Mahirah Kamaludin, Khalid Abdul Rahim
Institute Of Agricultural And Food Policy Studies, Putra Infoport, University Putra Malaysia mahirahkamaludin@gmail.com
Alias Radam
Department Of Management and Marketing, University Putra Malaysia
ABSTRACT
This study aims to estimate willingness to pay (WTP) of people in the state of
Kelantan, Malaysia for improvement in their domestic water services. Their water sector has been a long debate since their poor water services have threatened people’s daily routine such as water disruption, water leakage, smelly and dirty water supply. The water services should be managed effectively based on efficient water pricing since any improvement in services involve high cost in its operation. A
Contingent Valuation Method (CVM) was employed to 552 households in the state for analysis regarding to the services and Probit model was used to analyse the data obtained through the survey. The results show that bid price, household income and household size have statistically significant impact on WTP and they are as expected in earlier studies. The calculated mean WTP is RM0.60 applied on the first 35m 3 and it is much higher from current water price. The new water price can be recommended for any improvement and upgraded services to high standard in services in the future which is satisfying consumer’s needs.
Keywords: Contingent Valuation method, Probit model, willingness to pay, water services, Kelantan.
1. INTRODUCTION
High rapid population and urbanization has increased the demand for water and is expected to undergo substantial changes in the future. Water companies should manage the problem as soon as possible in order to satisfy consumer’s needs. But, low water prices can limit water companies’ ability to enhance their service quality and cover their capital costs. Water supply failing to meet demand is putting pressure on consumers.
Water supply problems also have clear impacts on social stability and economic productivity, thus solutions in dealing with water shortages are needed. It is important to know how much amount that households are willing to pay in order to help with the development and upgrading projects in water services. The World Bank Water Demand
Research Team (1993) points out that it is important to provide the consumers with services that what they are really wanted and how much they are willing to pay.
Problems in water services in Kelantan are a long standing issue due to its failure in supplying excellent water services to their
[45]

consumers. Currently, water service in the state is supplied by the sole provider, Air
Kelantan Sdn. Bhd. (AKSB). The water service in Kelantan was privatized in October 1995 and before this it was under the Water Supply
Division, Public Works Department. The situation of the water supply was very bad and it was not keeping pace with the demand, causing consumer dissatisfaction until the industry was privatized. The company operates in ten districts which are Kota Bharu,
Bachok, Pasir Puteh, Pasir Mas, Tanah Merah,
Jeli, Tumpat, Machang, Kuala Krai and Gua
Musang. Most of Kelantan’s population is concentrated in one-third of the state area with the availability of water in its alluvial subsurface terrain (Zamri, 2009). About 80% of the population lives in the northern region of the low alluvial plain and the remaining
20% live in the southern parts on higher ground. Kelantan’s water sources consist of
60% from surface water and 40% from groundwater (Zamri, 2009). Presently,
Kelantan is the largest groundwater operator in Malaysia (The Malaysian Water Association,
2011). Groundwater utilization is significant in less developed states such as Kelantan, and
Sabah and groundwater is only seen as a substitute for surface water in certain places
(Lee, 2007). Groundwater will be an essential source in meeting future water demands for the public supply.
The objectives of this study are to determine how much people in Kelantan are willing to pay (WTP) for improvement in the quality of domestic water services and what are possible determinants affecting their WTP. The organisation of this paper as follows, Section 1 and Section 2 introduces the background of the study, Section 3 explains the methodology and its application in the study, Section 4 demonstrates the results and discussions and finally Section 5 concludes the study.
2. BACKGROUND
Kelantan is one of the states in Malaysia, whose capital and royal seat is situated in Kota
Bharu. Kelantan is the most Northeastern state of Peninsular Malaysia and it is bordered by Narathiwat Province of Thailand to the north, Terengganu to the south-east, Perak to the west, and Pahang to the south. To the north-east of Kelantan is the South China Sea.
The state is flourishing with verdant paddy fields, rustic fishing villages and casuarinas lined beaches. Kelantan is an agrarian economy and the state is driven by the production of rubber, rice, and tobacco. The population of Kelantan was about 1.6 million in 2011 (Department of Statistic, 2012). The
9 th Malaysia Plan demonstrates that Kelantan is ranked at 13 th place in Malaysia based on development composite index by state and a large share of the population lives in rural areas. Kelantan is categorized as a state with high levels of poverty (4.8%) in Malaysia
(Economic Planning Unit, 2009).
A few states which are less developed in
Malaysia struggle to improve water coverage mostly in rural areas including Kelantan (Lee,
2011). The state demonstrates the lowest coverage of water supply for both urban
(57.9%) and rural (56.1%) areas by 2010
(Malaysian Water Association, 2011). Kelantan lacks financial sources in order to improve their water supply coverage to serve the public. Most water sources come from groundwater and groundwater utilization is most significantly in Kelantan (Malaysian
Water Association, 2011). However, the groundwater is faced with problems too, such as over extraction, subsidence and contamination. A statistic by the Malaysian
Water Association demonstrates water services complaints based on leakage, water quality, and water pressure, which shows that the rising complaint on water quality from
2009 to 2010. Most of people in Kelantan complaint about the quality of water supply which is not up to the standard, due to its
[46]

