Water 2019 , 11, x doi: FOR PEER REVIEW www.mdpi.comjournal water [609820]

Water 2019 , 11, x; doi: FOR PEER REVIEW www.mdpi.com/journal/ water
Article 1
Household willingness to pay for wastewater 2
treatment and water supply system improvement in a 3
Ger area in Ulaanbaatar city, Mongolia 4
Ariuntuya Byambadorj 1,2 and Han Soo Lee 1,* 5
1 Graduate School for International Development and Cooperation, Hiroshima University, 1 -5-1 6
Kagamiyama, Higashi -Hiroshima 739 -8529, Hiroshima, Japan 7
2 Property Relations Department, Ulaanbaatar City, Mongolia ; [anonimizat] 8
* Correspondence: leehs@hiroshima -u.ac.jp ; Tel.: + 81-82-424-4405 9
Received: date; Accepted: date; Published: date 10
Abstract: This study aims to identify the current conditions of the water supply, sewage system, 11
wastewater treatment and sanitation conditions of Ger (traditional nomadic house) areas in 12
Ulaanbaatar city and to investigate the willingness of residents to pay for improvements in the water 13
supply and wastewater treatment system in terms of capital costs and operation & management 14
costs. The study uses a contingent val uation method (CVM) and applies a Tobit model. The 15
problems in the Ger area are a lack of a drainage system, the use of unimproved sanitation 16
technology, and an unsafe water supply, in addition to the direct discharging of wastewater into pit 17
latrines, soa k pits, yards, and streets by the residents of Ger areas. A field survey was conducted in 18
the Damba planning unit, which is defined by the Urban Development Trend 2030. It is located in 19
the north -eastern part of Ulaanbaatar city, and the residents all live in Ger and owner -built houses. 20
The survey collected 298 samples from residents in the Damba planning unit. The average total 21
willingness to pay for the water supply and wastewater treatment facility installation was 1000.0 22
(thous. MNT), the average total willingness to pay for the operation and management costs was a 23
maximum of 3.0 (thous. MNT) per month. The Tobit model results show that the willingness to pay 24
for the installation is significantly influenced by income level, education, current payment for water 25
and housing. The willingness to pay for operation and management costs is strongly influenced by 26
the family size and income level and not strongly influenced by the housing and water 27
consumption. 28
Keywords: Willingness -to-pay; wastewater treatment; Ger area; Tobit model ; Ulaanbaatar City 29
30
1. Introduction 31
Currently, water, sanitation, and hygiene are a global concern and a priority area in the 32
international development sector [1]. 33
Ulaanbaatar, Mongolia, had a population of over 1.4 million in 2016 and is experiencing many 34
environmental, health and socio -economic problems due to unplanned urban expansion. Sixty 35
percent of the total population of Ulaanbaatar reside in peri -urban infor mal settlements, called Ger 36
areas (a Ger is a portable round tent used in traditional nomadic settlements). A lack of a safe water 37
supply and unimproved sanitation have been found to be the key issues in the Ger areas of Mongolia. 38
Simple, unimproved and un ventilated pit latrines and soak pits are generally used for on -site 39
sanitation and household greywater, resulting in unhygienic living conditions [2]. 40
Ger areas are located around the city core (primarily to the north), and due to their lower 41
populatio n density, they comprise most of the settled areas of the city. Ger area buildings are rarely 42
over two stories high. The most distinguishing feature of the Ger areas is the 2 -metre -high wooden 43

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fences that enclose all the residential plots and their typical ly irregular layout. Housing in the Ger 44
areas is predominantly single -family and is a mix of traditional Gers and owner -built houses 45
constructed of either brick or wood. 46
The daily average water consumption of 7.3 litres per person in the Ger areas is low c ompared 47
to the world standard of 25 litres and far lower than the 291 litres average used by residents of the 48
city core. The low usage in the Ger areas can be attributed to deficiencies in the water supply system, 49
constraints due to problems with other ser vice sectors and traditional water use practices. 50
