Willingness to pay for improvement in drinking water: Palar Basin, Vellore District in Tamil Nadu

Abstract

The present paper examines the willingness to pay for drinking water in the Palar river basin due to tannery water pollution which affects the health of the people. The tannery industry provides employment, mainly from socially and economically backward sections of the society in Vellore district in Tamil Nadu. The study also suggests policy measures to control the water pollution in Palar River.

Full text

Malaya Journal of Matematik, Vol. S, No. 1, 550-559, 2021
https://doi.org/10.26637/MJMS2101/0126
Willingness to pay for improvement in drinking
water: Palar Basin, Vellore District in Tamil Nadu
A. Xavier Susairaj1and A. Premkumar2
Abstract
The present papers examine the willingness to pay for drinking water in the Palar river basin due to tannery water
pollution which affects the health of the people. The tannery industry provides employment, mainly from socially
and economically backward sections of the society in Vellore district in Tamil Nadu. The study also suggests
policy measures to control the water pollution in Palar River.
Keywords
Water pollution, willingness to pay, health problems. Willingness to accept.
1,2Department of Economics, Sacred Heart College (Autonomous), Tirupattur-635601, Tamil Nadu, India.
Article History: Received 01 November 2020; Accepted 10 February 2021 c
2021 MJM.
Contents
1Introduction .......................................550
2Research Problem.................................550
2.1 Research Questions ..................551
2.2 The Study area ......................551
3Economic Model for Willingness to Pay .......... 551
4Review of Literature...............................552
5Methodology ......................................553
6Results and discussion ...........................553
6.1 Locality and Willingness to Pay ...........553
6.2 Occupation .........................554
6.3 Household size ......................554
6.4 Income ............................554
7Expenditure .......................................555
7.1 Household collecting water from different sources
555
7.2
Number of members affected by water-borne sick-
ness and WTP ..........................
555
7.3
Number of times affected by Water-Borne Sickness
556
8Conclusion ........................................558
References ........................................558
1. Introduction
Water is one of the most important commodities, for the suste-
nance of human life. It is a public good [1]. Aval Vikkadan
2020 found that 75% of the families in India don’t have water
facilities. 2 Lakh Indians were died due to good drinking
water 60 crore people Daily shortage of water. 84 percent of
the people living in some areas they don’t have pipe water
facilities in their house. There is a growing demand for water
in developing countries due to growing populations and ur-
banization the population has no access to safe drinking water,
and the majority of urban residents struggle to cope with both
low and intermittent water supply conditions.
2. Research Problem
Pollution from leather tanneries and the resulting health im-
pacts are a world-wide problem. Because of the cheap labour
availability and tax pollution regulation, leather tanning has
become a popular export-earning industry in countries like
India, Pakistan and Bangladesh [2]. There is no real study
that provides quantitative estimates of health and other envi-
ronmental costs that can be attributed to tannery pollution. It
is a well known fact that there is a strong correlation between
the quality of water and various human diseases. Tanneries
convert animal skins/hides into high quality leather. The pro-
cess requires several chemicals, through which the tanneries
discharge considerable quantities of organic and inorganic
wastes into the Palar River. The tannery effluents contain
heavy metals such as chromium, a known carcinogen, and
other harmful chemicals [3]. The tannery waste effluents also
contain pathogenic organisms, such as bacteria, viruses and

Willingness to pay for improvement in drinking water: Palar Basin, Vellore District in Tamil Nadu — 551/559
parasites. People using the Palar River water therefore are
suspected being exposed to these chemicals and pathogens
when they use the contaminated water for drinking. The use
of tannery-polluted water and air causes health effects such
as acute diarrhea, birth defects, cancer, allergy, and other dis-
eases [4]. A preliminary study conducted in the basin reports
several people actually suffering from some of these diseases
[5].
Past water quality economic studies conducted in India
recognize that there are quantifiable costs associated with
illness resulting from polluted water [6]. People suffering
water-borne illnesses end up spending five types of costs:
medical expenses toward treating illness, costs of averting
likely illnesses, lost wages, and disutility (discomfort) arising
from the illness, and reduction in life expectancy due to water-
borne illness [7]. Poor water quality is a serious concern
especially to most that live in the area without water treatment
system, which is very common in the Palar river basin. As it
is estimated that only 50 percent of the population in Vellore
district have access to safe drinking water, many residents
have observed water borne disease from their water sources
in recent [8]. The discharge of untreated tannery waste waters
from settlements is exacerbating the water pollution.
