




































Ecology, Economy and Society–the INSEE Journal 2 (2): 75–108, July 2019 

 
RESEARCH PAPER 
 

Estimating Willingness to Pay for Wastewater 
Treatment in New Delhi: Contingent Valuation 
Approach 
 
Vasudha Chopra  and Sukanya Das  
 
Abstract: Given the increasing demand pressure on water resources coupled with 
supply holdups and institutional failures, fresh-water resources are increasingly 
susceptible to depletion and could potentially add to water stress in India. A vast 
demand-supply gap necessitates water conservation, including recycling measures. 
India has a great potential in wastewater treatment, and one of the ways to address 
it is decentralisation of wastewater treatment given its environmental benefits. 
Based on the Contingent Valuation Method (CVM), this study assesses Delhi urban 
households‘ willingness to pay for the Operation & Maintenance (O&M) costs of a 
local Wastewater Treatment Plants (WWTP) that supplies residential complexes 
treated water for toilet-flushing. The study found that if freshwater prices rise 
sufficiently for consumers, they may be willing to subsidise a decentralized WWTP 
to cover at least their non-potable water uses. In addition, the co-provision of such 
public goods can become an important supplement to urban municipal finance. 

Keywords: Community Participation, Contingent Valuation, Willingness To Pay, 
Wastewater Treatment, Double Hurdle Model. 

1. INTRODUCTION 

Global water resources are vulnerable to climate change and population 
growth and are affected by rising water demand, worldwide. In India, as 
also in the entire sub-continent, the rising population has reduced per capita 

                                                           
 PhD student, The University of Tennessee, Knoxville, Tennessee, United States; 

vasudhachopra08@gmail.com.  

 Associate Professor, Department of Policy Studies, TERI School of Advanced Studies, 
New Delhi, India; sukanya.das@terisas.ac.in. 

Copyright © Chopra and Das 2019. Released under Creative Commons Attribution-
NonCommercial 4.0 International licence (CC BY-NC 4.0) by the author.  

Published by Indian Society for Ecological Economics (INSEE), c/o Institute of Economic 
Growth, University Enclave, North Campus, Delhi 110007.  

ISSN: 2581-6152 (print); 2581-6101 (web). 

DOI: https://doi.org/10.37773/ees.v2i2.77  

https://doi.org/10.37773/ees.v2i2.77


Ecology, Economy and Society–the INSEE Journal [76] 

average freshwater availability by about 65% in the latter half of the 20th 
century and is expected to reduce further over the next 30 years (Kaur et al. 
2012). Overall, water demand is projected to increase by about 55% by 
2050 causing worldwide depletion of water resources (Mountford 2011). 
While India might not be critically water scarce at present, in terms of the 
Water Stress Index,1 some parts of northern and southern India (e.g., Uttar 
Pradesh, Andhra Pradesh, Telangana, and Odisha) are ‗critically‘ water 
scarce (Rijsberman 2006). 

The emerging groundwater problem in South Asia and the Middle East and 
their increasing populations are causing demand for water (for domestic 
uses, irrigation, and industries) to rise, leading to depletion of fresh-water 
aquifers on one hand and rising salinity and contamination of usable 
groundwater on the other (Barker, Koppen, and Shah 2000). India is no 
exception and hence needs to critically examine supply augmentation 
options by reusing treated wastewater to bridge the demand-supply gap. 
Municipal bodies are caught in a downward spiral of disrepair and are 
unable to meet demand or maintain effluent standards for treated 
wastewater. Inefficiency, low investment levels, financial unviability, and 
absence of customer orientation means customers face inadequate and 
unreliable low-quality water supply (Dutta, Chander, and Srivastava 2005). 
About 70-80% of domestic water use translates into wastewater production. 
According to Central Pollution Control Board (CPCB), in 2007, the 
combined wastewater generation from cities and towns was 38,354 million 
litres daily (MLD) whereas sewage treatment capacity was only 11,786 
MLD—a gap of about 26,568 MLD. In 2017, the gap, although lower, was 
still about 22,939 MLD. Presently, the average utilisation of the eight 
sewage treatment plants (STP) owned by Delhi Government is 52% of total 
installed capacity, which is rated as gross underutilization (Gautam et al. 
2017). 

In developed nations, treated wastewater is used for agriculture and 
industrial purposes, however, avenues for its use in urban areas are rising. 
Often, scarcity is caused due to mismanagement in the distribution of water 
resources (CSE 2013). The city water-excreta survey2 portrays a succinct 
picture of demand-supply gap and leakage losses in metro cities and other 
classifications. Based on water supply norms of Central Public Health and 
Environmental Engineering Organisation (CPHEEO), water demand is in 

                                                           
1 According to this index, a country is defined as being water scarce when its water resource 
availability is less than 1000 cubic metres per capita (Sengupta 2011). 
2 The survey was published in 2005-06 by Centre for Science and Environment (CSE). It 
depicts the water supply scenario of 71 cities in India. 



[77] Vasudha Chopra and Sukanya Das 

excess of supply in 35% of the 71 cities surveyed with the highest shortfall 
and demand-supply gap in metropolitan cities (Narain 2012). 

In India, according to CPCB, urban areas generate 22,900 MLD of 
domestic wastewater, which has surpassed industrial wastewater generation 
(13,500 MLD). However, the treatment capacity for domestic wastewater is 
only 25% (Sengupta 2011). Water recycling and reuse have proved to be a 
feasible and sustainable way of water management (Anderson 2003). This is 
demonstrated by Chennai Metro Water Supply & Sewage Board‘s 
(CMWSSB) zero-water discharge programme in which all wastewater 
generated is treated and fully reused. As of 2018, 15% of Chennai‘s water 
demand is met from recycled water (International Water Association 2018). 
About 40% of domestic water demand of newly built houses and 8% of 
industrial water demand is met from the treated wastewater (International 
Water Association 2018). In Bengaluru, recycled water is used for toilet 
flushing and landscaping in apartment buildings. In a recent study, 67% of 
survey respondents in Bengaluru city expressed willingness to buy recycled 
water (Ravishankar, Nautiyal and Seshaiah 2018). Wastewater treatment has 
great potential, and it may be feasible for the users and governments to co-
sponsor setting up Wastewater Treatment Plants (WWTP). In fact, WWTPs 
are a pre-requisite to the overall development of urban metropolises as they 
internalise the negative environmental externalities of urbanisation. 

The primary objective of this study is to find out urban residents‘ 
willingness to pay (WTP) for the maintenance of a decentralised WWTP 
that treats domestic wastewater (―grey‖ water) for non-potable reuse. The 
study identifies main factors that affect monetary participation as measured 
by WTP and environmental attitude of individuals. Contingent Valuation 
Method (CVM) was used for valuation of a non-market environmental 
good i.e. treated wastewater and its reuse. The survey responses were 
elicited through an individual Contingent Valuation questionnaire using 
payment card format for the WTP bids, and econometric analysis was 
undertaken to determine the impact of household perceptions, socio-
economic and demographic characteristics on the WTP bids of urban 
households. Subsequently, the average WTP and net benefits were 
computed. The water saving potential was computed in monetary terms as a 
reduction in the freshwater bill3 when recycled water was reused by 
households.  

                                                           
3 The new freshwater bill is the original bill less the water-savings based on water 
consumption level plus the share of O&M cost of the treatment plant that is payable by the 
residents. 



Ecology, Economy and Society–the INSEE Journal [78] 

The paper contributes to (1) the existing contingent valuation literature in a 
developing country context with respect to wastewater treatment; (2) a 
novel econometric identification strategy; and (3) providing the numerical 
value of water saving potential for urban households in Delhi and its 
potential benefits for the households. The next section describes various 
technical options to treat domestic wastewater and some examples from the 
Indian context. Section 3 focuses on description of CVM, theoretical 
framework and related literature. Section 4 depicts the present study area, 
survey data and empirical strategy. Section 5 discusses salient findings, and 
section 6 describes study limitations. The last two sections conclude the 
paper and highlight policy implications.  