condition, smelly and coloured water supply.
A research by the Association of Water and
Energy Research Malaysia (2011) points out some cases in Kelantan with dirty and smelly water supply, low coverage performance and frequent water disruptions. Presently, water prices seem too cheap and it is unable to generate enough revenue to cover the full cost of capital investment, operation and maintenance. Kelantan ranked at 3 rd place in
2011 which demonstrates lowest water prices for domestic water services (RM0.55 for first
35m 3 ). If the price is too low, the water company is unable to maintain and sustain its operations and if the price of water is too cheap it leads to water wastage. Low water price and its sufficient supply makes consumers take water for granted in water consumption.
2.1 Consumer’s Willingness to Pay
WTP describes the maximum amount consumer’s are willing to give up for certain benefits in exchange for others, rather than without having it. WTP presents the value of a good to an individual as what they are willing to pay, sacrifice or exchange for it. Based on law of demand, the lower the price of a good, the larger the quantity consumers wish to pay
(Browning and Zupan, 2004). There are many previous studies that have existed about WTP for water services such as in Larson, Lew and
Onozaka (2001), Farolfi, Mabugu and
Ntshingila (2007), Vasquez et al. (2009) and
Bockstael, McConnell and Strand (1989).
Sarala Devi et al. (2009) argue that any water pricing system should consider factors such as
WTP ability and financial, affordability, socioeconomic, managerial, technical, and political factors.
3. METHODOLOGY
The consumer’s maximum willingness to pay for improvement in water services at Q 1 , while the status quo provision of water services is
Q 0 . When consumers are confronted with a question either to accept or reject the improvement program from Q 0 to Q 1 , it is important to ask consumers regarding their
WTP in order to get the proposed change.
New utility theoretical framework (Y 0 – WTP) shows how the respondents attain satisfaction when the income reduces by their willingness to pay. This study exhibits that consumer’s derived utility can be expressed as in Equation
1:
V (P
0
, Q
0
, Y
0
, Z
0
) V (P
1
, Q
1
, Y
0
- WTP, Z
1
)
(1)
The consumer’s WTP for water service improvement is a function of these variables
(provision of water services, income and other consumer’s characteristics). Equation 2 demonstrates how the changes in income affecting the WTP. WTP as a dependent variable which is consumer’s willingness to pay for a transformation in water services and it can be expressed in a linear regression form as below:
WTP = β
0
+ HINC β
1
+ WPRC β
2
+ HSZ β
3
+ e
(2)
Where;
WTP = Probability of saying “Yes” or
“No” to offered prices.
WPRC = Bid water price expressed in
Ringgit Malaysia (RM).
HINC = Household’s income expressed in Ringgit Malaysia (RM).
HSZ = Household size expressed in number of individual.
3.1 Contingent Valuation Method (CVM)
The CVM can be used to estimate nonmarketed goods such as environmental assets, amenities and services, and it elicits the preferences for non-marketed goods (United
Nations Environment Programme, 2007).
Benefits are measured directly rather than inferred from a demand curve in CVM. In the
CVM, respondents are asked whether or not
[47]

