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Performance Measurement of Water Supply Services: A Cross-Country Comparison between India and the UK

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Abstract

Increased emphasis on efficiency improvements has brought the benefits of internal savings and improved service deliveries of water supplies to the developed countries. However, water supply services in developing countries often exhibit large inefficiencies making it difficult to expand coverage or improve supply services. A comparative assessment of efficiencies in the developed and developing countries is therefore essential to estimate the actual lag that developing countries typically experience, and to arrive at an estimate of their efficiency improvements. This manuscript makes an effort in this direction by undertaking a cross-country benchmarking study on the level of inefficiencies prevailing in Indian urban water supply services (IUWSS) relative to the utilities in the UK—England and Wales. This manuscript uses data envelopment analysis approach to measure relative efficiencies of water supplies for a sample of 30 water utilities in India and the UK—England and Wales for the year 2015. The results indicate that there exist significant technical and scale inefficiencies in water supply operations of Indian municipalities compared with the UK—England and Wales. DEA results also show a significant scope for savings with respect to operating expenditures and non-revenue water, if Indian utilities were to adopt the best practices and managerial tools of the utilities in the UK—England and Wales. Further, it was found that IUWSS need a restructuring of their municipalities to become more efficient. This manuscript discusses the above results in the context of policy implications and suggests few mechanisms that are relevant from a developing country perspective for possible improvement in water supply services.

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Notes

  1. Utilities may appear to be efficient when benchmarked with peers of the same country but may have considerable differences in the level of service delivery with respect to international standards and may exhibit large inefficiencies when benchmarked with international peers, more so in the developing world.

  2. Almost exclusively the studies on the performance measurement of water supply services were carried out at a country level both in developed and developing nations (see Abbott and Cohen (2009)). Some of the recent studies focused on cross-country comparison of performance for water supply services (see De Witte and Marques (2010); Nauges and Van den Berg (2010)), but these studies are limited in number in comparison with the studies carried out at a country level.

  3. This technique is particularly useful in the efficiency measurement of public utilities (such as Indian water supply services) where the knowledge of production function and cost function are relatively scarce (De Witte and Marques 2010).

  4. Most of the state water regulatory authorities do not include provisions for regulating water service delivery and are solely named as water resource regulatory authorities (see PRAYAS (2009)).

  5. Although Thirteenth Finance Commission (2010–2015) of India recommended performance grants to improve SLBs for ULBs, only few states qualify and claim the grant (see NIUA (2018)).

  6. Few studies that focus on examining the effect of ownership structure (public vs private) reported that this variable does not affect efficiency (ex.: Wallsten and Koser 2005; Garcia-Sanchez 2006; da Silva e Souza et al. 2007), while few studies reported that ownership structure has an effect on efficiency (Raffiee et al. 1992; Bhattacharyya et al. 1994). Further, similar pattern of results can be observed with the studies related to scale and scope economies (see Abbot and Cohen (2009)).

  7. The most intricate issue of international benchmarking studies is the lack of comparability of data. The definitions and measurement of performance indicators vary across countries inducing heterogeneity in the sample and thus, making it difficult to carry out cross-country comparisons (De Witte and Marques 2010).

  8. Historically, it has been a common practice for water utilities in Scotland and Europe to benchmark and compare themselves against the utilities in England and Wales. Since its privatization, water companies in England and Wales improved quality of service delivery, enhanced their efficiency levels and provided service at overall reduced costs (GWI 2018). Hence, water supply utilities across the world (specifically in Scotland and Europe) benchmark themselves against highly efficient utilities in England and Wales to compare their performance levels and identify the scope for improvements possible in water service delivery.

  9. The CCR model was built on the idea of Farrell (1957), which is concerned with the estimation of technical efficiency and efficient frontier.

  10. Note that the CRS assumption is only appropriate when all the DMUs in the sample are operating at an optimal scale.

  11. The input-orientated model looks at the proportion by which inputs can be contracted to produce same amount of outputs. While, the output-orientated model looks at the proportion by which outputs can be expanded with same amount of inputs.

  12. For water utilities input orientation appears to be more suitable as inputs (labor costs, material costs, etc.) act as decision variables that may be adjusted accordingly, while outputs (measured by the water delivered or by the population served) are price inelastic and are often fixed (Bonacina et al. 2014). Moreover, the cross-country benchmarking studies so far in the literature used input orientation to measure the technical efficiencies of sample utilities (see De Witte and Marques (2010); da cruz et al. (2012)). Hence, the DEA models in this manuscript use input orientation to measure technical efficiency.