 

2. TREATMENT OPTIONS 

In order to be reused, domestic wastewater is treated with specific 
processes determined based on the level of effluent standards and reuse 
purpose. Wastewater reclamation involves various levels of treatment 
including primary (bar screens, grit chambers, skimming tank followed by a 
primary sedimentation tank), secondary (trickling filter, activated sludge 
process, secondary settling tank, oxidation ponds and sludge treatment) and 
tertiary treatment i.e. removal of residual organic matter (Tchobanoglous 
and Burton 1991). Each treatment level is associated with a certain level of 
water pollution as well as purpose, and pollution level decreases with an 
increase in the level of treatment. Benefits of wastewater reclamation 
include conservation of fresh-water resources, prevention of over-
extraction and pollution reduction of water bodies. 

Recently, there has been a paradigm shift with studies demonstrating 
wastewater treatment to its reuse, especially for industrial and agricultural 
purposes. Some metropolitan cities in India like Chennai and Bengaluru are 
working on tapping the potential for wastewater treatment and reuse. In 
Chennai, it is compulsory for every community to treat wastewater. The 
water balance study for wastewater reuse (toilet flushing and irrigation) in 
Sangamam, Tamil Nadu shows that potable fresh-water demand reduced by 
120 litres per capita daily (LPCD) (CPCB, 2008). For non-potable purposes, 
secondary treatment like Activated Sludge Process (ASP), Sequencing Batch 
Reactor (SBR), Moving Bed Biological Reactor (MBBR) and Membrane 
Bioreactor (MBR) are preferred to achieve the required effluent standards. 
Table 1 provides processes4 and their effluent standards, that is Biological 
Oxygen Demand (BOD) and Suspended Solids (SS). MBR is a popular 

                                                           
4 Detailed description of processes is provided in Table A6 in annexure. 



[79] Vasudha Chopra and Sukanya Das 

Table 1: Output Water Quality 

Technology BOD  
(mg/L) 

SS  
(mg/L) 

ASP <20 <30 

SBR <10 <10 

MBBR <30 <30 

MBR <5 <5 

Source: Sugam, Jain and Neog (2017) 

 

technology given its capacity to treat wastewater to prescribed river water 
quality levels but is costly (Kamyotra and Bhardwaj 2011). The costs are 
calculated for 1 MLD, hence, there are limitations in scaling down the costs 
due to the presence of scale economies (Sugam, Jain and Neog 2017). In 
terms of scale economies for 
centralized versus decentralized 
plants, there are cost differences, 
however, decentralized plants are 
designed to operate at a smaller 
scale as compared to centralized 
ones (Massoud, Tarhini and Nasr 
2009). 

The benefit of decentralized plants is the feasibility of community 
involvement in sharing Operation & Maintenance  (O&M) costs that reduces 
monetary burden for government budgets. Since this is on a need basis, it 
will also not lead to wasted potential as opposed to centralised treatment 
plants. In the end, the assessment of centralised plants often indicates 
wasted potential as well as issues of improper piping that lead to mixing of 
treated and untreated water hence wasting the entire effort of treatment in 
the first place (Narain 2012). Although devoid of scale economies, the 
decentralised plants are better suited in the urban as well as rural context. 

 

3. CONTINGENT VALUATION METHOD 

The contingent valuation method (CVM) accounts for necessary scenario 
change for which valuation is done i.e. treatment of domestic wastewater 
through construction of WWTP and subsequent improvement in water 
quality from q0 to q1. The method assesses total consumer welfare from the 
utility through improved water quality. This utility benefit can be measured 
using the Hicksian Compensating Variation (CV) (Weldesilassie et al., 
2009).5 














 0101 0111
hhhhh U,q,peU,q,peCV  (1) 

where, eh (p, q, Uh) is household‘s expenditure function, p is price vector, q 
is the level of public good and the household‘s utility i.e. Uh. The superscript 
0 refers to the current scenario (no provision of public good) and 
superscript 1 refers to improved scenario (with the provision of public 

                                                           
5 The Contingent Valuation framework helps to determine what amount of income change 
would be required to compensate the consumer for the payment. For a ―good‖ change, 
wherein utility is higher than before (an assumption), CV correctly measures the WTP 
(Varian, 1992). 



Ecology, Economy and Society–the INSEE Journal [80] 

good). The compensating variation is equivalent to the maximum amount 
of money that can be appropriated from the consumer, that is shadow price 
or WTP of an individual (Weldesilassie et al. 2009). ―Willingness to pay is 
defined as the maximum price a buyer accepts to pay for a given quantity of 
goods or services‖ (Gall-Ely 2009). 

Equation (1) can also be written as the integral of the shadow price of the 
environmental good: 

dqU,q,peCV h

q

q

hh 




 

01

1

0

 (2) 

where, due to unobservable nature of the shadow price function πh (p, q, 
Uh) = −∂e (p, q, Uh) / ∂q, the willingness to pay is elicited through CVM.  

If individual‘s income in the utility function is Yi and the payment for the 
environmental good is hi for an individual i, in the context of Random 
Utility Model, a respondent will be willing to pay hi (which raises the quality 
of the environmental public good, that is treated wastewater, from q0 to q1) 
if: 

),z,y(U),z,hy(U iiiiiii  11  (3) 

where, z vector contains socio-economic and demographic characteristics. 

Equation 3 simply states that an individual will be willing to pay a certain 
bid if they are better off with the payment and the public good provision 
(Dutta, Chander and Srivastava 2005). This gives rise to the probability of 
an individual saying yes, expressed as: 

)},z,y(U),z,hy(UPr{)yesPr( iiiiiiii  11  (4) 

The utility function of an individual is additively separable into a 
deterministic and random component. 

iiiiii )zy(U),z,y(U   (5) 

Let the deterministic part (non-stochastic) of the utility function be as 
follows: 

)hy(zU iiiii   (6) 

The stochastic part is the error term i.e. . The utility difference is 
calculated as: 

)hz(UU iiiii  01  (7) 



[81] Vasudha Chopra and Sukanya Das 

0 )iiiii hzPr()yesPr(  (8) 

where, α is the difference of vector of coefficients to respective socio-
economic and demographic variables between two scenarios i.e. 01i - . 

The behaviour assumptions for error term necessitate independent and 
identical distribution (IID) with zero mean (Dutta, Chander, and Srivastava 
2005). Usually, logit (logistic CDF) or probit (standard normal CDF) 
models are used depending upon the choice of the cumulative distribution 
function (CDF) for the data obtained. However, WTP calculation is not 
derived from the Random Utility Model for payment card format. Prior 
CVM studies have measured WTP both for setting up a common treatment 
plant as well as improving the capacity of an existing plant (Rollins et al. 
1997; Tziakisa et al. 2009). A recent study in Bengaluru states that 
decentralized wastewater treatment plants are an attractive solution to the 
rising water scarcity problems in urban India (Kuttuva, Lele and Mendez 
2018). They distinguish between the voluntary role of the public in adopting 
the system versus incentives used to gain compliance and conclude that 
even though such systems are a partial solution to addressing the scarcity 
issue, voluntary adoption is quite challenging, and consumers must be 
incentivized for adoption. The study highlights prior arguments made 
against centralized wastewater treatment as a non-optimal solution as 
compared to the decentralized treatment that leads to larger environmental 
benefits (Kuttuva, Lele, and Mendez 2018). Given that other cities in urban 
India face similar constraints in terms of water scarcity and sub-optimal 
performance of centralized wastewater treatment plants, the present study 
offers a potential solution.  

The framework proposed here requires an institutional setting where a 
government-sponsored public good is made available to people and they 
pay a monthly subscription, so it fits into the literature that measures 
benefits of decentralization of treatment plants. A global overview depicts 
the advantages of reusing wastewater, for instance, in Australia, wastewater 
reuse decreased energy consumption and demand for fresh water. In places 
like Lebanon and Palestine, the grey water is used to irrigate crops rather 
than domestic reuse, however, it also has shown positive impacts for 
demand reduction (Madungwe and Sakuringwa 2007). A study in Greece 
elicited residents‘ willingness to pay for the provision of a central 
wastewater treatment plant using CVM through a questionnaire on their 
socio-economic, demographic background and valuation concerns. The 
study found that about 97% of the residents agreed to pay for construction 
and their mean WTP was about 93% of the fresh-water price (Tziakisa et al. 
2009).  



