In spite of the relevance of different techniques and data, the estimates of price elasticity of water demand in the majority cases show that it is inflexible. Income elasticity, which is the topic of a lesser number of studies, in the majority studies get a rate lower than 1, but then water demand also found to be inelastic with relation to income changes.
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Arbues et al. (2003), Dalhuisen et al. (2003) and Espey et al. (1997) show a particular review of literature about this subject. Differentiation of the derived price and income elasticities is attributed to many factors. Functional specification, aggregation level, data characteristics and estimation issues are responsible for significant differences among elasticity values (Dalhuisen et al,. 2003). Regarding price elasticity, a significant factor is the duration of the analysed period. Many studies have analyzed short-run against long-run elasticities of water demand, concluding that the first one is lesser than the other (Dandy et al., 1997; Nauges and Thomas, 2000; Martinez-Espineira, 2002). This can be attributed to the fact that consumers need more time to acclimatize or buy water-saving appliances and equipment. The outcome of the meta-analysis made by Dalhuisen et al. (2003) is remarkable, demonstrating that price elasticities are usually smaller (water demand is more elastic) for higher income communities.
A significant factor in demand studies for the researched commodity is the recognition of substitute and complementary commodities to each other, because changes in their price affect the demand for this commodity. It is not easy to find a commodity that can be accepted as a full substitute for water that is demanded by consumers.
Minten et al (2008) apply OLS estimation and find household size elasticity of 0.31 and income elasticity of 0.11 in their study of eight rural communities in Madagascar.
Willingness to pay (WTP) studies through contingent valuation was conducted to detect the possible value to users of an improvement in water supply. Studies show that households are willing to pay between 0.5% and 10% of the income for improved water services. Garn (1998) mentions differences in cost (or price), water quality perceptions, reliability, and level of service between existing and improved supplies in rural areas as significant in affecting demand. Empirical findings indicate that to demand a high level of service, rural households are willing to pay more for improved water supply and services and are already spending substantial amounts to circumvent low services (Whittington et al., 1990; Mangin, 1991; Brookshire and Whittington, 1993; Altaf, 1994). In Kathmandu, Nepal, Whittington et al. (2002) found that householdsâ€™ willingness to pay for improved water services is much higher than their current water bills, where unconnected households are WTP a monthly average of US$ 11.67 for private connections. In the case of Ghana, London Economics (1999) find that urban households are WTP Â¢13,209 per month for a compound tap whilst this increases to Â¢13,432 per month for having in-house pipe connections. The decision by households to use improved water sources among other alternatives has received attention and has been modelled through a discrete choice approach. Mu et al. (1990) approach this choice problem by assuming that the decision to be adopted for improved water sources is independent of the quantity of water consumed thereof.
Merret (2002) criticises this approach as it ignores the fact that households use multiple water sources for multiple purposes. Asante et al. (2002) found that educational level and household income are important in determining the likelihood of households using improved water sources in the Volta basin of Ghana. However, their regression analysis does not include the price of improved water, an important decision variable often used as a tool in water demand management strategies. They attribute the observed high absolute price elasticity to high levels of poverty for consumers of private water companies. The study also finds that low water quality (peroxide by salinity) significantly reduced water demand whilst household income has no significant effect on quantity demanded. However, their study presents a serious drawback by excluding water price. A tool employed in water management to regulate demand.
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Using seemingly unrelated regressions, Acharya and Barbier (2002) find that in two areas (four villages) in the Hadejis-Jamaâ€™are floodplain in northern Nigeria, time devoted to water collection did not significantly explain water demand by households that only collect water. However, time significantly determines water demand by households which both collect and purchase water where a 1% increase in collection time decrease the demand for collected water by 3.19% and increase the demand for purchased water by 1.69%. Whilst the price of water does not explain water demand by this group of households (i.e., those who collect and purchase water), price of purchased water is significant in explaining the demand for purchased water where a 1% increase results in a 166.7% decrease in its demand for purchased water by both groups of households. Household size significantly explains the demand for collected and purchased water for these two groups of households.
System of consumer demand equations have been mostly applied in the area of food, meat and alcohol demand in developed and transition countries. Very few studies employ this theoretically consistent methodology on water demand studies. A notable one is by Pashardes et al. (2001) who applied the Quadratic Almost Ideal Demand System (QAIDS) model to estimate residential water demand in Cyprus and to derive welfare implications for changes the water pricing system. Their results point to water as a necessity with an average income elasticity of 0.32, ranging from 0.25 (lowest income group) to 0.48 (highest income group), indicating that water is more of a necessity to poorer households. Plausible reasons advanced for this unexpected result is the complementary role of water to large houses having the swimming pools, large lawn gardens, and other luxury goods such as washing machines purchased by rich households. Another reason is that affluent households use more water and thus pay more under the prevailing increasing block tariff system. They also observe that poorer households are more responsive or sensitive to water price changes (elasticity of – 0.79) than wealthier households (elasticity of -0.39). Based on this evidence of a decline in water price responsiveness from poor to rich households, the authors suggest the inappropriateness of using price as a water management tool.
Nearly all researches conducted particularly in developing countries model water as a homogenous commodity. Mu et al. (1990) recognize the heterogeneous character of water in less developed countries, maybe due to characteristic diversities, the requirement to deal with water and distances travelled to obtain water.
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