water paper eaere 2009

7/28/2019 Water Paper EAERE 2009

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Efficiency in the Malaysian Water Industry: A DEA and RegressionAnalysis

Lee Yoong Hon and Cassey LeeNottingham University Business School

University of Nottingham, Malaysia CampusJalan Broga, 43500 Semenyih

Selangor Darul Ehsan, MalaysiaTel:+603-89248269Fax: +603-89248019

E-Mail: [email protected]

Abstract

This study undertakes an empirical analysis of efficiency in the Malaysian water sectorusing the Data Envelopment Analysis (DEA) approach, the Malmquist Total FactorProductivity (TFP) indices and regression analysis (Tobit/OLS) as well. Overall, theDEA analysis finds that the mean technical efficiency of the water sector in Malaysia tobe about 66%, indicating a significant room for improvements in technical efficiency inthe Malaysian water sector. There are also significant differences in the efficiency in thewater sector across the different states. However, efficiency gains based on ownershipstatus could not be ascertained solely from the evidences in this paper.

Keywords: Water Utilities, Privatization, Efficiency, DEA, Tobit Regression


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Efficiency in the Malaysian Water Industry: A DEA and RegressionAnalysis

1. Introduction

The privatization of the water sector has generated a great amount of interest in the

measurement and comparison of efficiency levels in the sector. Regulators have

empirically estimated efficiency levels at private water companies in order to set limits

to water tariff revisions. Scholars have also been interested in assessing the impact of

privatization in the sector by comparing efficiency levels in both the private and public

water companies.

Most of the researches on the measurement of efficiency in the water sector are focused

on the experience of developed countries such as the United Kingdom and the United

States. Increasingly, more studies on efficiency levels in the water sector are being

carried out in developing countries such as Brazil. Cross-countries studies on the

relationship between ownership and efficiency have also become increasingly popular.

The two most widely used to estimate efficiency in the water sector are the Data

Envelopment Analysis (DEA), econometric analysis based on the estimation of

stochastic frontier models and also the use of regression analysis (RA).1

1 Under ideal conditions, both RA and DEA should be able to identify the true firm-level efficiency andproduce similar results. However, in practice they tend to produce strikingly different results particularlywhen it comes to smaller samples. Similarly many studies that use both DEA and stochastic frontieranalysis (SFA) often find contrasting results.


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In the past, the treatment and distribution of water were primarily undertaken by the

public sector. Since the early 1990s, a number of states have opted to privatize their

water sector. More recently, public debate has centered on whether privatization of the

water sector has yielded any benefit to society. A key issue in this debate is the impact

of privatization on the efficiency in the water sector. To date, there has been no

published quantitative empirical evidence on the efficiency of the water sector in

Malaysia. There is a significant gap in the larger body of research literature

particularly because Malaysia's experience is often examined and cited in discussions

on developing countries' experiences in privatization

The objective of this paper is to carry out an empirical analysis of efficiency in the

Malaysia water sector. This paper will employ the DEA approach to estimate the levels

of efficiency in the country's water sector and also perform some productivity analysis

over a selected time period. The paper opted for a DEA approach because we wanted to

assess the relative efficiency between the various state-water utilities and measure the

scope of improvement available to the respective units. For instance, although Ofwat

(Office of Water Services, the economic regulator for water and sewerage industry in

England and Wales) used regression analysis in their reports, they also recognized the use

of DEA in the relative efficiency analysis. In 1994, Ofwat used DEA to confirm the

results of the operating cost regression models as it provided a useful challenge to the

results of the preferred method (Ofwat, 1999).

In any event, since the true functional form for the water industry efficiency analysis is

often not clear, the use of DEA may have the added benefit since it does not require the


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specification of a functional form to be fitted. In this respect, it avoids the danger of

fitting the wrong functional form (Cubbin & Tzanidakis, 1998). DEA does not require

any specification of the functional form of the production relationship but develops a

frontier relating inputs to outputs.

In addition, the Malmquist productivity index in the DEA approach is used to measure

productivity in the industry, i.e. the outward movement of the industry frontier

(technological improvements) and the movement of companies towards the frontier

(catching-up). Such assessment of total factor productivity (TFP) growth (the real unit

cost of production) can also be seen in the Ofwat reports on the water industry in

England and Wales.

The outline of the rest of the paper is as follows. Section 2 provides a brief background

of the Malaysian water sector. This is followed by a discussion of the existing literature

on efficiency in the water sector in Section 3. The data and methodology used in the

study are discussed in Section 4. The main empirical results are presented in Section 5,

6 and 7. Section 8 concludes.

2. The Water Sector in Malaysia

Under Malaysias Federal Constitution, water resources fall under the jurisdiction of the

respective states.2 In the past, the treatment and distribution of water was undertaken by

state water agencies. These may differ from state to state and can either be: State Public

2 Malaysia is divided into 13 states and three federal territories, namely Kuala Lumpur, Labuan andPutrajaya.


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Works Department (PWD), State Water Supply Department (WSD) or State Water

Supply Board (WSB). Since the early 1990s, many states have allowed greater private

sector participation in the sector. In the early stages (1990-1994) of privatization of the

water sector in Malaysia, a few states chose to privatize water treatment only. Since the

mid-1990s, a few states have implemented a more comprehensive privatization program

by first, corporatizing state water companies and subsequently followed by the sale of

equity in these companies to the private sector (see Table 1).

[Insert Table 1]

The states that have fully privatized the provision of water services include the more

developed states (in terms of GDP per capita) such as Selangor, Pulau Pinang and

Johor. In most of these cases, state governments continue to hold substantial equity in

the privatized water companies. A few states (Labuan, N.Sembilan and Sabah) have

chosen a dual structure water system - whereby distribution is undertaken by state

agencies and water treatment is privatized via concessions. Some of the smaller states

(Melaka and Perlis) and less-developed states (Kedah, Sarawak and Pahang) have

chosen to maintain a public water provision system.

The regulatory structure in the Malaysian water sector is fairly complex due to the co-

existence of a variety of state-level and federal-level water supply institutions. There are

both state-level and federal-level regulators in Malaysia. The type of state-level

regulatory agency depends on the institutional status of the water service provider within

each state. Self-regulation is practiced in states where water is supplied by government


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agencies such as the public works department and the water supply department. For

corporatised bodies and privatized companies, new state regulatory agencies are created.

