IDEAS home Printed from https://ideas.repec.org/a/eee/streco/v67y2023icp211-219.html
   My bibliography  Save this article

Cost and quality of service performance in the Chilean water industry: A comparison of stochastic approaches

Author

Listed:
  • Maziotis, Alexandros
  • Sala-Garrido, Ramon
  • Mocholi-Arce, Manuel
  • Molinos-Senante, Maria

Abstract

The evaluation of efficiency can be of great value to water companies and regulators to adopt policies and design incentives to enhance performance. This study delves into the implications of employing distinct methodologies, namely the classical Stochastic Frontier Analysis (SFA), Bayesian SFA, and Stochastic non-parametric Envelopment of Data (StoNED), to evaluate cost and quality of service efficiency within the water industry. Chilean water companies reported average efficiencies of 0.623, 0.583, and 0.522 using the SFA, BSFA, and StoNED approaches, respectively. Furthermore, the SFA analysis suggested that the performance of water companies experienced a decline of −0.59% per year from 2010 to 2018. In contrast, the BSFA and StoNED estimations indicated an opposite trend, with annual performance improvements of 0.51% and 0.17% respectively, over the same period. These findings underscore the critical role of selecting appropriate methodologies when interpreting and comparing efficiency results for making informed long-term decisions.

Suggested Citation

  • Maziotis, Alexandros & Sala-Garrido, Ramon & Mocholi-Arce, Manuel & Molinos-Senante, Maria, 2023. "Cost and quality of service performance in the Chilean water industry: A comparison of stochastic approaches," Structural Change and Economic Dynamics, Elsevier, vol. 67(C), pages 211-219.
    • Handle: RePEc:eee:streco:v:67:y:2023:i:c:p:211-219
    DOI: 10.1016/j.strueco.2023.07.011
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0954349X23001017
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.strueco.2023.07.011?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Carvalho, Pedro & Marques, Rui Cunha & Berg, Sanford, 2012. "A meta-regression analysis of benchmarking studies on water utilities market structure," Utilities Policy, Elsevier, vol. 21(C), pages 40-49.
    2. Fernandez C. & Koop G. & Steel M.F.J., 2002. "Multiple-Output Production With Undesirable Outputs: An Application to Nitrogen Surplus in Agriculture," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 432-442, June.
    3. Kuosmanen, Timo & Saastamoinen, Antti & Sipiläinen, Timo, 2013. "What is the best practice for benchmark regulation of electricity distribution? Comparison of DEA, SFA and StoNED methods," Energy Policy, Elsevier, vol. 61(C), pages 740-750.
    4. Aude Le Lannier & Simon Porcher, 2014. "Efficiency in the public and private French water utilities: prospects for benchmarking," Applied Economics, Taylor & Francis Journals, vol. 46(5), pages 556-572, February.
    5. Walter, Matthias & Cullmann, Astrid & von Hirschhausen, Christian & Wand, Robert & Zschille, Michael, 2009. "Quo vadis efficiency analysis of water distribution? A comparative literature review," Utilities Policy, Elsevier, vol. 17(3-4), pages 225-232, September.
    6. Jamasb, Tooraj & Orea, Luis & Pollitt, Michael, 2012. "Estimating the marginal cost of quality improvements: The case of the UK electricity distribution companies," Energy Economics, Elsevier, vol. 34(5), pages 1498-1506.
    7. Ricardo S. Ehlers, 2011. "Comparison of Bayesian models for production efficiency," Journal of Applied Statistics, Taylor & Francis Journals, vol. 38(11), pages 2433-2443, January.
    8. Dilawar Khan & Muhammad Nouman & József Popp & Muhammad Asif Khan & Faheem Ur Rehman & Judit Oláh, 2021. "Link between Technically Derived Energy Efficiency and Ecological Footprint: Empirical Evidence from the ASEAN Region," Energies, MDPI, vol. 14(13), pages 1-16, June.