2.1 Research Questions
The following research questions are formulated. To assess
if how much the residential water users are willing to pay for
improved water services, what are the major health problems?
Research Objectives
1.
To investigate households willingness to pay for im-
proved water services in the Vellore District.
2. Identify factors that influence their willingness to pay
2.2 The Study area
This study will be conducted in the Vellore district of Tamil
Nadu, India. As per the 2011 census, the population of the
Vellore district was 3.4 million. The district covers an area
of 488,664 hectares and comprises of 7 taluks and 20 blocks,
753 village panchayats and 4827 villages. It receives a rainfall
of 990.3mm, per year.
In Tamil Nadu there are 662 tanneries and 230 in Vellore
district alone. These tanneries process animal skins and hides
and convert them to non-biodegradable stable and quality
leathers. This conversion process includes de-hairing, re-
moval of flesh and fat, and treatment with either plant extracts,
synthetic chemicals and chrome tanning. Several chemicals
including salts and heavy metals are used in chemical tanning.
Such processes result in large quantities of effluents.
The tanneries of Vellore district with a processing capacity
of about 289 thousand kg per day are estimated to discharge
300 million liters of effluents. The tannery sludge is charac-
terized by a large variety of organic and inorganic compounds
such as sodium chloride and chromium sulphate are exten-
sively used in the chemical tanning the resultant sludge is rich
in both sodium and chromium. High concentration of these
chemicals could deteriorate soil and ground water quality. The
subsequent entry of these metals into food chain may cause
health disorders in animals and human beings [10].
3. Economic Model for Willingness to Pay
Water is a commodity which is not traded in the market and
it is considered non-market commodity (check this statement
again). To estimate willingness to pay for water, non-market
valuation technique is needed. Non-market valuation helps to
estimate environmental goods in money term [11].
Consumer choice for, both market and non-market com-
modity depends upon price as well as utility function. It is
called as compensated demand function, otherwise known as
Hicksian demand function. Consumers would like to maxi-
mize their utility from both quantity and quality of goods and
services, according to their income and budget constraints.
The utility functionU(q,m)(3.1)
q=water quality
m=composite of all market goods
The expenditure functionE(p,q,u)(3.2)
The consumer expenditure function E(p,q,u)is the minimal
cost to the consumer for achieving utility level and quality
product, when the price is
p
. The expenditure function is
increasing function of ”price’ and ”utility’ and decreasing
function of ’quality’.
Since the consumer want to stay with the same utility, it is
appropriate to use expenditure minimization problem.
Min(m+Pm)(3.3)
Subject to
U=U(q,m)
where composite goods are equal to
one (Pm =1).
To solve the minimization problem and to obtain Hicksian
demand for the corresponding goods, Lagrange’s multiplier
can be used. The Hicksian demand is given as:
Hd=Hd(p,q,u∗)(3.4)
By substituting the values of Hicksian demand in the mini-
mum expenditure function, we can calculate the minimum
expenditure
E∗=E(p,q,u∗)(3.5)
Minimum expenditure need to achieve fixed level of utility
u∗
by using q level of water quality and it is determined by the
price of other goods, the fixed level of utility and the water
quality.
The expenditure function derivation, with respect to price
gives corresponding Hicksian demand function.
∂E/ˆpi =Hd(p,q,u∗)(3.6)
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Willingness to pay for the improved water services is the
integration of the marginal willingness to pay for the improved
water quality.
W T P =−Zq∗
q
∂E(q,u∗)/∂qXdq (3.7)
This is the maximum amount of money, the consumer would
be willing to pay in order to enjoy improved quality. The
willingness to pay for the improved water quality is
W T P =E(p,q,u)−E(p,q∗,u)
Where,
q
is polluted water and
q∗
is an improved level of
quality.
The difference in expenditure is either due to compensat-
ing surplus or equivalent surplus. If the utility level is in the
initial stage it is known as compensating surplus, if it is in
the final stage then it is equivalent surplus. WTP depends on
age, location, occupation, income, household education level;
waterborne diseases etc. [17]. To get various determinants of
WTP the following regression model is used.