Generally, state-level regulatory agencies undertake economic regulation such as setting

tariff levels. On the other hand, Federal-level regulatory agencies undertake technical

regulation and the coordination of matters involving: (a) several states (such as inter state

water transfers) and, (b) funding from the Federal government. Other Federal-level

agencies are also involved in regulatory matters in the sector. The Environmental

Health Engineering Section under the Ministry of Health monitors the quality of

drinking water supply in the country. The Department of Drainage and Irrigation under

the Ministry of Natural Resources and Environment undertakes river basin management

including the monitoring of pollution levels in rivers. The National Water Resource

Council (NWRC) is a council comprising of representatives from Federal and state

governments that is in charge of formulating national level policies.

More recently, a number of regulatory reforms have been undertaken. On 18 January

2005, the Malaysian Parliament amended the Constitution to affect the transfer of the

jurisdiction of water supply management from the respective states to the Federal

government. With this change, the Federal Government now has full control over water

supply management in the country. Essentially, the amendment is to enable the Federal

Government involvement in the water services sector, in order to establish a regulated

water services industry in the Peninsular Malaysia. In May 2006, the government

enacted two legislations that further transformed the industry, namely the Water Services


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Industry Bill 2006 (WSI) and National Water Service Commission Bill 2006 (SPAN, the

Malay acronym for Suruhanjaya Perkhidmatan Air Negara).

These proposed changes are expected to overcome some of the perceived shortcomings

and weaknesses of the existing water institutions in Malaysia, namely: (a) the lack of

coordination amongst various stakeholders,(b)ineffective regulatory structure and poor

enforcement, (c) capital expenditure constraints, and (d) varied success of privatization

of water supply projects. The problem of capital expenditure constraints is attributed to

the inability of existing operators to obtain sufficient revenues to cover capital

expenditures (investment). This, in turn, is due to water tariffs being currently set at

less than full-cost recovery levels. It also due to the problem of non-revenue water

(NRW) i.e. water that are produced but not billed to consumers due to leakages, under-

meter registration, and pilferage. Alarmingly, the average percentage of NRW in

Malaysia is very high, recording a figure of 39.3 percent in 2002 (Malaysia Water

Association, 2004).

3. Literature Review

A comprehensive discussion of the use of DEA in the water sector can be found in

Thanassoulis (2000a and 2000b). In both papers, the author provides estimates of the

potential operating cost savings in the water sector. An important stage in the application

of DEA in the water sector is the choice of input and output variables. According to

Thanassoulis (2000a), the main inputs in the model of water distribution is the operating

expenditure while the list of potential outputs include the number of connections, the


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length of mains (pipes), total volume of water delivered to clients, total water delivered to

household, total water delivered to businesses and pipe bursts. Due to the strong

correlation between the potential outputs, the paper adopted the output variables of the

number of connections, the length of mains (pipes), total volume of water delivered to

clients. According to Thanassoulis (2000b), the rationale for the number of connections

and length of pipes as output variables is because the two variables capture the scale size

of the water network and hence are expected to influence the operating expenditure. The

same applies to total water delivered, as it is a measure of the work done by companies

conveying water and hence should also influence the expenditure as well.

In Thanassoulis (2000b), the operating expenditure is used as an input while variables

such as the number of supply connections, the length of the main (reflecting the

dispersion to clients) and the amount of water delivered as were used as outputs in the

modeling of water distribution. In addition, he also assumed constant returns to scale

since the issue the paper was dealing with the issue of governmental regulation. This is

because the scale size of the water companies is largely inherited and is likely to be

outside the managerial control in the short-run. Thanassoulis (2000a), argued that scale

size are likely to be dependent on contextual variables such as population served, the

dispersion of the population and so on, hence out of the control of the companies

observed.

In their study of the water industry in Australia, Coelli and Walding (2006), used the

variables of the number of connections and the volume of water delivered as output

variables while operating expenditures and the length of pipes (as a proxy for capital)


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served as inputs. They found that the average firm in the water industry had a technical

score of 90.4 percent. In addition, in the productivity analysis of the industry from

1995/96 to 2002/03, they used a few different models and found that the total factor

productivity (TFP) growth to be in the range of -1.7 percent to + 1.1percent.

However, Saal et al (2004), in their study of impact of privatization on the water and

sewerage industry, reported that the water and sewerage companies (WASCs) were

characterized by diseconomies of scale. They found that diseconomies of scale had

consistently influenced productivity growth rates adversely both before and after

privatization. Over the period of study (1985 2000), the impact was found to have

caused a 0.37 percent reduction in the average annual productivity growth rates. The

study also went on to report that technical change was significantly higher after

privatization although its methodology could not proved whether the source of

improvement was due to the improved incentives from privatization or the new

environmental regulation.

In its 1994 periodic price review, the Office of Water Services (OFWAT) used both

regression analysis and DEA to analyse the comparative efficiency in the water industry.

Their model used data like the amount of water delivered; the length of mains; the

proportion of water delivered to measured non-households; connections density captured

by the number of properties per km of mains; the number of mains burst per km of mains;

the ratio of peak to average flow; the proportion of distribution derived from surface

sources; the proportion of water treated in larger works and many others. The results

were then used in the process of price determination.


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Other works in the water industry using DEA includes Tupper and Resende (2004) in the

case of the Brazilian water and sewerage system; Anwandter and Ozuna (2002) on the

public water sector reforms in Mexico and Saal and Parker (2001) in their study of the

productivity and price performance for the privatized sewerage and water companies in

England and Wales.

4. Data and Methodology

4.1 Data

Data for the study is obtained from theMalaysia Water Industry Guide published by the

Malaysian Water Association in collaboration with the Water Supply Department (under

the Ministry of Energy, Water and Communications). The data covers both financial and

non-financial variables at the state/district level for the period between 1999-2005.

However, data for the state of Sarawak is divided into four categories - Kuching, Sibu,

LAKU and a fourth category representing all other districts. Data for the state of Selangor

includes the Federal Territory of Kuala Lumpur.

