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Scholars
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African Journal of Environmental Economics and Management ISSN 2375-0707 Vol. 5 (6), pp. 341-347, 
November, 2017. Available online at www.internationalscholarsjournals.org © International Scholars Journals 

 

Author(s) retain the copyright of this article. 
 
 
 

         Full Length Research Paper 
 

Willingness to Pay (WTP) for pipe-water connection in 
Makululu compound of Kabwe, Zambia 

 

Yordanos Gebremeskel1, Nyambe Mubita2, Bupe Simuchimba1, James Mulenga1 
 

1
Mulungushi University. School of Social Sciences. Department of Economics. Box 80415. 

2
University of Barotseland. School of Natural Resources. 

 

Accepted 05 October, 2017 
 

The availability of safe drinking water is dependent on better and improved water sources for households. 
Access to safe drinking water especially piped water is one of the challenges faced by households in Zambia. 
This study aims to understand the factors that determine the willingness to pay for pipe water connection on 
low income peri-urban settlements in Zambia. Double bounded questions which have the advantage of 
including a follow up dichotomous question after the first dichotomous choice question are used. An interval 
regression model is used to analyze the collected data. The variables of interest are purification, family size, 
water quality, education and income. The results obtained show that most of these variables have the expected 
signs. Further, the results suggests that an increase in income will lead to an increase in the willingness to pay 
to have access to safe drinking water sources. The results also suggest that the distance travelled to fetch 
water from a community tap and level of household education play a role in the willingness to pay for access to 
pipe water connection. We estimated that the sample household mean WTP is K283.77 ($38.14) which is roughly 
33% of the commercial connection charge.  
 
Keywords: Contingent valuation, double-bounded, interval regression, WTP, Zambia. 
 
INTRODUCTION 
 
Water is essential for human survival. It is for this reason 
that in 2010, the United Nations General Assembly 
declared safe and clean drinking water and sanitation a 
human right essential to the full enjoyment of life and all 
other human rights (UNICEF, 2014). Notwithstanding its 
importance, access to improved water still remains a 
challenge to access safe drinking water. As a result, most 
people continue to be exposed to waterborne diseases 
such as cholera, diarrhoea, dysentery, typhoid fever 
among others. According to the World Health 
Organisation (WHO), water borne diseases are among 
the major causes of morbidity and mortality in most 
developing countries particularly among children under 
five years old. Contaminated drinking water is estimated 
to cause 502, 000 diarrhoea death each year (WHO, 
2016).  
 
 
Corresponding author E-mail: yorsink@yahoo.com; 
yordanosge@gmail.com 

    Zambia has made progress in increasing access to 
improved drinking water. The proportion of the population 
with access to an improved source of drinking water 
increased from 24 percent in 2007 to 65 percent in 2014 
(ZDHS, 2015). Table 1 below shows that, the percentage 
of total population piped into dwelling/yard/plot stands at 
16 percent in 2014. The progress has been necessitated 
by the various programmes that the government together 
with its cooperating partners have implemented such as 
the National Rural Water Supply and Sanitation Program 
(NRWSSP) and National Urban Water Supply and 
Sanitation Program (NUWSSP). Despite this progress, 
access to improved drinking water in general and piped 
water in particular continues to be a challenge in Zambia. 
In comparison to its neighbouring countries, as is shown 
in Table 2 (Annex), Zambia has among the lowest levels 
of access to drinking water. 
    The health and time saving benefits of access to clean 
water, as private or communal, by households in developing 



Gebremeskel et al.      342 
 
 
 
