Abstract

Safe water is described as an important resource for the survival of mankind. The outbreak of the Covid-19 pandemic has made safe water ‘super’ important and critical for the survival of mankind. Most developing countries, especially in Africa, incur additional costs in order to enjoy improved, if not safe domestic water supply. Using the averting expenditure method, this study estimates how much urban households in the Greater Accra Region of Ghana spend to improve the quality of domestic water they use. The study provides evidence that households spend Ghs84.30 ($14.70) per month, which constitutes 13.25% of their income. These estimates are very informative to the supplier in determining the economic viability of making the required quality of water available to households.

HIGHLIGHTS

  • The study presents the first averting behaviour estimate for domestic water use in Ghana.

  • The study provides evidence that households spend Ghs84.30 ($14.70) per month, which constitutes 13.25% of their income.

  • The study shows that sachet and bottled water dominate the drinking water choices.

  • The study shows that pipe-borne water is mainly used for general household non-drinking purposes.

INTRODUCTION

Access to potable water and residential water supplies in Ghana and other developing countries has been increasing steadily. These success stories follow the concerted global efforts in driving goals such as the Millennium Development Goals (MDGs) and the Sustainable Development Goals (SDGs) in addition to national-level policies. In Ghana, the gains include, but are not limited to, meeting the drinking water target under the MDGs a decade before the deadline, continuous increase in water coverage, reduction in water-related diseases and its associated impact on infant and child mortality rates (Monney & Antwi-Agyei 2018). Recently, the percentage of people using safely managed drinking water services in Ghana rose from 33.12% in 2015 to 36.41% in 2017 (World Bank 2019).

Despite the gains, several households are still without a connection to the nation's pipe-borne water network. As pointed out by van den Berg & Nauges (2012), generally, households in such situations face hauling costs to access water, they pay relatively higher prices and use relatively lower quality non-piped water sources. A common approach by most Ghanaian water users is to adopt averting behaviour as an improvement mechanism to domestic water before use. However, this approach involves time and effort, cost of averting materials or technique, relatively higher water cost, etc. Regardless of the averting behaviour, water quality for domestic use remains questionable. Currently, urban areas in Ghana experience inadequate supply of domestic water because of rising population levels and the high cost incurred by the Ghana Water Company Limited (GWCL) in improving the polluted sources of surface water. This gloomy situation is worse for rural communities where there is a lack of piped-water infrastructure layout. In the few rural communities with piped-water supply, water hardly flows, hence their reliance on boreholes provided mainly by the Community Water and Sanitation Agency (Amoah 2017). The need for stakeholders to act now follows the uncertainty that surrounds the supply of safe domestic water and associated health-related impact on human capital now and in the future. Safe drinking water is water with microbial, chemical and physical characteristics that meet WHO guidelines or national standards on drinking water quality (WHO 2015; www.who.int/water_sanitation_health/mdg1/en).

The primary question this study seeks to answer is, can the averting expenditure be estimated to determine the willingness-to-pay (WTP) for domestic pipe-borne water supply in residences? One of the revealed preferred methods that has proven reliable for the estimation of WTP for water supply is the averting behaviour method (ABM) (see Van Houtven et al. 2017). In this study, we define ABM as the actual practice(s) undertaken to prevent the harmful effect of water contamination. The associated cost incurred for engaging in these practices is the averting cost or expenditure. Unfortunately, there is a paucity of studies on this method in developing countries (Pattanayak et al. 2010). To the best of our knowledge, no study has provided water-related averting behaviour estimates in Ghana. However, there have been studies on other WTP applications such as contingent valuation method, travel cost method, and hedonic price method in Ghana. Some of these studies include Boadu (1992), Whittington et al. (1992), Amoah (2017, 2018) and Amoah & Moffatt (2017). In other jurisdictions, some previous studies have used ABM to provide estimates to inform policy decisions regarding the supply of safe domestic water. For example, in Korea, Um et al. (2002) estimated WTP for improved water quality using the conventional ABM. They found that for 10 mg/L of suspended solid concentration in tap water from 335 to 325 mg/L, households' WTP ranged from $0.70 to $1.70/month. However, for their perception ABM, WTP was observed to be higher and ranged from $4.2 to $6.1. Similarly, in June 1987, the water supplied in College Township, a community in central Pennsylvania with approximately 5,000 people, was contaminated with perchloroethylene. The Department of Environmental Resources was concerned about potential health risks of the chemical to the population and provided avoidance steps to households. The water users were left to their fate to decide on the usage of the water. Using household-level data, Abdalla (1990) estimated the economic losses from the groundwater contamination using ABM. The households were surveyed using the mail method, and 1,045 completed questionnaires were received. The WTP for households engaging in an averting behaviour was estimated at $21/month/household.