W T P =β0+β1W D +β2Age +β3HSize +β4Gender
+β5Ed u +β6Occu +β7NE M +β8MIF
+β9NW BS +β10NT A +β11W BS
+β12DI S +β13QW +β14HT +β15 PCW
+β16T C +β17IC +ε
Where
β0= Constant
βi(i=1,
2,. . . 17)= Regression coefficients
W T P = Willingness to Pay (in Rs.)
WD = Rural or Urban ward (Dummy variable
taking value 1 for urban and 0 for rural)
Age = Age of the household (years)
HSize = Household size (Family size in numbers)
Gender = Sex of the household (Dummy variable
taking value 1 for male and 0 for female)
Ed u = Educational level of the respondent (0=no
schooling; 1 =primary; 2 =secondary and
3 =high school; 4 =higher secondary;
5 =higher education
Occu = Occupation of the household (1=agriculture;
2=own business; 3 =govemment
employee and 4 =others )
NE M = Number of earming members in the family
MIF = Monthly income of the family in rupees
NW BS = Number of family members affected by
water bome sickness
NT A = Number of times affected by water
bome diseases
W BS = Incidence of water bome disease (Dummy
variable taking value 1 if affected by
Waterbome disease and 0 otherwise)
DIS = Distance between public tap and home
QW = Water quality index
HT = House type (1=hut; 2 =tiled; 3 =asbestos
sheet and 4 =concrete)
PCW = Consumption of water per month
TC = Total cost (Rs.) of treatment for water
bome disease
IC = Individual water pipe connection to home
(Dummy variable taking value 1 for Individual
water pipe connection and 0 otherwise)
ε= Error term Dependent Variable: WTP (Rs.)
4. Review of Literature
The world health Organization estimates 1.8 million people in
developing countries die every year from diarrhea and cholera,
out of these 90 percent are children under the age of five years.
While 88 percent of diarrheal diseases are attributed to unsafe
water supply, inadequate sanitation and hygiene [13]. Whit-
tington’s research in the area of WTP is the most prolific. He
describes a study from Onitsha, Nigeria [14] which illustrates
how levels of payment for water equate to the financing of
urban water supply infrastructure development. The World
Bank’s (1997) approach to estimating levels of WTP is by
application of the 5% rule. Rogerson [12] advocates further
research, but at the household level in order to assess levels
of WTP more accurately. Schur [18] believes it is the state’s
responsibility to ensure all citizens have access to sufficient
water supplies and sanitation facilities. Vira [19] refers to it
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as a matter of entitlement.
Agudelo presented a report for various methods that can
be used for valuation of water. In Conclusion, he agreed
with Freeman that the economic perspective - i.e. the adop-
tion of WTP is economic efficiency as the central criterion to
undertake the valuation of water resources is rational and con-
venient, and that adopting it is more helpful than abandoning
it.
WTP experiences in developing countries, The WTP sur-
vey for safe drinking water has been conducted in many places
around the world including cities in India. Roy conducted a
WTP survey in 2002 in a particular ward in Calcutta Municipal
Corporation for safe drinking water. A survey was conducted
among 240 households selected both from residential and
slum area. They also find it useful to control for the variable
-family size, A multiple Linear Regression Model is used to
estimate the contribution of various determining factors of
WTP and judge their statistical significance. They reported
that WTP of households vary within a wide range of Indian
Rupees (INR) 0.0023/litre to INR 1.06/litre (Almost free to
0.023 US $) of purified water. Spending power of households
and educational background are important determining factor
in WTP.
Jalan and Somanathan In their study in 2003, a randomly
selected group of 1000 households in Gurgaon, a suburb of
New Delhi, India they used the WTP method to test whether
(or not) their drinking water had tested positive for fecal con-
tamination.
Marie conducted a survey to study the perception of the
service and the willingness to pay for water for the higher
income group in Vijaywada. The survey indicates that 77%
of the group considers that water is cheap. Their preference
for improvement would go for an increase of quality (81% of
the households), to have the preference. For the connection
charges, households are willing to pay around Rs. 2,600.
