national multiple family submetering and allocation billing program ...

udget. In fact, the median monthly water bill for an average U.S. customer in 1998 was less
than $16 (Cavanagh, Hanemann, and Stavins 2001). Furthermore, the typical resident is
unaware of how much water they are using for any given purpose, thus contributing to the price
inelasticity.
One of the most hotly contested subjects in estimating price elasticities revolves around
the price variable. The price variable is a dicey subject because often, monthly water bills are
not straightforward calculations. Most studies have used either a marginal or average price.
Marginal price is based on the cost of providing the next unit of water, whereas average price
simply divides the total cost by the total water use. Gibbs (1978) was the first to argue for the
use of marginal rather than average price. Billings and Agthe (1980) employ marginal price as
well as a difference price variable. However, price perception tests found that customers respond
to average price rather than marginal price in electricity bills (Shin 1985) and water bills
(Nieswiadomy 1992). This finding could be testament to the fact that the more complex the
water bill, the more difficult it is for customers to establish the link between usage and cost.
There are also countless rate structures that utilities can choose to meet their revenue and
customer needs. The three main types: flat, uniform, and block rates, can take many different
forms. Several studies, including Camp (1978), Young et al. (1983), Stevens, Miller, and Willis
(1993), Corral (1997), and Cavanagh, Hanemann, and Stavins (2001) have examined the role of
price structures on demand. The effects of various price structures in the Tucson, Arizona,
metropolitan area have also been extensively studied (Young 1973, Cuthbert 1989, Billings and
Day 1989, Martin and Kulakowski 1991). In a study that extrapolates single-family water usage
to multi-family housing, Goodman (1999) used marginal price and estimated an elasticity of -
0.70. Hewitt and Hanemann (1995) used a discrete/continuous choice model, finding one of the
highest estimates of elasticity (-1.6), although it may have been influenced by summer irrigation.
There have also been studies that examined price elasticity over the long term versus the
short term. By definition, in the “long term” all costs are variable, so the consumer has more
options to adjust to prices. In the short run, at least some costs are fixed, so the consumer is
more constrained. Studies have indicated that there is a significant time lag before residents fully
react to price increases (Schneider 1991, Carver and Boland 1980, Espey, Espey, and Shaw
1997). In addition, studies have shown that price can be an effective tool during water shortages
(Moncur 1989, Corral 1997, Pint 1999).
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