they would pay or accept a specific amount for a program or policy in question. The method entails by asking question in this study such as “Are you willing to pay RM x for improved in domestic water services?”. The respondents choose whether to “take it” or
“leave it” to that proposed price. The CVM uses closed-ended format that can be answered using a simple "yes" or "no" regarding to their WTP. It can be presented by using model formulation which followed
Hanemann et al. (1991) as below:
Prob { No }  Prob { WTP max
<
PRICE }  G (PRICE; θ)
PRICE }  1 - G (PRICE; θ)
(3)
Prob { Yes }  Prob { WTP max
(4)
>
Equation 3 explains that if the proposed price amount (PRICE) is more than consumer’s willingness to pay, then they are not willing to pay for that amount. However, if the bid is below their true maximum willingness to pay amount, the probability of answering to that amount is “yes” based on Equation 4, it implies they maximize utility and willing to pay for that specific amount.
3.2 Probit Model
This study has dependent variable which elicits “yes” or “no” responses to WTP question, so probit regression are suitable to estimate the model. The Probit model is based on normal distribution as below (Gujarati,
2003):
Prob (Y) =
1
√2𝜋
∫ 𝑧
−∞ 𝑒
−𝑠2
2 ds
(5)
Assuming Y is a binary which has possible outcome recoded as 1 or 0, based on respondent answering yes/no to the questions. Thus, particularly we assume the model as:
Prob (Y = 1 |𝑋 𝑖
) = Φ (X i
β)
(6)
Where Prob represents probability of answering to the question, Φ is cumulative distribution function (CDF), and β can be estimated by maximum likelihood. Y * i
signifies the value of one if respondents are willing to pay for improvement, otherwise the value of zero, if they are against the program.
Y * i
= {
1 𝑖𝑓 𝑤𝑖𝑙𝑙𝑖𝑛𝑔 𝑡𝑜 𝑝𝑎𝑦, 𝑌
0 𝑖𝑓 𝑛𝑜𝑡, 𝑌 𝑖
∗ 𝑖
∗ > 0,
≤ 0,
(7)
Equivalently, we prove that both models in
Equation 6 are corresponding as follows:
Prob (Y = 1 |𝑋 𝑖
) = Prob (Y* > 0) = Prob (X i
β + e i
> 0) (8)
= Prob (e i
> - X i
β)
= Prob (e i
< X i
β)
= Φ (X i
β)
Estimated parameters can be achieved by maximizing the following log likelihood procedure:
log L (β) = ∑ 𝑛 𝑖=1
{𝑦 𝑖
(𝑥 𝑖 𝛽 )) } 𝑙𝑛 𝛷 (𝑥
(9) 𝑖 𝛽 ) + (1- 𝑦 𝑖
) ln (1- Φ
Then, the calculated mean WTP can be derived as stated in below equation
(Cameron, 1988):
WTP


0

n
 i -

2
1
 i
X
(10)
Where β
0
is estimated constant, β
1
is the coefficient for the proposed price amount, and β i
is the coefficient for socio-economic characteristics of respondents.
3.3 Sampling
There were 552 of domestic water services users who have registered active accounts with AKSB as respondents in this study. A sample survey was conducted for domestic users which come from urban and rural areas in Kelantan and they were from 10 of districts
[48]

in Kelantan. This study uses stratified random sampling based on the districts in the state as shown in Table 1.
Table 1: Total of Respondents According to the Districts
Tanah Merah
Kuala Krai
Gua Musang
Machang
Jeli
Total
45
38
34
35
17
552
District
Kota Bharu
Pasir Mas
Tumpat
Bachok
Pasir Puteh
4. RESULTS AND DISCUSSION
No. of
Respondents
168
70
55
46
44
The distributions of the respondents were not equivalent in each district since the districts did not have the same total numbers of population. The respondents were selected randomly in each stratum.
These findings in Table 2 present that the average household size was 5 with standard deviation of
2.59. The average age of respondents were 38 years and most of them were female (50.2%).
Household income was RM 4077.90 per month in average.
Table 2: Socio Demographic Profile of Respondents
Characteristics
Gender
Male
Female
Age
20 – 30 years
31 – 40 years
41 – 50 years
51 – 60 years
61 – 70 years
>71 years
Race
Malay
Chinese
Indian
Others
Household Size
1 – 5 people
6 – 10 people
>10 people
Frequency
275
277
27.7
30.8
29.3
9.6
1.8
0.7
513
32
3
4
271
266
15
Education Level
PhD/Master
Bachelor
Diploma/Certificate
Secondary level
Primary level
No Education
Numbers of working
22
139
200
134
50
7
Percent (%)
49.8%
50.2%
27.7%
30.8%
29.3%
9.6%
1.8%
0.7%
92.9%
5.8%
0.5%
0.7%
49.1%
48.2%
2.7%
4%
25.2%
36.2%
24.3%
9.1%
1.3%
Mean
38.34
5.10
SD
10.99
2.59
[49]