The data set used in this study includes the following variables:-

CONS: total consumption3 (m3)

CONN: number of connections

LENG: length of mains (km) OPEX: operating expenditure (Ringgit Malaysia, RM)

3 A proxy for total water delivered.


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Thannasoulis (2000a and 2000b), modeled the water distribution by using operating

expenditure as the input while the number of supply connections, the length of the main

(reflecting the dispersion to clients) and the amount of water delivered were treated as

outputs. The inputs should reflect the resources used and the outputs, the volume and

quality of activities encapsulated in the function being modeled. Since the function

concerns the pumping of water essentially, the key assets used are pumps, manpower and

the distribution main (Thanassoulis, 2000a).

In this respect, following the same approach, this paper will use the variables of total

expenditure (OPEX) as an input variable while the outputs in our distribution model are

the total consumption (CONS), the number of connections (CONN) and the length of the

main pipes (LENG).

4.2 Data Envelopment Analysis

Data Envelopment Analysis (DEA) is an analytical tool that can assist in the

identification of best practices in the use of resources among a group of organizations. It

is a mathematical programming production frontier approach and relative efficiency is

measured in relation to the constructed frontier. The frontier is constructed using a

piecewise linear combination that connects the set of best practice observations in the

sample, yielding a convex efficient frontier.

The term Data Envelopment Analysis was first used in 1978 by Charnes, Cooper and

Rhodes. Their approach applied the efficiency concept outlined by Farrell. Farrell (1957)

decomposed efficiency of a firm into its technical and price (allocative) efficiency


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where is a scalar, is a Nx1 vector of constants while X and Y are the output and

input vectors respectively. The value of will be the efficiency score for the i-th firm. It

will satisfy less than or equal to 1, with a value of 1 indicating a point on the frontier

and hence a technically efficient DMU. To account for VRS, the same equation can be

modified with the convexity constraint: N1= 1, where N1 is a Nx1 vector of ones. Such

convexity constraint ensures that an inefficient unit is only benchmarked against

similar size peers.

The CRS linear programming problem, under the output orientation, can be defined as:

max , ,

st -yi + Y 0,

xi - X 0,

N1= 1.

0 (2)

where is a scalar, is a Nx1 vector of constants while X and Y are the output and

input vectors respectively. This approach forms a convex hull of intersecting planes

which envelop the data points more tightly than the CRS conical hull and thus provides

technical efficiency score which are greater than or equal to those of the CRS model. A

scale efficiency score (SE) can be derived (for each firm) by dividing the CRS technical

efficiency score by the VRS technical efficiency score. This score also takes a value

between 0 and 1 inclusive.


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4.3 Productivity measures under DEA

For the productivity analysis, Fare et al (1994) have shown that the DEA methodology

can be used to obtain estimates of Malmquist total factor productivity (TFP) index

numbers. Essentially, The Malmquist Index approach is a chained index approach, which

measures changes in productivity relative to a base year. Changes in productivity can be

decomposed into components due to changes in technical efficiency (catching up) and

movements due to changes in technology (technological change). Meanwhile, changes in

a firms technical efficiency can be decomposed into change due to pure technical

efficiency change (managerial efficiency) and changes due to scale efficiency (plant size

optimality). The Malmquist DEA approach derives an efficiency measure for one year

relative to the prior year, while allowing the best frontier to shift.

The Malmquist TFP index measures the TFP change between two data points by

calculating the ratio of the distances of each data point relative to a common technology.

If the period t technology is used as the reference technology, the Malmquist (input-

oriented) TFP change index between period s (base period) and period t can be written as

( )),(

),(,,,

0

00

ss

t

tt

t

ttss

t

xyd

xydxyxym = (3)

Alternatively, if the period s reference technology is used, it is defined as

( )),(

),(,,,

0

00

sss

tt

s

ttss

s

xyd

xydxyxym =

(4)

Note that in the above equations the notation ),(0 tts

yxd represents the distance from the

period t observation to the period s technology. When t =s, this distance is equivalent to

the technical efficiency scores defined earlier. A value ofm0 greater than 1 indicates TFP


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growth from the period s to period twhile a value of less than 1 will otherwise, indicate a

TFP decline.

Fare et al. (1994) specifies an output based Malmquist productivity change index:

( )( )

( )

( )

( )

2

1

0

0

0

00

,

,

,

,,,,

=

ss

t

tt

t

ss

s

tt

s

ttssxyd

xyd

xyd

xydxysxym (5)

An equivalent way of writing this would be

( )( )

( )

( )

( )

( )

( )

2

1

0

0

0

0

0

00

,

,

,

,

,

,,,,

=

ss

t

ss

t

tt

t

tt

s

ss

s

tt

t

ttssxyd

xyd

xyd

xyd

xyd

xydxyxym (6)

The above equation (technical efficiency change) can be broken into two parts, namely

the efficiency change component and the technical change component.

Efficiency change =( )

( )ss

s

tt

t

xyd

xyd

,

,

0

0

(7)

and

Technical change =( )

( )

( )( )

2

1

0

0

0

0

,

,

,

,

ss

t

ss

s

tt

t

tt

s

xyd

xyd

xyd

xyd (8)

(Note: The efficiency change (7) can be decomposed into scale efficiency and pure

technical efficiency components. In addition, technical change above in equation (8) also

refers to technological change)

The four distance measures in equation (4.3)are calculated by solving four DEA-like

linear programming problems. The required LPs are:


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td0 (yt,xt ) = min , ,

st -y0t + Yt 0,

x0t - Xt 0,

0 (9)

sd0 (ys , xs) = min , ,

st -y0s + Ys 0,

x0s - Xs 0,

0 (10)

td0 (ys , xs,) = min , ,

st -y0s + Yt 0,

x0s - Xt

0,

0 (11)

sd0 ( yt , xt ) = min , ,

st -y0t + Ys 0,

x0t - Xs 0,

0 (12)

These four LPs must be solved for each firm in the sample.


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4.4 Other Methodological Issues

4.4.1 DEA-related Specification

From a regulation point of view, the use of constant returns to scale in the model

appeared to be reasonable. According to Thanassoulis (2000b), given that the regulators

duty is to safeguard public interest, assessments should be made under constant returns to

scale irrespective of the nature of the scale returns characterizing efficient production.

Essentially, given the nature of the industry, the companies have no control over their

size other than through mergers and acquisitions (which incidentally, subject to

regulatory approval anyway). In any event, assets have long lives and so, management (at

least in the short run) cannot change their scale of operation to exploit returns to scale.