Relatively, most developing countries use low-quality domestic water, however, only a handful of studies have focused on averting behaviour. For example, Vásquez et al. (2009) used a variety of averting measures to investigate private investment choices that improve water quality for households in Parral, Mexico. The study found that apart from bottled-water cost, several other home-based methods are used for tap water treatment. The estimated cost of this method was found to constitute 7.5% of household income. In a more recent study, Tarfasa & Brouwer (2013) used one of the stated preference methods, the choice experiment (CE), to investigate public benefits of urban water supply improvements in Ethiopia. They revealed that the estimated average monthly averting cost was 32.1 Ethiopian birr (approximately $1) per household. The authors concluded that averting behaviour is essential to water use in Hawassa, Ethiopia.

We infer that because the degree of water contamination or water quality is not the same across countries and communities (be it developed or developing), averting behaviour and associated cost estimates can also not be the same. The pieces of evidence from the previous studies reviewed, and the findings of the current study are externally validated by Amoah & Moffatt (2017), who have shown with empirical literature that households generally are willing to spend an average of 2%–18% of their income to improve the quality of their domestic water.

The main contribution of the study is using ABM and a survey approach to determine how much it costs urban households in the Greater Accra Region (GAR) to improve the quality of water before use. In addition, it provides a demand-side estimate to inform private sector decisions for water sector investment. Also, this study contributes to the empirical literature on averting behaviour and domestic water use in response to the paucity of literature on the application of ABM in developing countries, as acknowledged by Pattanayak et al. (2010).

Urban water situation in Ghana

Ghana is a fairly water-endowed country. The Ghana Water Company Limited (GWCL) is a state-owned company responsible for urban water supply which is almost entirely from surface water resources. The GWCL, therefore, has abundant surface water resources to meet the current and future urban consumptive demand (Yirenkyi-Fianko 2019). Unfortunately, the quality of surface water and groundwater sources keep deteriorating at an alarming rate. This is mainly attributed to rapid population growth without a corresponding replacement and expansion in infrastructure, pollution from mining areas, improper waste disposal, chemicals from agriculture and fishing, etc. (see Yeleliere et al. 2018). In recent times, the deteriorating nature of surface and groundwater sources have made averting behaviour a common practice with urban households. Available data from the Ghana Statistical Service (GSS 2014) shows that sachet water is the main source of household drinking water while the pipe-borne water is for general use (washing, bathing, cleaning, etc.).

METHODS

Data collection process

This study focuses on the ten districts in the GAR as defined by the 2010 Housing and Population Census by the GSS (2012). The Region has a total population and number of households of 4,010,054 and 1,036,426, respectively. Out of the latter, the urban and rural household population constitutes 766,955 and 269,471, respectively. Given that the rural case is dire and piped infrastructure is lacking, we focused on the urban area, hence our reference sampling frame is 733,955 households. In anticipation of a representative sample, the Yamane (1967) sample size formula was used to compute the sample size which yielded approximately 400 households. The study over-sampled to as high as 1,650 households.

The instrument used for the data collection was a structured questionnaire. The first section of the questionnaire had the demographic or socio-economic data of the respondent (personal data), the other sections had water, sanitation and other environmentally related questions. This included improved water valuation questions such as averting behaviour and expenditure.

The questionnaire was administered by 20 trained fieldworkers who were supervised by four coordinators. The principal investigator was responsible for the overarching supervision and ethical adherence by the entire team. Before the commencement of the main survey, the team undertook a pre-pilot survey before the actual pilot survey. This was done to avoid possible trial and error before the main survey. All responses in both pilot surveys were analysed to inform possible amendments of some of the questions before the survey. It is important to point out that the pilot datasets were not included in the final dataset.

Given the unplanned nature of settlements in most urban areas in Africa, of which the GAR is no exception, we applied a multistage probability sampling technique. This was achieved by first defining the various districts as unique clusters, followed by listing the communities in each cluster as prescribed by the Town and Country Planning Department. Households within each community were also listed and randomly selected until our expected quota was reached. In all, based on a multistage probability sampling technique, this study relies on 1,648 observations after two observations were excluded due to missing data points. This gives a very high response rate of about 99% which is very common in most surveys in the GAR (e.g., Amoah et al. 2019).