countries have been widely documented. Our main objective 
in this study is to assess their willingness to pay for private 
pipe-water connection. This is because of the fact that 
households in our study site already had access to clean 
water from communal taps supplied by the water utility 
company. Thus study sought to estimate willingness to pay 
for improved water supply through pipe water connection in 
Bwacha constituency, Kabwe, and to determine the factors 
(variables) that influence households’ WTP for improved 
water supply. Makululu is an unplanned settlement 
characterised by lack of social amenities such as piped 
water among others.  
    The Contingent Valuation Method (CVM) is used to 
estimate monetary measure of preference by directly asking 
respondents’ willingness to pay for changes in quantity or 
quality of a good or service ((Haab & McConnell, 2002)). 
The Dichotomous Choice Contingent Valuation Method 
(DC–CVM) can be presented in different forms. The single 
bounded DC–CVM asks respondents for a predetermined 
bid amount for their vote or rejection. Alternatively in double 
bounded format we offer a follow-up price (bid) for the 
respondent after getting a response for the initial bid. The 
follow-up bid in double bounded take a higher value if the 
respondent’s answer for the first bid was YES and a lower 
value if the answer was NO. 
    Estimation of household’s demand for water usually 
conducted by capturing key indicators of household 
characteristics. Nauges and Whittington (2010) well 
documented the various empirical researches made on 
water demand in various countries. Recent notable studies 
in African countries, using various methodologies, have 
established that income, family size, time spent to fetch 
water from existing sources, level of education, age of 
respondent, bid value, perceived quality of current water 
supply are the main factors influencing households WTP for 
improved water supply services (Gulyani et al. 2005; Tarfasa 
and Brouwer, 2013, 2015; Genius et al 2008.) 
    Despite the fact that various studies have been 
undertaken to determine the willingness to pay and the 
factors that determine the willingness to pay (WTP) for 
quality water in developing countries around the world, we 
didn’t find enough literature relating to WTP for improved 
water in Zambia particularly employing the CVM. One 
particular study done in the North Western Province of 
Zambia by Aymar (2004) using descriptive analysis 
concluded that willingness to pay depends to a large extent 
on income level. The higher the income, the higher 
willingness to pay. 
The paper proceeds as follows. In the next section we 
present the model of willingness to pay. Section 3 discusses 
study site and sampling technique. Sections 4, 5 and 6 are 
for results, discussion and conclusion respectively. 

 
METHODOLOGY 
 
Willingness to Pay Model 
 
An application of a contingent valuation questionnaire 
using the dichotomous choice model renders two 

answers. These are dichotomous answer (  if the 

individual answers yes and  if the answer is no) 
given a question about paying a predetermined amount 

of . 
 
The WTP can be estimated by modelling as the following 
linear function: 

     
    (1) 

Where  is a vector of explanatory variables,  is a 

vector of parameters to be estimated and  is the error 
term. The respondent will answer yes when his WTP is 
greater than the suggested amount that is when 

.  
WTP and Double-bounded contingent valuation 
 

We can assume that an individual is asked if he is willing 

to pay an amount  for a given change in the provision of 
a given good or service. If the individual answers no then 

we can infer that , if he answers yes then

.  
But Hanemann et al. (1991) suggested that the follow-up 
dichotomous question can be asked after the first 
dichotomous question. This approach, called double-
bounded models, is assumed to generate more efficient 
estimation than a single question or a single question 
followed by an open ended question. In the case of 
double bounded model, a follow-up dichotomous 
question is asked after the first dichotomous choice 
question. If the individual answers yes to the first 
question then he/she is asked about his WTP for a higher 
amount. If he answers no to the first question then a 
lower amount is offered. The second questions is 
endogenous in the sense that the amount asked depends 
on the answer obtained for the first question which is 
exogenous. The method renders with two answers for 
each respondent. 

If we call the first bid amount  and the second one , 

where  then 
i. The individual that answers yes to the first 

question and no to the second, then . This implies 

. 
ii. If the answers are yes for both questions, then 

. 
 

iii. If the individual’s answer are no to the first 

question and yes to the second, then . 
iv. Finally, if the individual’s responses are no for 

both questions, then we will have  
Econometric estimation of double-bounded or interval 
data model 
 

Let’s define  and  as the dichotomous variables that 
capture the response to the first and second closed 
questions, then the probability that an individual answers 
yes to the first question and no to the second can be



343     Afr. J. Environ. Econ. Manage. 
 
 
 

Table 1. Use of drinking water sources (percentage of population) as of 2015: selected countries. 
 

Country 

Urban  Rural  Total 

Total 
improved 

Piped on 
premises 

 Total 
improved 

Piped on 
premises 

 Total 
improved 

Piped on 
premises 

Botswana 99 96  92 45  96 74 

South Africa 100 92  81 38  93 73 

Namibia 98 69  85 34  91 51 

Malawi 96 33  89 3  90 8 

Zimbabwe 97 74  67 5  77 28 

Swaziland 94 75  69 27  74 37 

Zambia 86 36  51 2  65 16 

Congo, Dem. Rep. 81 17  31 1  52 8 

Mozambique 81 25  37 1  51 9 
 

Source: UNICEF and WHO, 2015. 