The sanitation practices in Ger areas has significant and cumulative impacts on the soil and 51
groundwater. Although pathogens from human waste eventually degrade in the ground, these 52
processes are slowed by the cold climate, with an average annual temperature of 1.0 ℃[3], an average 53
summer temperature of 15 ℃ and an average winter temperature of -19℃[4]. The large amounts of 54
waste going into the soil can be transported by ground water and surface water during the spring 55
thaw and summer rains. Other impacts concern the health of the residents due to the lack of hygienic 56
sanitation, which likely contribute directly to higher disease rates. The lack of adequate wastewater 57
disposal systems is one barrier to increasing the quantity of water supplied to residents. The lack of 58
basic urban services and infrastructure in the Ger a rea settlements has become a source of urban 59
environmental issues such as air, water and soil pollution [4]. 60
There are few studies that have measured improvements in household water supply (use) and 61
wastewater treatment in the Ger area of Ulaanbaatar. Some of them studied decentralized wastewater 62
treatment plants (WWTPs) [5, 6] and research about exposure to water supply, sanitation, and 63
hygiene -related hazards [1, 2] . 64
A series of reports, Ger Area Upgrading Strategy of Ulaanbaatar City [4, 7, 8] , have been 65
prepared by the UN Habitat for the development of Ger areas. They assess growth prospects, land 66
requirements, environmental issues and development constraints, and profile opportunities and 67
constraints in all the service and infrastructure areas (water supply, sanitation, solid waste 68
management, heating, electricity, street lighting, roads and footpaths, transportation services, flood 69
control and drainage, health services, emergency services, ed ucation, and greening) in Ger areas, 70
establish the legal and institutional structure of land management and planning, and identify the 71
specific issues related to the Ger areas. 72
Many studies on willingness to pay (WTP), applied to non -market products, allow ed the 73
contingent value model (CVM) to obtain the economic valuation of the proposed service, based on 74
an improvement in the living conditions of the beneficiaries [9, 10, 11] . The Tobit analysis offers a 75
straightforward technique for evaluating the WTP data as it allows for zero bids [10]. 76
There is no study on residents’ WTP for a water supply and wastewater treatment in the Ger 77
areas of Ulaanbaatar. Therefore, in this context, the main objective of this research is to estimate the 78
WTP for improvements in the water supply and wastewater treatment system of the Ger areas in 79
Ulaanbaatar city by analysing the results of a field survey by appl ying the Tobit model. Moreover, 80
we applied the ordinary least -square (OLS) method for analysing the survey results to compare with 81
the results from the Tobit model. The study area and field survey are described in Section 2. The data 82
and methodology are pr esented in Section 3, and the results and conclusions are described in Sections 83
4 and 5, respectively. 84
2. Study area and Field survey 85
2.1. Study area 86
Ulaanbaatar is the capital and the largest city of Mongolia and serves as the economic and 87
cultural centr e of the country ( Fig. 1 ). The Ulaanbaatar Ger areas are now permanently occupied. 88
While the housing density of the Ger areas is too high for a traditional lifestyle, it is too low for the 89
efficient provision of basic urban services to low -income household s. Small Ger areas could rely on 90
the adjacent developed city core for basic urban services, but now that the Ger area population has 91
increased to over 58% [4] of the total city the resulting spatial expansion is forcing new settlements 92
many kilometres away from the developed city core area. 93

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The Damba planning unit, one of the Ger districts defined by Urban Development Trend 2030 94
(Fig. 1 ), is chosen for the study area since it has 1 decentralized wastewater treatment plant ( 100 m3 95
per day ) in the centre, supporting limited number of households due to sewage line connection. 96
Therefore, it would be useful to investigate the willingness to pay for the wastewater treatment 97
