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

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  • 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
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    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. 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.
    4. 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.
    5. 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.
    6. 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.
    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. 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.
    9. 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.
    10. 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.
    11. 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.
    12. 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.
    13. 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.
    14. 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.
    15. 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.
    16. 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.
    17. 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.
    18. Timo Kuosmanen, 2008. "Representation theorem for convex nonparametric least squares," Econometrics Journal, Royal Economic Society, vol. 11(2), pages 308-325, July.
    19. Jim Griffin & Mark Steel, 2007. "Bayesian stochastic frontier analysis using WinBUGS," Journal of Productivity Analysis, Springer, vol. 27(3), pages 163-176, June.
    20. 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.
    21. 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.
    22. 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.
    23. Abbott, Malcolm & Cohen, Bruce, 2009. "Productivity and efficiency in the water industry," Utilities Policy, Elsevier, vol. 17(3-4), pages 233-244, September.
    24. 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.
    25. 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.
    26. 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.
    27. 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.
    28. 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.
    29. 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.
    30. 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.
    31. 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.
    32. 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.
    33. 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.
    34. 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).
    35. 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.
    36. 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.
    37. 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.
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