Demand for Household Water in Buon Ma Thuot, Viet Nam: Evidence from Households Revealed

Estimating household water demand using Revealed and contingent behaviors: evidence from Viet Nam

Jeremy Cheesman[*], Jeff Bennett[†] and Tran Vo Hung Son[‡]

Abstract: This article separately estimates water demand by households utilizing (i) municipal water exclusively and (ii) municipal water and household well water in Buon Ma Thuot, Viet Nam. Demand estimates are obtained from a panel dataset formed by pooling household-level data on observed municipal water purchases and stated intended water usage contingent on hypothetical water prices. Estimates show households using municipal water exclusively have very price inelastic demand, whereas households using both municipal and household well water have more price elastic, but still inelastic, simultaneous water demands and readily substitute between water sources in response to increasing prices. Household water usage is conditioned by water storage and supply infrastructure, income and socio-economic attributes. The demand estimates are used for forecasting municipal water usage as well as the municipal water supply company’s likely revenue stream following an increase to the municipal water tariff and also for modeling consumer surplus losses from municipal water supply disruptions.

Keywords: urban water demand, household production function, revealed preference, contingent behavior.

1Introduction

Household water demand analyses are an economic cornerstone for demand side water management, developing efficient water tariff schedules and water infrastructure cost benefit analyses. Meta-analyses profiling the household water demand literature concentrate on developed country applications (Espey et al. 1997, Arbues et al. 2003, Dalhuisen et al. 2003). These applied studies from developed countries mainly estimate demand from households’ observed water purchases from a single municipal water supplier, municipal water’s multi-part block tariff, household income, socio-economic attributes and sometimes climatic and structural factors, typically finding household water demand is both price and income inelastic. Household water’s price and income inelasticity is normally linked to water being a non-substitutable input in many household uses and also because household water expenditures only account for a small percentage of most households’ budgets (Arbues et al. 2003).

Less work has been directed towards estimating household water demand in less developed countries (LDC’s). Strand and Walker (2005) estimated a –0.32 household own price elasticity using a survey dataset from 17 cities in Central America and Venezuela. Their analysis shows households drawing water from more than one source have source specific water demand and also that in-household water infrastructure is a stronger demand determinant than water price. Using data from seven Cambodian towns, Basani, Isham et al. (2008 forthcoming) estimated households’ own price elasticity for municipal water supplies between -0.40 and -0.50. Combining household data from El Salvador and Honduras, Nauges and Strand (2007) estimated non-tap water demand elasticities as a function of water cost, defined as the sum of water’s purchase price and hauling costs, between -0.40 and -0.70. Rietveld, Rouwendal et al. (2000) estimated an own price elasticity of -1.2 for a cross-section of Indonesian households. Acharya and Barbier (2002) estimated linear water demands for Nigerian households that (i) exclusively collected water, (ii) exclusively purchased water from vendors, or (iii) hauled and purchased water. Households purchasing water exclusively had an estimated own price elasticity of –0.067, whereas collecting and purchasing households’ own price elasticity for purchased water was –0.073.

Estimating price elasticity requires that water’s price varies. However, water may be purchased at a constant price, as is the case when a municipal water supplier charges the same tariff for every cubic meter of water it delivers, or unpriced, in the sense of not having a tariff, as occurs when a household draws its water from a private well. Both of these situations complicate household water demand estimation, but both, and especially the latter, are frequently features of household water use in LDC’s. Stated preference techniques can be applied for constructing the price usage relationships needed for estimating household water demand functions in both these situations (Freeman 2003). Stated preference techniques construct hypothetical markets, using these for simulating respondents’ preferences for scarce resource allocation. When available, households’ real water purchasing histories, such as their water bills, can be used as an empirical anchor point for investigating each household’s likely water usage in novel water pricing situations. Confirming convergent validity between a household’s observed water purchases and stated preferences shows the same underlying preference structure is being used for making actual and hypothetical water purchases. Analyses pooling revealed and stated preference data (Adamowicz et al. 1994, Ben-Akiva et al. 1994, Englin and Cameron 1996, Adamowicz et al. 1997, Huang et al. 1997, Acharya and Barbier 2002, Boxall et al. 2002, Earnhart 2002, Hanley et al. 2003) generally show pooling increases estimated parameters’ efficiency and robustness, especially when estimates are based on small datasets (Englin and Cameron 1996, Haab and McConnell 2003, Hanley et al. 2003, Birol et al. 2006).