family members
0 – 3 members
4 – 7 members
>8 members
Household Income
(monthly)
Less than RM 2,000
RM2,001 – RM 4,000
RM4,001 – RM 6,000
RM6,001– RM 8,000
RM8,001– RM 10,000
>RM10,001
162
154
127
51
22
36
481
65
6
87.1%
11.8%
1.1%
29.3%
27.9%
23%
9.2%
4%
6.5%
4.1 Contingent Valuation Method (CVM) Analyses
Respondents answered the proposed water price with “Yes” and “No”, which are coded as “Yes = 1” and “No = 0”. The respondents are offered five groups of bidding price in this study. Table 3 presents consumer’s response to five groups of bidding price in the survey according to the districts. The distribution of price bids and respondents in each districts were not distribute equally since the population in each districts were not equivalent. Respondents were offered with different water price bid which were the prices in the range of RM 0.61 (10% increase from current price), RM 0.63 (15% increase from current price), RM 0.66 (20% increase from current price), RM0.69 (25% increase from current price), and RM0.72 (30% increase from current price). Table 3 portrays percentage of respondents rejecting the offered price as the price bid increases. About 70 of respondents reject the highest price bid (RM0.72) and only 21 of respondents are willing to pay the proposed water price.
Table 3: Willingness to Pay According to the Districts in Kelantan
Price
Bid
(per m 3 )
District
Kota Bharu
Pasir Mas
Tumpat
Bachok
Pasir Puteh
Tanah Merah
Kuala Krai
Gua Musang
Machang
Jeli
Total
RM 0.61
No
12
12
10
9
5
6
3
1
2
1
61
105
4.2 Probit Model Results
Yes
16
2
1
1
3
3
4
6
5
3
44
RM 0.63
No
10
13
10
7
4
7
4
2
2
1
60
121
Yes
33
1
1
2
5
2
4
5
5
3
61
RM 0.66
No
20
12
9
8
4
7
5
2
3
2
72
116
Yes
1
5
2
19
2
2
3
5
4
1
44
RM 0.69
No
17
14
9
9
6
8
6
6
3
1
79
119
0
2
0
3
1
2
1
4
2
2.13
4077.90
Yes
25
40
RM 0.72
No
9
8
5
8
13
11
6
5
4
1
70
91
1.18
2720.711
Yes
0
1
4
8
1
0
1
1
3
2
21 n
168
70
55
46
44
45
38
34
35
17
552
[50]

The data is analysed by using LIMDEP, NLogit
Version 9 and value of 0 is given to respondents who rejected the idea for improvement, saying “No” to offered price.
Then the value of 1 is for “Yes” answer if they accept the offer. Based on Table 4, explanatory variables have a unique statistically significant contribution to the model. The ‘Bid Price’ variable has a negative sign and significant at 1% level as predicted. It signifies that under hypothetical market, as offered bid price increases, the probability of saying “Yes” decreases among consumers.
Respondents reject the offered price as it increases and this is consistent with demand theory since consumers react to higher prices.
Household income is a significant determinant since it illustrates positive sign and significant economic theory which states that WTP for
“good” increases with income, thus household income should have positive sign. People tend to spend more and households with high level of income are willing to pay higher for water bill if there is an increase in water prices. The household size variable shows negative relationship with WTP but significant at 5% level. It demonstrates that as household size increases, the WTP decreases. The more household members in the house, there will be a reduction in WTP. It has been demonstrated that the more households consume water, the less they are willing to pay (Farolfi, 2007). They demand more water consumption but higher water price can be a burden for them. Thus, it consistent with demand theory; as price increase, there will at 1% level too. The determinant suits to be a decrease in quantity demanded.
Table 4: Final Regression Results of Probit Model
Variables
Constant
Bid Price
Household Income
Household size
Log Likelihood
Restricted Log Likehood
Chi squared
McFadden
R-squared
Percentage Correct
Probit Model
Coefficient t-value
3.8989
-6.0315
0.0000094
-0.0539
-349.7500
-366.1891
32.8782***
3.912***
-4.075***
3.927***
-2.380**
Note: (**) 5% level, (***) 1% level.
0.0448
62.14%
Regression was performed to elicit consumer’s WTP towards improvement in domestic water services and the probit model has three explanatory variables (bid price, household income and household size) which are statistically significant. In probit model, the chi-squared shows 32.87 and the value
McFadden R squared value is 0.0448. The R
Square expresses an indication of the amount of variation in the dependent variable clarified by the model. The model correctly classified
62.14% of cases.
4.3 Estimation of Mean Willingness to Pay
(WTP)
The estimated mean of WTP ranges from
RM0.4637 to RM0.7621 in this study. We estimate consumer’s willingness to pay for improved domestic water services in Kelantan by referring to Equation 10.
WTP = [β
0
+ (β
HINC
* HINC + β
HSZ
* HSIZE)] / -
(β
PRICE
) (11)
[51]

