency of US accounting firms before and after the Sarbanes-Oxley Act," Omega, Elsevier, vol. 37(5), pages 951-960, October.
    20. Blaeschke, Frédéric & Haug, Peter, 2014. "Does Intermunicipal Cooperation Increase Efficiency? Evidence from the Hessian Wastewater Sector," IWH Discussion Papers 11/2014, Halle Institute for Economic Research (IWH).

    More about this item

    Keywords

    Data Envelopment Analysis (DEA); Stochastic Frontier Analysis (SFA); input efficiency; output efficiency; Variable Return to Scale (VRS); Constant Return to Scale (CRS); Corrected Ordinary Least Squares (COLS);
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C30 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - General
    • L25 - Industrial Organization - - Firm Objectives, Organization, and Behavior - - - Firm Performance

    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:ora:journl:v:1:y:2017:i:1:p:39-48. See general information about how to correct material in RePEc.

    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 bibliographic 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.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catalin ZMOLE (email available below). General contact details of provider: https://edirc.repec.org/data/feoraro.html .

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

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.