improvement from local residents. It is located in the northeast part of Ulaanbaatar city, and residents 98
in the area all live in Ger and owner -built houses. The population of the Damba unit was 99
approximately 32,100, and the number of households was approximately 8,700 in 2010 [12]. In the 100
Damba area, in addition to the decentralized wastewater treatment plant, there are 20 main line – 101
connected water supply stations, 7 truck water su pply stations, and 12 bathhouses. 102

Figure 1. The study area, Damba planning unit in the Ulaanbaatar City, Mongolia 103
2.2. Field survey 104
To estimate the WTP for improving the water supply and wastewater treatment system, a field 105
survey was conducted in the Damba planning unit in September 2017. To assess the WTP of residents 106
for improved domestic water service, a random sampling process was used to select households in 107
the Damba area to be interviewed. The survey team contacted 300 households. A total of 298 108
questionnaires were completed with a response rate of 99%. Among the 298 observations, 14 outliers 109
were excluded from the analyses, and the remaining 284 observations were kept in the sample. 110
The questionnaire included five sections: (1) respondent identification, (2) household 111
information, (3) household water supply and consumption, (4) wastewater sanitation, and (5) WTP 112
for the cost of construction and for the operation and management. Of the 284 responses included in 113
the analysis, 105 (37%) indicated a zero WTP for water supply and wastewater treatment. Figure 2 114
illustrates the summary of the field survey. The questionnaire used in the field survey can be found 115
in the Supplementary Material section. 116
The household’s WTP was elicited through the following two -part questioning framework: 117
a) Would you be willing to pay an _____ fee to access a water supply (WS) and wastewater 118
treatment (WWT) fa cility? (Yes or No) 119
b) If yes, how much would you be willing to pay for the installation (capital cost)? (0=0, 1=less 120
than 1000.0, 2=1000.0 -1500.0, 3=1500.0 -2000.0, 4=higher than 2000.0 (thous. MNT)). 121
A “No” response to item a) implied a zero WTP in item b). 122
(a) Ulaanbaatar City
(b) Damba Planning Unit

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Figure 2. Summary of a field survey result 123

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3. Data and Methodology 124
The WTP is used as a measure of non -market goods’ price, based on the assumptions of rational 125
choice and utility maximization. If a change is proposed for a non -market good and a person believes 126
that the change makes them better off in some way, that person may be willing to pay for this change, 127
and so the WTP reflects a person’s economic valuation of the good in question. [13]. 128
Contingent valuation (CV) is the most appropriate method for describing and valuing the 129
(hypothetical) environmental changes that may be incurred through the provision of a wastewater 130
treatment facility. The important feature that distinguishes CV from oth er methods (e.g., the travel 131
cost and hedonic pricing methods) is that CV captures the total economic value of the benefits 132
concerned. This means that the values derived from CV incorporate both a ‘use’ and a ‘non -use’ 133
component. In the case of a wastewate r treatment facility, the value can be defined as the household 134
WTP for the benefits that they will receive from the use of a wastewater treatment facility. The 135
benefits could include their reduced exposure to odours, pollution of local ditches or streams, and 136
the inconvenience of arranging for bathing and cleaning. The non -use value relates to an individual’s 137
WTP for the benefits in the absence of any likelihood that they will personally be a direct beneficiary 138
[14]. 139
WTP for non -market amenities is typically expressed using a Hicksian equivalence or a 140
compensating surplus measure [10]. Under this approach, the parameters of the inverse Hicksian 141
demand function are estimated. A Hicksian compen sation measure of consumer 𝑖’s WTP for an 142
increase in the level of non -market amenities is given by: 143
𝑊𝑇𝑃 𝑖=𝑓(𝑞,𝑌,𝑇𝑖 )=[𝑒𝑖(𝑝0,𝑞0,𝑈0)=𝑌0]−[𝑒𝑖(𝑝0,𝑞1,𝑈0)=𝑌1] (1)