In Varanasi Singh made an attempt to know the consumers’
willingness to pay and their affordability of cost of water
through a bidding game. They found that about 37% of popu-
lation has a willingness to pay for the sum of INR 40 against
the existing charge of INR 20 per month for water supply.
Astana studied the economic behaviour of the poor in
collecting the safe drinking water. The study reveals that
perception of health benefits by the people is significant and
they are prepared to spend significantly higher amount of time
in collection of safe water as opposed to unsafe water.
Kaliba presents the result of a CVM survey conducted
in Tanzania where the surveyed community is willing to pay
32 shilling per 20Litre of water which is about twenty five
percent above the existing tariff [15].
AKM studied the willingness to pay / use issues in Dhaka,
Bangladesh. They opined that the city water supply and sani-
tation system suffers from various problems including those
of technological, policy, planning, coordination, managerial
aspects. Some of these can be explained by low willingness
to pay [16]. In 1993, ADB found in MP (a state in India) that
nearly 80% of the households were willing to pay a monthly
fixed charge of INR100 for improved water supply, while 13%
were willing to spend up to INR200; the remaining house-
holds were willing to spend only below INR100. Joel studied
the managerial and administrative aspects of the water and
sanitation service.
5. Methodology
The data for the analysis will come from a combination of
primary, secondary and published sources, the present study
will be carried out in two taluks of Vellore district in Tamil
Nadu. The researcher purposively selected four blocks namely
Katpadi, Walaja, Vaniyambadi and Ambur to compare and
contrast people’s willingness to pay for improved drinking
water supply, Choice of villages/Urban, On the basis of the
water quality; all the villages in four sample blocks were
selected. Choice of the Households, from each selected urban
and rural wards, 10 percent of the households will be select
at random. Total number of sample is 500, consisting of 200
urban households and 200 rural households. The individuals
from these four blocks will be randomly selected. A structured
face-to-face interview will be conducted by a group of trained
interviewers.
6. Results and discussion
A detailed primary household survey was conducted from
Vellore district to find out the quality of water and quantity
of water used in households for various day-to-day activities.
The household composition of respondents in each sample
includes head of the household, in most cases female or male
i.e., the spouse, or both husband and wife. This chapter is
divided into two parts. The socio-economic characteristics of
the sample households are presented in the first part.
The second part of this section investigates the factors
influencing households’ WTP for improved water. It is well
known that environmental pollution leads to serious negative
impacts on health and on various economic activities.
Willingness to pay for improved water
This section is composed of descriptive statistics on willing-
ness to pay for improved water and socio-economic variables.
Age of the respondents
Age group of the respondents is the mean age is 40.76. Out of
the total number of 500 respondents, 210 (42 percent) respon-
dents are males and 290 (58 per cent) respondents are females.
Table 1 : Age of Respondents and Willingness to Pay
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Age Mean (Rs.) NS.D. (check this value again) Minimum Maximu mRange
20 −30 403.32 104 206.54 100.00 800.00 700.00
31 −40 386.47 156 175.37 100.00 950.00 850.00
41 −50 398.05 149 202.12 100.00 1100.00 1000.00
51 and above 352.42 91 197.74 100.00 1000.00 900.00
Total 387.23 500 194.52 100.00 1100.00 1000.00
In the semi-interview questionnaire, the amount respondents
are willing to pay for water supply was asked. Table 1 re-
veals that the younger respondents are willing to pay more
compared to other age group members. The standard devi-
ation shows that there is marked gap between respondents
belonging to specific age-group.
6.1 Locality and Willingness to Pay
Awareness about water pollution and its consequences are
comparatively more in the urban areas than in rural areas.
Urban residents are willing to pay for improved water supply
more than their rural counterparts. Maximum amount in the
rural ward is Rs. 900.00 in the urban places it is Rs.1100.00.
Higher standard deviation for urban residents indicate higher
variability which means not all respondents are willing to pay
higher amounts.
Table 2: Distribution of Respondents by gender and willing
to pay (%)
Amount of Willingness to Gender Total
Pay (Rs. for 3 months) Male Female
Less than 200 50.95 49.05 100.00
(25.71) (17.93) (21.2)
201 −400 40.22 59.78 100.00
(34.29) (36.90) (35.8)
401 −600 36.81 63.19 100.00
(28.57) (35.17) (32.6)
601 −800 46.81 53.19 100.00
(10.48) (8.62) (9.4)
801 −1000 50.00 50.00 100.00
(0.95) (0.69) (0.8)
More than 1000 0.00 100.00 100.00
(0.00) (0.35) (0.2)
Total 42.00 58.00 100.00
(100) (100) (100)
No. of Respondents 210 290 500
Notes: Figures in parentheses denote column percentages.