Hence in the short-run at least, companies cannot move to a most productive scale size

(see Banker, 1984) and should be instead, assessed based on their current scale size.

Efficiency conditions can be analyzed through an input-orientated or an output-orientated

approach. In the case of the former, outputs are maximized given the amount of inputs

while in the latter; inputs are minimized given the level of outputs. The DEA approach

selected in this paper is an input-orientated approach. According to Coelli (1996), one

should select an orientation according to which quantities (inputs or outputs) the

managers have most control over. Since in this paper, we have use operating expenditures

as input while total length of pipes, total connections and total consumption of water as

outputs, we believe that the managers should have most control over the operating

expenditures. The length of pipes or the number of connections or the total consumption

is harder to change, especially in the shorter run period as opposed to operating

expenditures. As such, the approach is an input-orientated one.


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Erbetta and Cave (2006) in their study on the regulations and efficiency incentives in the

water and sewerage industry in England and Wales also adopted an input-orientated

approach. According to them, the adoption of an input-orientated approach should be

preferred in the case of water public utilities since the demand level the suppliers face

could be seen as exogenous.

4.4.2 Regression Analysis (RA)

The use of DEA with RA in the water sector has been widely employed in many research

work, most notably Ofwats comparative efficiency exercise for the water industry in

England and Wales. Others who have followed a similar take include Cubbin and

Tzanidakis (1998) and Kirkpatrick at el (2004, 2006). The former opted for a

comparative assessment of both methods while the latter pursued a Tobit regression on

the DEA scores on various explanatory factors in order to uncover other possible

explanatory factors for efficient performance. Our approach is more akin to Kirkpatrick

et als. The DEA efficiency scores are submitted to a regression (both Tobit and OLS) on

a set of explanatory factors (deemed crucial in the performance of the water entities).

5. Efficiency in the Malaysian Water Sector5.1 Technical Efficiency

Using the DEA, this study finds that the mean technical efficiency over the period

between the periods of 1999 to 2005 to be about 66%. This implies that there is still a

significant room for improvements in the technical efficiency of the Malaysian water

sector. The average technical efficiency ranged between 56.9% and 72.2% (see Table 2).


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[Insert Table 2]

Over the period 1999-2005, the pattern of those achieving full efficiency and their

ownership status did not reveal a consistent association although several of the privatized

entities managed to be on the efficient frontier for most of the seven-year period. As far

as technical efficiencies are concerned, 1999 saw Kelantan and P.Pinang recording 100%

while they were joined by N.Sembilan the following year as the group which was

operating on the efficient frontier. 2001 saw Terengganu and Kelantan recording 100%

while they were joined by P.Pinang, Perak and Sarawak in the following year as the

group which was operating on the efficient frontier. Meanwhile, 2003 also had five

technically efficient entities although P.Pinang was replaced by Labuan in the quintet.

The remaining two years of 2004 and 2005 had lesser efficient entities with both years

having three and four each respectively. Over the seven-year period, Kelantan, P.Pinang

and Terengganu appeared the most on the efficient frontier with Kelantan being efficient

in every year while the other two had 100% technical efficiency scores in four out of the

7 years (refer to Table 3).

A comparison over the 7-year period (2001-2005) saw Kelantan having the highest mean

technical scores (100%). It also has consistently been the most efficient unit over the 7-

year period. The other units ranked high in the mean technical scores comparison are

P.Pinang in second place (with 97.7%) while Terengganu came in third place with 95.2%

(refer to Table 3). Overall, the mean technical efficiency for the period was 66% thus

indicating that on average over the 7 year period, these units inputs could have been

reduced by 34% while still achieving the same levels of outputs. Managerial efficiency


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efficient benchmarking group is P.Pinang7, which has shown high standards in efficiency

over the 7 year period.

Table 5 shows the water units that form the efficient frontier over the 7-year period. The

units which are on the frontier will be referenced as a peer in the peer count. Kelantan,

P.Pinang and Terengganu are consistently referenced as a peer over the 7-year period.

These units have consistently formed the efficient frontiers over the years in the study.

This means that these units have the best practices which should be benchmarked by the

other state water utilities in order to catch up to the efficient frontier. Terengganu is the

unit which have been referenced the most times over the 7 year period (total peer count of

48 times added up over the 7-year period) while both Kelantan and P.Pinang were also

consistently defining the efficient frontier. In terms of mean technical scores, Kelantan

and Penang occupied the top two positions although in terms of being referenced, the duo

is behind Terengganu, recording 39 and 32 times respectively over the 7year period of

study compared to Terengganus 48 times.

[Insert Table 5]

5.2 Total Factor Productivity

Essentially, the total factor productivity (TFP) is a multiplicative compound of technical

efficiency8 change and technical (technological) change. In this respect, the major cause

of productivity improvements can be ascertained by comparing the values of efficiency

7 P.Pinang is also one of the states (together with Johor) which consistently tops in profitability. Its non-revenue water figures and domestic water tariff were also consistently the lowest in the country. SeeSantiago (2005) for a note of the success of the P.Pinang water utility.8 Technical efficiency is multiplicative compound of pure technical efficiency and scale efficiency.


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change and technological change indices. Put differently, the productivity improvements

described can be the result of efficiency gains (loss), technological progress (retardation9)

or both. Table 6 shows the unweighted TFP changes. All indices are means and the TFPs

are calculated with reference to the previous period's technology.

[Insert Table 6]

From the Malmquist indices, the water utility with the highest total factor productivity

(TFP) growth is surprisingly, Labuan. This comes from a 12.5% increase in technical

efficiency change over the years 1999-2005 which were significantly due to its

improvement in scale efficiency rather than managerial efficiency. On the other hand,

Terengganu was the most poorly performed entity in terms of TFP, partly due to the fact

that it was already operating on the efficient frontier for many years and also it had a

significant fall in the seventh year which could have led to a much lower mean technical

efficiency change. Overall, the industrys mean of TFP showed a decline of 2.9% over

the period of 1999-2005, the main reason being the fall in technological change (-7.1%)

which was greater than the rise in technical efficiency change (+4.6%).

