Consolidating the water industry: an analysis of the potential gains from horizontal integration in a conditional efficiency framework


The German potable water supply industry is regarded highly fragmented, thus preventing efficiency improvements that could happen through consolidation. Focusing on a hypothetical restructuring of the industry, we use a cross-section sample of 364 German water utilities in 2006, applying Data Envelopment Analysis, to analyze the potential efficiency gains from hypothetical mergers between water utilities at the county level. A conditional efficiency framework is applied to account for the water utilities’ operating environments. A conditional order-m approach is applied for the detection of potential outlying observations. Merger gains are decomposed into a technical efficiency effect, a harmony effect and a scale effect. The greatest efficiency improvement potentials turn out to result from reducing individual inefficiencies while pure merger gains are found to be low. The results suggest improving incentives for efficient operations in water supply and a consolidation of the smallest water utilities.

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  1. 1.

    Water utilities in Germany are natural monopolies in their service areas. Public water utilities currently do not face an effective price control. Private water utilities face an ex-post price control based on German cartel law. This price control however only is executed in the federal state of Hesse. There is currently no effective regulation of German public and private water utilities.

  2. 2.

    Subsampling describes the generation of pseudo samples from the original data sample using resampling without replacement whereas the m-bootstrap represents resampling with replacement (Simar and Wilson 2011a).

  3. 3.

    See Cazals et al. (2002) for details on the order-m approach.

  4. 4.

    See Coelli et al. (2005), Thanassoulis et al. (2008) or De Witte and Marques (2010b) for overviews of alternative outlier detection procedures.

  5. 5.

    A short overview of possible approaches to incorporate the operating environment into DEA is given in Coelli et al. (2005).

  6. 6.

    In our analysis we focus on hypothetical mergers between water utilities located in the same Landkreis, the German equivalent of a county. For the application of the conditional efficiency approach, structural variables are re-calculated for the integrated companies.

  7. 7.

    Under the assumption of NDRS and thus of additivity for the underlying technology set, the overall merger gains \(E^J\) will always be less than or equal to one in a standard DEA framework. Due to the application of the conditional efficiency framework in our analysis, the set of peer units for a merged water utility is likely to be different as compared to the individual pre-merger companies due to the re-calculation of environmental variables. It is thus possible that hypothetically integrated companies lie outside the NDRS technology, i.e. it is possible to achieve overall merger gains greater than one.

  8. 8.

    As of 2014, Germany had made no major structural or regulatory changes to their water supply since 2006.

  9. 9.

    On average, the excluded observations are smaller than the remaining observations. We note that the excluded observations might be relevant for the merger analysis since merger gains are more likely to accrue for small water utilities. However, due to missing or erroneous data, it is necessary to exclude these observations.

  10. 10.

    The consideration of firms with different degrees of vertical integration would furthermore complicate the analysis of horizontal integration gains through simultaneous changes in horizontal and vertical firm characteristics.

  11. 11.

    All variables used in our analysis only represent the potable water activities of the companies. We are however aware of possible scope effects between the services provided by multi-utilities. Given our model specification, this might only be the case for labor input, e.g. due to a shared management overhead. We assume this effect to be small. The sample of 364 observations contains 121 observations of water-only companies and 63 observations of water and sewerage companies. The remaining companies provide one or more services in addition to water supply. Such integrated firm structures can impact potential merger benefits, which cannot be captured by our model. Mühlenkamp (2012) and Rottmann (2010) provide some general overview over German multi-utilities. Other studies, e.g. by Farsi et al. (2008), Fraquelli et al. (2004), and Piacenza and Vannoni (2004), focus on the analysis of multi-utilities in other countries.

  12. 12.

    With a median level of 1.15 million cubic meters of final water deliveries, the sampled water utilities deliver more than the national average of 0.76 million cubic meters per water utility (Statistisches Bundesamt 2009). This might be explained by the poor data availability for smaller companies, since they are often part of the municipal administration.

  13. 13.

    We are aware of other potential factors and characteristics of the water utilities’ operating environments with a possible influence on efficiency. However, given data availability, we can only include three z-variables in our analysis.

  14. 14.

    A similar assumption on the exogeneity of water losses in the short run is made in Zschille and Walter (2012).

  15. 15.

    As indicated by the minimum value of the share of water losses, the sample includes observations with very low shares of water losses of below 1 %, which is unrealistic from an engineering perspective. Since we however can observe a continuum of water utilities with similarly low losses, we do not remove such observations from the data sample.

  16. 16.

    One explanation might be our focus on vertically integrated companies with own water production and distribution. Such vertically integrated utilities usually use groundwater resources, while surface water resources like e.g. reservoir or river water are usually used by larger bulk water supply companies, which are not part of our final sample.

  17. 17.

    All calculations are conducted using the statistical software R with the additional packages “Benchmarking” version 0.18 by Bogetoft and Otto (2011), “FEAR” version 1.13 by Wilson (2008) and “np” version 0.40-3 by Hayfield and Racine (2008).

  18. 18.

    As a rule of thumb, Simar (2003) suggest \(\sqrt{(}n)/n\) as a reasonable upper bound for the share of outliers in a data sample. In the case of the \(n=364\) observations, this rule of thumb suggests an upper bound of 5.24 % of outliers in the sample. The 19 deleted observations correspond to a share of outlying observations of 5.22 %.

  19. 19.

    We note that the removed observations might represent extreme best practices even when controlling for the operating environment. However, to ensure the validity of the results, we decide to remove the detected potential outliers, thus leading to more conservative estimates of potential merger gains. The application of robust methods, like the order-m approach, would be beneficial in the subsequent merger analysis. However, by construction, the merger analysis approach relies on full frontier measures like DEA.

  20. 20.

    A Landkreis is the German equivalent of a county as defined by the NUTS 3 code of the NUTS-classification (Nomenclature des unités territoriales statistiques).

  21. 21.

    The idea of aggregating water utilities originates from observations of consolidated water utilities on the county level especially in East Germany. We cannot guarantee that the simulated mergers represent merger cases between really neighboring water utilities. However, due to the usually low number of water utilities within a county, the merger simulation approach appears reasonable.

  22. 22.

    Environmental variables of hypothetically merged entities are re-calculated by aggregating the underlying raw data used to calculate the environmental variables of the individual pre-merger companies.

  23. 23.

    Calculated as the sum of water delivered to final customers and the amount of bulk water supplies.

  24. 24.

    The opposite case is also possible if an inefficient water utility takes over an efficient one.


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We thank the participants of the XII European Workshop on Efficiency and Productivity Analysis (EWEPA) in June 2011 in Verona, Italy, and the participants of the 39th Annual Conference of the European Association for Research in Industrial Economics (EARIE) in September 2012 in Rome, Italy. In particular, we thank David Saal, Christian von Hirschhausen and Astrid Cullmann for discussions and suggestions. We further thank three anonymous referees for helpful comments substantially improving our paper. The usual disclaimer applies.

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Correspondence to Michael Zschille.

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This paper is produced as part of the project Growth and Sustainability Policies for Europe (GRASP), a Collaborative Project funded by the European Commission’s Seventh Research Framework Programme, Contract number 244725.

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Zschille, M. Consolidating the water industry: an analysis of the potential gains from horizontal integration in a conditional efficiency framework. J Prod Anal 44, 97–114 (2015).

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  • Water supply
  • Horizontal integration
  • Data Envelopment Analysis
  • Conditional efficiency
  • Nonparametric estimation

JEL Classification

  • C14
  • L22
  • L95
  • Q25