    9. Humberto Brea-Solis & Sergio Perelman & David Saal, 2017. "Regulatory incentives to water losses reduction: the case of England and Wales," Journal of Productivity Analysis, Springer, vol. 47(3), pages 259-276, June.
    10. Jim Griffin & Mark Steel, 2007. "Bayesian stochastic frontier analysis using WinBUGS," Journal of Productivity Analysis, Springer, vol. 27(3), pages 163-176, June.
    11. Johnson, Andrew L. & Kuosmanen, Timo, 2012. "One-stage and two-stage DEA estimation of the effects of contextual variables," European Journal of Operational Research, Elsevier, vol. 220(2), pages 559-570.
    12. van den Broeck, Julien & Koop, Gary & Osiewalski, Jacek & Steel, Mark F. J., 1994. "Stochastic frontier models : A Bayesian perspective," Journal of Econometrics, Elsevier, vol. 61(2), pages 273-303, April.
    13. Astrid Cullmann, 2012. "Benchmarking and firm heterogeneity: a latent class analysis for German electricity distribution companies," Empirical Economics, Springer, vol. 42(1), pages 147-169, February.
    14. Ngobeni, Victor & Breitenbach, Marthinus C, 2021. "Production and Scale Efficiency of South African Water Utilities: The Case of Water Boards," MPRA Paper 106242, University Library of Munich, Germany.
    15. Tsionas, Mike G., 2023. "Combining data envelopment analysis and stochastic frontiers via a LASSO prior," European Journal of Operational Research, Elsevier, vol. 304(3), pages 1158-1166.
    16. Amaral, António L. & Martins, Rita & Dias, Luís C., 2023. "Operational drivers of water reuse efficiency in Portuguese wastewater service providers," Utilities Policy, Elsevier, vol. 83(C).
    17. Elvira Silva & Pedro Macedo & Isabel Soares, 2019. "Maximum entropy: a stochastic frontier approach for electricity distribution regulation," Journal of Regulatory Economics, Springer, vol. 55(3), pages 237-257, June.
    18. Timo Kuosmanen & Andrew Johnson & Antti Saastamoinen, 2015. "Stochastic Nonparametric Approach to Efficiency Analysis: A Unified Framework," International Series in Operations Research & Management Science, in: Joe Zhu (ed.), Data Envelopment Analysis, edition 127, chapter 7, pages 191-244, Springer.
    19. Ha Thu Vu & Sean Turnell, 2010. "Cost Efficiency of the Banking Sector in Vietnam: A Bayesian Stochastic Frontier Approach with Regularity Constraints," Asian Economic Journal, East Asian Economic Association, vol. 24(2), pages 115-139, June.
    20. Koop, Gary & Osiewalski, Jacek & Steel, Mark F. J., 1997. "Bayesian efficiency analysis through individual effects: Hospital cost frontiers," Journal of Econometrics, Elsevier, vol. 76(1-2), pages 77-105.
    21. E Thanassoulis, 2002. "Comparative performance measurement in regulation: the case of English and Welsh sewerage services," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 53(3), pages 292-302, March.
    22. Thanassoulis, Emmanuel, 2000. "DEA and its use in the regulation of water companies," European Journal of Operational Research, Elsevier, vol. 127(1), pages 1-13, November.
    23. Yaguo Deng & Helena Veiga & Michael P. Wiper, 2019. "Efficiency evaluation of hotel chains: a Spanish case study," SERIEs: Journal of the Spanish Economic Association, Springer;Spanish Economic Association, vol. 10(2), pages 115-139, June.
    24. Guerrini, Andrea & Romano, Giulia & Leardini, Chiara, 2018. "Economies of scale and density in the Italian water industry: A stochastic frontier approach," Utilities Policy, Elsevier, vol. 52(C), pages 103-111.
    25. Lee, Chia-Yen & Johnson, Andrew L. & Moreno-Centeno, Erick & Kuosmanen, Timo, 2013. "A more efficient algorithm for Convex Nonparametric Least Squares," European Journal of Operational Research, Elsevier, vol. 227(2), pages 391-400.