This evidence suggests that the over the 7 year period from 1999 to 2005, technology in

the industry as a whole, regressed by an average of 7.1% although technical efficiency

improved overall for the period by 4.6% (refer to Table 7). As far as technical efficiency

change, there were improvements in both managerial (pure technical) and scale

9 Retrogress in technology in this context could mean any developments that could affect water distributionor production in an adeverse way, e.g. burst pipes that lead to increase in rhe non-revenue water and so on.


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efficiency. The former reflects that management caliber, processes and skills have overall

actually improved by 2.9% while scale efficiency too has also improved (+1.7%)

suggesting better operating size among the water utility plants. However, technological

change registered retardation (regress) over the period, with a fall of 7.1%. A possibility

of this reversal in technology could be due to any adverse developments that could have

hampered the operations of the water distribution(or output being adversely affected due

to the technology and equipment used) for instance; increase in broken pipes, aging pipes

or increased water pilferaging that may have led to greater levels of non-revenue water

(NRW) for the utilities. It is also interesting to note that in terms of technological change,

none of the water utilities had recorded any significant improvements in terms of

utilization of better technologies to become more efficient. From Table 6, the results

showed that none of the state water utilities actually had scores of over 1.000 in their

mean technical (technological) change indices.

[Insert Table 7]

Overall, the slight drop in total factor productivity suggests that the overall labour and

capital productivity had slightly regressed. As such, a more concerted effort by all parties

in the industry especially the government is warranted to reverse the slide. In this respect,

the Malaysian government or the privatized entities may need to increase their

investments to expand their coverage (e.g. expand the length of pipes or the number of

connections as seen in the output slacks)and further renew the water infrastructure.


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6. Tariff, Financial Performance and Ownership: A qualitative analysis

Overall, we noticed that the results from the analysis seemed to suggest that the efficient

units are linked with lower tariff rates. Kelantan, P.Pinang and Terengganu which were

the top three-ranked state water utilities in our DEA analysis had also among the lowest

water tariffs10

. In fact, P.Pinang has one of the lowest water tariffs in the country and the

world11 while also recording the highest profits among all water providers in the country.

Terengganu meanwhile, came in second in terms the water rates ranking in the country.

Coincidentally, the lowly-ranked utilities seemed to record the highest rates, for instance,

Labuan, Sabah, Johor and Selangor which had poor mean DEA scores were all at the

lower end of the rankings (refer to Table 8). However, the issue of water tariff is complex

in Malaysia hence any suggestions of it linking to the issue of efficiency may be

ambiguous at best. For instance, many states use an increasing block tariff structure

for industrial and commercial water tariffs12 while quite a few others (Melaka,

Terengganu, Perlis, Kelantan and Sabah) opt for flat rate tariffs for industrial and

commercial users.

[Insert Table 8]

In any event, in terms of average price of water13, we compared the 17 utilities over the

seven year period and while there was no exact pattern, those with lower DEA scores

10 Referring to private users rates only for the year 2004.11 A 1999 comparative study of water charges in 65 cities and towns in 38 countries in Asia, Europe, Africaand the United States indicated that P.Pinang had the lowest tariff rates (Santiago, 2005).12 Such block structures are however, not very steep i.e. the block increments are relatively small. Inany event, the National Water Services Commission regulates the water industry includinglicensing, tariffs and the capital expenditure of these water utilities.13 Total revenue divided by total consumption.


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24

tended to be on the higher side. For instance, Johor, Sabah and Selangor (which were

ranked 13th

, 16th

and 17th

respectively in the mean DEA scores rankings) had the highest

average price levels (refer to Table 9). These states were also among the ones with the

highest water tariffs, incidentally. Overall, in the seven-year period, the average price in

the industry recorded was 0.958269 sen per cubic meter, a low price if compared to many

industrialized countries in the world14.

[Insert Table 9]

Meanwhile, as far as the profitability analysis on the respective units efficiency scores

is concerned, the pattern that was found was rather vague, at best. Over the seven year

period, although profitability was poor for some of the poorly ranked entities like Sabah

and Selangor (ranked the bottom two), Johor (ranked 13 th in DEA scores) tops the

ranking nonetheless with an almost 96 million ringgit profit. However, those with higher

DEA scores were also found to be generally profitable as well, as efficient units like

Penang, Terengganu, Kelantan all had very good financial returns, with Penang being the

second highest earner in the country behind Johor. Overall in the seven-year period, the

Malaysian water sector experiences a revenue-cost deficit of about RM13.5339

million. Six of the states had financial deficit over this period (see Table 9). Of

these, states with large deficits were Selangor (-RM412 million) and Sabah (79.11

million) which were incidentally, also the two lowest-ranked states in our DEA

14 A recent 2007 survey of 14 countries indicated that the average municipal water prices ranged from 66cents (or about RM2.31 [assuming at an exchange of RM3.50 to US$1] ) in the US to up to $2.25 inDenmark and Germany. In fact, over the past 5 years, municipal water rates have increased by an averageof 27% in the US, 32% in the UK, 45% in Australia, 50% in South Africa and 58% in Canada (Clark,2007). In Malaysia, the average price of water increased by 22% from 2001 to 2005.


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25

analysis. However, there is no distinct pattern when analysing the profitability data

with ownership status15

.

Finally, when matched the results of the DEA analysis with the institutional setting

(ownership) in various states, no distinct pattern was again, found regarding the issue of

efficiency and privatization over the period of study. However, it is interesting to note

that all three best performers according to the DEA results (i.e. P.Pinang, Terengganu and

Kelantan) are privatized (or corporatized) entities. Nonetheless, Selangor and Johor (both

privatized) did not adhere to the trend. In any event, a regression was performed using

ownership as an independent dummy variable (and a few others) with DEA as dependent

variable and the results revealed in the next section.

6.1.2 Privatization and Policies: A qualitative analysis

The argument of privatization is that the move can reduce the governments burden and

free up some of the budget for other areas in greater need. This argument is substantiated

to a certain extent as seen in the many state utilities losses over the years. From 1999-

2005, six water utilities have recorded losses on average with five of them being public-

owned utilities. Only Selangor, a privately owned entity recorded losses although its loss

was the biggest among the pool.

In any event, transforming water from a common good to a private good that is subjected

to the market mechanism has been debated and criticized by many in the name of basic

15 A simple OLS regression using profitability as the dependent variable and a dummy variable forownership (2005 data) indicated that ownership status is not a significant variable for profitability. Lee(2007) also previously found the same results using panel regression on the Malaysian data from 1999 to2003.


