Benchmarking Municipal Efficiencies A Preliminary Study

1. Benchmarking Municipal EfficienciesA Preliminary Study W.J. Brettenny S.G. Hosking G.D. Sharp Pretoria, 14 June 2013

2. Overview • What is efficiencyand how is it measured? • What variables should be used in RSA? • Results and Discussion

3. What is efficiency? • Producing large quantities (or high quality) of output is not enough, this be accompanied by a minimal use of raw materials, labour and other available resources. • A firm’s capacity to maximiseoutputs(services) while minimising their use of inputs (resources) is an indicator of the efficiency of the firm (Bogetoft and Otto, 2010).

4. What is efficiency? • A firm’s success can be gauged by how successfully it • generates a maximum level of outputs for a given set of inputs (Farrell, 1957) or • limits the amount of inputs used to achieve a desired level of output. • This measures the econometric efficiency of a firm. • We will consider a water service authority (WSA) to be a firm that is responsible for taking a set of inputs (labour, operating expenditure, etc) and converting them into valuable outputs (water delivered, etc).

5. Has it been done before? • Econometric efficiency analysis has been used in numerous different fields both in the private and public sector. • These include banks, sports, prisons, traffic control, schooling, water and electricity supply etc. • There are two main approaches used: • Data Envelopment Analysis (DEA) • Stochastic Frontier Analysis (SFA)

6. Has it been done before? • Econometric efficiency analysis in the field of water service provision is also not a new concept. • Numerous countries have used these methods to gauge the efficiency of their water service provision for example: • UK region (Ashton (2003); Bottasso and Conti (2003)) • Italy (Fraquelli and Moiso (2005)) • Slovenia (Filippini et al. (2008)) • UK region (Cubbin and Tzanidakis (1998); Thanassoulis (2000)) • Japan (Aida et al. (1998)) • Brazil (Tupper and Resende (2004)) • Spain (García-Valiñasa and Muñiza (2007)) • Peru (Berg and Lin (2008)) SFA DEA

7. How do we measure efficiency? • The efficiency of a WSA () can be thought of as (Cooper et al., 2006): or equivalently as:

8. How do we measure efficiency?

9. Data Envelopment Analysis (DEA) • DEA uses a linear programming approach to solve the this problem. • Using a linear programming technique one must select one of two approaches: • Input minimisation • Output maximisation • In a water service provision the “input minimisation” approach is typically used as outputs (water supplied etc.) are considered to be exogenous.

10. Data Envelopment Analysis (DEA) • For this preliminary study we consider the Banker-Charnes-Cooper (BCC) (Banker et al., 1984) model which uses varying returns to scale (VRS). • The use of this approach will allow us to determine Pure Technical Efficiencies (Managerial Efficiencies). • These are all RELATIVE efficiency measurements.

11. CRS and VRS?

12. Data Envelopment Analysis (DEA) • Input minimisation algorithm for DEA: Subject to

13. What Variables Should be used? • For this preliminary study we use data from the 2010 municipal year. • The number of variables used must conform to: • The selection of variables at this stage is observational. We considered the most common variables used in the literature and used these in our study.

14. What Variables Should be used? See Singh et al (2008) (pg 88) for DEA additional studies

15. What Variables Should be used? • Thanassoulis (2000a) provided a review of the use of DEA in the regulation of water service providers in the UK. Possible input and output variables for the use in a DEA model for water service providers (Thanassoulis, 2000a, pg. 8)

16. What Variables Should be used? • Owing to their dominance of the reviewed literature as well as the availability of the data in RSA, the following inputs and outputs were used in the current study. • Inputs: • Operating Expenditure (R ‘000) • Number of Employees • Outputs: • Number of Connections • Length of the Mains (km) • Water Input into the System (kl) We thus require municipalities for assessment

17. Data Sources • The data used for this exploratory research came from the following sources: • StatsSA • P9115: Non-financial Audit of Municipalities • P9114: Financial Audit of Municipalities • WRC Document • TT 552/12: The State of Non-Revenue Water in South Africa (2012)

18. Missing / Illogical Data • Data compiled from various sources, require valid data for every variable. • All municipalities with missing data were excluded. • All municipalities with illogical data were omitted, for example: • Amatole DM; Length of mains = 1 927 668 km.