Econometric modelling

The principal aim of modelling the averting expenditure is to calculate the conditional averting average cost (conditional mean) instead of just the mean of averting cost (unconditional). Second, this study seeks to determine the drivers of averting behaviour and provide evidence that supports the internal and external validity of the study. The data show that approximately 10% of the sample do not incur any averting cost as they enjoy regular flow of safe piped water that does not require an averting behaviour. In the regression and the averting cost estimates, the study excluded the 10% to only focus on households that engage in averting behaviour and have incurred an expenditure in that respect. Thus, the discrete dependent variable, total averting expenditure or cost per household (), depends on socio-economic, demographic, context relevant variables and district dummies. Following Amoah et al. (2019), the econometric model is specified as: 
formula
where is as already defined, is a vector of independent variables used for the estimation, and are the unknown parameters to be estimated. The stochastic term, , follows a standard normal distribution. In order to interpret the results as elasticities, we transformed the model into a natural logarithm form. The raw data and their descriptive statistics are presented in Table 1 before transformations. From Table 1, the averting cost per day reported ranges from a minimum of Ghs0.06 (6 pesewas or 1 cent) and a maximum of Ghs36.48 ($6.29). The unconditional mean averting expenditure or cost is Ghs4.36 ($0.75) per day. The average household income per month is Ghs636.37 ($109.72) which is quite close to the national estimate of Ghs544 ($93.79) (GSS 2008). The average age of respondents is 39 years with 89% of household heads being males. The majority (59%) of the respondents are married. Sixty-eight per cent claim they have reliable or regular supply, yet they still engage in averting behaviour. This shows that the quality of water is not always as perfect or safe as one would expect. The household life cycle variable shows the various stages of households with the average representation being families with or without children.
Table 1

Descriptive statistics

StatsAvertingHH-incomeMaleMSAgeAge2ReliabilityLife cycleAccess
Mean 4.36 636.37 0.89 0.59 39.30 1,685.04 0.68 3.28 1.27 
Median 3.19 380.00 1.00 1.00 37.00 1,369.00 1.00 3.00 1.00 
SD 3.97 581.35 0.31 0.49 11.86 986.86 0.47 1.36 0.44 
Skewness 2.25 2.08 − 2.49 − 0.37 0.31 0.79 − 0.76 − 0.10 1.06 
Kurtosis 12.65 8.17 7.18 1.14 2.17 3.00 1.58 2.78 2.13 
Minimum 0.06 160.00 0.00 0.00 18.00 324.00 0.00 1.00 1.00 
Maximum 36.48 4,400.00 1.00 1.00 72.00 5,184.00 1.00 7.00 2.00 
1,490.00 1,604.00 1,648.00 1,648.00 1,648.00 1,648.00 1,489.00 1,645.00 1,619.00 
StatsAvertingHH-incomeMaleMSAgeAge2ReliabilityLife cycleAccess
Mean 4.36 636.37 0.89 0.59 39.30 1,685.04 0.68 3.28 1.27 
Median 3.19 380.00 1.00 1.00 37.00 1,369.00 1.00 3.00 1.00 
SD 3.97 581.35 0.31 0.49 11.86 986.86 0.47 1.36 0.44 
Skewness 2.25 2.08 − 2.49 − 0.37 0.31 0.79 − 0.76 − 0.10 1.06 
Kurtosis 12.65 8.17 7.18 1.14 2.17 3.00 1.58 2.78 2.13 
Minimum 0.06 160.00 0.00 0.00 18.00 324.00 0.00 1.00 1.00 
Maximum 36.48 4,400.00 1.00 1.00 72.00 5,184.00 1.00 7.00 2.00 
1,490.00 1,604.00 1,648.00 1,648.00 1,648.00 1,648.00 1,489.00 1,645.00 1,619.00 

RESULTS AND DISCUSSION

In Table 2, we show the various sources of water being used by urban households in the GAR. The main sources are categorized into water for drinking and water for general use. For the drinking water sources, it is observed that approximately 82% of the respondents use sachet or bottled (packaged) water. This is due to unreliability and poor quality from the pipe-borne system. Hence, most households depend on the commercial (public or private) standpipe water sources which are relatively reliable because of available storage facilities. Consistent with our evidence, the Ghana Statistical Service (GSS 2014) reports that in the urban areas of Ghana, sachet water constitutes the main source of household drinking water and where households use pipe-borne water, they mostly rely on either private or public standpipes.

