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The economic performance of Swiss drinking water utilities

Abstract

This paper measures the performance in terms of costs of Swiss drinking water utilities accounting for environmental factors. We estimate a translog stochastic variable cost frontier using two different techniques on an unbalanced panel of 141 water distribution utilities over the years 2002–2009, for a total of 745 observations. Results show that exogenous factors have an impact on variable cost. More precisely, we find that the share of pumped over total extracted water, population density, altitude and meteorological factors (maximum 30 days temperature and extreme precipitation events) have a significant impact on variable cost. Likelihood ratio tests emphasize the importance to include observed heterogeneity in the estimations. Efficiency rankings provided by models accounting for exogenous factors and their counterparts without them are however relatively similar. On the contrary, the efficiency ranks differ strongly between alternative estimation techniques. In assessing the economic performance of utilities, the most important choice thus seems to be about the way unobserved heterogeneity is treated.

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Fig. 1
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Notes

  1. 1.

    The globally flexible Fourier functional form (Gallant 1981) could offer an even more flexible solution. However, it would increase the number of parameters to be estimated and result in a further loss of degrees of freedom (Filippini et al. 2007), which is why it is not estimated in this paper.

  2. 2.

    Possible interactions between output and exogenous factors seem intuitively appealing, as the impact of environmental conditions on variable cost may vary with utility size. The model was also estimated including interaction terms between output and exogenous factors. However, none of these proved to be statistically significant and a likelihood ratio test rejected the model including interactions in favour of the restricted one.

  3. 3.

    For a detailed description of the different estimation methods, see Kumbhakar and Lovell (2000) and Greene (2005a, b).

  4. 4.

    We thank an anonymous referee for this suggestion.

  5. 5.

    As only time-invariant exogenous factors are accommodated in this model, the mean values of the variables are used for time-varying environmental variables. This is not problematic for density and the share of pumped water, which display very little within group variability. However, it entails some loss of information for both meteorological factors that vary from year to year.

  6. 6.

    For the TRE model, εit = vit + uit + wi.

  7. 7.

    In 2003, 1 CHF = 0.74 USD = 0.66 EURO.

  8. 8.

    We have information about FTE in 2009 only. For the previous years, the survey reports the total number of employees working part time and the total number of employees working full time only. We assume that the FTE of part-time employees in 2009 is constant over the whole period. For those utilities for which we do not have FTE for 2009, part-time employees correspond to the median FTE of utilities of comparable size. To test the possible impact of this variable on our results, we have estimated the cost frontier and inefficiency scores using alternative labour cost data from the Swiss Federal Office of Statistics based on the median gross salary in 7 Swiss regions. Results are very similar and the main conclusions are unchanged. Therefore, we use the much more precise utility-specific cost of labour data instead of the regional median salaries.

  9. 9.

    The estimated equation is the following:

    $$ \ln ({\text{networklength}}_t) = 0.69 \,(0.43) + 0.81 \, (0.05) \, \ln({\text{networklength}}_{t-1}) + 0.11 \, (0.04)\, \ln({\text{sum of investments}}) $$

    With SEs in brackets. R2 = 0.87.

  10. 10.

    Detailed results are available upon request.

  11. 11.

    Time-variance was tested only in the model where heterogeneity is directly included in the cost frontier. Indeed, the BC model could not be estimated with heterogeneity included in the inefficiency distribution as models did not converge.

  12. 12.

    Detailed results of the estimation of the Cobb-Douglas cost frontier are available upon request.

  13. 13.

    The inclusion of other regional dummies (for example accounting for statistical regions in Switzerland) also produced a significant positive coefficient for temperature.