where 𝑒(.) is the consumer’s expenditure function, U is the respondent’s utility, Y is the 144
minimum level of income necessary to maintain utility given the prices and quantities, 𝑞0 and 𝑞1 145
represent the quantities of the non -market good 𝑞1>𝑞0 and 𝑇𝑖 is a vector of characteristics 146
influencing the tastes and preferences of consumer 𝑖. If 𝑌1>𝑌0, then 𝑞1 is preferred to 𝑞0 and the 147
consumer would be willing to pay for the additional level of amenities up to the point where utility 148
was unchanged. If 𝑌1≤𝑌0, the 𝑞1 is not preferred to 𝑞0 and a non -positive level of compensating 149
surplus is implied. In this case, a corner solution is implied, and the consumer will report a zero bid 150
in the survey on their WTP for the additional amenities offered in 𝑞1. 151
Additional flexibility in the interpre tation of zero bids in a WTP survey will allow the use/non – 152
use decision implied by zero bids to be generated by a process separate from that governing the 153
continuous WTP revealed by potential users. In terms of the bid function, the parameters of 𝑓(.) are 154
permitted to vary between users and non -users. This procedure allows the variables to exert 155
differentiated influences on the discrete use/non -use decision and the continuous WTP for the 156
subsample of users. This distinction is important because constraining the bid function to be 157
generated by the same processes for both users and non -users may introduce specification biases into 158
the estimation of the bid function and may thus result in misleading inferences. Allowing such 159
flexibility has intuitive appeal beca use non -users may reveal a different set of tastes and preferences 160
for the non -mark et good through their zero bids [10]. 161
One of the models that is being increasingly used is the Tobit analysis, a model dev ised by Tobin 162
[15] in which it is assumed that the dependent variable has a number of values clustered at a limiting 163
value, usually zero [16]. For example, the data on the demand for consumable goods often have 164
values clustered at zero; data on hours of w ork often have the same clustering. The Tobit technique 165
uses all of the observations, both those at the limit and those above it, to estimate the regression line, 166
and it is preferred, in general, over alternative techniques that estimate the regression lin e only with 167
the observations above the limit [16]. 168
3.1. Standard Tobit model 169
In the standard Tobit model [17], there is a dependent variable y that is left -censored at zero: 170
𝑦𝑖∗=𝑥𝑖′𝛽+𝜀𝑖 (2)

Water 2019 , 11, x FOR PEER REVIEW 6 of 11
𝑦𝑖={0 𝑖𝑓 𝑦𝑖∗≤0
𝑦𝑖∗ 𝑖𝑓 𝑦𝑖∗>0 (3)
where, the subscript 𝑖= 1,…,𝑁 indicates that the observation 𝑦𝑖∗ is an unobserved ( “latent” ) 171
variable, 𝑥𝑖 is a vector of explanatory variables, 𝛽 is a vector of unknown parameters, and 𝜀𝑖 is a 172
disturbance term. 173
3.2. Censored regression model 174
The censored regression model is a generalization of the standard Tobit model. The dependent 175
variable can be either left -censored, right -censored, or both left-censored and right -censored, where 176
the lower and/or upper limit of the dependent variable can be any number: 177
𝑦𝑖∗=𝑥𝑖′𝛽+𝜀𝑖 (4)
𝑦𝑖={𝑎 𝑖𝑓 𝑦𝑖∗≤𝑎
𝑦𝑖∗ 𝑖𝑓 𝑎<𝑦𝑖∗<𝑏
𝑏 𝑖𝑓 𝑦𝑖∗≥𝑏 (5)
where, 𝑎 is the lower limit, and b is the upper limit of the dependent variable. If 𝑎=−∞ or 𝑏=∞, 178
the dependent variable is not left -censored or not right -censored, respectively. 179
3.3. Estimation method 180
Censored regression models (including the standard Tobit model ) are usually estimated by the 181
maximum likelihood (ML) method. Assuming that the disturbance term 𝜀 follows a normal 182
distribution with a mean of 0 and a variance of 𝜎2, the log -likelihood function is: 183
log𝐿=∑[𝐼𝑖𝑎logΦ(𝑎−𝑥𝑖′𝛽
𝜎)+𝐼𝑖𝑏logΦ(𝑥𝑖′𝛽−𝑏
𝜎)+(1−𝐼𝑖𝑎−𝐼𝑖𝑏)(log∅(𝑦𝑖−𝑥𝑖′𝛽
𝜎)−log𝜎)]𝑁
𝑖=1 (6)
where ∅(.) and Φ(.) denote the probability density function and the cumulative distribution 184
function, respectively, of the standard normal distribution, and 𝐼𝑖𝑎 and 𝐼𝑖𝑏 denote indicator 185
functions with 186
𝐼𝑖𝑎={1 𝑖𝑓 𝑦𝑖=𝑎
0 𝑖𝑓 𝑦𝑖>𝑎 (7)
𝐼𝑖𝑏={1 𝑖𝑓 𝑦𝑖=𝑏
0 𝑖𝑓 𝑦𝑖>𝑏 (8)
The log -likelihood function of the censored regression model , Eq. (5), can be maximized with 187
respect to the parameter vector (𝛽′,𝜎′) using standard non -linear optimization algorithms. 188