Where, β
HINC
, β
HSZ
and β
PRICE show estimated variables for household income, household size and price bid. Thus, the mean WTP computed is RM 0.60 for probit model applied on the first 35 m 3 . It increases by about 9.09% from the current water price (RM0.55 applied on the first 35 m 3 ).
5. CONCLUSION
This study has shown that people in Kelantan are willing to pay more than the current water price, which is RM0.60 applied on the first 35 m 3 by employing probit model. It demonstrates the three factors that most influencing people’s willingness to pay for improved services based on findings of CVM approach such as price bid, household income and household size. The signs indicate as expected in earlier studies. The high increase in water demand requires the authorities and water companies to think of on new strategies to attain efficient water services. Cheap water prices will limit implementation of infrastructure projects to upgrade facilities and it implies low value of water, though the sources are valuable and insufficient. The government should regulate the market by controlling water prices in the state. The prices should be reliable in order to balance and protect both sides; producers and consumers. Water prices should reflect the cost of water production in order to promote market efficiency. If the water price is increased, it can lessen the financial burden of water companies by providing needed funds for further improvement in infrastructure, upgrade services and financial development.
Revising prices can help the water companies to reduce cost somewhat but they still have to operate until the optimum production is achieved. The consumers will be aware to conserve water and avoid water wastage due to an increase in the water price. Participating in awareness programs makes the consumers accountable for water conservation and appreciates the value of water, the most valuable among natural resources. Since water is considered as a ‘public good’, it should be allocated to the consumers at the best condition.
REFERENCE
Association of Water and Energy Research
Malaysia. (2011). National water services industry restructuring –
Kelantan state case study. Kuala
Lumpur.
Bockstael, N.E., McConnell, K.E., and Strand,
I.E. (1989). Measuring the benefits of improvements in water quality: the
Chesapeake Bay. Marine Resource
Economics. 6: 1-18.
Browning, E.K., and Zupan, M.A. (2004).
Microeconomics: Theory and
Applications (8 th edition). United
States of America: John Wiley & Sons,
Inc.
Cameron, T.A. (1988). A new paradigm for valuing non-market goods using referendum data: maximum likelihood estimation by censored logistic regression. Journal
Environmental Economics and
management. 15: 355-379.
Department of Statistic (2012). Kelantan at a
Glance: Population in 2011. http://www.statistics.gov.my/portal/i ndex.php?option=com_content&id=5
26&lang=en&negeri=Kelantan
Economic Planning Unit. (2009). Incidence of poverty, by ethnicity, strata and state, Malaysia 1970-2009.
Household income and poverty. http://www.epu.gov.my/c/document
_library/get_file?uuid=5bf3a7ca-
37a7-4ebb-96c6-
634ce17141dc&groupId=34492
[52]

Farolfi, S., Mabugu, R. E., and Ntshingila, S. N.
(2007). Domestic water use and values in Swaziland: a contingent valuation analysis. Agrekon. 46 (2):
157-169.
Gujarati, D. (2003). Basic Econometrics (4 th edition). New York: Mc GrawHill.
Hanemann, W.M. (1994). Valuing the environmental through contingent valuation. Journal of Economics
Perspectives. 8 (4): 19-43.
Larson, D.M., Lew, D.K., and Onozaka, Y.
(2001). The public’s willingness to pay for improving California’s water
quality. Paper presented at Annual
Meeting of Western Regional project
W-133. Miami: Florida.
Lee, C.H.K. (2007). Social policies and private sector participation in water supply –
the case of Malaysia. Retrieved from
United Nations Research Institute for
Social Development, program on
Social Policy, Regulation and Private
Sector Involvement in Water Supply at: http://www.unrisd.org/
Lee, C.H.K. (2011). Privatization, water access and affordability: evidence from
Malaysian household expenditure data. Economic Modelling, 28, 2121-
2128. http://www.sciencedirect.com/scienc e/article/pii/S0264999311001143
Malaysian Water Association. (2011).
Malaysia water industry guide. Kuala
Lumpur: MWA.
Sarala Devi, J., Joseph, B., Karunakaran, K.,
Anurdha, B., and RamaDevi, K.
(2009). People’s attitudes towards paying for water. Current Science. 97
(9): 1296-1302
United Nations Environment Programme.
(2007). Guidelines for conducting economic valuation of coastal ecosystem goods and services
(UNEP/GEF/SCS Technical Publication
No.8). Thailand: United Nations
Environment Programme.
Vasquez, W.F., Mozumder, P., Hernandez-
Arce, J., and Berrens, R.P. (2009).
Willingness to pay for safe drinking water: evidence from Parral, Mexico.
Journal of Environment Management.
90: 3391-3400.
World Bank Water Demand Research Team
(1993). The demand for water in rural areas: determinants and policy implications. The World Bank
Research Observer. 8 (1): 47-70.
Zamri, W. M. (2009). Groundwater for domestic needs in Kelantan. Water
Malaysia, 20, pp. 6-10.
[53]