Table 2

Sources of water for drinking and general use in urban Greater Accra Region

Main source of water for drinking
Main source for general use
SourcesFrequencyPer centSourcesFrequencyPer cent
Indoor plumbing 14 0.85 Indoor plumbing 406 24.76 
Private inside standpipe 43 2.61 Private inside standpipe 345 21.04 
Water truck/Tanker service 0.49 Water truck/Tanker service 456 27.8 
Water vendor(gallons) 0.24 Water vendor(gallons) 56 3.41 
Pipe in neighbouring household 24 1.46 Pipe in neighbouring household 88 5.37 
Private outside standpipe 22 1.34 Private outside standpipe 145 8.84 
Public standpipe 95 5.77 Public standpipe 81 4.94 
Borehole 64 3.89 Borehole 36 2.2 
Protected well 21 1.28 Protected well 26 1.59 
River/Stream/Lake/Dam 0.3 Unprotected well 0.06 
Sachet/Bottled (packaged) water 1,346 81.77 Sachet/Bottled (packaged) water 
Total 1,646 100 Total 1,640 100 
Main source of water for drinking
Main source for general use
SourcesFrequencyPer centSourcesFrequencyPer cent
Indoor plumbing 14 0.85 Indoor plumbing 406 24.76 
Private inside standpipe 43 2.61 Private inside standpipe 345 21.04 
Water truck/Tanker service 0.49 Water truck/Tanker service 456 27.8 
Water vendor(gallons) 0.24 Water vendor(gallons) 56 3.41 
Pipe in neighbouring household 24 1.46 Pipe in neighbouring household 88 5.37 
Private outside standpipe 22 1.34 Private outside standpipe 145 8.84 
Public standpipe 95 5.77 Public standpipe 81 4.94 
Borehole 64 3.89 Borehole 36 2.2 
Protected well 21 1.28 Protected well 26 1.59 
River/Stream/Lake/Dam 0.3 Unprotected well 0.06 
Sachet/Bottled (packaged) water 1,346 81.77 Sachet/Bottled (packaged) water 
Total 1,646 100 Total 1,640 100 

In the case of water for general use (washing, bathing, cleaning, etc.), most households mainly depend on pipe-borne related sources. Given that pipe-borne sources do not flow reliably, most households use water-saving containers to reserve water during piped-water opening or flowing days. Such sources are susceptible to contamination, hence the need for averting practices. Evidently, the GSS (2014) reports that households in urban areas mainly use piped-system and/or tanker services (pipe-borne or borehole or wells).

Overall, our results suggest that most households in the urban areas rely mainly on packaged water for drinking while pipe-borne sources are used for other general household purposes. This evidence may be driven by pipe-borne water quality concerns in the GAR.

Next, we show how most households improve their quality of water before use. Table 3 shows that 36.47% of households buy disinfectant chemicals (e.g., Dettol, alum, camphor, etc.) to treat their water before use, while only 9.88% buy filters. The low patronage for filters is because most households use either bottled or sachet water for drinking and prefer to use other quality improvement methods for bathing, cleaning, washing, and laundry, among others.

Table 3

Averting behaviour in urban Greater Accra Region

Averting behaviour practisedMethod of computationFrequencyPer cent (%)
Applying disinfectant chemicals Cost of chemical used 550 36.47 
Allowing water to settle Opportunity cost of waiting time using the current minimum wage 352 23.34 
Boiling Estimated cost of fire used 457 30.31 
Filtering Cost of filter 149 9.88 
Total  1,508 100 
Averting behaviour practisedMethod of computationFrequencyPer cent (%)
Applying disinfectant chemicals Cost of chemical used 550 36.47 
Allowing water to settle Opportunity cost of waiting time using the current minimum wage 352 23.34 
Boiling Estimated cost of fire used 457 30.31 
Filtering Cost of filter 149 9.88 
Total  1,508 100 

Note: Bottled water and sachet water are used by almost all households.

Averting expenditure/cost analysis

From Table 4, we report and discuss the conditional mean as it represents the predicted average based on the drivers of household averting expenditure. The results show that households spend as high as Ghs84.30 ($14.70) per month to improve the quality of their domestic water. This constitutes 13.25% of the household's take-home income. The unconditional mean is not used because it is prone to either overestimation or underestimation. In the case of this study, it overestimates the averting cost by 7.30 percentage points.

