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Service quality, scale economies and ownership: an econometric analysis of water supply costs

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Abstract

Issues related to service quality are crucial for water utility management and regulation. Omitting these aspects, especially when they are treated as exogenous, can lead to large biases in estimating cost functions as well as to misleading information concerning technology. In this article, we integrate the output multi-dimension in the cost function, considering delivered water volume and service quality as being endogenous. Network-related scale measures and private versus public ownership are investigated with the objective of evaluating how endogenous quality may affect their impact on costs. A translog cost model is estimated from a dataset of US water utilities. It is shown that including the quality level of the delivered services has a significant impact on scale economies and ownership effects. Significant economies of scope confirm the existence of trade-offs between water production and service quality.

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Notes

  1. At the end of the 1990s, many countries created working groups with the aim of defining performance indicators of water utilities, including indicators of service quality for the consumer. At the international level, we can mention the “Performance Indicators” group of the IWA (International Water Association) and the World Bank and, at the national level, the work of AWWARF (American Water Works Association Research Foundation) in the US, AFNOR (French Standards Association) in France and OFWAT (Office of Water Services) in England (see Alegre et al. (2006) for more information on performance indicators for water supply utilities).

  2. See numerous reports of PSIRU (Public Services International Research Unit) at http://www.psiru.org/.

  3. Gertler and Waldman (1992) identify and estimate a (quality-adjusted) cost function in the presence of endogenous and unobserved quality.

  4. In a recent theoretical research paper, Martimort and Sand-Zantman (2006) attempted to explain the shape of delegation contracts with respect to the quality of infrastructure in the water industry. The authors showed that the level of risk maintained by the local government increases with the quality of the infrastructure and that full privatization emerges for the worst quality infrastructures, whereas the best ones remain under public management. However, the paper does not address the quality of the service that is assumed to be observable and heavily regulated.

  5. Mosheim (2006) modeled a shadow cost function that incorporated water quality and ownership effects. However, this study only took water quality production using only one regulated contaminant, turbidity, into account.

  6. Saal and Parker (2000) use a multiple output translog cost function model with quality-adjusted outputs and highlight the importance of quality drivers to properly assess economies of scale and scope in the water industry.

  7. Conti (2005) analyzed and discussed papers dealing with the relative efficiency of public and private operators (and papers on the effects of privatization) in the water supply industry.

  8. Different firms may be in charge of one or more operations in the water supply process.

  9. Costs related to groundwater extraction are higher, whereas treatment costs are lower than for surface water, which is generally less clean.

  10. The (conditional) variable cost function conveys the same information as the original production process (see Chambers 1988). The capital variables can in fact have a large impact on the explanation of variable costs and the definition of the optimal level of infrastructures. Moreover, given that we observe water utilities for only one year, it is likely that the capital is not adjusted to its price variations (i.e., the fixed inputs do not automatically minimize the costs). By estimating a total cost function, we would have made the implicit assumption that at each period, the capital expenses are optimal, which may not be the case in practice.

  11. The AWWA is an international non-profit educational association dedicated to safe water. Founded in 1881 as a forum for water professionals to share information and learn from each other for the common good, the AWWA is the authoritative resource for knowledge, information and advocacy for improving the quality and supply of water in North America and beyond. The AWWA currently has more than 50,000 members, including 4,700 utilities.

  12. During the 1990s, water service privatization took the form of public-private partnerships. “The range of choice extends from (1) outsourcing of various services such as provision of supplies and meter reading; (2) private contract operation and maintenance of existing plants; and (3) contracts for the integrated design, construction, and subsequent operation of new facilities (DBO contracts). Nonetheless, investor-owned companies have historically played and continue to play an important role in providing water services in the United States” National Research Council (2002).

  13. We tried other variables based on disaggregated information obtained from the AWWA survey of 1996: water-related customer inquiries about billing, water-related customer inquiries about water quality, water related customer inquiries about other problems not previously listed. However, the rough proxy variable of consumers’ concerns about water service quality appears to be best suited for the best fitted model.

  14. Mosheim (2006) uses turbidity as a water quality indicator. However, among the different quality factors measured, turbidity is an indicator with many missing values.