Ecology, Economy and Society–the INSEE Journal [82] 

Another study in Greece ensured the full operational capacity of an existing 
treatment plant and concluded that about 69% of respondents were willing 
to pay, and the total amount contributed by respondents covered the 
amount required for the full operational capacity of the existing plant 
(Kontogianni et al. 2003). These studies follow a similar methodology; 
however, they look at individual households rather than a community. In 
communities, the mindset of respondents is different and often leads to 
comparatively lower zero responses. Loomis et al. (2000) estimate the total 
economic value of restoring selected ecosystem services in a river basin and 
estimate WTP and other total economic value measures using CVM. Rollins 
et al. (1997) elicited WTP by implementing the CVM and concluded a 
significant willingness to pay. The studies that focus on wastewater 
treatment and reuse have implications for water demand, that is, the 
provision of such public goods and perceptions about them have 
implications for water demand by respondents.  

 

4. PRESENT STUDY 

The study is based in New Delhi, which is not only the capital of India but 
is also becoming a hub for activities causing pollution. There are 4 major 
districts i.e. North, South, East and West Delhi as is visible in Figure 1. The 
Population of West Delhi (about 25 lakhs) and South Delhi (about 27 
lakhs) is highest among all major divisions which correspond to high water 
demand and usage relative to other divisions (District Census 2011). Delhi‘s 
colossal water shortage problem coupled with massive population growth 
has led to groundwater exploitation. The water demand-supply gap was 
estimated at 173 MLD (million litres per day) and rose up to 2,149 MLD 
after accounting for leakage loss (Narain 2012). Groundwater resources 
cater to about 12% of the city‘s water needs, however, it is underestimated 
given unaccounted use of alternatives (hand pumps, illegal tube wells etc.). 
This has led to groundwater salinity and a fall in water tables up to 30-45 
meters below the ground level in West, North West and South West Delhi 
(Economic Survey 2014-15). India‘s per capita water availability is 
decreasing and is expected to fall from a level of 5177 cubic meter per year 
in 1951 to 1140 cubic meter by 2050 which warrants need for efficient 
water management (Kaur et al. 2012).  



[83] Vasudha Chopra and Sukanya Das 

Figure 1: Districts in New Delhi 

 
Source: Wikipedia (2019) 

Domestic sewage is about 80% of the household water supply,6 which 
contributes to more than 80% of the pollution load (Narain 2012). CPCB 
estimates 4,426 MLD wastewater generation in Delhi. This wastewater 
needs to be treated in a Municipal Wastewater Treatment Plant (MWWTP) 
to remove harmful chemicals and waste. Although the number of treatment 
plants and capacity has increased over time; as of 2008, the treatment 
capacity was 3,250 MLD at 100% capacity utilization. In other words, only 
about 70% of the wastewater generated in Delhi could be treated (Narain 

2012). There was no domestic reuse and the treated water was dumped into 
drains that nullified the whole process. As of 2018, the installed capacity is 
only 75% of the total wastewater generated in Delhi, and the utilization rate 
is about 75% (Gupta, Singh and Gandhi 2018). 

 

This raises a question about improvement in centralised wastewater 
treatment plants and disposal process and their complementarities with 
decentralized plants (Libralato, Ghirardini and Avezzù 2012). This study 

                                                           
6 Domestic Sewage consists of black water (containing faecal matter) as well as greywater 
(coming from household activities but not containing faecal matter); this study focuses on 
reuse of domestic greywater. 



Ecology, Economy and Society–the INSEE Journal [84] 

focuses on decentralised STPs as the focus is on domestic wastewater reuse 
within households. In addition, effects of improvement in centralized 
(government-owned) STPs have been widely studied in the literature. 
Although economies of scale may not hold with decentralized STPs, it is 
important to recognize that decentralized STPs do not always correspond 
to small scale.7 However, the decentralized approach is helpful in inducing 
wastewater reuse at a lower cost8 through savings in the cost of piping for 
supplying the treated water as compared to centralized STPs. Moreover, 
decentralised STPs favour reuse instead of discharge into the environment 
(Libralato, Ghirardini and Avezzù 2012). Given the economy, efficiency 
and utility, Decentralized Wastewater Treatment Systems (DEWATS) 
jointly set up by community and municipality are becoming popular for 
greywater treatment (including wastewater from kitchen, washing machine 
and bathwater, etc.). Vigyan Vijay, an NGO based in New Delhi, has set up 
a wastewater treatment plant in Vasant Vihar [capacity: 45 KLD (Kilo 
Litres Daily); reuse available: 40 KLD] as well as a kitchen waste biogas 
plant with funding from the residents and the Municipal Corporation of 
Delhi in order to scale up micro-sized plants (Seshadri 2011). Hence, co-
funding is not only a proposed strategy for decentralized treatment plants, 
but it has also already been implemented in India. 

4.1. Study Specifications 

This study is based on a primary survey of 167 urban households in eight 
group housing societies (GHS) in New Delhi.9 Households were identified 
based on stratified random sampling, where a stratum is defined as urban 
households living in group housing societies (each GHS has a common 
Resident Welfare Association (RWA)). The societies were chosen given 
their proximity to municipal WWTP that treated their domestic wastewater. 
Within each society, households were randomly chosen.10 As much as 
possible, the choice of households was random, but it is still subject to a 
sampling error. The households were surveyed from December 2015 – 
February 2016 and one representative from each household who was 
involved in household decisions was interviewed.11 The survey 
questionnaire12 was designed to elicit responses in three information 

                                                           
7 The scale (in KLD or MLD) will depend on a society if the study is to be generalized to 
relatively larger societies as compared to our sample. 
8 The additional capital and maintenance costs for decentralized STPs have been discussed 
under cost estimates. The dual piping is not initiated in Delhi; hence no reasonable estimate 
may be utilized as per Indian Institute of Technology, Roorkee experts. 
9 This represents 6.1 % of total households in 8 societies as of 2016. 
10 On average, less than 10% of households within each society refused to be interviewed. 
11 The 8 survey areas and number of respondents is included in the annexure. 
12 The instructions for questionnaire are included in the annexure. 



[85] Vasudha Chopra and Sukanya Das 

categories: (a) Attitudes, knowledge and behaviour, (b) Economic valuation 
questions and (c) Socio-economic characteristics. The pilot survey took 
place in December 2015 in Punjabi Bagh (West Delhi), where seven 
households were surveyed to test and, if need be, amend the questionnaire 
in order to obtain all required information. The scenario structured for 
CVM in the context of Delhi was quite similar to a study that was 
conducted in Chennai to elicit consumer willingness to pay for wastewater 
management and reuse (Das, Bouzit and Cary 2016). After ensuring the 
appropriateness of the survey questionnaire, the rest of the households 
(160) were surveyed during January – February 2016. The survey was 
conducted by way of a personal interview with a Payment Card Format 
Questionnaire in which a set of bids was provided, and the respondents 
chose their maximum WTP. There was only one scenario change13 that was 
provided to the respondents along with its characteristics. 

Before the pilot study was implemented, the following steps were taken in 
reducing CVM biases. To begin with, as all households in the survey sample 
pay the water bill according to consumption levels, they were familiar with 
water use cost. Further, all survey interviews were conducted by the same 
interviewer in order to minimize bias. All respondents were briefed that 
their freshwater bill would reduce by a certain proportion depending on 
their consumption levels. The interviewees were also told that there was no 
increased monetary burden, in case they ―dislike‖ the payment vehicle 
(Whittington 2010). It was taken care that the time spent in asking the 
questions was more and less same for all interviewees and that the 
interviewer did not indulge in any personal talk in order to avoid anchoring 
or influencing the answers of the participants. Also, for all societies, the 
individual households were chosen randomly, and the survey was 
completed on the same day to avoid survey information being passed on by 
neighbours. The remaining biases are discussed in the limitations. The bid 
amounts14 used for economic valuation were calculated using time series 
data on per capita water consumption in Delhi for the past 10 years. The 
payment vehicle is a tax over and above the existing monthly water bill and 
can be thought of as a monthly payment to the RWA to financially support 
the O&M costs of the treatment plant. 

