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

Abstract

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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Notes

  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.

References

  1. Abbott M, Cohen B (2009) Productivity and efficiency in the water industry. Util Policy 17(3–4):233–244

    Article  Google Scholar 

  2. Antonioli B, Filippini M (2001) The use of a variable cost function in the regulation of the Italian water industry. Util Policy 10(3–4):181–187

    Article  Google Scholar 

  3. Ashton JK (2003) Capital utilisation and scale in the English and Welsh water industry. Serv Ind J 23(5):137–149

    Article  Google Scholar 

  4. Bagdadioglu N, Waddams Price C, Weyman-Jones T (2007) Measuring potential gains from mergers among electricity distribution companies in Turkey using a non-parametric model. Energy J 28(2):83–110

    Article  Google Scholar 

  5. Ballance T, Saal DS, Reid S (2004) Investigation into evidence for economies of scale in the water and sewerage industry in England and Wales. Stone & Webster Consultants, London

    Google Scholar 

  6. Bogetoft P, Otto L (2011) Benchmarking with DEA and SFA. R package version 18

  7. Bogetoft P, Wang D (2005) Estimating the potential gains from mergers. J Prod Anal 23(2):145–171

    Article  Google Scholar 

  8. Bottasso A, Conti M (2009) 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 

  9. Bruno C (2012) Vertical and horizontal integration in public utilities. Evidence from telecom EU operators and Italian water regulatory agencies. PhD thesis, Università degli studi di Bergamo, Faculty of Engineering

  10. Bădin L, Daraio C, Simar L (2010) Optimal bandwidth selection for conditional efficiency measures: a data-driven approach. Eur J Oper Res 201(2):633–640

    Article  Google Scholar 

  11. Bădin L, Daraio C, Simar L (2012) How to measure the impact of environmental factors in a nonparametric production model. Eur J Oper Res 223(3):818–833

    Article  Google Scholar 

  12. Bundesministerium für Wirtschaft und Arbeit (2005) Wasserleitfaden: Leitfaden zur Herausbildung leistungsstarker kommunaler und gemischtwirtschaftlicher Unternehmen der Wasserver- und Abwasserentsorgung. Bundesministerium für Wirtschaft und Arbeit Berlin, Dokumentation 547

  13. Bundesregierung (2010) Stellungnahme der Bundesregierung zum XVIII. Haupt-gutachten der Monopolkommission 2008/2009. Drucksache 17/2600

  14. Bundesverband der Energie- und Wasserwirtschaft (2008) 118. Wasserstatistik der Bundesrepublik Deutschland. wvgw Wirtschafts- und Verlagsgesellschaft Gas und Wasser mbH, Bonn

  15. Cazals C, Florens JP, Simar L (2002) Nonparametric frontier estimation: a robust approach. J Econom 106(1):1–25

    Article  Google Scholar 

  16. Coelli TJ, Walding S (2006) Performance measurement in the Australian water supply industry: a preliminary analysis. In: Coelli TJ, Lawrence D (eds) Performance measurement and regulation of network utilities, 1st edn. Edward Elgar, Cheltenham, pp 29–61

    Google Scholar 

  17. Coelli TJ, Rao D, O’Donnell CJ, Battese GE (2005) An introduction to efficiency and productivity analysis, 2nd edn. Springer, New York

    Google Scholar 

  18. Corton M (2011) Sector fragmentation and aggregation of service provision in the water industry. J Prod Anal 35(2):159–169

    Article  Google Scholar 

  19. Daraio C, Simar L (2005) Introducing environmental variables in nonparametric frontier models: a probabilistic approach. J Prod Anal 24(1):93–121

    Article  Google Scholar 

  20. Daraio C, Simar L (2007) Conditional nonparametric frontier models for convex and nonconvex technologies: a unifying approach. J Prod Anal 28(1–2):13–32

    Article  Google Scholar 

  21. Daraio C, Simar L (2014) Directional distances and their robust versions: computational and testing issues. Eur J Oper Res 237(1):358–369

    Article  Google Scholar 

  22. 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 

  23. De Witte K, Marques RC (2010a) Designing performance incentives, an international benchmark study in the water sector. CEJOR 18(2):189–220

    Article  Google Scholar 

  24. De Witte K, Marques RC (2010b) Influential observations in frontier models, a robust non-oriented approach to the water sector. Ann Oper Res 181(1):377–392

    Article  Google Scholar 

  25. De Witte K, Marques RC (2011) Big and beautiful? On non-parametrically measuring scale economies in non-convex technologies. J Prod Anal 35(3):213–226