Abbreviations

BC:

Battese and Coelli

DEA:

Data envelopment analysis

FTE:

Full time equivalent

PL:

Pitt and Lee

SFA:

Stochastic frontier analysis

SGWA:

Swiss Gas and Water Industry Association

TRE:

True random effect

References

  1. Abbott M, Cohen B (2009) Productivity and efficiency in the water industry. Utili Policy 17:233–244

    Article  Google Scholar 

  2. Abrate G, Erbetta F, Fraquelli G (2011) Public utility planning and cost efficiency in a decentralized regulation context: the case of the Italian integrated water service. J Prod Anal 35(3):227–242

    Article  Google Scholar 

  3. Aigner D, Lovell CAK, Schmidt P (1977) Formulation and estimation of stochastic frontier production functions models. J Econ 6:21–37

    Article  Google Scholar 

  4. Antonioli D, Filippini M (2001) The use of variable cost function in the regulation of the Italian water industry. Utili Policy 10:181–187

    Article  Google Scholar 

  5. Baranzini A (1996) Structure des coûts des stations d’épuration en Suisse et gestion efficace des eaux usées. Swiss J Econ Stat 132(4):515–538

    Google Scholar 

  6. Battese GE, Coelli TJ (1992) Frontier production functions, technical efficiency and panel data: with application to paddy farmers in India. J Prod Anal 11:251–273

    Google Scholar 

  7. Battese GE, Coelli TJ (1995) A model for technical inefficiency effects in a stochastic frontier production function for panel data. Empir Econ 20:325–332

    Article  Google Scholar 

  8. Berg S, Marques R (2011) Quantitative studies of water and sanitation utilities: a literature survey. Water Policy 12(5):591–606

    Article  Google Scholar 

  9. Bottasso A, Conti M (2003) Cost inefficiency in the English and Welsh water industry: an heteroskedastic stochastic cost frontier approach. Economics discussion papers 573, University of Essex, Department of Economics

  10. Bottasso A, Conti M (2008) Scale economies, technology and technical change in the water industry: evidence from the English water only sector. Reg Sci Urban Econ 39(2):138–147

    Article  Google Scholar 

  11. Carvalho P, Marques RC (2011) The influence of the operational environment on the efficiency of water utilities. J Environ Manag 92(10):2698–2707

    Article  Google Scholar 

  12. Carvalho P, Marques RC, Berg S (2012) A meta-regression analysis of benchmarking studies on water utilities market structure. Utili Policy 21:40–49

    Article  Google Scholar 

  13. Christensen LR, Jorgenson DW, Lau LJ (1973) Transcendental logarithmic production frontiers. Rev Econ Stat 55:28–45

    Article  Google Scholar 

  14. Coelli T, Perelman S, Romano E (1999) Accounting for environmental influences in stochastic frontier models: with application to international airlines. J Prod Anal 11:251–273

    Article  Google Scholar 

  15. Conti M (2005) Ownership relative efficiency in the water industry: a survey of the international empirical evidence. Econ Internazionale 58(3):273–306

    Google Scholar 

  16. Corton ML (2011) Sector fragmentation and aggregation of service provision in the water industry. J Prod Anal 35:159–169

    Article  Google Scholar 

  17. Cowing TG, Holtmann AG (1983) Multiproduct short run hospital cost functions: empirical evidence and policy implications from cross-section data. South Econ J 49:637–653

    Article  Google Scholar 

  18. De Witte K, Dijkgraaf E (2010) Mean and bold: on separating merger economies from structural efficiency gains in the drinking water sector. J Oper Res Soc 61(2):222–234

    Article  Google Scholar 

  19. De Witte K, Marques RC (2010) Designing performance incentives, an international benchmark study in the water sector. Central Eur J Oper Res 18(2):189–220

    Article  Google Scholar 

  20. De Witte K, Saal D (2010) Is a little sunshine all we need? On the impact of sunshine regulation on profits, productivity and prices in the Dutch drinking water sector. J Regul Econ 37(3):219–242

    Article  Google Scholar 

  21. Di Cosmo V (2012) Ownership. To be published in Journal of Industry, Competition and Trade, Scale Economies and Efficiency in the ItalianWater Sector