4.2. Survey data and Econometric Framework 

During the survey period (January – February 2016), the data entry was 
done manually and cross-checked after survey was completed to avoid data 

                                                           
13 The scenario change (in the questionnaire included in annexure) has been constructed 
after consultations with field experts, hydrologists, municipal officials and academicians. 
14 Assuming a 4-person household, the final water bill was Rs. 950. 



Ecology, Economy and Society–the INSEE Journal [86] 

entry errors. The data being of categorical nature required transformations 
to facilitate data analysis using Stata.15 In terms of outliers and errors in 
reporting, data was not always consistent. It was observed that two 
households with same water consumption had a slightly different bill 
amount, but the analysis results are robust to that. 

Given that the data had many zero responses that depicted people‘s 
unwillingness to pay, an empirical model that accounts for the meaning of 
zeros fits the data well. With such data, heckit type 2 or ―hurdle‖ models 
were suitable as they assumed a 2-step decision. In addition, the model 
assumed that factors affecting consumer participation have a different 
impact on consumption/payment hence a two-step process is followed 
(Humphreys, 2013). It also allowed for covariates to be different for two 
processes i.e. participation and payment. These models were a less 
restrictive version of the Tobit Model, which assumes one process explains 
both decisions. According to Jones‘ Alternatives, hurdle models are best 
suited for ―genuine zero‖ responses when the participation and payment 
decision is taken simultaneously. Hence, the Double Hurdle Model was 
employed (Humphreys 2013). In the first step, a probit regression is run for 
the participation decision, where a dummy for whether the respondent is 
willing to participate in the contingent market is regressed on explanatory 
variables (Jones 2000). The second step is a truncated Ordinary Least 
Squares (OLS) regression to model the payment decision by those 
individuals that choose to participate in the contingent market. Since a part 
(116 respondents) of the total sample population (167 respondents) was 
modelled for the second decision, the truncated OLS is used instead of a 
simple OLS regression model. 

The econometric representations given below characterize participation (9) 
and payment (10). 

)Z,DEM,HC,SEC(Y iiii1  (9) 

)Z,DEM,HC,SEC(fY iiii2  (10) 

where, Y1 is participation dummy, Y2 is WTP,   standard normal CDF, f is 

truncated regression CDF, SEC is socio-economic characteristics, HC is 
household characteristics, DEM is demographic characteristics and Z is 
environmental characteristics. The Z vector includes awareness dummy 
(regarding wastewater disposal and treatment) and pollution dummy 
(opinion about pollution due to uncovered sewer drains). 

                                                           
15 Stata, a statistical package, has also been used for the entire estimation of the empirical 
model described in this section in order to obtain findings. 



[87] Vasudha Chopra and Sukanya Das 

Table 2: Summary Statistics 

Name Mean (Std. Dev) 

Age (years) 45.4 (17.0) 

Monthly Income (Rs.) 72997.0 (28591.5) 

Monthly Expenditure on Food (Rs.) 17481.0 (8758.6) 

Total Monthly Expenditure (Rs.) 40304.9 (16419.5) 

Monthly Water Bill (Rs.) 583.9 (371.8) 

Monthly Water Consumption (KL) 21.4 (8.6) 

Children 1.5 (0.8) 

Household Members 3.9 (1.4) 

Toilets 2.4 (0.9) 

Source: Authors‘ calculations based on field survey data 

 

5. SURVEY FINDINGS 

5.1 Empirical Results 

A total of 167 urban households from 8 societies across 4 districts of Delhi 
participated in the survey. All societies had an existing MWWTP 
responsible for treating the domestic wastewater, 70% of which were found 
to be grossly underutilized, and those that were fully utilized had a very low 
treatment capacity as compared to others. Reasons for such poor 
performance have been documented in studies by CPCB as well as others 
(Kaur et al. 2012). Improper design, power cuts and lack of maintenance 
have been highlighted as the main reasons behind the neglect of STPs. Due 
to the lack of proper functioning of the facilities, most STPs remain closed 
or underutilised (Kaur et al. 2012).  

Of the respondents surveyed, 60.5% were males. About 54% of the 
respondents possessed an undergraduate degree, 38% were post-graduates 
and 8% stated higher secondary level as their highest educational 
qualification. About 67% of the respondents were aware of wastewater 
disposal and treatment as well as the existence of an STP nearby. Overall, 
69% of the respondents were willing to pay for wastewater treatment in a 
contingent improved scenario and reuse of treated wastewater. Of those 
who stated motivation behind the willingness to pay (51 respondents), 49% 
said it was for the environmental protection for future generations. These 
specific questions in the survey directly translated into economic variables 
of interest in order to employ the econometric strategy. The description of 
the variables is presented in annexure. Table 2 provides summary statistics. 
The average age of the respondents was 45 years and they played a role in 
household decision-making. Majority of the households had a single child, 
and some had more than 3. On average, the survey households had four 
members each and at least two toilets, and they came under a high tax 
bracket. 



Ecology, Economy and Society–the INSEE Journal [88] 

Table 3: Regression Results of the Survey 

Variables Tier 1: Probit 
Coefficient 

(Standard Error) 

Tier 1: Probit 
Marginal Effects 

(at mean) 

Tier 2: Truncated 
OLS Coefficient 
(Standard Error) 

Age of respondent 0.0152 
(0.0154) 

.0019 
(.001) 

-1.841** 
(.838) 

Education dummy 0.236 
(0.638) 

.0305 
(.083) 

99.362 
(136.792) 

Pollution 1.747*** 
(0.486) 

.2258*** 
(.086) 

111.81** 
(48.757) 

Awareness dummy 3.010*** 
(0.480) 

.3891** 
(.161) 

40.590 
(37.493) 

Female 0.756* 
(0.438) 

.0977 
(.071) 

-46.435** 
(23.99) 

Monthly income 0.0000645*** 
(1.76e-05) 

0.00000834*** 
(2.79e-06) 

.001229* 
(.0006) 

No. of Children -0.156 
(0.270) 

-.0202 
(.037) 

- 

No. of Toilets - - 39.727*** 
(11.976) 

Constant -7.397*** 
(1.787) 

- -100.222 
(159.502) 

Observations 167 167 116 

Source: Authors‘ calculations based on field survey data 
Note: Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1 

 

To reiterate, 69% of the respondents were willing to pay for improved 
wastewater treatment services and reuse. This directly corresponds to 
whether they were aware of improper wastewater disposal and its negative 
environmental consequences. Of the respondents who were unwilling to 
pay (31% of the total sample), about 52% believed the government should 
pay for such improvements. During the interview, many respondents said 
that they did not believe that the municipality put the resources to correct 
use, although it was not formally captured. Hence, it is speculated that this 
belief was triggered due to a trust deficit of consumers in the municipalities 
given their opaque management system. A double-hurdle model for 
participation and payment decision was employed and results for both tier 1 
and 2 are specified in Table 3. 

Female respondents were more likely to pay for wastewater treatment, 
however, their WTP bid was significantly lower than that of male 
respondents. There were some contradictory results obtained in the 
literature with respect to the gender of the respondent (Genius & 



[89] Vasudha Chopra and Sukanya Das 

Tsagarakis 2006). However, Bilgic (2010) found that female respondents in 
Turkey were willing to pay lower than their male counterparts given a 
greater role of males in decision making, which is consistent with the 
present study. Household income was also an important and significant 
factor in determining participation and WTP amounts. Some studies use 
food expenditure as a proxy for income, given the inherent problem of 
underreporting; food expenditure has a positive impact on WTP estimates 
(Bilgic 2010). Whittington et al. (1993) had similarly observed that higher 
income has a causal link with higher willingness to pay. Awareness level of 
the individual regarding wastewater treatment is also a key determinant. 
Level of education showed no significant effect on WTP for improved 
wastewater infrastructure in a study in Canada (Rollins et al. 1997). Result of 
the present study is consistent with that and it is possible that correlation16 
between awareness and education level may be high or it could also be a 
direct result of low variation in education level in the sample.  