 
 
 
 

Table 2. Socio-economic characteristics of surveyed households. 
 

Variable Name Description  Frequency Percent 

Gender Gender of the respondent   

 Female 63 42 

 Male 87 58 

Education Educational level of the respondent   

 No formal education 8 5.3 

 Primary 46 30.7 

 Secondary 68 45.3 

 Tertiary 28 18.7 

Family size Number of family members living in the household   

 Less than 5 84 56 

 5 up to 10 62 41.3 

 More than 10 4 2.7 

Income Average monthly income   

 Less than K100 9 6 

 K100 – K150  30 20 

 K150 – K200 30 20 

 More than K200 80 53 

 
 
 
 

expressed as .Given this 

and under the assumption that 

and , the probability of each one of the cases 
is given by: 

 

 

 

 

 
 
Using symmetry of the normal distribution: 

 
The other three probabilities, the probability that an 

individual answers  are also 
captured in a similar fashion.  

Then, a log-likelihood function can be used to estimate 𝛽 
and σ using maximum likelihood estimation.  

 

Where , ,  and,  are the dummy variables 1 
and 0 denoting the group to which the i

th
 respondent



Gebremeskel et al.      344 
 
 
 
belongs. A given respondent contributes to the logarithm 
of the likelihood function in only one of its four parts. 
 
Description of study area 
 
Our study is conducted in Kabwe, one of the 11 districts 
of the Central province of Zambia. The specific locality, 
Makululu Compound in Bwacha constituency, is an 
unplanned settlement located in Kabwe district of lying 
between latitude 14° 27' S and longitude of 28° 27'E. It is 
along the Great North Road, 139 kilometers North East of 
Lusaka. Kabwe was established as a Lead and Zinc 
mining town around 1902 and almost all of the mines 
were closed in mid 1990s that created high 
unemployment and poverty level on sizable residents. 
The total population of Bwacha constituency in 2010 
was85, 397that belongs to 17,185 households (CSO, 
2011:25). The settlement can be described as poor 
housing units made of mud, with poor sanitation and safe 
drinking water supply. Poor sanitary practices, like 
disposal of human excreta usually leads to contamination 
of water and therefore water-borne diseases Phiri (2016). 
Majority of the population are not in the formal 
employment sector with low levels of professional skills 
and education.  
    We have applied a stratified and systemic sampling 
techniques for the primary data collection. There are five 
wards in Makululu compound and three wards were 
considered to reach 150 respondents. These were 
Makululu ward, Chililalila and Moomba Ward. A pre-test 
was conducted on 5 households to confirm the 
applicability of the questionnaire in general and the 
contingent valuation technique in particular. It was found 
that all the questions including the WTP bids were easily 
understood by the respondents. For the final data 
collection, proportionate stratified sampling was applied 
and each ward had fifty households sampled. 
 
RESULTS  
 
Descriptive statistics 
 
This section discusses both the descriptive statistics and 
the regression estimation. It also includes explanations of 
the variables which are used for describing and 
estimating the willingness to pay for pipe water 
connection in Bwacha. Furthermore, the section 
discusses the relevant variables used for the WTP 
estimation. 
    The respondents were given a brief introduction of the 
benefits from connecting to a commercial water pipe line. 
A total of 150 respondents were asked double bounded 
questions with K200 being the initial bid and K100 and 
K500 were the follow-up bids. We did not find anyone 
opposed the proposed project of private pipe water 
connection. Table 3 (see Annex)shows frequency and 
percent of selected variables to describe the sample 