Table 4

Averting expenditure for urban Greater Accra Region

MeasuresExpenditure/HH/Day in GhsExpenditure/HH/Month in GhsShare of incomeCalculated expenditure/Month for urban Greater Accra Region in Ghs
Mean averting expenditure 4.36 ($0.76) [4.16–4.56] 130.80 ($22.80) [124.80–136.8] 20.55% 96,001,314.00 ($16,734,174.00) [91,597,584.00–100,405,044.00] 
Conditional mean averting expenditure 2.81 ($0.49) [2.75–2.87] 84.30 ($14.70) [82.50–86.10] 13.25% 61,872,406.5 ($10,789,138.50) [60,551,287.50–63,193,525.50] 
MeasuresExpenditure/HH/Day in GhsExpenditure/HH/Month in GhsShare of incomeCalculated expenditure/Month for urban Greater Accra Region in Ghs
Mean averting expenditure 4.36 ($0.76) [4.16–4.56] 130.80 ($22.80) [124.80–136.8] 20.55% 96,001,314.00 ($16,734,174.00) [91,597,584.00–100,405,044.00] 
Conditional mean averting expenditure 2.81 ($0.49) [2.75–2.87] 84.30 ($14.70) [82.50–86.10] 13.25% 61,872,406.5 ($10,789,138.50) [60,551,287.50–63,193,525.50] 

Note: Ghs 1 = (US$0.17) [*] implies 95% confidence interval.

In urban GAR of Ghana, the estimated total averting cost for improved water supply is deemed very informative to policy-makers as well as the supplier (GWCL). The total averting cost provides an estimate of the additional amount households would be willing to pay for improvement in their domestic water supply to avoid averting behaviour. Next, we investigate the drivers of averting cost.

Regression analysis of drivers of averting cost

To commence our discussion, we perform some diagnostic tests to ensure the validity of our results. In cross-sectional data analysis, multicollinearity and heteroskedasticity tests are a pre-requisite. To ensure that no exact linear relationship exists among the independent variables, we used the pairwise correlation matrix to compute their respective degree of correlation. From the test results (Table A1 in the Appendix), the highest degree of correlation is found between age and marital status with a correlation coefficient of approximately 0.29. It suggests that there is no concern for multicollinearity among the variables in the data. The other problem is heteroscedasticity, which arises when there is unequal variance in the error term. To control for this in our study, the coefficients are estimated with White's heteroskedasticity-consistent robust standard errors. In addition, we do not assume homogeneity among the districts, so we introduced district fixed effects to account for possible bias that may arise as a result of differences in the districts. We report an R-squared (coefficient of variation) of 15% which fits the minimum value for reliable valuation studies as proposed by Mitchel & Carson (1989). The F-statistics of 8.73 and a p-value of 0.00 provide evidence that the overall model is fit for prediction. Following the evidence from the diagnostic tests, we confidently argue that the estimated results are not severely biased and can be trusted in making policy decisions. The interpretation and discussion of our results assume that all other factors are held constant (ceteris paribus).

In line with the theoretical construct which satisfies an internal validity test, we argue that a consumer's demand is expected to vary with his/her income. If the good is a normal good, we expect that an increase in income will increase consumption of the good in question. In Table 5, we found that there is a positive and statistically significant relationship between income and averting expenditure. That is, if a household's income increases by 1%, the average averting expenditure will increase by 0.0880%. This suggests that water is not just a normal good but a necessity, and households with higher incomes are willing to spend more to improve the quality of water they use. This is consistent with theoretical expectations and justifies the internal validity of the model estimated.