Another important factor that contributes towards participation is the 
respondents‘ knowledge and perceptions of pollution caused by sewage 
(positive and significant). Groundwater pollution and coastal pollution have 
been used in drinking water improvement studies and results are in line 
with economic theory as groundwater is a major source for drinking in 
Greece (Tziakisa et al. 2009). In line with mainstream literature, the payment 
decision in this survey is influenced by perceived pollution level. In 
addition, gender and income have a significant impact on the amount that a 
respondent is willing to pay for wastewater treatment. The number of 
toilets (a proxy for water usage) is a significant determinant of the WTP 
bids chosen by respondents. The economic rationale being that those who 
consume more water and pay higher monthly water bill are the ones who 
can afford to do so; they have a high probability of choosing a higher bid 
than low consumption households. Any reduction is beneficial to high 
consumption households and leads to a large potential freshwater and 
monetary saving. 

Usually, it is expected that respondents having high water consumption 
have a higher contribution to the treatment of used water. The analogy is 
similar to the Kyoto Protocol for climate change—the countries that emit 
more carbon must abate at a higher rate. Other measures of water usage 
identified in the literature are the water bill and water consumption. Those 
who pay water bill that is inclusive of wastewater or sewerage charges 
would usually like to pay less for wastewater treatment (Rollins, et al. 1997). 

                                                           
16 To ensure problem of multicollinearity is dealt with, if any, correlation between education 
and awareness is evaluated and it is not high as well as a joint F-test shows significance. 



Ecology, Economy and Society–the INSEE Journal [90] 

A Study done in Greece on wastewater improvement concludes that 
consumers who consume more water are willing to pay a higher amount; 
these are cases of larger families with more no. of children and a high 
willingness to pay for their wellbeing (Tziakisa et al. 2009). The salient 
findings from the survey are listed below. 

1. Relatively high level of awareness among people of wastewater 
treatment and disposal: As many as 69% of the total respondents were 
aware of wastewater treatment concerns in contrast to 44% reported by 
a similar study in Bengaluru (Ravishankar, Nautiyal and Seishaiah 2018). 
However, this awareness is lower in comparison to Australia and 
Florida, possibly because recycled water is already being reused in those 
countries (Po, Nancarrow and Kaercher 2003). 

2. A majority of respondents distrust government‘s fund management: 
More than half (53%) of total respondents were not willing to pay 
government directly, consistent with a similar choice-experiment study 
of WTP of local people near the Ganges that found that even though 
people valued the improvement in wastewater quality, most of them 
were not willing to pay due to a substantial amount of distrust in the 
fund management of authorities (Birol and Das 2012).  

3. Nearly half (48.5%) of the respondents felt that wastewater treatment 
was important regardless of cost. Also, 36.2% (42) of the total 
respondents (116) who were willing to pay for wastewater treatment 
stated their motivation as protection of the environment for future 
generations, while 76% of the respondents (89) felt that wastewater 
treatment was very important regardless of the cost. 

5.2. Willingness to Pay 

The average willingness to pay varies across the survey respondents due to 
the existence of heterogeneity in awareness levels, socio-economic 
characteristics and environmental attitudes. The average WTP varies within 
a range of Rs. 100 – Rs. 200.17 The potential benefits from scenario change 
are constructed using reduction in freshwater bill, water saving potential 
and approximate cost constructed based on estimated cost regressions 
(Starkl et al. 2018), which is compared to water bill in business as usual 
scenario. The comparison is systematically provided in section 5.3. The net 
benefits are calculated while accounting for operating and maintenance 
costs based on the premise that residents are responsible for O&M 

                                                           
17 Table A5 in annexure captures average WTP for each society. 



[91] Vasudha Chopra and Sukanya Das 

Table 4: Household Water Saving Potential 

Consumer 
Category 

Water 
Consumption 
(KL/month) 

Average 
water saving 
(KL/month) 

Average 
water saving 
(Rs./month) 

Potential Saving 
(percentage of 

average monthly 
water bill) 

1 (Low) 0-20 2.09 14.68 6.1% 

2 (Medium) 20-30 4.04 142.10 21.0% 

3 (High) Above 30 6.27 367.27 26.6% 

Source: Authors‘ calculations based on field survey data 

 

expenditure. The capacity18 of 200 KLD was chosen to minimize the sub-
optimal use, achieve cost efficiency and overcome the inherent problem of 
inefficiency in government-owned plants.  

5.3 Water Saving Potential  

The activity-wise water consumption was calculated by a study done in all 
major cities in India, including New Delhi (Shaban and Sharma 2007). In an 
urban household, bathing accounts for the highest proportion of water 
usage, followed by toilets flushing, and utensil washing (Shaban and Sharma 
2007). Based on this study, percentage of water consumption in toilets per 
month in Delhi (16.5%) was computed for all the households. This study 
computes the water saving potential that equals the volume of water used in 
toilets in line with its premise that replacement of freshwater with treated 
water is channeled towards non-potable household use.19 The absolute 
figure depends on total household consumption. Therefore, the households 
were categorized based on their consumption levels. To maintain congruity, 
we followed water consumption categories similar to those provided by 
Delhi Jal Board in their tariff order for domestic consumers. The tariff rates 
used in the calculation are the revised rates for the year 2015. 

The average water saving for three consumer categories (in volume and 
monetary value) are provided in Table 4. The interpretation of the results is 
quite straight-forward in that saving is proportional to consumption 
category: households in high consumption category had higher saving 
potential than those in low consumption category. For a medium category 
water consumer, average water saving was Rs. 140 per month; which is, to 
say the least, not insignificant compared to the average bill in that category 
(i.e. Rs. 675). Similarly, high usage category households on  average  save 

                                                           
18 Although the technology has been scaled down, costs cannot always be scaled down given 
scale economies hence, the scaled down costs from CEEW estimates are compared with 
cost functions estimated by Starkl et al. (2018). 
19 Please note that although non-potable includes a wide variety of uses, toilet reuse was not 
mentioned in the survey. 



Ecology, Economy and Society–the INSEE Journal [92] 

Rs. 367 on the average bill of Rs. 1378 (i.e. average monthly saving of 26% 
of the average water bill for the category). Users who are quite close to the 
upper threshold of categories have an incentive to reduce consumption20 
and save a lot on the water bill, that is, say a household uses 22 Kilo Litres 
(KL) of water per month, by reducing even 2 KL consumer would jump 
down to the lower category thus saving on the water bill. 

Based on these categories, if the average WTP is compared to average water 
saving in monetary terms, it gives a rough idea as to whether households are 
better off21 with the new scenario or not. For low and medium category 
users, the average WTP is higher than the average water saving per month 
whereas for the high category22 users, the potential water saving is much 
higher than average WTP. In Table 6, similar findings by Tziakisa et al. 
(2009) demonstrate WTP as a percentage of average water bill, which is 
40.5%, 24.5% and 9.2% for low, medium and high users, respectively. The 
reason that this figure is relatively low as compared to the finding in Greece 
(93%) may be because the fixed charge is also included in the original water 
bill instead of just comparing by the volumetric charge. Also, the fact that 
low consumption households are not really low income users is also 
important here. High users were quite less relatively in the sample and gave 
zero responses, which drives down the average WTP. This, however, does 
not correspond to a comparison of the two scenarios which is done later in 
this section. 

5.4 Cost Estimates  

There are different technologies for the secondary treatment of wastewater, 
some23 of these are listed in Table 5 along with their per KLD costs as 
estimated by Starkl et al. (2018). Council on Energy, Environment & Water 
(CEEW) computations for input cost24 of wastewater treatment 
technologies provide another comparison, however, it is subject to scale 
issues (Sugam, Jain and Neog 2017). These cost estimates take into 
consideration the following specific characteristics of a wastewater 

                                                           
20 This reduction can stem from various sources i.e. lower absolute consumption of 
freshwater without any increase in alternative sources or reusing treated water such that it 
displaces freshwater consumption. 
21 Table A2 in annexure provides comparison of average WTP based on consumer category 
(low, medium or high) and average water saving (in Rs.). 
22 The Average WTP of the high user category is less than that of the medium due to lower 
number of high users being in the sample and a higher percentage of them were not willing 
to pay as compared to the medium category users. 
23 There are many more technologies that exist, however, this study depicts some of the 
more commonly used ones across India. 
24 The reference values for the capital expenditure are obtained from CPCB; see table A4 ijn 
annexure for estimates scaled down for 200 KLD plant. 