Source: Primary Survey, Vellore District.
Gender and Willingness to pay It was pointed out earlier that
as women are required to do a substantial part of household
chores, they may come forward to pay more than the males.
From the above table the percentage of females willing to
pay more for safe water. Table 2. shows that in low amount
categories, more male respondents are found, their proportion
falls particularly in 400-601. In case of females, two groups –
201-400 and 401-600, their proportion is high.
6.2 Occupation
Occupation plays an important role in determining the stan-
dard of living of a person. The nature of the employment that
the members of the sample households engaged in are classi-
fied into four major categories viz, agriculture, own business,
government employee and others.
There is a clear cut relationship between the nature of
employment and willingness to pay for water supply. Govern-
ment employees are willing to pay the highest amount. They
are followed by own business households.
6.3 Household size
The family size of the households determines the quantity of
water. More the number of members in a household means
higher will be the demand for water and therefore people will
be willing to pay more. Hence the family size is considered im-
portant for the analysis.
Table 3: Respondents’ Occupation and WTP
Occupation Mean NS.D Minimum Maximum Range
Agriculture 354.80 112 155.03 100.00 800.00 700.00
Own business 442.37 77 231.39 100.00 1100.00 1000.00
Goverment employees 486.16 60 220.17 100.00 1000.00 900.00
Others 361.12 251 180.80 100.00 950.00 850.00
Total 387.23 500 194.51 100.00 1100.00 1000.00
Source: Primary Survey, Vellore District
Table 4: Size of the Households
Size of the family Number of families Mean S.D Minimum Maximum Range
Small family 300 3.50 0.615 2 4 2
Large family 200 5.57 0.793 5 9 4
Total 500 4.33 1.23 2 9 7
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Source: Primary Survey, Vellore District
In the present study, if a household has up to four members,
such a family is considered as small family and others as large
families. Sixty percent of the households are small families
(Table 4). It has been observed four percent households have
the lowest household size, i.e. with the households’ size of
two members whereas 0.2 percent of the households have
household size of 9 members, which is the highest.
The survey shows that when the household size is between
2 and 6 the amount of willingness to pay show up and down.
From seven to nine members’ house the willingness to pay
for the improved water gradually increase.
6.4 Income
The mean WTP value for sample households is estimated
to at Rs. 387.23 for three months while the maximum and
minimum WTP values are Rs. 100 and Rs. 1100 respectively.
The mean WTP value for the improved water supply scheme
is found to be lesser than what they are already paying. For
a month money paid by the households is nearly Rs. 175.
The insignificant mean WTP values across income categories
suggest ceteris paribus, i.e. the income difference does not
influence the WTP value significantly.
7. Expenditure
Though water is a free commodity in a theoretical sense,
Indian households today are required spend from their pocket
for various purposes related to water supply. They pay water
tax, repair pipelines and motor and other source of water etc
Table 5: Income-Wise Household’s Willing to Pay for Improved Water Supply
Income NMinimum Maximum Mean S.D Sum of
Category WTP Value WTP Value WTP Value WTP Value
less than Rs.4000 75 100.00 800.00 343.86 162.62 25790.00
4001 −6000 153 100.00 800.00 374.11 173.15 57240.00
Rs.6001-8000 108 100.00 800.00 330.12 189.42 35653.00
Rs.8001-10000 72 100.00 800.00 389.87 172.15 28071.00
92 100.00 1100.00 509.34 223.14 46860.00
Total 500 100.00 1100.00 387.22 194.52 193614.00
Source: Primary Survey, Vellore District
Table 6: Household Expenditure on Water for Different
Sources per Month
Categories Mean Maximum Minimum Std. Dev
Value of Amount Amount -iation
Amount Paid Paid Paid
Public Taps 28.69 100 00 21.69
Individual 22.47 55 00 24.28
connections
Hand Pump 19.10 125 00 24.85
Bore-well 105.30 600 00 147.96
Source: Primary Survey, Vellore District
Monthly expenditure actually incurred by the sample house-
holds on different sources of water supply is given in Table 6.