In this study, the analysis results of the OLS estimation method are compared to the Tobit 189
analysis results. 190
3.4. Marginal effect 191
The marginal effect indicates the effect of a change in the 𝑗𝑡ℎ variable on a dependent variable 192
of x. The marginal effects of an explanatory variable on the expected value of the dependent variable 193
is [17]. 194
𝑀𝐸 𝑖=𝜕𝐸[𝑦|𝑥]
𝜕𝑥𝑖=𝛽𝑗[Φ(𝑏−𝑥′𝛽
𝜎)−Φ(𝑎−𝑥′𝛽
𝜎)] (9)
where 𝑀𝐸 𝑖 is the marginal effect of the 𝑗𝑡ℎ explanatory variable, a is the lower limit and b is the 195
upper limit of the dependent variable. β is a vector of unknown parameters of the 𝑗𝑡ℎ explanatory 196
variable, and Φ(.) is the cumulative distribution function. 197

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Table 1. Survey results on water supply and wastewater treatment facility . 198
Improved Unimproved
Drinking water Hand well 11 River and Spring 8
Water supply station 250
Boreh ole with pump 13
Pipelin e connected 3
Sanita tion Pit latrine with concrete
stab 14 Pit latrine 260
Flash to septic tank 10 Nothing 0
Table 2. Descriptions of variables used in the field survey . 199
Variable Description
Agreement of WS and WWT facility improvement
(installation) payment 1 if individual willing to pay, 0 otherwise
Willingness to pay WS and WWT facility installation
(thousand MNT) 0=0, 1=less than 1000.0, 2=1000.0 -1500.0,
3=1500.0 -2000.0, 4=higher than 2000.0
Agreement of WS and WWT facility operation and
management (monthly) fee 1 if individual willing to pay, 0 otherwise
Willingness to pay WS and WWT facility operation
and management fee (monthly) (thousand MNT) 0=0, 1=less than 3.0, 2=3.0 -6.0, 3=6.0 -9.0 4=9.0 –
12.0, 5=Higher then 12.0
Education 1 if ind ividual has higher then high school
education, 0 otherwise
Family size Number of house hold member
Income level 1 (thousand MNT per Month) 1 if individual family income was less than
300.0, 0 otherwise
Income level 2 (thousand MNT per Month) 1 if individual family income was between
300.0 and 700.0, 0 otherwise
Income level 3 (thousand MNT per Month) 1 if individual family income was between
700.0 and 1200.0, 0 otherwise
Income level 4 (thousand MNT per Month) 1 if individual family income was between
higher then 1200.0, 0 otherwise
Housing 1 if individual family lives in own built house,
0 lives in ger
Time for water access 1 if lower than 30 minutes, 0 higher than 31
minutes
Water consumption (HH) Water use in a week (Liter)
Current monthl y payment of water supply
(thousand MNT) 0=0, 1=less than 1.0, 2=1.0 -3.0, 3=3.0 -5.0, 4=5.0 –
7.0, 5=Higher than 7.0
4. Results and Discussions 200
Table 1 shows the current water supply and wastewater treatment facility types and the number 201
of respondents. In the study area, 98.1% of the respondents can access an improved water supply but 202
only 23.7% said they are satisfied, 66.4% said it is just passable, a nd 8% said they are unsatisfied with 203
the water quality. Eight percent of the respondents could use improved wastewater treatment and 204
sanitation facilities, while 92% of the respondents could not use improved wastewater treatment and 205
sanitation facilities. 206
A number of factors were hypothesized to be relevant to the revealed WTP for improving the 207
water supply and wastewater treatment conditions. The dummy variable in the bid function equals 208
1 if the residents’ education is greater than high school and otherwi se equals 0. Additionally, if the 209
residents live in their own built house, then the dummy variable equals 1; otherwise, it becomes 0. 210
The expected effect of water access time and housing on WTP is uncertain. Residents living in their 211

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own built house were h ypothesized to have more opportunities to improve their water conditions; 212
thus, they were expected to have a greater WTP for the improved water supply and wastewater 213
treatment. Four categorical income variables were defined to represent monthly income leve ls. WTP 214
is expected to increase as income increases and water consumption increases. The expected effect of 215
the current monthly payment and education on water supply (thous. Mongolia Tughrik (MNT)) is 216
unclear . 217
Table 3 . Statistics of variables relevant to WTP for water supply and wastewater treatment . 218
WTP for maintenance WTP for operation &
