Benchmarking Municipal Efficiencies A Preliminary Study

Benchmarking Municipal EfficienciesA Preliminary Study

W.J. BrettennyS.G. HoskingG.D. Sharp

Pretoria, 14 June 2013

2

Overview

• What is efficiency and 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 maximise outputs (services) while minimising their use of inputs (resources) is an indicator of the efficiency of the firm (Bogetoft and Otto, 2010).


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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).


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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)


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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:


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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.


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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?


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Data Envelopment Analysis (DEA)• Input minimisation algorithm for DEA:

Subject to


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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.

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Author Ctry Inputs OutputsAida et al. (1998) Japan Number of employees; operating

expenses (before depreciation); net plant; equipment value, population size; length of pipes.

Operating revenues; the amount of water billed net of leakage**

Cubbin and Tzanidakis (1998)

England and Wales

Operating expenditure. **

NC.: prop of distribution to non households

Volume of water delivered; length of water main

Thanassoulis (2000b)

United Kingdom

Operating expenditure.** Volume of water delivered; length of water main; number of connections served

Tupper and Resende (2004)

Brazil Labour expenses; operational costs (e.g. materials); other operational costs (e.g. interest on debts)

Water produced; treated sewage; population served (treated sewage); population served (water). **

García-Valiñasa and Muñiza (2007)

Spain Operational costs. **

N.C.: Rainfall

Volume of water delivered; length of water main; population supplied with water

Berg and Lin (2008)

Peru Operating costs, number of employees, number of water connections. **

Volume of water billed; number of customers, coverage of service; continuity of service.

What Variables Should be used? Pretoria, 14 June 2013

See Singh et al (2008) (pg 88) for DEA additional studies

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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.Potential Inputs Potential Outputs

Operating Expenditure

Number of connections served

Length of mains (dispersion)

Water delivered to clients (metered and non-metered) Measured amount of water delivered

Estimated remainder of water delivered

Expenditure incurred for repairs (pipe bursts)

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

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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)

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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.

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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 ().


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• Fully technically efficient district/metro municipalities (VRS)

CON LEN INP EXP_W EMP DEA_EFF

O.R. TAMBO DM 334138 1823 71832332 558958 354 100%

CITY OF JOHANNESBURG MM 559367 11296 502956153 4034615 2691 100%

CITY OF TSHWANE MM 423414 9141 265845467 1319808 930 100%

EKURHULENI MM 497570 10077 322249616 2273901 960 100%

ETHEKWINI MM 442721 11643 332941393 2669202 2582 100%

UMGUNGUNDHLOVU DM 162446 1655 58492009 41244 131 100%

CAPRICORN DM 246428 3684 33555165 54019 302 100%

WEST COAST DM 72666 1871 20417029 53772 82 100%

CENTRAL KAROO DM 15178 280 3611000 867 10 100%


Results – Metro/District Municipalities

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• Best inefficient municipalities and their target input values.

EXP_W EMP DEA_EFF EXP_W* EMP*

NELSON MANDELA BAY MM 522289 1497 67.3% 351252 414

UGU DM 572150 483 64.1% 366927 310

UTHUKELA DM 157560 650 38.1% 59775 247

UTHUNGULU DM 211016 138 83.4% 67401 115



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• Peer groups.

INEFFICIENT MUNICIPALITY PEER GROUP

UTHUNGULU DM UMGUNGUNDHLOVU DM (51%); WEST COAST DM (48%); EKURHULENI MM (1%)



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

CON LEN INP EXP_W EMP DEA_EFF

CAMDEBOO LM 11287 226 6590000 6321 28 100%

NDLAMBE LM 17289 346 3368070 8890 57 100%

BAVIAANS LM 4159 83 619612 1309 6 100%

MANGAUNG LM 167954 3401 79085845 307971 860 100%

TSWELOPELE LM 13169 263 2863509 6893 31 100%

MOQHAKA LM 46082 922 11744810 42400 126 100%

EMFULENI LM 229610 4592 79559382 331464 403 100%

KUNGWINI LM 41503 830 24190000 45144 54 100%

DR JS MOROKA LM 52373 225 23400000 52525 179 100%

RUSTENBURG LM 134983 2700 35581439 260609 121 100%

DRAKENSTEIN LM 54025 1080 15975000 52788 149 100%


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


BUFFALO CITY LM 285941 686 97.0% 277401 354

MATJHABENG LM 197887 126 91.4% 180888 115

NOKENG TSA TAEMANE LM 29242 21 91.7% 26849 19

HANTAM LM 8063 11 85.3% 6861 9

KAROO HOOGLAND LM 1602 12 91.3% 1464 7


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


MIDVAAL LM 74304 69 44.6% 29193 31

LESEDI LM 43704 166 37.2% 16219 62

MOGALAKWENA LM 69539 111 24.1% 16749 27

NAMA KHOI LM 20784 68 36.4% 7550 25


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Results – Local Municipalities• Peer groups.