[93] Vasudha Chopra and Sukanya Das 

Table 5: Technology Input Cost 

Technology Total Capital Expenditure 
(Rs. ‗000) 

Total Annual Operating Expenditure 
(Rs. ‗000) 

SBR 644.4 – 5118.8 317.8 

MBBR 410.6 – 6256.4 24 – 92.6 

MBR 12200 - 13530 477.8 

Source: Starkl et al. (2018) 

 

treatment plant: (1) Capacity of 200 KLD (for 350-400 households); (2) 
secondary treatment along with secondary sludge handling; (3) 100-150 m2 
land area requirement; (4) gardening and toilet flushing reuse; (5) BOD and 
SS standards. 

Starkl et al. (2018) estimated cost functions based on actual observed capital 
and maintenance costs of over 50 STPs all over India using all widely 
available treatment technologies, including the four described in this study. 
Starkl et al. (2018) calculated the capital expenditure and the O&M 
expenditure for different technologies per KLD in order to account for 
scale economies.25  

Membrane Bioreactor requires much higher capital and operating 
expenditure; however, it provides a higher output water quality. As per 
CPHEEO guidelines,26 BOD must be less than 10 mg/L and SS must be 
nil in order to reuse water for non-potable purposes (toilet flushing, surface 
irrigation). There exist trade-offs with respect to water quality standards and 
costs between MBR and MBBR, hence, there is no ideal technology that 
can be justified as the best for all criteria, and this fact is well-supported in 
the literature (Starkl et al. 2018). Giving higher preference to effluent 
standards, this study uses O&M costs for SBR and MBR that are borne by 
the beneficiary community, while the governing agency incurs capital costs 
as infrastructure investment.  

The total freshwater cost for community members after the scenario change 
has been portrayed for each consumer category in comparison to their 
average original freshwater bill (before scenario change) to assess the 
benefits (Refer Table 6). O&M costs were calculated for all societies based 
on total resident population. As the number of residents differs across 
societies, a range of O&M shares is provided (Table A3). The new 
freshwater bill is calculated using two components, original freshwater bill 
less the reduction i.e. average water saving and O&M cost share for 
recycled water.  

                                                           
25 The values are time adjusted as per changes in Indian Rupee (Rs.). 
26 The analogous standards as per US EPA (Environmental Protection Agency) are less than 
10 mg/L for BOD and SS. 



Ecology, Economy and Society–the INSEE Journal [94] 

Table 6: Comparison of Scenario A and B in Terms of Freshwater Bill 

Consumer 
Category 

Avg. water 
saving 

(Rs./month) 

Avg. Original 
Freshwater Bill 
(Rs./month) 

New Freshwater 
Bill SBR 

(Rs./month) 

New Freshwater 
Bill MBR 

(Rs./month) 

1(Low) 14.68 241.14 285.31 – 358.87 314.94 – 425.54 

2 (Medium) 142.10 675.32 592.07 – 665.63 621.7 – 732.3 

3 (High) 367.27 1378.57 1070.15 – 1143.71 1099.78 – 1210.38 

Source: Authors‘ calculations based on field survey data; O&M costs based on 
calculations by Starkl et al. (2018). 

 

Based on the above comparison, it can be concluded that low category 
users do not benefit (the new bill is higher). However, it must be noted that 
here the division among residents is equal,27 whereas the water bill 
reduction isn‘t. The medium and high consumption users benefit more 
from the scenario change based on the SBR technology as their total cost of 
water reduces even assuming the higher range value. High category users 
particularly get a sizeable reduction in their freshwater bill, that is Rs. 367, 
and benefit the most from such a decentralized STP construction and water 
reuse. 

 

6. STUDY LIMITATIONS 

The contingent valuation method, a stated preference approach, is prone to 
certain biases given its very nature. Some of those are mentioned in section 
4 along with efforts to minimize the same. Other plausible biases include 
hypothetical bias, that is an overestimation of WTP given that consumers 
do not make choices subject to a budget constraint (Whittington 2010). CV 
studies may also be prone to an enumerator bias, that is people expressing 
positive willingness to pay in order to satisfy the interviewer (Whittington 
2010). Prior literature finds that these biases do not exist at least at a 
statistically significant level, that is the revealed and stated preferences are 
quite similar for the context of developed countries (Carson et al. 1996). 
However, there are mixed findings with respect to developing countries.  

Whittington (2010) points out that there are two scenarios wherein the 
revealed preferences could deviate from the stated ones, that is in case of 
voluntary contributions. However, in this study, the participants were 
explained potential savings in the water bill, so contribution does have 
some future benefit which could reduce the bias to an extent. The other 

                                                           
27 There exist alternatives for proportional contribution based on water consumption and 
saving. 



[95] Vasudha Chopra and Sukanya Das 

scenario is a hypothetical choice in laboratory settings, which does not 
apply to a field setting. The study is based on a small sample size; however, 
it does not cause any statistical significance issues. It seems large enough to 
obtain mean effects for WTP and its determinants. Although respondents 
were told about non-potable reuse of recycled wastewater, the questionnaire 
did not specifically mention toilet flushing to avoid any misgivings. Also, 
there were some households for which data was not consistent, that is even 
with same water consumption for two households, they had a slightly 
different bill, which points out potential inconsistencies with water meters 
that measure consumption. With respect to CEEW cost estimates, it is 
subject to the issue of scaling down due to the presence of scale economies. 
However, to overcome that cost estimates provided by Starkl et al. (2018) 
for a plant as small as 1 KLD are employed. Since these cost estimates are 
fitted regressions based on a sample of 50 STPs, these are not free from the 
errors of fitted cost regression lines.  

The study is largely focused on the computation of benefits. However, 
costs provide a good perspective in terms of assessment of net benefits. 
The scope of this study far exceeds the calculation of net present valued 
benefits and payback period, which are interesting future research avenues. 
Based on a similar survey, the study can be replicated for the entire Delhi 
region and other states. This study may be generalized, to an extent, to 
residential societies in Delhi with common trends with respect to different 
socioeconomic characteristics such as income, education, etc. The societies 
that were surveyed for this study also contained a mix of middle-income 
and high-income households. It may not, however, be generalized at the 
state or national level given the different socio-economic characteristics of 
residents as well as differences in cooperation levels within societies. 

 

7. CONCLUSION 

Wastewater reclamation has been a subject of considerable attention 
especially with respect to industries and agricultural reuse. However, this 
study aims at extending it to the domestic sector. The study utilized primary 
field data that was beneficial in raising awareness about water reuse and 
wastewater treatment among individuals. This was essential given awareness 
is a direct determinant of contribution towards a cleaner environment. This 
result is in line with studies done in major districts of Canada, where prior 
awareness contributes to a higher WTP for a wastewater infrastructure 
project (Rollins et al. 1997). As economic theory dictates, socio-economic 
and demographic characteristics (gender, income) significantly determine 
whether a household will choose to participate in the contingent market. 



Ecology, Economy and Society–the INSEE Journal [96] 

Overall, the survey reveals that 30% of the urban population living in 
residential colonies of New Delhi is still unaware of wastewater treatment. 
The analysis concludes that medium and high usage consumers benefit the 
most based on technology choices. However, for low and medium users 
paying for expensive technology, that is MBR, the government must offer 
some subsidy in order to make them at least as better off as before. For low 
usage consumers, the study recommends a subsidy of at least Rs. 73 a 
month considering the lower threshold of range for freshwater with MBR 
technology (Table 6), and for medium usage consumers, a subsidy of at the 
most Rs. 57 a month considering the higher threshold. Hence, co-financed 
decentralized STPs will benefit28 the urban residential colonies in Delhi. 
This analysis is based on adapted suitable cost estimates that may be subject 
to error and whether the households adopt the practice voluntarily or 
otherwise is not the focus. However, it is another way forward for any 
extensions to the context of urban cities in India. 

