IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v8y2020i2p233-d319197.html
   My bibliography  Save this article

Comparing Groups of Decision-Making Units in Efficiency Based on Semiparametric Regression

Author

Listed:
  • Hohsuk Noh

    (Department of Statistics, Sookmyung Women’s University, Seoul 04310, Korea)

  • Seong J. Yang

    (Department of Statistics (Institute of Applied Statistics), Jeonbuk National University, Jeollabuk-do 54896, Korea)

Abstract

We consider a stochastic frontier model in which a deviation of output from the production frontier consists of two components, a one-sided technical inefficiency and a two-sided random noise. In such a situation, we develop a semiparametric regression-based test and compare the technical efficiencies of the different decision-making unit groups, assuming that the production frontier function is the same for all the groups. Our test performs better than the previously proposed ones for the same purpose in numerical studies, and also has the theoretical advantage of working under more general assumptions. To illustrate our method, we apply the proposed test to Program for International Student Assessment (PISA) 2015 data and investigate whether an efficiency difference exists between male and female student groups at a specific age in terms of learning time and achievement in mathematics.

Suggested Citation

  • Hohsuk Noh & Seong J. Yang, 2020. "Comparing Groups of Decision-Making Units in Efficiency Based on Semiparametric Regression," Mathematics, MDPI, vol. 8(2), pages 1-16, February.
    • Handle: RePEc:gam:jmathe:v:8:y:2020:i:2:p:233-:d:319197
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/8/2/233/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/8/2/233/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Chuan-hua Wei & Chunling Liu, 2012. "Statistical inference on semi-parametric partial linear additive models," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 24(4), pages 809-823, December.
    2. Leopold Simar & Valentin Zelenyuk, 2006. "On Testing Equality of Distributions of Technical Efficiency Scores," Econometric Reviews, Taylor & Francis Journals, vol. 25(4), pages 497-522.
    3. Banker, Rajiv D. & Zheng, Zhiqiang (Eric) & Natarajan, Ram, 2010. "DEA-based hypothesis tests for comparing two groups of decision making units," European Journal of Operational Research, Elsevier, vol. 206(1), pages 231-238, October.
    4. Fan, Jianqing & Jiang, Jiancheng, 2005. "Nonparametric Inferences for Additive Models," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 890-907, September.
    5. Liang, Hua, 2006. "Estimation in partially linear models and numerical comparisons," Computational Statistics & Data Analysis, Elsevier, vol. 50(3), pages 675-687, February.
    6. Li, Qi, 2000. "Efficient Estimation of Additive Partially Linear Models," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 41(4), pages 1073-1092, November.
    7. Christopher O’Donnell & D. Rao & George Battese, 2008. "Metafrontier frameworks for the study of firm-level efficiencies and technology ratios," Empirical Economics, Springer, vol. 34(2), pages 231-255, March.
    8. Meeusen, Wim & van den Broeck, Julien, 1977. "Efficiency Estimation from Cobb-Douglas Production Functions with Composed Error," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 18(2), pages 435-444, June.
    9. Ferrara, Giancarlo & Vidoli, Francesco, 2017. "Semiparametric stochastic frontier models: A generalized additive model approach," European Journal of Operational Research, Elsevier, vol. 258(2), pages 761-777.
    10. Rajiv D. Banker & Ram Natarajan, 2008. "Evaluating Contextual Variables Affecting Productivity Using Data Envelopment Analysis," Operations Research, INFORMS, vol. 56(1), pages 48-58, February.
    11. Yanyuan Ma & Jeng-Min Chiou & Naisyin Wang, 2006. "Efficient semiparametric estimator for heteroscedastic partially linear models," Biometrika, Biometrika Trust, vol. 93(1), pages 75-84, March.
    12. B. Golany & J. E. Storbeck, 1999. "A Data Envelopment Analysis of the Operational Efficiency of Bank Branches," Interfaces, INFORMS, vol. 29(3), pages 14-26, June.
    13. Noh, Hohsuk & Van Keilegom, Ingrid, 2020. "On relaxing the distributional assumption of stochastic frontier models," LIDAM Reprints ISBA 2020044, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    14. Lee, Hakyeon & Park, Yongtae & Choi, Hoogon, 2009. "Comparative evaluation of performance of national R&D programs with heterogeneous objectives: A DEA approach," European Journal of Operational Research, Elsevier, vol. 196(3), pages 847-855, August.
    15. J. David Cummins & Mary A. Weiss & Hongmin Zi, 1999. "Organizational Form and Efficiency: The Coexistence of Stock and Mutual Property-Liability Insurers," Management Science, INFORMS, vol. 45(9), pages 1254-1269, 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. J. David Cummins & María Rubio-Misas, 2022. "Integration and convergence in efficiency and technology gap of European life insurance markets," Annals of Operations Research, Springer, vol. 315(1), pages 93-119, August.