Table 5

Ordinary least squares regression results

VariablesLog-log modelRobust standard errors[95% Conf. interval]
Socio-economic characteristics    
HH-income(Log) 0.0880** (0.039) [0.01089 0.16509] 
Gender = 1, male 0.0192 (0.098) [−0.17339 0.21177] 
Marital status (MS) = 1 0.1238** (0.060) [0.00550 0.24207] 
Age of respondent in years (age) −0.0476** (0.019) [−0.08463 −0.01063] 
Age of respondent in years squared (age20.0005** (0.000) [0.00007 0.00095] 
Family life cycle characteristics    
Life cycle = 2, new couple (<= 1 yr) 0.6530*** (0.192) [0.275441 1.03054] 
Life cycle = 3, family with children (<teenage) 0.8127*** (0.107) [0.60335 1.02202] 
Life cycle = 4, family with teenagers (<‘launching’ children) 0.9923*** (0.111) [0.77415 1.21036] 
Life cycle = 5, family with ‘launching’ children 1.0924*** (0.112) [0.87319 1.31165] 
Life cycle = 6, several adults living together 1.0506*** (0.192) [0.67453 1.42662] 
Residential characteristics    
Reliability of water supply = 1 −0.2786*** (0.067) [−0.40918 −0.14804] 
Access to toilet in residence = 1 −0.1259* (0.066) [−0.25633 0.00453] 
Districts (fixed effects)    
Abokobi 0.1116 (0.102) [0.31188 0.08865] 
Accra 0.3219(0.169) [0.00871 0.65256] 
Ada-Foah 0.1098 (0.145) [0.39459 0.17504] 
Adenta 0.2939** (0.146) [0.00845 0.57944] 
Amasaman 0.0964 (0.147) 0.19253 0.38528] 
Ashiama 0.2325 (0.168) [0.09625 0.56132] 
Dodowa 0.5655*** (0.178) [0.21649 0.91443] 
Tema 0.1668 (0.130) [0.0873 0.42089] 
Teshie-Nungua 0.0955 (0.139) [0.17724 0.36824] 
Weija (reference category) XXXX (XXXX) [XXXX XXXX] 
Constant 0.8272* (0.436)  
Turning point for age and age2 (nlcom) 46.6212***   
Observations 1,282   
F (21, 1,260) 8.73***   
R-squared 0.15   
VariablesLog-log modelRobust standard errors[95% Conf. interval]
Socio-economic characteristics    
HH-income(Log) 0.0880** (0.039) [0.01089 0.16509] 
Gender = 1, male 0.0192 (0.098) [−0.17339 0.21177] 
Marital status (MS) = 1 0.1238** (0.060) [0.00550 0.24207] 
Age of respondent in years (age) −0.0476** (0.019) [−0.08463 −0.01063] 
Age of respondent in years squared (age20.0005** (0.000) [0.00007 0.00095] 
Family life cycle characteristics    
Life cycle = 2, new couple (<= 1 yr) 0.6530*** (0.192) [0.275441 1.03054] 
Life cycle = 3, family with children (<teenage) 0.8127*** (0.107) [0.60335 1.02202] 
Life cycle = 4, family with teenagers (<‘launching’ children) 0.9923*** (0.111) [0.77415 1.21036] 
Life cycle = 5, family with ‘launching’ children 1.0924*** (0.112) [0.87319 1.31165] 
Life cycle = 6, several adults living together 1.0506*** (0.192) [0.67453 1.42662] 
Residential characteristics    
Reliability of water supply = 1 −0.2786*** (0.067) [−0.40918 −0.14804] 
Access to toilet in residence = 1 −0.1259* (0.066) [−0.25633 0.00453] 
Districts (fixed effects)    
Abokobi 0.1116 (0.102) [0.31188 0.08865] 
Accra 0.3219(0.169) [0.00871 0.65256] 
Ada-Foah 0.1098 (0.145) [0.39459 0.17504] 
Adenta 0.2939** (0.146) [0.00845 0.57944] 
Amasaman 0.0964 (0.147) 0.19253 0.38528] 
Ashiama 0.2325 (0.168) [0.09625 0.56132] 
Dodowa 0.5655*** (0.178) [0.21649 0.91443] 
Tema 0.1668 (0.130) [0.0873 0.42089] 
Teshie-Nungua 0.0955 (0.139) [0.17724 0.36824] 
Weija (reference category) XXXX (XXXX) [XXXX XXXX] 
Constant 0.8272* (0.436)  
Turning point for age and age2 (nlcom) 46.6212***   
Observations 1,282   
F (21, 1,260) 8.73***   
R-squared 0.15   

Dep variable: averting expenditure or household's amount spent on water treatment per day (log).

Robust standard errors in parentheses: ***p < 0.01, **p < 0.05, *p < 0.1. (Note: coefficients approximate to 4 decimal places.).

Also, we introduced gender into the model because gender disparity matters in household choices. We found a positive relationship between gender and averting expenditure, albeit insignificant. That is, regarding averting behaviour, gender is statistically immaterial.

Again, we argue that the behaviour of a single financial and health decision-maker and that of a joint financial and health decision-maker cannot assume homogeneity. The decisions regarding household choices in a marital home are generally assumed to be jointly taken while singles take individual decisions without consultation. In this study, we found that there is a positive and statistically significant relationship between those who are married and averting expenditure. That is, if the respondent is married, on average, his/her averting behaviour will increase by 0.1238% relative to the unmarried. This is intuitively not farfetched. Relatively, the married are likely to use more litres of water, care more about each other's health and support each other financially. Thus, they can afford to spend more to improve the quality of their domestic water as compared to a single resident who uses less water, cares for himself and solely absorbs all household financial burden.