    Google Scholar 

  22. Farsi M, Filippini M (2009) An analysis of cost efficiency in Swiss multi-utilities. Energy Econ 31(2):306–315

    Article  Google Scholar 

  23. Filippini M (1996) Economies of scale and utilization in the Swiss electric power distribution industry. Appl Econ 28:543–550

    Article  Google Scholar 

  24. Filippini M, Hrovatin N, Zoric J (2007) Cost efficiency of Slovenian water distribution utilities: an application of stochastic frontier methods. J Prod Anal 29:169–182

    Article  Google Scholar 

  25. Fraquelli G, Moiso V (2005) Cost efficiency and economies of scale in the Italian water industry. Conference of the Società italiana di economia pubblica. Pavia, 15–16 September

  26. Gallant R (1981) On the bias in flexible functional forms and an essentially unbiased form: the fourier flexible form. J Econ 15(2):211–245

    Article  Google Scholar 

  27. Gander B (2009) Climate change and water suppliers: informations and adaptation strategies. Gwa 89:241–249

    Google Scholar 

  28. Garcia S, Thomas A (2001) The structure of municipal water supply costs: application to a panel of French local communities. J Prod Anal 16:5–29

    Article  Google Scholar 

  29. Garcia S, Moreaux M, Reynaud A (2007) Measuring economies of vertical integration in network industries: an application to the water sector. Int J Ind Organ 25(4):791–820

    Article  Google Scholar 

  30. Greene WH (2005a) Fixed and random effects in stochastic frontier models. J Prod Anal 23:7–32

    Article  Google Scholar 

  31. Greene WH (2005b) Reconsidering heterogeneity in panel data estimators of the stochastic frontier model. J Econ 126:269–303

    Article  Google Scholar 

  32. Growitsch C, Jamasb T, Wetzel H (2011) Efficiency effects of observed and unobserved heterogeneity: evidence from Norwegian electricity distribution networks. Energy Economics, Available online 4 November 2011, ISSN 0140-9883, 10.1016/j.eneco.2011.10.013. (http://www.sciencedirect.com/science/article/pii/S0140988311002635)

  33. Huang CJ, Liu JT (1994) Estimation of a non-neutral stochastic frontier production function. J Prod Anal 5:171–180

    Article  Google Scholar 

  34. Jondrow J, Materov I, Lovell K, Schmidt P (1982) On the estimation of technical inefficiency in the stochastic frontier production function model. J Econ 19:233–238

    Article  Google Scholar 

  35. Kilchmann A (2003) Distributeurs d’eau et analyse concurrentielle. Gwa 6:411–418

    Google Scholar 

  36. Kopsakangas-Savolainen M, Svento R (2008) Estimation of cost-effectiveness of the Finnish electricity distribution utilities. Energy Econ 30(2):212–229

    Article  Google Scholar 

  37. Kumbhakar SC, Lovell CAK (2000) Stochastic frontier analysis. Cambridge University Press, Cambridge

    Book  Google Scholar 

  38. Kumbhakar SC, Ghosh S, McGuckin JT (1991) A generalized production frontier approach for estimating determinants of inefficiency in US dairy farms. J Bus Econ Stat 9(3):279–286

    Google Scholar 

  39. Luís-Manso P (2005) Water institutions and management in Switzerland. Lausanne: Ecole Polytechnique Fédérale de Lausanne, College of Management of Technology, MIR-Report-2005-001

  40. Marques RC, Berg SV, Shinji Y (2011) Performance benchmarking analysis of Japanese water utilities. University of Florida, Department of Economics, PURC Working Paper

    Google Scholar 

  41. Meeusen W, van den Broeck J (1977) Efficiency estimation from Cobb-Douglas production functions with composed error. Int Econ Rev 18(2):435–444