    26. Andrew Johnson & Timo Kuosmanen, 2011. "One-stage estimation of the effects of operational conditions and practices on productive performance: asymptotically normal and efficient, root-n consistent StoNEZD method," Journal of Productivity Analysis, Springer, vol. 36(2), pages 219-230, October.
    27. Jondrow, James & Knox Lovell, C. A. & Materov, Ivan S. & Schmidt, Peter, 1982. "On the estimation of technical inefficiency in the stochastic frontier production function model," Journal of Econometrics, Elsevier, vol. 19(2-3), pages 233-238, August.
    28. David Saal & David Parker & Tom Weyman-Jones, 2007. "Determining the contribution of technical change, efficiency change and scale change to productivity growth in the privatized English and Welsh water and sewerage industry: 1985–2000," Journal of Productivity Analysis, Springer, vol. 28(1), pages 127-139, October.
    29. Timo Kuosmanen, 2008. "Representation theorem for convex nonparametric least squares," Econometrics Journal, Royal Economic Society, vol. 11(2), pages 308-325, July.
    30. Abbott, Malcolm & Cohen, Bruce, 2009. "Productivity and efficiency in the water industry," Utilities Policy, Elsevier, vol. 17(3-4), pages 233-244, September.
    31. Dilawar Khan & Muhammad Nouman & Arif Ullah, 2023. "Assessing the impact of technological innovation on technically derived energy efficiency: a multivariate co-integration analysis of the agricultural sector in South Asia," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 25(4), pages 3723-3745, April.
    32. Li, Hong-Zhou & Kopsakangas-Savolainen, Maria & Xiao, Xing-Zhi & Tian, Zhen-Zhen & Yang, Xiao-Yuan & Wang, Jian-Lin, 2016. "Cost efficiency of electric grid utilities in China: A comparison of estimates from SFA–MLE, SFA–Bayes and StoNED–CNLS," Energy Economics, Elsevier, vol. 55(C), pages 272-283.
    33. Efthymios G. Tsionas & Subal C. Kumbhakar, 2014. "FIRM HETEROGENEITY, PERSISTENT AND TRANSIENT TECHNICAL INEFFICIENCY: A GENERALIZED TRUE RANDOM‐EFFECTS model," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 29(1), pages 110-132, January.
    34. Kumbhakar, Subal C. & Tsionas, Efthymios G., 2005. "Measuring technical and allocative inefficiency in the translog cost system: a Bayesian approach," Journal of Econometrics, Elsevier, vol. 126(2), pages 355-384, June.
    35. Tsionas, Efthymios G., 2003. "Combining DEA and stochastic frontier models: An empirical Bayes approach," European Journal of Operational Research, Elsevier, vol. 147(3), pages 499-510, June.
    36. Zainab Bibi & Dilawar Khan & Ihtisham ul Haq, 2021. "Technical and environmental efficiency of agriculture sector in South Asia: a stochastic frontier analysis approach," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 23(6), pages 9260-9279, June.
    37. Timo Kuosmanen & Mika Kortelainen, 2012. "Stochastic non-smooth envelopment of data: semi-parametric frontier estimation subject to shape constraints," Journal of Productivity Analysis, Springer, vol. 38(1), pages 11-28, August.
    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. Molinos-Senante, Maria & Maziotis, Alexandros, 2022. "Evaluation of energy efficiency of wastewater treatment plants: The influence of the technology and aging factors," Applied Energy, Elsevier, vol. 310(C).
    2. Preciado Arreola, José Luis & Johnson, Andrew L. & Chen, Xun C. & Morita, Hiroshi, 2020. "Estimating stochastic production frontiers: A one-stage multivariate semiparametric Bayesian concave regression method," European Journal of Operational Research, Elsevier, vol. 287(2), pages 699-711.
    3. Liu, Fangmei & Li, Li & Ye, Bin & Qin, Quande, 2023. "A novel stochastic semi-parametric frontier-based three-stage DEA window model to evaluate China's industrial green economic efficiency," Energy Economics, Elsevier, vol. 119(C).
    4. Maria Nieswand & Stefan Seifert, 2016. "Operational Conditions in Regulatory Benchmarking Models: A Monte Carlo Analysis," Discussion Papers of DIW Berlin 1585, DIW Berlin, German Institute for Economic Research.
