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The economic performance of Swiss drinking water utilities


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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  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.



Battese and Coelli


Data envelopment analysis


Full time equivalent


Pitt and Lee


Stochastic frontier analysis


Swiss Gas and Water Industry Association


True random effect


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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).

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

JEL Classification

  • C23
  • D24
  • L25
  • L95
  • Q25