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26

human rights. With privatization, decisions relating to water provisioning or access to

water will be based on profit considerations and not public health, social equity and

environmental sustainability. Specifically, with water privatization, the move involves

organizing the water supply system around the rules of the market which is likely to be

based on profit generations, stock price values of firms or other kinds of investment

options (Santiago, 2005).

Having said that, part of the profitability problem of the water entities could be traced

back to the fact that their prices had not been set to full cost recovery levels. Privatization

is likely to mitigate this as the consumer prices will then reflect the levels that are

expected to meet the full operating and maintenance cost of water facilities and services.

Incidentally, the two highest average price of water from 1999 to 2005 were from Johor

and Selangor, both privatized entities.

6. Regression

Although the number of firms16 were rather limited, we nonetheless proceeded with a

regression analysis in a bid to provide a more rigorous assessment of the matter. The

qualitative analysis in the previous section complements this part appropriately. In any

event, we regressed the DEA efficiency scores on a host of independent variables which

we felt that would affect the water utilities performance. The independent variables

include the economic conditions (state GDP per capita), the non-revenue water

percentage and a dummy variable to capture the effects of ownership status. As data on

16 Although there were 17 water entities in the DEA analysis, we had to restrict the analysis in this sectionto 13 due to data limitations.


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27

the state GDP were only available in five-year intervals, we performed two Tobit17

regressions; one for 2000 and another for 200518

. The results are as follows:-

2000; DEAS = 1.1567 1.09E-11GDP 0.0084NRW + 0.2514OWNERSHIP (13)

2005; DEAS = 1.3380 1.13E-11GDP 0.0106NRW + 0.0670OWNERSHIP (14)

with DEAS = DEA scores, GDP = GDP per capita for the respective

states, NRW = non-revenue water, OWNERSHIP = ownership dummy

variable (1 denoting private entity and 0 otherwise).

The coefficient of GDP and non-revenue water exhibited the expected signs (refer to

Table 10 and 11) for both the selected years. Both have negative coefficient thus

indicating that income per capita and percentage of non-revenue water were adversely

affecting the state utilities performances. Both GDP per capita and non-revenue water

were significant in both the years of 2000 and 200519. Higher GDP leading to lower

efficiency scores may be rationalized along the argument that greater amount of

economic activity demands greater need for water thus putting greater pressure on the

operational and distributional process, not to mention the higher likelihood of incidences

of water theft and pilferages that can easily lead to higher non-revenue water levels. In

any event, non-revenue water was also a negative factor, another expected result as

greater occurrence of such problems are likely to hamper the firms performance in terms

17 The Tobit regression is used by many researches in this area as the dependent variables value isrestricted between 0 and 1. In this respect, for estimation, maximum likelihood technique is preferred.18 Unreported OLS regressions were also performed with R2 of 0.620 and 0.577 for 2000 and 2005respectively.19 Note that for non-revenue water, it was only significant at 10% for the year 2000. The rest of the caseswere all at 5% level of significance.


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28

of total output and total expenditure incurred thus causing much technical inefficiencies.

However, ownership had positive coefficient in both years thus indicating improvements

in performance from privatization although it was insignificant in both years.20

[Insert Table 10 and 11]

Overall, the results suggest that the issues of non-revenue water variable and level of

economic activity may play a significant role in the efficiency in the water utilities. In the

case of the non-revenue water, thefts, pilferage and so on has been a serious issue for

many years and these water utilities and the authorities must work together to curb this

problem. The negative correlation between GDP per capita and efficiency is also

strengthened by the fact that we also found significant results when we use GDP growth

rate21 instead of per capita figures.

Nonetheless, the positive coefficient in the case of ownership indicates efficiency gains in

the face of privatization although this was not substantiated in the test of significance

hence the evidence offered here may not be overwhelming. In addition, the brief

qualitative analysis in the earlier part of section 6 also did not seem to offer substantial

arguments in favour of privatization. Nonetheless, an OLS regression involving all the 17

state water utilities was performed linking only the DEA efficiency scores to ownership

status over the period of 1999-2005 (see Table 12). The results surprisingly, found the

dummy ownership coefficient significant (at 5% level of significance) and had a positive

20 At 5% significance level.21 Unreported OLS and Tobit regression using mean values (2001-2005) of GDP growth, age of meter,ownership and non-revenue water also found significance in the GDP growth coefficient.


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29

value indicating efficiency superiority in the case of the private entities. The results could

be partly a reflection of the performances of Terengganu, Kelantan and Penang (all three

being privatized entities) which has consistently performed at the frontier level over the

seven year period.

[Insert Table 12]

7. Conclusion

The performance of the water sector has been a key developmental issue in Malaysia.

Under the countrys Federal Constitution, water is a state matter, resulting in a very

diverse set of water institutions. Some states have opted to privatize their water sector

while water provision remains entirely in the public sector in other states. More recently,

a number of changes have been undertaken in the water sector. This includes the

establishment of a centralized federal regulator. A key issue in future regulatory

initiative in the countrys water sector is likely to include the measurement and

benchmarking of performance across the different states. Such an attempt is likely to

rekindle the public-private debate on the optimal institution for water provision. In light

of the importance of performance benchmarking in the Malaysia water sector, this study

investigates the efficiency in the water sector across the different states in Malaysia using

the DEA approach, the Malmquist productivity index and regression analysis as well.

Overall, based on the DEA approach, the mean technical efficiency of the water sector in

Malaysia is about 66% (in terms of percentage distance from the efficient frontier) during

the period from 1999 to 2005. This implies that there is significant room for


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30

improvements in technical efficiency in the Malaysian water sector. There are also

significant differences in efficiency in the water sector across the different states. The

findings in the paper also showed that the state water utilities of Kelantan, P.Pinang and

Terengganu were the most efficient according to our DEA model. Kelantan had the

highest mean technical efficiency (under constant returns to scale assumption) score over

the seven-year duration while P.Pinang and Terengganu came in second and third

respectively. Meanwhile, the most inefficient states were Labuan, Sabah and Selangor,

with Sabah being the worst.