INEFFICIENT MUNICIPALITY PEER GROUP

MATJHABENG LM RUSTENBURG LM (53%); KUNGWINI LM (39%); EMFULENI LM (7%)

MOGALAKWENA LM BAVIAANS LM (46%); KUNGWINI LM (33%); CAMDEBOO LM (11%); TSWELOPELE LM (11%)


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Results


Average Technical Efficiency Average Technical Efficiency (<1)0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Local Municipalities

District / Metro Munic-ipalities

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Results – by ProvincePretoria, 14 June 2013

EC FS GT KZN LIM MP NW NC WC0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0.92742 0.764170.7251333333333330.7647 0.241 0.72352 1 0.63202 0.782950.83655 0 1 0.77112 1 0 0 0 1

Local Eff Ave Dist/Metro Eff Ave

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Comparison with Blue Drop Score -Metro/District Municipalities


BDS DEA_EFF

CITY OF JOHANNESBURG MM 98.4 100%

EKURHULENI MM 96.8 100%

CITY OF TSHWANE MM 96.4 100%

ETHEKWINI MM 96.1 100%

UMGUNGUNDHLOVU DM 64.7 100%

WEST COAST DM 60.4 100%

CAPRICORN DM 55.9 100%

CENTRAL KAROO DM 45.6 100%

O.R. TAMBO DM 22.2 100%

UTHUNGULU DM 37.2 83.4%

NELSON MANDELA BAY MM 95.1 67.3%

UGU DM 87.4 64.1%

UTHUKELA DM 66 38.1%

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Comparison with Blue Drop Score


CITY OF J

OHANNESBURG M

M

EKURHULENI M

M

CITY OF T

SHWANE M

M

ETHEKWINI MM

UMGUNGUNDHLOVU DM

WEST COAST

DM

CAPRICORN DM

CENTRAL KAROO DM

O.R. TAMBO DM

UTHUNGULU DM

NELSON M

ANDELA BAY M

M

UGU DM

UTHUKELA DM

0

10

20

30

40

50

60

70

80

90

100

Blue Drop Score Efficiency

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Comparison with Blue Drop Score – Local Municipalities


BDS DEA_EFFDR JS MOROKA LM 95.7 100%RUSTENBURG LM 95.1 100%MANGAUNG LM 95 100%EMFULENI LM 94.5 100%

DRAKENSTEIN LM 91.7 100%BAVIAANS LM 52.6 100%

TSWELOPELE LM 49.9 100%KUNGWINI LM 42.5 100%NDLAMBE LM 37.8 100%

CAMDEBOO LM 37.4 100%BUFFALO CITY LM 95.2 97.0%

NOKENG TSA TAEMANE LM 57.6 91.7%MATJHABENG LM 47.3 91.4%

KAROO HOOGLAND LM 39 91.3%HANTAM LM 68.5 85.3%MIDVAAL LM 73.6 44.6%LESEDI LM 58.8 37.2%

NAMA KHOI LM 22 36.4%MOGALAKWENA LM 46.6 24.1%

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Comparison with Blue Drop Score


DR JS M

OROKA LM

RUSTENBURG LM

MANGAUNG LM

EMFULE

NI LM

DRAKENSTEIN LM

BAVIAANS LM

TSWELO

PELE LM

KUNGWINI LM

NDLAMBE LM

CAMDEBOO LM

BUFFALO

CITY LM

NOKENG TSA TAEMANE LM

MATJHABENG LM

KAROO HOOGLAND LM

HANTAM LM

MIDVAAL LM

LESE

DI LM

NAMA KHOI LM

MOGALAKWENA LM

0

10

20

30

40

50

60

70

80

90

100

Blue Drop Score Efficiency

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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.

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ReferencesPretoria, 14 June 2013

• 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 Royal Statistical 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’. Applied Economics 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 Conferenza società italiana

di economia pubblica. Finanziamento del settore pubblico. • 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’. European Journal 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 of Management 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 Envelopment Analysis 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.

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Benchmarking Municipal Efficiencies A Preliminary Study

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