management
Total sample Yes No Yes No
Mean Std
Dev Mean Std
Dev Mean Std
Dev Mean Std
Dev Mean Std
Dev
Agreement of WS and
WWT facility
improvement
(installation) payment 0.639 0.481 – – – – 0.840 0.367 0.208 0.408
Willingness to pay WS
and WWT facility
installation (thousand
MNT) 0.827 0.785 1.206 0.663 – – 1.044 0.766 0.258 0.510
Agreement of WS and
WWT facility operation
and management
(monthly) fee 0.708 0.456 0.908 0.290 0.330 0.473 – – – –
Willingness to pay WS
and WWT facility
operation and
management fee
(monthly) (thousand
MNT) 1.708 1.546 2.260 1.432 0.728 1.241 2.437 1.286 – –
Education 0.599 0.491 0.634 0.483 0.525 0.502 0.661 0.475 0.434 0.499
Family size 3.489 1.586 3.598 1.691 3.343 1.379 3.587 1.617 3.224 1.457
Income level 1
(thousand MNT in
Month) 0.151 0.359 0.089 0.286 0.257 0.439 0.116 0.322 0.231 0.424
Income level 2
(thousand MNT in
Month) 0.372 0.484 0.408 0.493 0.307 0.464 0.365 0.483 0.372 0.486
Income level 3
(thousand MNT in
Month) 0.067 0.250 0.084 0.278 0.040 0.196 0.079 0.271 0.038 0.194
Income level 4
(thousand MNT in
Month) 0.021 0.144 0.034 0.180 0 0 0.026 0.161 0.013 0.113
Housing 0.668 0.472 0.713 0.453 0.590 0.494 0.690 0.464 0.590 0.495
Time for water access 0.853 0.355 0.886 0.319 0.810 0.394 0.866 0.342 0.813 0.392
Water consumption
(HH) 298 276 301 283 290 262 306 297 281 231
Current monthly
payment of water
supply (thousand MNT) 1.919 1.215 2.096 1.173 1.554 1.189 1.978 1.156 1.795 1.354
Variable definitions are contained in Table 2, and a summary of the statistics for the entire 219
sample and for the censored and uncensored sub -samples are presented in Table 3 . The mean WTP 220
for the water supply and the improved wastewater treatment facility installation for the entire sample 221
was less than 1000.0 (thous. MNT). In the subsample (62.8%) that answered “yes” on question F1 (If 222
there is a chance to improve the water supply and the wastewater (sanitation) conditions, how much 223

Water 2019 , 11, x FOR PEER REVIEW 9 of 11
would you pay for it?), the WTP for the installation of the water supply and wastewater treatment 224
facility was 1500.0 (thous. MNT) . 225
Table 4 . Result of ordinary least squares (OLS) and the Tobit model . 226
WTP for maintenance
OLS Tobit
coefficient coefficient Marginal Effect
Intercept -0.1322695 -0.9634944 .
Education 0.1194276 * 0.1929599 * 0.1376600
Family size 0.0500528 0.0746763 0.0532760
Income level 1 (thousand
MNT in Month) -0.3618264 * -0.6099464 * -0.4351500
Income level 2 (thousand
MNT in Month) -0.0768330 -0.0569057 -0.0405980
Income level 3 (thousand
MNT in Month) 0.0448670 0.0018459 0.0013169
Income level 4 (thousand
MNT in Month) 0.3785073 0.4430555 0.3160800
Housing 0.3398268 ** 0.4614334 ** 0.3292000
Time for water access -0.0567885 -0.1155886 -0.0824630
Water consumption (HH) -0.0001149 -0.0001377 -0.0000981
Current monthly payment of
water supply (thousand MNT) 0.1010606 * 0.1943611 ** 0.1386600
log sigma 0.34769
log-likelihood -250.0031 -279.5164
Multiple R -squared 0.1553000
P-value 0.0000508
Signif. codes : 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 227
The mean WTP for the operation and management (O& M) of the water supply and wastewater 228
treatment improvement facility from the entire sample was less than 3.0 (thous. MNT). In the 229
subsample (66.3%) that answered “yes” on question F7 (If answer F -1 is yes, what would you pay for 230
water, wastewater and sani tation fees for monthly (O&M)?), the WTP for O&M was 6.0 (thous. MNT) . 231
A greater proportion of those respondents that indicated a zero bid on both the installation and 232
the O&M WTP were from lower income categories (Income levels 1 and 2). 233
In the subsample indicating a positive WTP for the installation of the water supply and 234
wastewater treatment facility, 62% of the respondents have an education level greater than high 235
school, 9% are at income level 1, 41% are at income level 2, 8% are at income level 3, 3% are at income 236
level 4, 71% live in a detached house, 87% can access water within 30 minutes, and 88% are willing 237
to pay for O&M. 238
In the subsample indicating a positive WTP for the O&M of the water supply and wastewater 239
treatment improvement, 65% of the re spondents have an education level greater than high school, 240