    Article  Google Scholar 

  42. Nauges C, van den Berg C (2008) Economies of density, scale and scope in the water supply and sewerage sector: a study of four developing and transition economies. J Regul Econ 34:144–163

    Article  Google Scholar 

  43. Nelson RA (1989) On the measurement of capacity utilization. J Indus Econ 37(3):273–286

    Article  Google Scholar 

  44. OcCC (2008). Le climat change—que faire? Le nouveau rapport des Nations Unies sur le climat (GIEC 2007) et ses principaux résultats dans l’optique de la Suisse. OcCC—Organe consultatif sur les changements climatiques, Berne, 47 pp. ISBN: 978-3-907630-34-1

  45. Picazo-Tadeo A, Sáez-Fernández F, González-Gómez F (2009a) The role of environmental factors in water utilities technical efficiency. Empirical evidence from Spanish companies. Appl Econ 41:615–628

    Article  Google Scholar 

  46. Picazo-Tadeo A, Sáez-Fernández F, González-Gómez F (2009b) Accounting for operating environments in measuring water utilities’ managerial efficiency. Service Indus J 29:761–773

    Article  Google Scholar 

  47. Pitt MM, Lee LF (1981) The measurement and sources of technical inefficiency in the indonesian weaving industry. J Dev Econ 9:43–64

    Article  Google Scholar 

  48. ProClim (2005) Canicule de l’été 2003. Rapport de synthèse, ProClim, Berne. ISBN 978-3-907630-16-7

    Google Scholar 

  49. Reifschneider D, Stevenson R (1991) Systematic departures from the frontier: a framework for the analysis of firm inefficiency. Int Econ Rev 32:715–723

    Article  Google Scholar 

  50. Renzetti S, Dupont D (2003) Ownership and performance of water utilities. Greener Manag Int 42(1):9–19

    Article  Google Scholar 

  51. Renzetti S, Dupont D (2009) Measuring the technical efficiency of municipal water suppliers: the role of environmental factors. Land Econ 85:627–636

    Google Scholar 

  52. Saal D, Reid S (2004) Estimating opex productivity growth in English and welsh water and sewerage companies 1993–2003. Research paper 0434. Aston Business School, Ashton University

  53. SGWA (2002–2010). Résultats statistiques des distributeurs d’eau en Suisse: années 2000 à 2009. SGWA, Zurich

  54. Torres M, Morrison PC (2006) Driving forces for consolidation or fragmentation of the U.S. water utility industry: a cost function approach with endogeneous output. J Urban Econ 59:104–120

    Article  Google Scholar 

  55. Walter M, Cullmann A, von Hirschhausen C, Wand R, Zschille M (2009) Quo vadis efficiency analysis of water distribution? A comparative literature review. Utili Policy 17:225–232

    Article  Google Scholar 

  56. Zschille M, Walter M (2012) The performance of German water utilities: a (semi)-parametric analysis. Appl Econ 44:3749–3764

    Article  Google Scholar 

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Acknowledgements

We thank the Swiss Gas and Water Industry Association (SGWA) for providing the data. We are grateful for the helpful comments from three anonymous referees, David Maradan, Philippe Thalmann and the participants of the XII European Workshop on Efficiency and Productivity Analysis, 21-24 June 2011, especially David Saal. This research has been financed by the Network of competencies in economics and management of the University of Applied Sciences Western Switzerland (HES SO) and by National Centre of Competence in Research (NCCR) Climate. The findings, interpretations, conclusions and any remaining errors are the authors’ own.

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Correspondence to Anne-Kathrin Faust.

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Faust, AK., Baranzini, A. The economic performance of Swiss drinking water utilities. J Prod Anal 41, 383–397 (2014). https://doi.org/10.1007/s11123-013-0344-0

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Keywords

  • Stochastic frontier analysis
  • Environmental factors
  • Heterogeneity
  • Drinking water distribution

JEL Classification

  • C23
  • D24
  • L25
  • L95
  • Q25