respondents. The sample respondents are more male, 
educated, and moderate family size & income.   
Table 4 (see Annex) shows that majority, 70%, of the 
respondents are fetching drinking water from communal 
taps built by the utility company Lukanga Water and 
Sewerage. The responses on the assessment of water 
quality shows that 65(43.3%) and 83(55.4%) of the 
respondents feel that the water quality is good and very 
good respectively. This perception of the quality of 
drinking water also reflected by high percentage, 86.7%, 
of respondents who are not using any water purification 
method. For three quarter (75%) of the respondents it 
takes more than 7 minutes to fetch water from the 
nearest communal tap. Out of this percentage, 20% of 
the respondents need to walk for more than 30 minutes 
to access the communal tap. A more than 10 minutes 
walking is usually estimated as more than 1 kilometre 
(0.6 miles) with a normal walking style of an adult person. 
Majority of our respondents have access to communal 
taps (70%) as their primary source of drinking water and 
they perceived the quality as good and very good (98%). 
The high rating of the water quality correlate with less 
tendency to use water purification techniques (13%). 
Though the majority of the households have access to 
communal tap water, they need to walk more than a 
kilometre. Given most of them need to fetch water more 
than once per day, the time cost of fetching water is high. 
Table 5 below shows frequency and percentage of the 
double bounded responses as ‘yes-yes’, ‘yes-no’, ‘no-
yes’ and ‘yes-yes’. The bids were preceded by WTP 
question stated as: 
 ‘It is difficult to state the actual cost of connecting your 
household to the nearest water network. The prices 
stated below are chosen simply for the purpose of our 
study. If you are willing to be connected to the water 
system, are you willing to pay a one-off out of pocket 
payment?’ 
    Table 6 shows summary statistics of each independent 
variable used in the interval regression model which is 
presented in table 7. These variables are expected to 
affect the WTP for safe drinking water. Water quality, 
family size, education level and income are expected to 
have a positive sign in the regression model.  
 
Regression Estimation 
 
Table 7 presents results from a double bounded interval-
regression estimation. We made the estimation using 
Stata’s intreg censored regression command. The table 
presents the coefficient (marginal WTP), standard error, 
significance level, and confidence interval. We found that 
the overall model is statistically significant as measured 
by log likelihood and wald chi square.  
    Most of the estimated coefficients have the expected 
signs. According to the estimation result, education level 
and income level of the household are positive in sign 
and statistically significant at 5% and 1% respectively.  



345     Afr. J. Environ. Econ. Manage. 
 
 
 

Table 3. Water source, perceived quality, purification and estimated time to fetch. 
 

Variable Name Description  Frequency Percent 

Source Source of drinking water   

 Hand dug well 2 1.3 

 Borehole 43 28.7 

 Communal tap 105 70 

Water quality Respondent’s perception of current water quality   

 Poor 2 1.3 

 Good 65 43.3 

 Very Good 83 55.4 

Purification Water purification method used   

 Chlorination 11 7.3 

 Boiling 7 4.7 

 Filtration 2 1.3 

 No purification 130 86.7 

Time Time taken to fetch water from the nearest source   

 Less than 7 minutes 38 25.3 

 15 – 20 minutes 51 34 

 20 – 30 minutes 30 20 

 30 – 40 minutes 19 12.7 

 More than 40 minutes 12 8 

 

 
Table 4. Distribution of double bounded responses for initial amount of 
K200 and follow-up K500. 
 

Responses Number of responses Percentage 

Yes –Yes 33 22.0 

Yes – No 46 30.7 

No – Yes 33 22.0 

No-No 38 25.3 

 
 
 

Table 5. variables used in the regression and their expected signs. 
 

Variable Type Description Mean Std. Dev Sign 

Educational level Ordered 
categorical 

0 if primary; 1 if basic; 2 if 
secondary; 3 if tertiary 

1.782 .066 + 

Income per month Ordered 
categorical 

0 if <K100; 1 if K100-K150; 2 if 
K150-K200; 3 if  >K200 

2.217 .079 + 

      

Family size Ordered 
categorical  

0 if 1 – 5; 1 if 5 – 10;  2 if >10 0.60 .057 -/+ 

Distance from the nearest 
community tap (in 
minutes) 

Ordered 
categorical 

0 if <7 ; 1 if 15-20; 2 if 20-30; 3 if 
30-40; 5 if >40 

1.414 .098 + 

Purification Nominal  0 = no purification; 1=purification .92 .021 + 

 
 
 
 
Whereas distance has a negative sign and is statistically 
significant at 5% 
 
Mean WTP Estimation 

 
The estimation of mean WTP is made using the mean values 
and the marginal WTP values of the variables used in the 
regression estimation. 