 

8. POLICY IMPLICATIONS 

Awareness of all individuals must be augmented in order to tackle 
environmental problems with suitable incentives. In fact, as mentioned in 
the paper, Delhi Government could think about replicating a mandatory 
framework used in the context of Chennai. In addition, in Kerala, the newly 
built residential buildings also include the construction costs of the WWTP 
within the cost of the house, therefore ensuring the residents finance the 
public good. To deal with lack of trust in local bodies, transparency and 
effective policy implementation can be targeted. The government can 
introduce a subsidy scheme for households that participate in water 
conservation such that they can be incentivised, and possibly other 
households can be motivated to invest in environmental protection. Given 
that co-funding leads to some reduction in spending by the government, it 
would be interesting to try and assess whether a subsidy can be constructed 
that leaves everyone at least as better off as before. This gives way to 
potential savings in water bill and environmental benefits from cleaner 
drains. This study suggests a possible way of creating environmental and 
monetary benefits not only for the new apartment buildings but also for 
existing residential colonies. This, of course, depends on additional 
safeguards that treatment plants run at full capacity and are economically 
viable. Although the study might inform policy, it is far from providing a 
well-structured policy, for which further research is encouraged on 

                                                           
28 These results are based on cost estimates that may change overtime and hence, are subject 
to dynamics of freshwater price changes and changes in O&M costs. 



[97] Vasudha Chopra and Sukanya Das 

consumer attitudes, awareness and social acceptance that may be helpful 
while designing policies. 

 

ACKNOWLEDGEMENTS 

The authors sincerely thank the anonymous referees for their critical 
comments which significantly helped in improving the quality of the paper. 

 

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[101] Vasudha Chopra and Sukanya Das 

Figure A1: Survey areas in New Delhi 

33 

21 

27 

12 

31 

17 
14 

12 

0

5

10

15

20

25

30

35

Respondents

 

Source: Authors‘ calculations based on field survey data 

ANNEXURE A 

 

 

 

 

 

 

 

 

 

 

 
 

Table A1: Description of Variables 

Name of 
Variable 

Definition Type 

Age The age of a person in completed years that 
relates to his / her last birthday prior to the date 
of enquiry. 

Discrete 

Education The highest general and technical education 
levels attained by the person determine what is 
known as educational standard. 

Categorical 

Occupation The nature of economic activity performed by a 
person is his/her occupation. 

Categorical 

Female Identifies the gender of the respondent. The 
dummy = 1 if respondent is a female, 0 
otherwise. 

Binary 

Monthly 
Income 

The monthly income includes all income 
accruing from economic activity i.e. 
wage/salaried employed. 

Continuous 

Awareness 
Dummy 

= 1 if the respondent is aware about wastewater 
disposal and related services, 0 otherwise. 

Binary 

Children Equal to the no. of children born to the 
respondent. 

Discrete 

Sewer Drain 
Pollution 

=1 if the respondent faces pollution from 
uncovered sewer drain, 0 otherwise. 

Binary 

Toilets Equal to the no. of toilets/washrooms that are 
used in a respondent‘s household. 

Discrete 

Source: NSSO (2001) for first three variables, and authors‘ own compilation for 
the rest. 



Ecology, Economy and Society–the INSEE Journal [102] 

 

Table A2: Comparison of Average WTP, Average water saving & Average Water 
Bill 

Consumer 
Category 

Water 
Consump-
tion (KL / 

month) 

No. of 
Obser-
vations 

Average 
water saving 
(KL/month) 

Average 
water saving 

(Rs./ 
month) 

Average 
WTP 
(Rs./ 

month) 

Average 
Freshwater 
Bill (Rs. / 
Month) 

1(Low) 0-20 57 2.09 14.68 97.89 241.14 

2 (Medium) 20-30 88 4.04 142.10 165.85 675.32 

3 (High) Above 30 14 6.27 367.27 127.5 1378.57 

Source: Authors‘ calculations based on field survey data 
Note: The average freshwater bill is based on full consumption quantity, however, 
average WTP is based on the use of treated water for non-potable purposes, 
majorly toilet flushing, as freshwater saving potential measured in KL. The average 
saving is through the reduction in water bill as a result of reduced consumption of 
freshwater. 

 

Table A3: Computation of O&M cost 

Consumer 
Category 

Water 
Consum-

ption  
(KL) 

Average 
water 
saving 
(KL) 

Average 
water 
saving 
(Rs.) 

Average 
WTP 
(Rs.) 

Average 
Fresh-

water Bill 
(Rs.) 

O&M – 
SBR  
(Rs.) 

O&M – 
MBR  
(Rs.) 

1 (Low) 0-20 2.09 14.68 97.89 241.14 58.85 –
132.41 

88.48 –
199.08 

2 (Medium) 20-30 4.04 142.10 165.85 675.32 58.85 –
132.41 

88.48 – 
199.08 

3 (High) Above 30 6.27 367.27 127.5 1378.57 58.85 –
132.41 

88.48 –
199.08 

Source: Authors‘ calculations based on field survey data; O&M costs based on 
calculations by Starkl et al. (2018). 
Note: All figures are per month. 

 

 

 

 

 

 

 

 

 

 



[103] Vasudha Chopra and Sukanya Das 

Table A4: Technology Input Cost (based on CEEW estimates) 

Techno-
logy 

Total Capital 
Expenditure* 

(Rs. ‗000) 

Annual Operating Expenditure 
(Rs. ‗000) 

Total Annual 
Operating 

Expenditure 
(Rs. ‗000) 

  Power Repairs Chemical Manpower  

Activated 
Sludge 
Process 
(ASP) 

2000 80.8 30 17 13.4 141.2 

Sequencin
g Batch 
Reactor 
(SBR) 

1800 66.8 23.2 17 8.2 115.2 

Moving 
Bed 
Biological 
Reactor 
(MBBR) 

1800 97.4 24.4 17 9.8 148.6 

Membrane 
Bioreactor 
(MBR) 

6400 132.4 44 132 24 332.4 

Source: Sugam, Jain and Neog (2017) 
Note: * signifies including land cost 

 

Table A5: Average WTP by society 

Area Average WTP 
(Rs./month) 

Total population of the 
society (no. of households) 

Dwarka 181.61 400 

Paschim Vihar 
(two societies) 

152.18 400 

Paschim Puri 165.5 250 

Punjabi Bagh 196.85 200 

Tilak Nagar 200.83 350 

Preet Vihar 173.25 300 

Shalimar Bagh 182.11 400 

Vasant Kunj 101.25 450 

Source: Authors‘ calculations based on field survey data 

 

 

 

 

 



Ecology, Economy and Society–the INSEE Journal [104] 

Table A6: Description of Technologies 

Technology Description 

Activated Sludge 
Process (ASP) 

The first process requires aeration tank that uses oxygen 
to break down the BOD components and a clarifier in 
the secondary process in order to separate sludge. 

Sequencing Batch 
Reactor (SBR) 

Involves three processes in a sequence—fill-aeration, 
settling and decantation to remove organic matter. 

Moving Bed 
Biological Reactor 
(MBBR) 

Like ASP, it uses aeration tank in order to treat the 
wastewater. 

Membrane 
Bioreactor (MBR) 

Amalgamation of activated sludge process and 
microfiltration in order to treat wastewater. 

Source: Kamyotra and Bhardwaj (2011) 

 

ANNEXURE B 

1. Instructions/Introduction for each respondent (before the survey 
questionnaire)29 

Hello, my name is Vasudha Chopra and I am a master‘s student at TERI University 
pursuing M.Sc. Economics. I am working on a study for my master‘s thesis that 
focuses on wastewater treatment. This is a field experiment in the economics of 
decision making.  

Would you like to participate for a short interview?  

I have a short questionnaire form with me for you to fill based on your true 
preferences in wastewater treatment and reuse (for non-potable purposes i.e. 
gardening, toilet flushing, cleaning of cars etc.). Some of the information is also 
contained in the questionnaire. If there are any questions or confusions while filling 
it out, please feel free to ask me to explain. 