The Malmquist productivity index computed indicated that the overall level of

productivity in the Malaysian water sector declined by 2.9% during the period from 1999

to 2005. During this period, deterioration in technological change offset improvements

in technical efficiency (both managerial and scale). Thus, even though there were

improvements in scale efficiencies (due to better operational size) and managerial

efficiency (operational management), many of the state water utilities were not able to

make use of current technology available to enhance their performance. This could be

due to non-revenue water in the form of leakages (old pipes and meters), water pilferages,

under-meter registration and so on.

The study also provided some preliminary analysis of the relationship between efficiency

(as measured by DEA technical scores) and other factor such as tariff levels, financial

performance and ownership (privatization). Qualitative analysis and some basic

comparative exercise from the study seemed to suggest that the efficient water provision

is likely to be linked to lower tariff rates and higher profitability. However, ownership


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31

does not seem to be linked to better profitability or lower prices. In any event, a Tobit

regression was also employed to provide a further test of the relationship between

efficiency and some suspected explanatory variables. The results indicated significant

relationships between the state income and non-revenue phenomena to the utilities

technical efficiencies. However, the link between ownership and efficiency gains could

not be ascertained solely based on the evidences presented in this paper. Nonetheless, it is

interesting to note the positive co-efficiency in the case of ownerships status in the

regressions, indicating efficiency improvements in the case of privatized ownerships.

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Appendices

Table 1: Water Supply Institutions in MalaysiaPublic Works Department

Kedah (1) Production and distribution by PWD(2) Privatized production and distribution by

Taliworks Consortium at Langkawi Island(3) Privatization of 5 treatment plants Syarikat

Air Utara

Sarawak (1) Production and distribution by PWD(2) Privatized production and distribution for Miri,

Bintulu and Limbang LAKU ManagementSdn Bhd

Labuan (1) Distribution by PWD(2) Management contract of production by Encorp

Utility Sdn Bhd

Perlis Production and distribution by PWD.

Water Supply Dept

Pahang Production and distribution by PWD

N.Sembilan (1) Distribution by WSD

(2) Privatization of 2 water treatment plants TaliworksConsortium, 10 years

Sabah (1) Distribution by WSD(2) Privatization of 3 water treatment plants Jetama SdnBhd, Timatch Sdn Bhd, Lahad Datu Water Supply 10year concessions

Water Supply Board

Perak (1) Distribution by WSD(2) Privatization of 3 water treatment plants 20 yearconcessions

Melaka (1) Production and distribution by WSB (Perbadanan AirMelaka)

Water Supply Co.

P.Pinang Privatized in 2000:

Production and distribution by PBA Holdings Berhad(state government share 55%)

Terengganu Corporatized in 1999Production and distribution by Syarikat Air TerengganuSdn Bhd (state government share 100%)

Selangor Privatized in 2002:(1) Monopoly distribution by Perbadanan Urus Air

Selangor (state government share 30%) since 2002(2) 7 water treatment plants (4 existing, 3 BOT)

operated by 5 firms with concession period 10 30years

Johor Privatized in 2001:Production and distribution by SAJ Holdings Sdn Bhd(state government share 0%), concessions period 30 years

Kelantan Privatized in 1996:Production and distribution by Air Kelantan Sdn Bhd(state government share 70%)

Source: MWA (2004)


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Table 2: Mean Efficiency Scores in the Malaysian Water Sector, 1999-2005

Year Technical Efficiency

1999 0.569

2000 0.643

2001 0.654

2002 0.7222003 0.689

2004 0.671

2005 0.714

Mean 0.666

Table 3: Yearly Technical Efficiency Scores in the Malaysian Water Sector,

1999 - 2005

CRSTE Efficiency ScoreState

1999 2000 2001 2002 2003 2004 2005 Mean

Kedah 0.657 0.642 0.803 0.816 0.845 0.848 1.000 0.801571Sarawak 0.493 0.629 0.779 1.000 1.000 0.999 1.000 0.842857

Labuan 0.148 0.169 0.235 0.298 1.000 0.273 0.359 0.354571

Perlis 0.715 0.962 0.977 0.971 0.268 1.000 0.946 0.834143

Pahang 0.615 0.816 0.837 0.853 0.804 0.806 0.723 0.779143

N.Sembilan 0.658 1.000 0.829 0.912 0.519 0.512 0.779 0.744143

Sabah 0.176 0.238 0.247 0.228 0.332 0.366 0.276 0.266143

Perak 0.570 0.540 0.631 1.000 1.000 0.762 0.903 0.772286

Melaka 0.502 0.486 0.601 0.595 0.638 0.444 0.536 0.543143

Kuching 0.508 0.478 0.533 0.609 0.563 0.622 0.724 0.576714

Sibu 0.457 0.428 0.444 0.488 0.455 0.496 0.602 0.481429

P.Pinang 1.000 1.000 0.933 1.000 0.932 0.979 1.000 0.977714

Terengganu 0.951 0.933 1.000 1.000 1.000 1.000 0.784 0.952571

Selangor 0.363 0.407 0.196 0.260 0.199 0.217 0.300 0.277429

Johor 0.307 0.593 0.588 0.658 0.592 0.462 0.491 0.527286

Kelantan 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1

LAKU22

0.555 0.609 0.489 0.584 0.572 0.618 0.706 0.590429

22 LAKU Management Sdn Bhd was incorporated in 1995.


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Table 4: Yearly Efficiency Ranking in the Malaysian Water Sector, 1999-2005

State CRSTE efficiency ranking

1999 2000 2001 2002 2003 2004 2005 1999 2005

Kedah 5 8 7 9 7 6 1 6

Sarawak 12 7 8 3 3 4 1 4Labuan 17 17 15 15 4 16 15 15

Perlis 4 4 3 6 16 1 5 5

Pahang 7 6 5 8 8 7 10 7

N.Sembilan 6 1 6 7 13 11 8 9

Sabah 16 16 16 17 15 15 17 17

Perak 8 11 9 2 2 8 6 8

Melaka 11 12 10 12 9 14 13 12

Kuching 10 13 12 11 12 9 9 11

Sibu 13 14 14 14 14 12 12 14

P.Pinang 2 2 4 4 6 5 1 2

Terengganu 3 5 1 1 1 1 7 3Selangor 14 15 17 16 17 17 16 16

Johor 15 10 11 10 10 13 14 13

Kelantan 1 3 1 5 5 1 1 1

LAKU 9 9 13 13 11 10 11 10(Note: in the event of more than 1 firm having 1.000 scores, units with the most referenced times are

ranked higher)