The estimated mean WTP for the 147 sampled 
households is K283.7748 ($38.14). The estimate of the 
mean WTP for the pipe water connection can be used to 
estimate the total benefits in the specific locality. 
Consequently, we attempted to expand the sample WTP 
estimate for the population of Bwacha. According to the 
Central Statistics of Zambia (CSO, 2011), there were 17,185  



Gebremeskel et al.      346 
 
 
 

Table 6. Interval regression estimation. 
 

Interval regression                                                       Number of obs   =        147 
Wald chi2(5)    =      47.31 
Log pseudolikelihood = -218.0028  Prob> chi2     =     0.0000 

 Coef. Robust 
Std. Err. 

z P>|z| [95% Conf. Interval] 

educ 50.81731 22.35321 2.27 0.023* 7.00583494.62879 

income 78.70924 14.73437 5.34 0.000** 49.83042107.5881 

famsize 29.74863 23.38308 1.27 0.203 -16.0813775.57863 

distance -98.20083 41.97465 -2.34 0.019* -180.4696-15.93202 

purify -73.84673 79.13106 -0.93 0.351 -228.940881.2473 

_cons 143.1545 97.65233 1.47 0.143 -48.2405  334.5496 

/lnsigma 5.220299 .0648312 80.52 0.000 5.093232       5.347365 

 
Observation summary: 

 
0     left-censored observations 
0     uncensored observations 
32   right-censored observations 
115       interval observations 

*Significant at 5%, ** significant at 1%. 
 
 
 
 

Table 7. Estimated mean willingness to pay (WTP).  

Mean WTP [95% Conf. Interval] 

283.7748 (USD 38.14) 248.7927         318.7568 

 

 

Table 8. Aggregate willingness to pay (WTP) of tap water connection in Bwacha, Zambia. 
 

Bwacha residents household mean WTP [95% Conf. Interval] 

4, 876, 669 (USD 655, 466) 92308.54     93210.62 

 

 
 
households in Bwacha in 2010. Multiplying the 810 
households by the mean WTP and it yields a total of  
approximately K4, 876,669 ($655,466), as shown in the 
following table. 

 
DISCUSSION OF RESULTS 
 
Interval regression model was used to investigate factors 
determine household’s WTP for tap-water connection. 
The analysis result also includes the estimation of WTP 
for sample households and the population. 
    The interval regression model shows that education 
level and household’s monthly income are the significant 
explanatory variables at 5% and 1% respectively. This is 
consistent with other studies (Jianjun et al. 2016; 
Parveen et al 2016; Twerefou, et al. 2015; Mezgebo and 
Ewnetu, 2015; Coster and Otufale 2014; Kwak et al. 
2013; Khan et al. 2010; Mbata 2007) which showed that 
education and income are significant determinants of 
WTP.  Education enlightens people about the importance 
clean water while income provides the households with 

ability to pay for the water. The type of purification 
technique used, the assessment of current water quality 
and family size found to be statistically insignificant to 
WTP for private water pipe-line connection.  
    It is found that the mean WTP for sample households 
is K283.77. This amount is a little higher than the 
estimated average households’ income that falls in the 
range K150 – K200. This confirm that the household’s 
expression of willingness to pay is within their capability 
to pay. Depending on the sign and magnitude of the 
coefficient of the variable, it is possible to forecast the 
change in WTP for a unit change of the variable 
concerned. For instance, the income variable is 
measured as ordered category between 0 and 3, as one 
unit change represent a change in income of K50. 
Therefore, based on the mean WTP estimation, a one-
unit increase in income category adds K78.70 to WTP. 
    The major contribution of this research is that it adds to 
the literature on willingness to pay estimation for Zambia 
where there remain evidences of WTP are scarce. To the 
best of the authors’, this study is the first of its type to



347     Afr. J. Environ. Econ. Manage.  
 
 
 
 
apply double bounded discrete choice model with interval 
regression to pipe water connection in Zambia. 
 