    2. Zarrin, Mansour & Brunner, Jens O., 2023. "Analyzing the accuracy of variable returns to scale data envelopment analysis models," European Journal of Operational Research, Elsevier, vol. 308(3), pages 1286-1301.
    3. Banker, Rajiv D. & Zheng, Zhiqiang (Eric) & Natarajan, Ram, 2010. "DEA-based hypothesis tests for comparing two groups of decision making units," European Journal of Operational Research, Elsevier, vol. 206(1), pages 231-238, October.
    4. Fadzlan Sufian & Fakarudin Kamarudin, 2014. "The impact of ownership structure on bank productivity and efficiency: Evidence from semi-parametric Malmquist Productivity Index," Cogent Economics & Finance, Taylor & Francis Journals, vol. 2(1), pages 1-27, December.
    5. Chien-Ming Chen & Magali A. Delmas & Marvin B. Lieberman, 2015. "Production frontier methodologies and efficiency as a performance measure in strategic management research," Strategic Management Journal, Wiley Blackwell, vol. 36(1), pages 19-36, January.
    6. Marta Suárez-Varela & María los Ángeles García-Valiñas & Francisco González-Gómez & Andrés J Picazo-Tadeo, 2017. "Ownership and Performance in Water Services Revisited: Does Private Management Really Outperform Public?," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(8), pages 2355-2373, June.
    7. Juan Piedra-Peña & Diego Prior, 2023. "Analyzing the effect of health reforms on the efficiency of Ecuadorian public hospitals," International Journal of Health Economics and Management, Springer, vol. 23(3), pages 361-392, September.
    8. Cuccia, Tiziana & Guccio, Calogero & Rizzo, Ilde, 2016. "The effects of UNESCO World Heritage List inscription on tourism destinations performance in Italian regions," Economic Modelling, Elsevier, vol. 53(C), pages 494-508.
    9. Ferrara, Giancarlo & Vidoli, Francesco, 2017. "Semiparametric stochastic frontier models: A generalized additive model approach," European Journal of Operational Research, Elsevier, vol. 258(2), pages 761-777.
    10. Silvia Saravia-Matus & T. S. Amjath-Babu & Sreejith Aravindakshan & Stefan Sieber & Jimmy A. Saravia & Sergio Gomez y Paloma, 2021. "Can Enhancing Efficiency Promote the Economic Viability of Smallholder Farmers? A Case of Sierra Leone," Sustainability, MDPI, vol. 13(8), pages 1-17, April.
    11. Mohammad Nourani & Qian Long Kweh & Evelyn Shyamala Devadason & V.G.R. Chandran, 2020. "A decomposition analysis of managerial efficiency for the insurance companies: A data envelopment analysis approach," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 41(6), pages 885-901, September.
    12. Shahbaz Nasir & Kaliappa Kalirajan, 2016. "Information and Communication Technology-Enabled Modern Services Export Performances of Asian Economies," Asian Development Review, MIT Press, vol. 33(1), pages 1-27, March.
    13. Gil, Guilherme Dôco Roberti & Costa, Marcelo Azevedo & Lopes, Ana Lúcia Miranda & Mayrink, Vinícius Diniz, 2017. "Spatial statistical methods applied to the 2015 Brazilian energy distribution benchmarking model: Accounting for unobserved determinants of inefficiencies," Energy Economics, Elsevier, vol. 64(C), pages 373-383.
    14. Chuan-hua Wei & Chunling Liu, 2012. "Statistical inference on semi-parametric partial linear additive models," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 24(4), pages 809-823, December.
    15. Roland Banya & Nicholas Biekpe, 2018. "Banking efficiency and its determinants in selected frontier african markets," Economic Change and Restructuring, Springer, vol. 51(1), pages 69-95, February.
    16. Bouali Guesmi & Teresa Serra & Allen Featherstone, 2015. "Technical efficiency of Kansas arable crop farms: a local maximum likelihood approach," Agricultural Economics, International Association of Agricultural Economists, vol. 46(6), pages 703-713, November.
    17. Gulati, Rachita & Kumar, Sunil, 2016. "Assessing the impact of the global financial crisis on the profit efficiency of Indian banks," Economic Modelling, Elsevier, vol. 58(C), pages 167-181.
    18. Chuanhua Wei & Jin Yang, 2020. "Stochastic restricted estimation in partially linear additive errors-in-variables models," Statistical Papers, Springer, vol. 61(3), pages 1269-1279, June.
    19. Wijesiri, Mahinda & Yaron, Jacob & Meoli, Michele, 2017. "Assessing the financial and outreach efficiency of microfinance institutions: Do age and size matter?," Journal of Multinational Financial Management, Elsevier, vol. 40(C), pages 63-76.
    20. Zervopoulos, Panagiotis & Emrouznejad, Ali & Sklavos, Sokratis, 2019. "A Bayesian approach for correcting bias of data envelopment analysis estimators," MPRA Paper 91886, University Library of Munich, Germany.

    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:gam:jmathe:v:8:y:2020:i:2:p:233-:d:319197. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.