12% are at income level 1, 37% are at income level 2, 8% are at income level 3, 3% are at income level 241
4, 68% live in a detached house, 85% can access water within 30 minutes, and 84% are willing to pay 242
for the facility installation. 243
People who have an education level greater than high school are willing to pay more. The effect 244
of water access time is that those who could not access water within 30 minutes had a lower WTP. 245
People who lived in their own built house were willing to pay more. 246

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With respect to the WTP for the installation, the OLS and Tobit analysis results are presented in 247
Table 4 . The significance of the individual variables in the WTP for the installation is very similar 248
between the O LS and Tobit models’ estimation. In each case, people who are educated (greater than 249
high school), who live in their own detached house, and who pay more currently for water show 250
more positive significance with a high WTP. Income level 1 was negatively sig nificant, indicating that 251
low-income people have a lower WTP. 252
With respect to the WTP for the O&M, the OLS and Tobit analysis results are shown in Table 5 . 253
The significance of the individual variables in the WTP for the O&M are similar between the OLS and 254
Tobit models’ estimations. In each case, people who have low income (income levels 1 and 2) and 255
who live in a detached house were negatively significant. People who access water within 30 minutes 256
were positively significant for the WTP. 257
In the Tobit model results, the family size indicates a high positive significance in the WTP for 258
the O&M. People who are educated (greater than high school) have a positive WTP. 259
5. Conclusions 260
This study aims to estimate the WTP of Ger residents for the capital costs and op erational costs 261
to install and operate an improved water supply and wastewater treatment facility in Ulaanbaatar 262
city using a contingent valuation method and a Tobit model. 263
The average total WTP for the water supply and wastewater treatment facility instal lation was 264
less than 1000.0 (thous. MNT), the average total WTP for the O&M was less than 3,000 MNT. People 265
who live in their own built house and are educated greater than high school are willing to pay more 266
for the water supply and wastewater treatment im provement. Water access time also affects the WTP 267
of those who could not access water within 30 minutes, as they indicated low WTP values. 268
The Tobit model shows that the WTP for the installation is significantly influenced by the income 269
level (income level 1 marginal effect = -0.45, income level 2 marginal effect = -0.04, income level 3 270
marginal effect = 0.0013, income level 4 marginal effect = 0.31), c urrent monthly payment of water 271
supply (marginal effect = -0.14) and housing (marginal effect = 0.32). The WTP for the O&M is also 272
significantly influenced by income levels (income level 1 marginal effect = -0.73, income level 2 273
marginal effect = -0.47, income level 3 marginal effect = -0.03, income level 4 marginal effect = -0.26) 274
but is not highly influenced by housing. 275
We hope that this study of the WTP for the water supply and wastewater treatment based on 276
field survey results can encourage public participation in the urban decision -making process and 277
assist various planners and authorities in formulating a suitable plan for the Ger area development, 278
Ulaanbaatar city, Mongolia. 279
Supplementary Materials: The questionnaire used in the field survey in Damba Planning Unit is provided as a 280
Supplementary Material. 281
Author Contributions: A.B. developed the concept of this study under the supervision of H.S.L. The analysis 282
was carried out by both authors. Both authors drafted the first version of the manuscript and worked on 283
improving and finalizing the manuscript. 284
Funding: This research is partly supported by the Gr ant-in-Aid for Scientific Research (17K06577) from JSPS, 285
Japan. 286
Acknowledgments: The first author is supported by The Project for Human Resource Developmen t Scholarship 287
(JDS), Japan. 288
Conflicts of Interest: The authors declare no conflict of interest. 289
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