  15. The symmetry restrictions are: \(\alpha _{ii'}=\alpha _{i'i}\), \(\beta _{jj'}=\beta _{j'j}\), \(\gamma _{kk'}=\gamma _{k'k}\). Linear homogeneity requires the following set of parameter restrictions: \(\sum _i \alpha _i = 1\), \(\sum _i \alpha _{ii'} = \sum _{i'} \alpha _{ii'} = 0\) and \(\sum _i \alpha _{yi} = \sum _i \delta _{ij} = \sum _i \eta _{ik} = 0, \forall j\) and \(k\). This is equivalent to dividing variable cost and input prices by the price of any input. In order to limit the number of parameters to be estimated, we chose this second solution. The reference price is the price of other inputs (\(w_M\)).

  16. Since \(\sum _i S_i = 1\), one of the cost shares is dropped to avoid singularity of the variance-covariance matrix of errors.

  17. See also the discussion of Urakami and Parker (2011) on the choice of variables included in the hedonic output function.

  18. Antonioli and Filippini (2001), Fabbri and Fraquelli (2000) and Mizutani and Urakami (2001) also identify network density and scale economies for Italian and Japanese water supply services, respectively.

  19. Economies of production density are also referred to as economies of volume by Torres and Morrison Paul (2006), or economies of output density by Bottasso and Conti (2009).

  20. Torres and Morrison Paul (2006) speak of a vertical dimension when dealing with the customer density as opposed to the horizontal dimension related to spatial network expansion.

  21. Garcia and Thomas (2001) use the number of communities served and the network length (designated as LENGTH) to define scale economies. The network length is strongly correlated with service area size, as already noted by Torres and Morrison Paul (2006), and eliminated from our analysis.

  22. Another approach for simultaneous equations with endogeneity is the full information maximum likelihood (FIML) estimation method. However, even if the FIML method does not require instrumental variables, the model must include the full equation system with as many equations as there are endogenous variables. Moreover, this method assumes that the equation errors have a multivariate normal distribution.

  23. There are \(G\) parameters to estimate for the unrestricted form of the cost system. However, since the last two equations consist of cost share equations derived from the cost function, there are a number of cross-equation parameter restrictions. Since all structural parameters enter the cost equation, whereas cost share equations contain subsets of the full parameter set, the total number of parameters to be estimated is the number of parameters \(K\) in the cost function.

  24. The small number of privately-owned water utilities prevents us from testing the more flexible translog form of the cost function for each of the two subsamples. However, in the pooled cost function, several cross-products with the ownership variable present parameters not significantly different from zero, meaning that the technologies are not significantly different according to the type of ownership.

  25. Marginal costs are computed from estimated average costs, \(\widehat{\text{ AC }}=exp(\hat{\alpha _0}+\ln w_M)\), and estimated cost elasticities with respect to output \(\widehat{\varepsilon _Y}\): \(\widehat{\text{ Cm }}=\widehat{\text{ AC }}\times \widehat{\varepsilon _Y}\), see also Eq. (10).

  26. Water losses are drinking water volumes, so that there is a share of variable costs that is allocated to the production of this undesirable output. This therefore confirms the existing trade-off between increasing production and decreasing losses (by repairing leaks). Garcia and Thomas (2001) show that the manager of the water utility does not minimize this lost water volume because the cost of “zero loss” is greater than increasing the production to satisfy the same demand. A similar interpretation may be made for other quality variables.

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Acknowledgments

The authors are very grateful to the two anonymous reviewers and the editor for their precious comments and advice which were useful to greatly improve the article. The UMR Economie Forestière is supported by a grant overseen by the French National Research Agency (ANR) as part of the “Investissements d’Avenir” program (ANR-11-LABX-0002-01, Lab of Excellence ARBRE).

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Authors and Affiliations

  1. Laboratory of Territorial Water and Environment Management (GESTE), UMR ENGEES-IRSTEA, ENGEES, 1 quai Koch, BP 61039, 67070,  Strasbourg Cedex, France

    François Destandau

  2. INRA, UMR 356 Économie Forestière, 54000,  Nancy, France

    Serge Garcia

  3. Laboratoire d’Économie Forestière, AgroParisTech, 54000,  Nancy, France

    Serge Garcia

  4. LEF, INRA-AgroParisTech, 14 rue Girardet, CS 14216, 54042,  Nancy, France

    Serge Garcia

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  1. François Destandau
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Destandau, F., Garcia, S. Service quality, scale economies and ownership: an econometric analysis of water supply costs. J Regul Econ 46, 152–182 (2014). https://doi.org/10.1007/s11149-014-9250-2

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