CONCLUSION AND POLICY IMPLICATIONS 
 
In this study we examine to what extent characteristics of 
the respondents (the household head) determine the 
likelihood that they are willing to pay a given amount of 
money for pipe water connection. The study used a 
double bounded contingent valuation method to estimate 
willingness to pay (WTP) for pipe water connection in 
Bwacha constituency, Zambia. The main objective of this 
study was to understand the factors that determine the 
willingness to pay for safe drinking water in a low income 
urban settlement in Zambia. An interval regression model 
is used to estimate coefficients of the variable and the 
WTP.  
The regression estimation result shows that most of the 
variables found to have expected signs. Among the five 
variables considered, two variable, namely income and 
education level of the respondents are statistically 
significant in determining the WTP for safe drinking water 
connection. The mean WTP of income is estimated to be 
K283.77 for a household. The Lukanga water company, 
the sole utility company in Kabwe, has a fixed pipe water 
connection rate based on the distance between a house 
and the nearest main pipe-line. Those houses within 10 
meters distance from the main pipe-line are charged 
K852 and the rate increases as the house located more 
than the 10 meters distance. Our estimation of mean 
WTP is roughly 33% of the connection charge.  
The findings of the study are vital for utility company, 
development partners and local government authorities. 
The connection charge for most households is 
‘expensive.’ Nevertheless, our research found that, with 
some financial or reduced connection charge, it is 
possible to provide access to safe drinking water to many 
urban dwellers who are deprived of these essential life 
sustaining resource for many years. Attainment of more 
years of formal education plays a paramount role in 
creating awareness about the many advantages of living 
a healthy life by committing resources by acquiring 
utilities like water. The study demonstrated the 
applicability of contingent valuation method to assess the 
demand and willingness to pay for access to safe 

drinking water in developing country like Zambia. Future 
research works should attempt to use other WTP 
estimation techniques to extend our findings.  
 
DISCLOSURE 
 
The authors declare no conflict of interest. 
 
REFERENCES 
 
Aymar, M. (2004). Ability and Willingness to Pay Study NWWSSC, 

Commercialisation of Water Supply and Sewerage Utilities in the 
North-western and Southern Provinces. Zambia: GTZ. 

Celine Nauges, D. W. (2010). Estimation of water demand in 
Developing countries. World Bank. 

Central Statistical Office. (2011). Zambia 2010 Census of 
Population and Housing. Lusaka, Zambia. 

Central Statistical Office. (2015). Demographic and Health Survey . 
Lusaka, Zambia: CSO. 

Genius, M., Hatzaki, E., Kouromichelaki, E. M., Kouvakis, G., 
Nikiforaki, s., & Tsagarakis, K. P. (2008). Evaluation consumers' 
willingness to pay for improved potable water quality and 
quantity. Water Resources Management, 22(12), 1825 - 1834. 

GUHA, S. (2007). Valuation of clean water supply by willingness to 
pay method in a developing nation: a case study in Calcutta, 
India. Journal of Young Investigators,17. 

Gulyani, S., Talukdar, D., & Kariuki, R. M. (2005, July). Universal 
(NOn)service? Water Markets, Household Demand and the Poor 
in Urban Kenya. Urban Studies, 42(8), 1247 - 1274. 

Haab & McConnell. (2002). Valuing Environment and Natural 
Resources. Massachusetts, USA: Edward Elgar Publishing, Inc. 

Hanemann, W. M., Loomis, J., & Kanninen, B. J. (1991). Statistical 
efficiency of double bounded dichotomous choice conitngent 
valuation. American Journal of Agricultural Economics, 73, 1255 - 
1263. 

Lopez-Feldman, A. (2012). Introduction to contingent valuation 
using Stata. MPRA. Toluca, Mexico: Centro de Investigacion 
Docenicia Economicas (CIDE0. 

Phiri, A. (2016). Risks of domestic underground water sourses in 
informal settlment in Kabwe. Environmental and pollution, 5(2). 

Solomon, T., & Roy Brouwer. (2013). Estimation of the public 
benefits of urban water supply improvements in Ethioia: a choice 
experiment. Applied Economics, 45, 1099-1108. 

Twerefou, D. T. (2015). Willingness-to-Pay for Potable Water in the 
Accra-Tema Metropolitan Area of Ghana. Modern Economy, 6, 
1285-1296. 

UNICEF. (2014). The rights to safe water and sanitation: current 
issues. No 3. USA. 

World Health Organization. (2016). World Health Statistics. France. 
Zambia Demographic and Health Survey (ZDHS). (2015). 

Demographic and Health Survey. Lusaka, Zambia. 

.
 
 