This article estimates demand for delivered water by households using (i) municipal water exclusively and (ii) municipal water and household well water in Buon Ma Thuot (BMT), Viet Nam. Buon Ma Thuot is located in Viet Nam’s Central Highlands region and is DakLakProvince’s largest town. The municipal water supply system was upgraded and expanded in 2003, resulting in connected households increasing their municipal water usage, and thereby diverting scarce water away from the region’s irrigated agriculture sector. The Buon Ma Thuot Water Supply Company (BMTWSC), the autonomous State agency responsible for operating the municipal water supply system, is meant to operate at full cost recovery. The fixed VND2,250 (USD1  VND15,500) per cubic meter tariff it charges is less than the VND4,000 per cubic meter average cost it estimates it incurs for delivering water to BMT’s households however. All households receiving municipal water supplies in BMT are metered and have their monthly household water bills calculated from their metered usage.

Approximately 75 percent of all permanent households in BMT are now connected to the municipal water supply system. A percentage of households already connected to the municipal system combine municipal water and water from at least one alternative source, such as private wells or water vendors. Little is known about households’ usage patterns from non-municipal water sources in BMT nor why households may prefer these sources’ water to municipal water. Madanat and Humplick (1993) found that households had preferences for water by source in specific uses and it is reasonable to expect the same thing here. For example, BMT’s households may prefer using municipal water for cooking and well water for drinking because they believe municipal water tastes and smells of chemicals. Nothing is known about how households using secondary water sources would alter usage between sources when responding to changes in the attributes of either the municipal or secondary source’s water. These substitution strategies carry important economic and water planning implications in BMT however, meaning a system of conditional water demands for households not using the municipal water source exclusively must be estimated.

This article’s main contributions lie first in developing the sparse literature on single and multiple source household water demand in Southeast Asia and second in the novel revealed and stated preference approach the article applies for estimating own and cross price elasticities for water when faced with an invariant municipal water price and unpriced household well water. Household water demand estimates are constructed from a survey dataset pooling households’ actual observed water usage at the existing municipal water tariff and their stated water usage preferences contingent on hypothetical water prices. The stated preference approach is based in the contingent behavior method, which works by eliciting individuals’ intended behavioral response to a hypothetical situation occurring, such as an increase in water price (Hanley et al. 2003). Acharya and Barbier (2002) have previously employed a contingent behavior approach in estimating Nigerian households’ water demand as a function of real and hypothetical vendor water prices and water hauling times.

In the remainder of this article the conceptual household water demand model, estimation and survey approaches are first described. Following a brief descriptive analysis of the survey data, household water demands are estimated from the panel dataset. Policy implications are discussed in section five and the demand estimates are used to forecast household municipal water usage and the BMTWSC’s revenue following an increase to the municipal water price. The consumer surplus losses imposed by binding water supply constraints are evaluated in section six in light of dry season water shortages that have historically plagued BMT. Section seven concludes.

2Specification and estimation technique

2.1Modeling household water usage

Household water usage is a function of an underlying decision making process that takes water usage preferences and constraints on acquiring water into account (Larson et al. 2006). When household labor is needed for collecting and preparing water, a household water demand model accounting for having to choose between allocating scarce household labor between water collecting and preparing usages and income generating work is required. Acharya and Barbier (2002) formally model the joint consumer producer household’s decision making when two water sources are available, with one source being free but requiring labor input and the other priced and not requiring labor input. The household seeks to maximize utility from water given the water sources available and the household’s income and labor constraints. The end result is the household water demand function, conditional on water source:

/ (1)

where is the water quantity used from source j, is the purchased water’s price, is the collected water’s shadow price, which is the marginal opportunity cost of foregone income from work, are two vectors describing water quality attributes such as turbidity, smell and taste of priced and collected water respectively and is a vector of household specific characteristics, including income and labor potential. When water is perfectly substitutable between sources, the utility maximizing household consumes water from both sources until the marginal rate of substitution from purchasing water and collecting water are equal, meaning the marginal opportunity cost of foregone work income equals the marginal water price. This household decision framework includes two corner solutions: firstly, when the opportunity cost of foregone work income due to water collection and preparation always exceeds water’s marginal price the household consumes priced water only and secondly, when the marginal water price always exceeds labor’s marginal opportunity cost then the household always collects water.