    Article  Google Scholar 

  26. Fabbri P, Fraquelli G (2000) Costs and structure of technology in the Italian water industry. Empirica 27(1):65–82

    Article  Google Scholar 

  27. Farrell M (1957) The measurement of productive efficiency. J R Stat Soc 120(3):253–281

    Article  Google Scholar 

  28. Farsi M, Fetz A, Filippini M (2008) Economies of scale and scope in multi-utilities. Energy J 29(4):123–143

    Article  Google Scholar 

  29. Filippini M, Hrovatin N, Zorić J (2008) Cost efficiency of Slovenian water distribution utilities: an application of stochastic frontier models. J Prod Anal 29(2):169–182

    Article  Google Scholar 

  30. Fraquelli G, Moiso V (2005) The management of cost efficiency in the Italian water industry. HERMES Research Center Working Paper 8

  31. Fraquelli G, Piacenza M, Vannoni D (2004) Scope and scale economies in multi-utilities: evidence from gas, water and electricity combinations. Appl Econ 36(18):2045–2057

    Article  Google Scholar 

  32. Garcia S, Thomas A (2001) The structure of municipal water supply costs: application to a panel of French local communities. J Prod Anal 16(1):5–29

    Article  Google Scholar 

  33. 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 

  34. García-Sánchez IM (2006) Efficiency measurement in Spanish local government: the case of municipal water services. Rev Policy Res 23(2):355–372

    Article  Google Scholar 

  35. Hayfield T, Racine JS (2008) Nonparametric econometrics: the np package. J Stat Softw 27(5):1–32

    Google Scholar 

  36. Hirschhausen Cv, Cullmann A, Walter M, Zschille M (2009) Fallende Preise in der Wasserwirtschaft: Hessen auf dem Vormarsch. DIW Berlin Wochenbericht 76(10):150–155

    Google Scholar 

  37. Kim HY, Clark RM (1988) Economies of scale and scope in water supply. Reg Sci Urban Econ 18(4):479–502

    Article  Google Scholar 

  38. Kristensen T, Bogetoft P, Pedersen KM (2010) Potential gains from hospital mergers in Denmark. Health Care Manag Sci 13(4):334–345

    Article  Google Scholar 

  39. Marques RC, De Witte K (2011) Is big better? On scale and scope economies in the Portuguese water sector. Econ Model 28(3):1009–1016

    Article  Google Scholar 

  40. Martins R, Coelho F, Fortunato A (2012) Water losses and hydrographical regions influence on the cost structure of the Portuguese water industry. J Prod Anal 38(1):81–94

    Article  Google Scholar 

  41. Mizutani F, Urakami T (2001) Identifying network density and scale economies for Japanese water supply organizations. Pap Reg Sci 80(2):211–230

    Article  Google Scholar 

  42. Monopolkommission (2010) Achtzehntes Hauptgutachten der Monopolkommission 2008/2009. Mehr Wettbewerb, wenig Ausnahmen. Nomos Verlagsgesellschaft, Baden-Baden

  43. Mosheim R (2006) A shadow cost function model of the US water industry incorporating water quality and ownership effects. In: Coelli TJ, Lawrence D (eds) Performance measurement and regulation of network utilities, 1st edn. Edward Elgar, Cheltenham, pp 243–265

    Google Scholar 

  44. Mühlenkamp H (2012) Zur relativen (In-)Effizienz öffentlicher (und privater) Unternehmen - Unternehmensziele, Effizienzmaßstäbe und empirische Befunde. Renaissance öffentlicher Wirtschaft. Nomos Verlagsgesellschaft, Baden-Baden, pp 21–48

    Google Scholar 

  45. Nauges C, Berg Cvd (2010) Heterogeneity in the cost structure of water and sanitation services: a cross-country comparison of conditions for scale economies. Oxf Dev Stud 38(2):199–217

    Article  Google Scholar 

  46. Piacenza M, Vannoni D (2004) Choosing among alternative cost function specifications: an application to Italian multi-utilities. Econ Lett 82(3):415–422

    Article  Google Scholar 

  47. Picazo-Tadeo A, Sáez-Fernández FJ, González-Gómez F (2009) The role of environmental factors in water utilities’ technical efficiency. Empirical evidence from Spanish companies. Appl Econ 41(5):615–628

    Article  Google Scholar 

  48. Racine JS (1997) Consistent significance testing for nonparametric regression. J Bus Econ Stat 15(3):369–379

    Google Scholar 

  49. Rottmann O (2010) Herausforderungen für die Innensteuerung von Stadtwerken aus der Interdependenz der Außensteuerungspostulate. Peter Lang Internationaler Verlag der Wissenschaften, Frankfurt am Main