Table 5: Summary of Peer Count in the Malaysian Water Sector, 1999-2005Peer Count (i.e. number of times chosen as peer to inefficient units)

1999 2000 2001 2002 2003 2004 2005 Total

Kedah 0 0 0 0 00 7 7Sarawak 0 0 0 4 3 0 1 7

Labuan 0 0 0 0 2 0 0 2Perlis 0 0 0 0 0 6 0 6

Pahang 0 0 0 0 0 0 0 0N.Sembilan 0 12 0 0 0 0 0 12

Sabah 0 0 0 0 0 0 0 0Perak 0 0 0 7 4 0 0 11

Melaka 0 0 0 0 0 0 0 0Kuching 0 0 0 0 0 0 0 0

Sibu 0 0 0 0 0 0 0 0

P.Pinang 13 8 0 3 0 0 8 32Terengganu 0 0 15 9 11 13 0 48

Selangor 0 0 0 0 0 0 0 0Johor 0 0 0 0 0 0 0 0

Kelantan 15 5 4 2 0 5 8 39LAKU 0 0 0 0 0 0 0 0


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Table 6: Individual State Water Companies Malmquist Index, 1999-2005

State

Water

Utilities

Technical

Efficiency

Change

Technological

Change

Pure

Technical

Efficiency

Change

Scale

Efficiency

Change

Total

Factor

Productivity

(TFP)

Ranking

of Mean

TFP

Kedah 1.072 0.941 1.000 1.072 1.009 3Sarawak 1.125 0.915 1.093 1.029 1.030 2

Labuan 1.159 0.934 1.348 0.860 1.082 1

Perlis 1.048 0.914 1.000 1.048 0.957 10

Pahang 1.028 0.913 1.000 1.028 0.939 14

N.Sembilan 1.029 0.927 0.978 1.052 0.954 11

Sabah 1.078 0.912 1.050 1.027 0.983 8

Perak 1.080 0.921 1.000 1.080 0.995 5

Melaka 1.011 0.941 0.976 1.036 0.951 12

Kuching 1.061 0.943 1.061 1.000 1.001 4

Sibu 1.047 0.931 1.031 1.016 0.975 9

P.Pinang 1.000 0.946 1.000 1.000 0.946 13Terengganu 0.968 0.930 0.961 1.007 0.901 17

Selangor 0.969 0.949 1.000 0.969 0.919 15

Johor 1.081 0.920 1.011 1.070 0.995 5

Kelantan 1.000 0.910 1.000 1.000 0.910 16

LAKU 1.041 0.943 1.032 1.008 0.982 7

Table 7: Annual Means of Malmquist Index, 2000 - 2005

Technical

Efficiency

Change

Technological

change

(Technical

change)

Pure

technical

efficiency

Scale

efficiency

Total

Factor

Productivity

change2000 1.144 0.921 1.011 1.132 1.054

2001 1.013 0.917 1.026 0.897 0.929

2002 1.115 0.897 1.054 1.058 1

2003 0.952 1.033 0.974 0.978 0.984

2004 0.978 0.936 0.995 0.983 0.915

2005 1.087 0.876 1.119 0.972 0.952

Mean 1.046 0.929 1.029 1.017 0.971


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Table 8: Domestic and Industry Water Rates, 2004

State Tariff Rates

(Domestic)

Ranking

(ascending

order)

Tariff Rates

(Industry)

Ranking

Kelantan 0.31 1 0.70 1

Pulau Pinang 0.31 1 0.94 4Terengganu 0.52 3 1.15 10

Kedah 0.53 4 1.20 12

Sarawak 0.56 5 1.19 11

Perlis 0.57 6 1.30 14

Pahang 0.57 6 1.40 15

Melaka 0.59 8 1.40 15

Bintulu 0.61 9 1.21 13

Kuching 0.62 10 1.06 5

Sibu 0.62 10 1.06 5

Sri Aman 0.62 10 1.06 5

Limbang 0.62 10 1.06 5Sarikei 0.62 10 1.06 5

Perak 0.67 15 1.45 17

N.Sembilan 0.68 16 1.59 18

Selangor 0.72 17 1.91 19

Labuan 0.90 18 0.90 2

Sabah 0.90 18 0.90 2

Johor 0.90 18 2.93 20

Mean - -Source : MWA (2006)


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Table 9: Mean profitability and average price (1999-2005)

State Average Price Average

profitability

Kelantan 0.925714 10238763

LAKU 0.967143 8044218

Pulau Pinang 0.707143 51542618Terengganu 0.831429 26160189

Kedah 0.804286 31025326

Sarawak 0.53 -10748418

Perlis 0.687143 -1563648

Pahang 0.795714 -21948668

Melaka 1.127143 19808744

Bintulu N/A N/A

Kuching 0.902857 6973674

Sibu 1.042857 623549.6

Sri Aman N/A N/A

Limbang N/A N/ASarikei N/A N/A

Perak 0.952857 31853339

N.Sembilan 1.005714 22118889

Selangor 1.254286 -4.12E+08

Labuan 1.017143 -8930064

Sabah 1.251429 -79112887

Johor 1.392857 96060341

Mean 0.952689 -13533900Source: MWA

Table 10: Tobit Regression (for year 2000 data)Variable Coefficient Std Error z-statistic Prob.

CGDPNRW

OWNERSHIP

1.1567-1.09E-11-0.00840.2514

0.21475.40E-120.00460.1588

5.3877-2.0150-1.79501.5827

0.00000.04390.07190.1135

Log likelihood 4.3524

Table 11: Tobit Regression (for year 2005 data)

Variable Coefficient Std Error z-statistic Prob.

CGDPNRW

OWNERSHIP

1.338-1.13E-11-0.01060.0670

0.20194.13E-120.00460.1321

6.6268-2.7340-2.31310.5071

0.00000.00630.02070.6121

Log likelihood 4.4514


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Table 12: OLS Regression (Ownership and Efficiency 1999- 2005)

Variable Coefficient Std Error t-statistic Prob.

COWNERSHIP

0.62460.1369

0.02840.0517

21.9482.646

0.00000.0093

R2

0.056

water paper eaere 2009

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