2.2Demand estimation

2.2.1Household well status

Obtaining unbiased water demand estimates requires that households drawing water from wells in BMT do so as a result of a random selection process. It is possible however that latent variables determine whether a household has a well or not. This potential source of sample selection bias is controlled for using Heckman’s (1979) two step estimation procedure. In the first step the discrete choice dependent variable (di) equals one if the household has a private well and zero if they do not. Assuming a normal probability distribution for the error term (ui), the decision model in probit form is:

/ (2)

where is a matrix vector of explanatory variables describing the household’s well status, a vector of unknown coefficients to be estimated and is the cumulative normal distribution. The inverse Mill’s ratio is calculated with the probit model’s estimated parameters and included in the second stage household water demand estimates. The inverse Mill’s ratio is:

/ (3)

where and are respectively the univariate standard normal cumulative distribution and the probability density functions.

2.2.2Conditional household water demand functions

For households using the municipal water supply only, their conditional household demand function is assumed to be:

/ (4)

Whereas the households using water from both municipal and private well sources have the conditional simultaneous demands:

/ (5)
(6)

Where the municipal water price is , is well water’s shadow price, describes household socio-economic characteristics including water supply infrastructure such as storage tanks and booster pumps and also the household’s inverse Mill’s ratio, the normally distributed idiosyncratic error term and the remainder are coefficients for estimating. These demand specifications exclude costs from preparing water for use, such as filtering or boiling water before drinking, because descriptive analyses, to be discussed subsequently, suggest these are likely immaterial. The demand equations also exclude water quality attributes, again because descriptive analyses showed BMT’s survey respondents viewed water quality as being near equal between municipal and household well sources and also because water quality perceptions are likely correlated with income and education (Whitehead 2005).

3Empirical application

Schedules of household water usage as a function of water prices are constructed in this analysis by pooling observed and contingent behavior data from Buon Ma Thuot’s urban and peri-urban households. The observed behavior data is municipal water usage by households at the existing municipal water tariff. The contingent behavior data is estimated by constructing how each household changes its water usage following hypothetical changes in water pricing. Because all households receiving the BMTWSC’s municipal water are metered, this data can be used for cross-validating households’ own water usage estimates and also for anchoring the contingent behaviour scenarios.

Survey development is discussed in detail in Cheesman, Son et al. (2007). The survey’s main objective was collecting household background data, including details on in-household water supply infrastructure, and estimating actual and contingent household water usage for BMT households’ seven main water usages, with these defined in pre-testing: (i) bathing and washing; (ii) preparing meals; (iii) drinking; (iv) cleaning; (v) laundry; (vi) outside (generally gardening); and (vii) home business. For estimating households’ revealed and stated preferences for water by household usage, the survey enumerator first assisted the respondents in estimating their average daily household water usage by source for the seven household usages. To do this, the enumerator walked through the respondents’ household, identifying with the respondents where activities using water were occurring. Following this initial identification, the enumerator worked with the respondents to estimate the amount of water used in each activity during a normal day. Because different household members are generally responsible for specific water usages, both the male and female household heads participated where possible. Having both household heads responding may reduce the potential for strategic behaviour because the respondents audited the other’s answers and there was open discussion on points of difference (Thomas and Syme 1988). The household members estimated their daily water usage via observation and demonstration. For water usages that were not daily, weekly usage figures were estimated.

After household daily or weekly water usages in the seven main household usages were estimated, the enumerator extrapolated monthly household water usage and water expenditure by water source. As a first step, the household’s estimated municipal water usage was compared to their latest available municipal water bill to check whether the respondents accurately estimated their monthly municipal water usage. Then, for estimating the monthly municipal water cost by usage, each usage’s estimated monthly municipal water usage was multiplied by the VND2,250 per cubic meter tariff charged by the BMTWSC. For calculating well water’s monthly cost by the seven household usages, estimated well water usage was multiplied by a volumetric shadow price of VND450 per cubic meter, which was the representative household’s calculated well water extraction cost defined by pre-testing. The shadow price was constructed using labor and pumping fuel costs only, with these being constructed from the average daily wage and fuel price observed from the pre-testing respondents. Separately estimating well water shadow prices for each responding household would be preferable to the averaging approach used, however in practice we found this preferable approach was prohibitively time consuming, distracting and often lead to enumerators incorrectly calculating shadow prices. Because the survey focus groups, pre-tests and discussions with local authorities suggested households were relatively homogenous in their acquiring, storing and well water usage (a finding also supported by this article’s descriptive statistics), we ultimately favored using a common shadow price for all households using well water. This simplified approach obviously has its limitations.