19. Grouping of South African WSA’s • Owing to the size as well as the number of municipalities available for assessment, the following groupings were made: • Metropolitan and District Municipalities (). • Local Municipalities ().

20. Results – Metro/District Municipalities • Fully technically efficient district/metro municipalities (VRS)

21. Results – Metro/District Municipalities • Best inefficient municipalities and their target input values.

22. Results – Metro/District Municipalities • Peer groups.

23. Results – Local Municipalities • Fully technically efficient local municipalities (VRS)

24. Results – Local Municipalities • “Best” inefficient municipalities and their target input values.

25. Results – Local Municipalities • “Worst” inefficient municipalities and their target input values.

26. Results – Local Municipalities • Peer groups.

27. Results

28. Results – by Province

29. Comparison with Blue Drop Score -Metro/District Municipalities

30. Comparison with Blue Drop Score

31. Comparison with Blue Drop Score – Local Municipalities

32. Comparison with Blue Drop Score

33. Conclusion and Way Forward • Results are highly preliminary and subject to change with additional data collection, verification and validation. • Incorporate the BDS Score into calculations of efficiency (as an input). • Compute and compare efficiencies using SFA. • Use DEA super efficiencies to discriminate between fully efficient WSA’s.

34. References • Bogetoft, P. and Otto, L. (2010). Benchmarking with DEA, SFA, and R. Vol. 157 of International Series in Operations Research & Management Science. Springer Science+Business Media, LLC. • Farrell, M. J. (1957). ‘The measurement of productive efficiency’. Journal of the RoyalStatistical Society. Series A (General), 120(3), pp. 253–290. • Cubbin, J. and Tzanidakis, G. (1998). ‘Regression versus data envelopment analysis for efficiency measurement: an application to the England and Wales regulated water industry’. Utilities Policy, 7(2), 75 – 85. • Ashton, J. K. (2000). ‘Cost efficiency in the UK water and sewerage industry’. AppliedEconomics Letters, 7(7), 455–458. • Bottasso, A. and Conti, M. (2003). Cost inefficiency in the English and Welsh water industry:An heteroskedastic stochastic cost frontier approach. University of Essex. • Fraquelli, G. and Moiso, V. (2005). Cost efficiency and economies of scale in the Italian water industry. XVII Conferenzasocietàitaliana di economiapubblica. Finanziamento del settorepubblico. • Filippini, M., Hrovatin, N. and Zoric, J. (2008). ‘Cost efficiency of Slovenian water distribution utilities: an application of stochastic frontier methods’. Journal of Productivity Analysis, 29, 169–182. • Thanassoulis, E. (2000a). ‘DEA and its use in the regulation of water companies’. EuropeanJournal of Operational Research, 127(1), 1 – 13. • Thanassoulis, E. (2000b). ‘The use of data envelopment analysis in the regulation of UK water utilities: Water distribution’. European Journal of Operational Research, 126(2), 436 – 453. • Aida, K., William, W., Jesús, T. and Toshiyuki, S. (1998). ‘Evaluating water supply services in Japan with RAM: a range-adjusted measure of efficiency’. Omega, International Journal ofManagement Science, 26(2), 207 – 232. • Tupper, H. C. and Resende, M. (2004). ‘Efficiency and regulatory issues in the Brazilian water and sewage sector: an empirical study’. Utilities Policy, 12(1), 29 – 40. • García-Valiñasa, M. A. and Muñiza, M. A. (2007). ‘Is DEA useful in the regulation of water utilities? a dynamic efficiency evaluation (a dynamic efficiency evaluation of water utilities)’. Applied Economics, 39(2), 245–252. • Berg, S. and Lin, C. (2008). ‘Consistency in performance rankings: the Peru water sector’. Applied Economics, 40(6), 793–805. • Cooper, W. W., Seiford, L. M. and Tone, K. (2006). Introduction to Data EnvelopmentAnalysis and Its Uses: With DEA-Solver Software and References. Springer Science+Business Media, Inc. • Banker, R., Charnes, A. and Cooper, W. (1984). ‘Some models for estimating technical and scale inefficiencies in data envelopment analysis’. Management science, 30(9), 1078–1092. • Singh, M. R., Mittal, A. K. and Upadhyay, V. (2011). ‘Benchmarking of North Indian urban water utilities’. Benchmarking: An International Journal, 18(1), 86 – 106.

35. Thank You