    Google Scholar 

  50. Saal DS, Parker D, Weyman-Jones T (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. J Prod Anal 28(1):127–139

    Article  Google Scholar 

  51. Saal DS, Arocena P, Maziotis A (2011) Economies of integration in the English and Welsh water only companies and the assessment of alternative unbundling policies. Aston University Birmingham, draft paper

  52. Saal DS, Arocena P, Maziotis A, Triebs T (2013) Scale and scope economies and the efficient vertical and horizontal configuration of the water industry: a survey of the literature. Rev Netw Econ 12(1):93–129

    Article  Google Scholar 

  53. Sauer JF (2005) The economics and efficiency of water supply infrastructure. Logos Verlag, Berlin

    Google Scholar 

  54. Simar L (2003) Detecting outliers in frontier models: a simple approach. J Prod Anal 20(3):391–424

    Article  Google Scholar 

  55. Simar L, Wilson PW (1998) Sensitivity analysis of efficiency scores: how to bootstrap in nonparametric frontier models. Manag Sci 44(1):49–61

    Article  Google Scholar 

  56. Simar L, Wilson PW (2007) Estimation and inference in two-stage, semi-parametric models of production processes. J Econom 136(1):31–64

    Article  Google Scholar 

  57. Simar L, Wilson PW (2008) Statistical inference in nonparametric frontier models: recent developments and perspectives. In: Fried HO, Lovell CAK, Schmidt SS (eds) The measurement of productive efficiency and productivity growth. Oxford University Press, New York, pp 421–521

    Google Scholar 

  58. Simar L, Wilson PW (2011) Inference by the m out of n bootstrap in nonparametric frontier models. J Prod Anal 36(1):33–53

    Google Scholar 

  59. Simar L, Wilson PW (2011b) Two-stage DEA: caveat emptor. J Prod Anal 36(2):205–218

    Article  Google Scholar 

  60. Statistisches Amt der DDR (1990) Statistisches Jahrbuch der Deutschen Demokratischen Republik, vol 35, 1st edn. Rudolf Haufe Verlag, Berlin

    Google Scholar 

  61. Statistisches Bundesamt (2009) Fachserie 19, Reihe 2.1: Öffentliche Wasserversorgung und Abwasserbeseitigung 2007. Statistisches Bundesamt, Wiesbaden

  62. Thanassoulis E (2000) The use of data envelopment analysis in the regulation of UK water utilities: Water distribution. Eur J Oper Res 126(2):436–453

    Article  Google Scholar 

  63. Thanassoulis E, Portela MCS, Despić O (2008) Data envelopment analysis: the mathematical programming approach to efficiency analysis. In: Fried HO, Lovell CAK, Schmidt SS (eds) The measurement of productive efficiency and productivity growth. Oxford University Press, New York, pp 251–420

    Google Scholar 

  64. Torres M, Morrison Paul CJ (2006) Driving forces for consolidation or fragmentation of the US water utility industry: a cost function approach with endogenous output. J Urban Econ 59(1):104–120

    Article  Google Scholar 

  65. Tupper HC, Resende M (2004) Efficiency and regulatory issues in the Brazilian water and sewage sector: an empirical study. Util Policy 12(1):29–40

    Article  Google Scholar 

  66. Urakami T (2006) Identifying scale economies for different types of water supply organizations in Japan. J Bus Adm Market 52(3):147–158

    Google Scholar 

  67. Urakami T, Parker D (2011) The effects of consolidation amongst Japanese water utilities: a hedonic cost function analysis. Urban Stud 48(13):2805–2825

    Article  Google Scholar 

  68. Viton PA (1992) Consolidations of scale and scope in urban transit. Reg Sci Urban Econ 22(1):25–49

    Article  Google Scholar 

  69. Walter M, Cullmann A, Cv Hirschhausen, Wand R, Zschille M (2009) Quo vadis efficiency analysis of water distribution? A comparative literature review. Util Policy 17(3–4):225–232

    Article  Google Scholar 

  70. Wilson PW (2008) Fear 1.0: a software package for frontier efficiency analysis with R. Socio Econ Plan Sci 42(4):247–254

    Article  Google Scholar 

  71. Zschille M (2013) Nonparametric measures of returns to scale: an application to German water supply. Empir Econ. doi:10.1007/s00181-013-0775-5

  72. Zschille M, Walter M (2012) The performance of German water utilities: a (semi)-parametric analysis. Appl Econ 44(29):3749–3764

    Article  Google Scholar 

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Acknowledgments

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). https://doi.org/10.1007/s11123-014-0407-x

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Keywords

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

JEL Classification

  • C14
  • L22
  • L95
  • Q25