On an average, the family ‘informally” pays around Rs.28.50
per month in order to get water supply regularly. In the same
way, the households collecting water from the hand pumps
too pay an informal amount to the local officials around Rs.
20,they are required to pay a minimum amount of Rs.30 to
get the panchayat water in villages and Rs.55 to get the corpo-
ration water in case of Vellore city.
7.1 Household collecting water from different sources
Since the water supply is not sufficient in one source, house-
holds collect water different sources. Residents in all the
villages use public taps, individual connections, hand pumps
and bore well. Some collect water from more than two sources.
Though they have many sources sometimes the available water
is not sufficient and they buy from the tankers.
Table 7: Household Collecting Water from Public Tap and WTP
Public Tap Mean NS.D Minimum Maximum Range
Collecting from public tap 373.80 360 193.21 100.00 1100.00 1000.00
Not collecting from public tap 421.74 140 194.28 100.00 800.00 700.00
Source: Primary Survey, Vellore District.
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Households those who are not collecting water from the
public tap are willing to pay more than those who are collect-
ing water from public taps. Nearly 72 percent of the people
draw water from pubic tap. Ninety- five percent of the people
pay Rs. 50 per month as a bribe either to the local authorities
or to the people who are responsible to get regular supply of
water.
It should be noted only 4.4 percent of the sample house-
holds consume less than 500 liters of water per week. Nearly
60 percent of the sample households are using 500 to 1000
liters of water per week. Around 95 percent are using less
than 1500 liters of water for a week. In 4001- 6000 cate-
gory, 63 percent of the sample households are using only less
than 1000 liters of water for a week. Table 8 also reveals
that income and water consumption are not having direct re-
lationship. Consumption of water depends upon not only on
income, but also on other factors such as household size. One
can also argue that when the supply water is more people may
be encouraged to consume more.
7.2 Number of members affected by water-borne sick-
ness and WTP
Number of members affected by water-borne sickness in the
households may have effect on the willingness to pay for the
improved drinking water.
If more members are affected, more will be the value
of willingness to pay for improved water. In contrast to the
expectation, households in the study area who did not get
affected during the study period is willing to pay more i.e
mean amount is Rs.458.47.
Table 8: Distribution of Households by Level of Water Consumption and Income
Households Income Household water consumption (in liters) Total
Category (Rs. per month) Below 500 501−1000 1001−1500 1501−2000 more than 2000
less than 1.33 78.67 18.67 −1.33 100
4000 (4.54) (19.80) (8.8) (100.00) (15.00)
4001 −6000 4.58 60.78 30.71 3.90 100
(31.81) (31.21) (29.56) (30.00)−(30.60)
6001 −8000 9.25 54.63 30.56 5.56 100
(45.46) (19.80) (20.76) (30)−(21.6)
8001 −10000 2.78 52.78 38.89 5.56 100
(9.91) (12.75) (17.61) (20)−(14.40)
I0001 and 2.17 53.26 40.21 4.35 100
above (9.91) (16.44) (23.27) (20)−(18.4)
Total 4.4 59.60 31.8 4.00 0.02 100
(100) (100) (100) (100) (100) (100)
Source: Primary Survey, Vellore District
Table 9: Distribution of Members Affected by Water Borne Sickness and WTP
NWBS Mean NS.D Minimum Maximum Range
None 458.47 129 220.95 100.00 1100.00 1000.00
One 362.77 318 175.67 100.00 900.00 800.00
Two 349.75 41 186.42 100.00 950.00 850.00
Three 391.81 11 234.76 100.00 800.00 700.00
Four 460.00 1 .460.00 460.00
Total 387.23 500 194.52 100.00 1100.00 1000.00
Source: Primary Survey, Vellore District
7.3 Number of times affected by Water-Borne Sick-
ness
If the water borne sickness occurs many times in a household,
it may like to get rid of the causes for the sickness. Since
people are aware that due to leather tanneries the water in their
area has got polluted and it affects their health. In order avoid
sickness they may be willing to come forward to pay more
for the improved water. . Households who got maximum
time willing to pay Rs.385.38 which is equal to the total mean
amount Rs. 387.23.