    5. Kuosmanen, Timo, 2012. "Stochastic semi-nonparametric frontier estimation of electricity distribution networks: Application of the StoNED method in the Finnish regulatory model," Energy Economics, Elsevier, vol. 34(6), pages 2189-2199.
    6. 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.
    7. José Luis Gallizo & Jordi Moreno & Manuel Salvador, 2015. "European banking integration: is foreign ownership affecting banking efficiency?," Journal of Business Economics and Management, Taylor & Francis Journals, vol. 16(2), pages 340-368, April.
    8. Chen, Zhongfei & Barros, Carlos Pestana & Borges, Maria Rosa, 2015. "A Bayesian stochastic frontier analysis of Chinese fossil-fuel electricity generation companies," Energy Economics, Elsevier, vol. 48(C), pages 136-144.
    9. Tsionas, Mike G., 2022. "Convex non-parametric least squares, causal structures and productivity," European Journal of Operational Research, Elsevier, vol. 303(1), pages 370-387.
    10. Alexandr Akimov & Paul Simshauser, 2018. "Performance measurement in Australian water utilities: Current state and future directions," Discussion Papers in Finance finance:201802, Griffith University, Department of Accounting, Finance and Economics.
    11. Lee, Chia-Yen & Johnson, Andrew L. & Moreno-Centeno, Erick & Kuosmanen, Timo, 2013. "A more efficient algorithm for Convex Nonparametric Least Squares," European Journal of Operational Research, Elsevier, vol. 227(2), pages 391-400.
    12. Li, Hong-Zhou & Kopsakangas-Savolainen, Maria & Xiao, Xing-Zhi & Tian, Zhen-Zhen & Yang, Xiao-Yuan & Wang, Jian-Lin, 2016. "Cost efficiency of electric grid utilities in China: A comparison of estimates from SFA–MLE, SFA–Bayes and StoNED–CNLS," Energy Economics, Elsevier, vol. 55(C), pages 272-283.
    13. Timo Kuosmanen & Yong Tan & Sheng Dai, 2023. "Performance analysis of English hospitals during the first and second waves of the coronavirus pandemic," Health Care Management Science, Springer, vol. 26(3), pages 447-460, September.
    14. Maziotis, Alexandros & Villegas, Andrés & Molinos-Senante, María & Sala-Garrido, Ramon, 2020. "Impact of external costs of unplanned supply interruptions on water company efficiency: Evidence from Chile," Utilities Policy, Elsevier, vol. 66(C).
    15. Saastamoinen, Antti & Kuosmanen, Timo, 2016. "Quality frontier of electricity distribution: Supply security, best practices, and underground cabling in Finland," Energy Economics, Elsevier, vol. 53(C), pages 281-292.
    16. Nieswand, Maria & Seifert, Stefan, 2018. "Environmental factors in frontier estimation – A Monte Carlo analysis," European Journal of Operational Research, Elsevier, vol. 265(1), pages 133-148.
    17. Kuosmanen, Timo & Saastamoinen, Antti & Sipiläinen, Timo, 2013. "What is the best practice for benchmark regulation of electricity distribution? Comparison of DEA, SFA and StoNED methods," Energy Policy, Elsevier, vol. 61(C), pages 740-750.
    18. Mekaroonreung, Maethee & Johnson, Andrew L., 2014. "A nonparametric method to estimate a technical change effect on marginal abatement costs of U.S. coal power plants," Energy Economics, Elsevier, vol. 46(C), pages 45-55.
    19. Saastamoinen, Antti & Bjørndal, Endre & Bjørndal, Mette, 2017. "Specification of merger gains in the Norwegian electricity distribution industry," Energy Policy, Elsevier, vol. 102(C), pages 96-107.
    20. Kuosmanen, Timo & Johnson, Andrew, 2017. "Modeling joint production of multiple outputs in StoNED: Directional distance function approach," European Journal of Operational Research, Elsevier, vol. 262(2), pages 792-801.

    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:eee:streco:v:67:y:2023:i:c:p:211-219. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/inca/525148 .

    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.