Age of the respondent has been used in the literature as a proxy for experience. According to Cameron & Englin (1997), age is crucial in valuation studies. It further provides an upper bound estimate on the respondent's experience. In this study, we found that as people age, they tend to focus on things other than spending to improve the quality of their water. That is, if age should increase by one year, the average averting expenditure will decrease by 0.0476%. However, if age should double, based on the respondent's experience, the average amount spent on averting expenditure will increase by 0.0005%. Age and age-squared together depict a U-shaped relationship with averting expenditure. This provides a minimum turning point of approximately 47 years with a 95% confidence interval ranging from a minimum threshold of 40 years to a maximum threshold of 53 years. This reflects the changing preferences of the respondent as the years go by.

In addition, we represented the household size with household life cycle to better represent the different phases of the household and their expected sizes. This seeks to help in understanding the level of the life cycle at which more is spent on averting behaviour. The intuition is that as the household advances in the life cycle, its water demand (use or consumption) increases. Indeed, such households would be expected to spend more on averting expenditure. To start with, we considered the case where there are only two people living together as a couple. (Here, we considered only respondents without children.) Next in the household life cycle is the family with children (below teenage age only), followed by the family with teenagers (excluding ‘launching’ children, i.e., those who are ready for marriage and may leave the house anytime soon), then the family with ‘launching’ children, and finally families with several adults living together. On average, we observed an increasing ordered effect on the trend of the household life cycle. Thus, there is a positive and statistically significant relationship between households in their advanced stage of the life cycle and averting expenditures. If a household advances by one unit in the life cycle, the average averting expenditure increases from 0.65% to 1.1% approximately. This evidence is consistent with the a priori expectation.

Again, we introduced a control variable for households with a regular pipe-borne water supply and compared them with households without access to regular pipe-borne water supply, where the latter is used as the reference category. We found a negative and highly statistically significant relationship between regular pipe-borne water supply and averting expenditure. That is, if a household has access to a regular supply of water through the piped system, the average amount they spend on averting decreases by 0.2786%. This implies that households with access to pipe-borne water on average spend less to improve domestic water before use. This may include the purchase of bottled water and sachet water for drinking, while the pipe-borne water is used for all other purposes without having to subject it to further water treatment. This suggests that some consumers do not trust the quality of the piped water for drinking, hence they resort to alternatives. The GWCL should find a way of winning back the trust of consumers regarding the quality of piped water to save consumers from expensive alternative sources.

Also, one reason why people treat their water before use is when they suspect that indiscriminate disposal of human or animal faecal matter has made its way into the type of water they use. It is expected that households without toilet facilities and piped water in residences are more likely to dispose of their faeces indiscriminately, which may end up in the non-piped domestic water for household use. We have evidence of a negative and statistically significant relationship between households with access to toilet facilities in their residence and averting expenditure. That is, if a household has access to a toilet facility in their residence, the average averting expenditure will decrease by 0.1259%.

District level analysis

After including the district dummies in the model, we observed differences in the results vis-à-vis signs and significance. This suggests that the case of engaging in averting behaviour and incurring an additional cost in order to improve domestic water supply is not homogeneous across the districts. From Table 2, we deduce from our data and results that Accra, Adenta and Dodowa exhibit positive and statistically significant relationships with averting behaviour. This suggests that, with Weija as the reference district, households in these districts are prone to engaging in averting behaviour because of water supply challenges, hence incurring averting expenditure. Grönwall & Oduro-Kwarteng (2018) reiterated an alternative approach to surviving the water challenges in these districts. They indicated that the GWCL has been supplying households in Dodowa and Adenta with groundwater once a week as a way of improving resilience to water shortages. Similarly, Vinorkor & Afari-Mintah (2008) have re-echoed the Ministry of Water Resources, Works and Housing's intentions to address the water supply challenges in Accra. However, statistical significance evidence was not found with those in Abokobi, Ada-Foah, Amasaman, Tema and Teshie. This is because the severity of the evidence in the aforementioned districts may not be the same across districts.

Heterogenous effect of averting expenditure cost

In most developing countries, gender inequality is found in every space where gender matters. Several studies have shown that women bear the water supply burden in most households. Further, this study finds it interesting to investigate which gender is most sensitive to water quality improvement, engages more in averting behaviour and bears most of the averting burden? The study used both parametric and non-parametric statistical tests to investigate whether the averting burden falls more on women than men or the reverse is true.