Identification of Factors influencing willingness to pay
The
R2
value - coefficient of determination of 0.19 implies
that 19 per cent of the variation in the dependent variable WTP
is explained by the fitted model. The ANOVA table shows the
overall fit of the model as indicated by the significance of the
F-ratio.
In this section, the nature and magnitude of the influence
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of the various explanatory variables included in the model on
the WTP for improved water facilities is discussed. Six out of
17 variables included in the model influence the WTP value
significantly (Table 5.53). The remaining variables although
not statistically significant, give us the expected sign.
The location factor - nature of the Ward (WD) influences
the WTP significantly at 5% level. Its coefficient is 38.278
and it is significant at 5% level. The variable WD is a dummy
variable taking the value 1 for urban location and 0 for rural
location (the description about each variable may be shifted to
the beginning of this part before the table). The variable has
expected positive sign. It shows that households in urban area
are willing to pay more by an amount of Rs. 38.278 towards
water supply This may due to the fact that the households in
the urban areas are much more aware of the consequences of
the polluted water and they like to have safe water supply.
The number of earning members in a household (NEM)
having employment is strongly significant and its coefficient
is 29.764 and it is positive and significant at 5 percent level.
This means that for every one earning member increase in the
family, the household will spend additionally about Rs. 30 per
three month towards water supply. The sign of the coefficient
is on the expected lines.
The researcher expected that the households with more
income would be willing to pay more than the households
with relatively less income. It is found that as expected, the
income of the households (MIF) positively and significantly
influences the WTP value. More clearly, the households with
low level of monthly income are willing to pay less than those
households with relatively higher level of household income.
Table 10: Results of Regression Analysis
Variable Regression coefficient coefficient Std. Error t-value p-value
Constant β0167.063 78.702 2.123 0.034
Ward β138.278∗∗ 17.336 2.208 0.028
Age β2−1.117 0.773 −1.444 0.149
Hsize β3−1.788 7.370 −0.243 0.808
Gender β4−7.446 16.685 −0.446 0.656
Education β5−4.710 9.976 −0.472 0.637
Occupation β6−10.027 6.658 −1.506 0.133
NEM β729.764∗15.321 1.943 0.053
MIF β80.014∗∗∗ 0.002 6.187 0.000
NWBS β9−25.716∗16.321 −1.976 0.098
NTA β10 −13.790 8.851 −1.558 0.120
WBS β11 19.785∗20.440 1.893 0.081
Distance β12 11.061 11.611 0.953 0.341
QWIndex β13 52.856∗∗ 23.280 2.270 0.024
HouseType β14 8.193 8.699 0.942 0.347
PCW β15 0.020 0.030 0.671 0.502
TCTR β16 0.000 0.001 0.074 0.941
Iconnection β17 9.352 23.238 0.402 0.688
R20.19
F6.663∗∗∗
Notes: (∗),(∗∗),(∗∗∗)−significant at 10%,5% and 1% respectively Source: Primary Survey, Vellore District.
Monthly income of the family (MIF) is strongly significant
at 1 percent level. Its coefficient is 0.014 and it is positive.
This implies that for every one rupee increase in monthly
income, the household is willing to pay 0.014 rupees towards
getting good water supply. This conclusion on the expected
lines because it is generally hypothesized that higher income
implies more power towards family expenditure.
Another variable that is significant is number of members
who are affected by water borne diseases (NWBS). Its coef-
ficient is -25.716 which is significant at 10% level only. The
sign of the coefficient is not on the expected lines because it
is generally hypothesized that if more members of the family
are affected by water borne disease, the household will spend
more on water quality.
Incidence of water borne sickness in the family (WBS) is a
dummy variable used in the model is also an important factor
influencing the willingness to pay for water. This variable
takes value 1 if there is incidence of water borne sickness and
0 otherwise. It s coefficient has expected positive sign with a
value of 19.785 and it is significant at 10 percent level. This
means that incidence of water borne sickness in the family
will induce an additional expenditure of Rs. 19.785 towards
water supply This is quite reasonable because households
will tend to spend more on water if the family members are
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Willingness to pay for improvement in drinking water: Palar Basin, Vellore District in Tamil Nadu — 558/559
affected by diseases due to poor quality of water.