  13. Note that if the number of DMUs in the sample increase from z to z + p, the only change in the model is the addition of p normalization constraints due to which the new optimal solution for any existing DMU will be less or equal to the previous optimal solution because of the reduction in feasible solution set. Therefore, by construction, joining separate data sets does not increase the efficiency score of the individual DMUs in comparison with their separate analysis. This is important aspect in international benchmark studies as the combination of national databases increase the number of observations in the sample (De witte and Marques 2010).

  14. The exchange rate fluctuations, unequal extent of outsourcing, dissimilarities in wage rates, taxes, and rates of return on capital could influence costs and induce heterogeneity between countries, which could be falsely taken as inefficiencies (De Witte and Marques 2010).

  15. IBNET promotes cross-country comparisons by providing robust data definitions and by sharing comparable information on a set of core indicators. Choosing variables/indicators from IBNET database limits the issue of “lack of comparability of data” in cross-country benchmarking.

  16. Most of the cross-country benchmarking studies used economic measures such as GDP or GNI per capita (Corton and Berg 2009); GNP per capita (Clarke et al. 2009); GRP per capita (De witte and Marques 2010) to account for economic differences in each country and capture country-specific economic circumstances affecting the performance of water supply services. This manuscript uses “income per capita” to normalize the economic differences between countries under examination.

  17. NRW is the difference between “total water produced and total water billed”. It includes the “physical losses” (pipe leaks and overflows), and the “commercial losses” (unpaid bills, unbilled authorized consumption, illegal connections, and metering inaccuracies). NRW forms an important indicator for performance measurement (see Yepes and Dianderas (1996); Alegre et al. (2006)), and water utilities are expected to achieve the “NRW reduction targets” set by regulators/governing authorities of their respective countries.

  18. OFWAT collects information from each of the water companies in the UK—England and Wales for performance assessments and price reviews.

  19. PAS project, India, has evolved as a major repository of urban water and sanitation database in India.

  20. It should be noted that universal access to water still remains a major issue in India and only 55% of urban households are connected with private household connections (IIHS 2014).

  21. IUWSS suffer from intermittent water supplies and restricted time supplies have an effect on costs, water losses, and manifest as the one of the sources of system inefficiencies (Nyathikala et al. 2018; Kulshrestha 2006).

  22. It should be noted that Gupta et al. (2012) uses the data from the year 2005 and Singh et al. (2010) uses the data from the year 1999 to measure the efficiencies of IUWSS. This manuscript however uses the data from the year 2015 to measure the efficiencies of IUWSS relative to the utilities in the UK—England and Wales. A comparison is made between these three studies in the above discussion, and irrespective of the differences in data and methodology used few of the IUWSS appear to be efficient when benchmarked with Indian peers but exhibit inefficiencies when benchmarked with international peers.

  23. In this case, the efficiency scores of highly regulated and incentivized water supply utilities in the UK—England and Wales and Wales are observed to be higher than the non-regulated water supply services in India (see Table 7).

  24. In developed countries such as the UK—England and Wales there is a large focus on cost minimization and efficiency improvement of water utilities. However, in developing countries such as India, no major focus has been laid on improving operational and service efficiencies of water supply services (Nyathikala et al. 2018).

  25. It should be noted that it is important to integrate quality measures and water losses in the cost efficiency modeling to prevent utilities from compromising on quality standards. Further, it is necessary to design penalizing mechanisms for utilities that do not comply to the quality/leakage reduction targets.

  26. The X-factor (in RPI-X) is generally called productivity offset because it reflects the degree to which the regulator believes the sector can improve its productivity (i.e., reduce costs) through efficiency improvements.

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Acknowledgements

The authors gratefully acknowledge the award of fellowship by Commonwealth Scholarship Commission of UK for 1 year to Sai Amulya Nyathikala who enabled this work. Authors are also grateful to the University of Durham, UK that hosted Sai Amulya Nyathikala, and to Professor Tooraj Jamasb for all the help and guidance.

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  1. Environmental Engineering Division, Department of Civil Engineering, National Institute of Technology, MANIT, Bhopal, Madhya Pradesh, 462003, India

    Sai Amulya Nyathikala & Mukul Kulshrestha

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Nyathikala, S.A., Kulshrestha, M. Performance Measurement of Water Supply Services: A Cross-Country Comparison between India and the UK. Environmental Management 66, 517–534 (2020). https://doi.org/10.1007/s00267-020-01333-1

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