From Table 6, we have evidence that there is no statistically significant difference between the males and the females in bearing averting cost. The evidence from the parametric and non-parametric tests provides robust results. By implication, we argue that there is no gender disparity gap when it has to do with improving water quality as human beings are naturally inclined to practices that may improve their health and well-being.

Table 6

Gender differences and averting expenditure cost burden

Parametric testNon-parametric test
Hypothesis (H1): Averting cost (male) ≠ Averting cost (female)
Two-sample t test with equal variances Two-sample Wilcoxon rank-sum (Mann–Whitney) test 
−0.3703 Pr(|T| > |t|) = 0.7112 −1.212 Prob > |z| = 0.2255 
Statistically insignificant Statistically insignificant 
Parametric testNon-parametric test
Hypothesis (H1): Averting cost (male) ≠ Averting cost (female)
Two-sample t test with equal variances Two-sample Wilcoxon rank-sum (Mann–Whitney) test 
−0.3703 Pr(|T| > |t|) = 0.7112 −1.212 Prob > |z| = 0.2255 
Statistically insignificant Statistically insignificant 

Again, to inform policy in subsidy allocation to different income groups, the study employs the poverty headcount index by the World Bank for lower middle-income countries and categorizes the respondents into low-income (poor) and high-income (non-poor) groups. Subsequently, we use both parametric and non-parametric tests to investigate whether there is a statistically significant difference between the poor and non-poor averting expenditures.

In Table 7, we find evidence to reject the null hypothesis that there is no statistically significant relationship between low- and high-income groups in their averting expenditures. That is, the high-income group is observed to spend more on averting expenditures than the low-income group. This study argues that for policy purposes, two scenarios are possible. First, in the case where the poor and the non-poor are exposed to the same sources (e.g., residential pipe-borne system), we expect the poor to consume less relative to the non-poor, hence the government can subsidize the consumption of the poor based on volume used. Second, in the case where both income groups are exposed to different water sources because of the ability to afford (e.g., the high-income group can afford bottled water), the government can subsidize the sources being patronized by the poor.

Table 7

Household income differences and averting expenditure/cost burden

Parametric testNon-parametric test
Hypothesis (H1): Averting cost (high income) ≠ Averting Cost (low income)
Two-sample t test with equal variances Two-sample Wilcoxon rank-sum (Mann–Whitney) test 
−3.0298 Pr(|T| > |t|) = 0.0025*** −2.261 Prob > |z| = 0.0238** 
Statistically significant Statistically significant 
Parametric testNon-parametric test
Hypothesis (H1): Averting cost (high income) ≠ Averting Cost (low income)
Two-sample t test with equal variances Two-sample Wilcoxon rank-sum (Mann–Whitney) test 
−3.0298 Pr(|T| > |t|) = 0.0025*** −2.261 Prob > |z| = 0.0238** 
Statistically significant Statistically significant 

***p < 0.01, **p < 0.05.

CONCLUSION

Despite considerable achievement in access to improved water over the years, substantial inequality in the supply of improved water by the GWCL still exists. Using household-level primary data of 1,648 observations, we have identified households' averting behaviours together with associated cost and drivers. This study has shown that, on average, a household is willing to pay Ghs84.30 ($14.70) per month to improve the quality of their domestic water. Our current estimate constitutes 13.25% of household take-home income.

For policy purposes, this study has provided a demand-side estimate necessary for determining the economic viability of connecting households in urban GAR to the regular piped supply network and ensure that improved quality water is served to households. Thus, to permanently end domestic water-borne diseases and provide safe pipe-borne water at a relatively cheaper price, this study is arguing that households are currently spending an average of 13.25% of their take-home income to enjoy improved water. This estimate depicts households' average WTP for safe pipe-borne water in urban GAR. For the supply of reliable safe pipe-borne water, this estimate is very informative for the GWCL and government in determining pricing and subsidy strategies for households, especially the poor and non-poor. Lastly, the evidence of households' WTP supports the call for the provision of residential reliable safe pipe-borne water for households in urban GAR and by extension, the entire country.

ETHICAL DECLARATION

The data collection process which is part of my PhD research was approved by the Ethics Committee, Faculty of Social Sciences, University of East Anglia, UK. The data were presented at a seminar as well as shared with key supervisors.

DATA AVAILABILITY STATEMENT

Data cannot be made publicly available; readers should contact the corresponding author for details.

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