Water quality index (QWindex) and its coefficient is 52.856
and it is significant at 5 percent level. Thus water quality has
on positive effect on WTP. The QWindex ranges from 1 to
4. So if the QWindex increases by 1, the households are will-
ing to pay extra Rs. 52.856 towards good water supply. The
positive sign of the coefficient of QWindex is on the expected
lines.
Hence, monthly income of the family (MIF), water qual-
ity index (QWindex), location (Ward), number of earning
members (NEM),incidence of water borne disease (WBS) and
Number of family members affected by water borne sickness
(NWBS) are statistically significant implying that these six
variables affect the amount willingness to pay for providing
good water supply The next variable is respondent’s Age on
WTP. Usually this variable affects willingness to pay for im-
proved quality positively; older the person more will be the
willingness to pay for improved water quality. In our analysis
respondents is not affected significantly.
This might attribute to those respondents who regarded
water services as an entitlement that should be provided by the
government. WTP for improved water quality and reliability
of supply is expected to be positively related to 8education.
Because of education people may understand better the conse-
quences of using unsafe water and the need to have reliable
water supply. Even the variable education is not significant
one related to willingness to pay for quality water. Because
they strongly believe it is the responsibility of the government.
Usually, the size of the household (Hsize) is expected to
have positive relationship with WTP value. Because, more
number of household members means higher will be the water
requirement and therefore the WTP value of these households
would be greater than the households with smaller size. How-
ever, the size of the household does not have any significant
influence on the WTP value. Even though this may be due
to the low level of household income, etc, the main reason
may be that households with more number of members will
be able to collect adequate amount of water from different
sources because of the availability of labour in the households
to collect water.
Generally, 10 gender is supposed to affect WTP. Because
females are the one who take care of domestic household
chores such travelling to other places to fetch water in times
of need, hence they will be willing to pay. However, the
negative sign of the variable Gender in the regression analysis
suggests that the females are willing to pay less than the males.
This may be attributed to the reason that the females’s control
over the financial resources in the family is generally weak
and therefore, they are not willing to pay more.
The variable 11 (DIS) distance between public tap and
house is insignificant. The researcher expected that the dis-
tance would affect the WTP value. However the households
may have some other source of which is closer to their house,
or they may be used to travel long distance to fetch water.
The next variable12 is occupation which is insignificant.
It does not play an important role in WTP. This variable on
the WTP value is insignificant implying that there is not much
difference among WTP values provided by the variable occu-
pation.
The education is strongly significant at 1% level. Its coef-
ficient is 39.75 and it is positive. This implies educated people
are more willing to pay than the illiterates and less educated.
When people are affected by any sickness they find out the
reason for the sickness and they will try to avoid the causes
for the sickness. Though many households have spent lot
of money for treating the water-borne sickness the variable
14total cost is not significantly related with willingness to pay.
The analysis gives insignificant result implying that people
are still to be aware of the environment and they need to avoid
unnecessary expenditure.
Even the 15number of times affected by water borne sick-
ness is insignificant. Even after affected by more than once
people still are not ready to pay for quality for their health
aspect. Lack of awareness, socio economic condition like
income, education etc., may be the cause for not willing to
pay more for improved water quality. Even factors like type of
house and individual water connection to home are not signif-
icantly coefficient. All together 11 variables have statistically
insignificant coefficients. The model given in the equation
above contains 17 variables and so it is difficult to use it for
predicting WTP as it requires the values of all the 17 variables.
Hence a stepwise regression analysis was performed to extract
best sub-set of variables from the 17 variables that will have
maximum explanatory power. Stepwise is a common method
used to select a subset of explanatory variables when they are
large in number. In these methods in each step explanatory
variables are one by one added or deleted.
8. Conclusion
The common effluent treatment plants should take place; pol-
lution industries must pay compensation to the local people.
Pollution by tanneries must be cheeked and damages. It is de-
sirable to have a planning committee in the pollution control
boards with skilled people in the subjects concern to formulate
location plans and to monitor their implementation.
Acknowledgment
Author acknowledges the Indian Council for Social Science
Research under IMPRESS grant (F.No. IMPRESS/P1236/429/
2018-19/ICSSR) for the financial support to conduct this
study.
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