Before starting, I would like to stress on the fact that I want you to answer in line 
with your true preferences as re-use of recycled water leads to monetary saving in 
freshwater bill.  

Thank you. 
 
2. Questionnaire 

A. HOUSEHOLD PROFILE 

1. Name and address of respondent: Name (optional); Address; Residing Area; 
Mobile/Telephone (optional) 

                                                           
29 While this script was written out initially, it was made sure that the interviewer memorizes 
the same and speaks out loud while quickly introducing himself/herself to the respondent 
and asking for permission to be interviewed. If the permission wasn‘t granted, then another 
random household was picked and so on. 



[105] Vasudha Chopra and Sukanya Das 

2. Age (in years) of the respondent: __ 

3. Gender of the respondent: choose from  Male; Female 

4. No. of members in the household (including you): __ 

5. Caste affiliation of the Respondent: choose from  General; Scheduled Tribe; 
Scheduled Caste; Other Backward Caste 

6. Education status of respondent: choose from  Illiterate; Higher Secondary (up 
to age of 18); Up to Primary education (up to the age of 11); Graduate; Secondary 
School education (up to the age of 16); Post Graduate and above 

7. Education status of decision maker: choose from  Illiterate; Higher Secondary 
(up to age of 18); Up to Primary education (up to the age of 11); Graduate; 
Secondary School education (up to the age of 16); Post Graduate and above 

8. Main occupation of respondent: choose from  Service; Student; Self-employed; 
Worker; Pensioner/Retiree; Unemployed; Other (if, specify __) 

9. Main occupation of decision maker: choose from  Service; Student; Self-
employed; Worker; Pensioner/Retiree; Unemployed; Other (if, specify __) 

10. Which class best describes your total monthly household income? choose from 

 Less than Rs. 10,000; Rs. 20,000 - Rs. 30,000; Rs. 40,000 - Rs. 50,000; Rs. 10,000 
- Rs. 20,000; Rs. 30,000 - Rs. 40,000; Higher than 50,000 

11. How much is your approximate monthly household expenditure on food 
items?__ 

12. Number of children in your home, if any? __ 

13. Since how many years have you been living in your home? __ 

14. Do you own or rent your home? choose from  Own House; Rented House 

15. Provide the no. of toilets in the house? __ 

B. HOUSEHOLD PROVISION OF EXISTING SEWERAGE AND WATER 
SERVICES: 

16. What are the sources for potable water (drinking/cooking)? choose from 

Piped water connection; Rainwater; Public Tap; Bottled/Canned Water; Bore 
well/Tube well/Dug well; Surface Water (river, pond, canal etc.) 

17. What are the sources for non-potable water (bathing/washing/cleaning etc.)? 

choose from Piped water connection; Rainwater; Public Tap; Bottled/Canned 
Water; Bore well/Tube well/Dug well; Surface Water (river, pond, canal etc.) 

18. How much is your monthly household consumption of water (in kilolitres or 
units)? __ 

19. How much is your average monthly water bill (in rupees)? Rs. __ 



Ecology, Economy and Society–the INSEE Journal [106] 

20. How much was the water consumption last year? __KL 

C. WARM UP AND AWARENESS QUESTIONS 

21. Do you know where the household wastewater is being disposed? choose from 

 Central Sewerage System; Groundwater Seepage; River; Roadside Drains / 
Sewer Drains; Canal; Do not know  

22. Are you aware that the domestic wastewater is being treated in a Sewage 

Treatment Plant? choose from  Yes; No; Do not know 

23. Are you aware of existence of STPs owned by the government in order to treat 

wastewater in your society? choose from  Yes; No; Do not know 

24. Is there a sewer drain nearby your area? choose from  Yes; No; Do not know 

25. How would you describe water quality of sewer drain in your area? choose from 

 Very poor; Poor; Adequate; Good; Very good; Do not know 

26. How are the sanitation facilities in your society? choose from  Excellent; 
Adequate; Very poor; Good; Poor; Do not know 

27. If you answered poor or very poor to the previous question, then how many 
times have you suffered from illness in the last 5 years? __ 

28. Are you suffering from pollution due to the sewer drain? choose from  Yes; 
No; Do not know 

29. If a sewer drain exists nearby then have you experienced any illness due to its 

pollution in the last 5 years? choose from  Yes; No; Do not know. 

D. PERCEPTION ABOUT ENVIRONMENTAL QUALITY  

30. Which statement do you agree with the most? choose from  Wastewater 
treatment is very important regardless the cost; Wastewater treatment is important 
while holding the current cost; Wastewater treatment is sufficient and we should 
cut down on cost.  

31. Cleaning of the nearby sewer drain may have various benefits, which among the 
following are the most important for your residing area? express your preference in 

order from 1 to 6 (1 signifies most important; 6 signifies least important)  
Eliminates odor; Eliminates insects and mosquitoes; Avoid groundwater 
contamination; Reduce pollution; Reduce health problems; Lower the degradation 
of the environment 

32. Treatment of domestic wastewater may have various benefits, which among the 
following are the most important according to you? express your preference in 

order from 1 to 5 (1 signifies most important; 5 signifies least important)  Water 
recycling and reuse; Lower water pollution; Improving health situation; 
Conservation of scarce freshwater; Protection of the environment 



[107] Vasudha Chopra and Sukanya Das 

33. In order for conserving the scarce freshwater in India, treatment and reuse of 

domestic wastewater would be: choose from  Very Important; Somewhat 
Important; Not important; Important; Less Important 

E. CONTINGENT VALUATION 

o Huge wastewater generation from households in Delhi 
o Only 55% of this wastewater is treated currently 
o Causes enormous environmental and water pollution, groundwater 
contamination as well as health problems 
o There is a need for treatment of the remaining untreated wastewater 

Current Scenario (A):  There is no treatment plant set up near your society. The 
STPs by the government only partially treat the wastewater. 
o There is an environmental degradation involving groundwater depletion.  
o The untreated wastewater and poorly managed sewer drains cause health problems. 

Improved Scenario (B):  A treatment plant is set up near your society which treats 
the wastewater and this can be reused for non-potable purposes like cleaning of 
cars and gardening.  
o Water bill reduces since now fresh water is not being used for a few non-potable 
purposes.  
o There is an environmental improvement and due to better management of water in 
sewer drains there are lower health problems in your area. 

34. Suppose that Wastewater plant were to be constructed, this will result in an 
improvement in the quality of environment from Scenario A to B. Would you be 
willing to pay higher water and sewage taxes for environmental improvement? 

choose from  Yes; No; Do not know 

35. If you answered yes to Q34, what amount are you willing to pay per month? 

choose from  Rs. 95; Rs. 114; Rs. 142; Rs. 161; Rs. 190; Rs. 237; Rs. 256; Rs. 285; 
Rs. 304; Rs. 332; None of the mentioned; if any other amount, Rs. __.  

36. If you chose none of the above for Q35, would you be willing to pay a lower 

amount per month for the improvement of the environment? choose from  Rs. 
47; Rs. 66; Rs. 95; Rs. 114; Rs. 142; Rs. 161; Rs. 190; Rs. 209; None of the above  

37. Would you be willing to pay a higher amount for environmental improvement 

than the ones offered before? choose from  Yes; No; Do not know 

38. If you answered yes to Q37, how much are you willing to pay for 

environmental improvement? choose from  Rs. 190; Rs. 285; Rs. 380; Rs. 475; 
Rs. 237; Rs. 332; Rs. 427 

39. If you are willing to pay for environmental improvement then please state your 

motivation to do so: choose from  Wastewater management problem is 
important; Enjoy contributing for a cleaner environment; Protection of 
environment for future generations; Water is being over-utilized and its 



Ecology, Economy and Society–the INSEE Journal [108] 

conservation is important; Enjoy contributing for other reasons; Other reasons if 
any  

40. If you are NOT willing to pay for environmental improvement then state your 

reasons: choose from  The government should pay for water conservation rather 
than introducing new taxes; Cannot afford to pay more taxes; Not interested in 
paying for wastewater management; Not interested in paying for a cleaner 
environment; This problem is not a priority; Water is not being over-utilized and 
there is no need for water conservation 


