Estimation of the effects of price and billing frequency on household water demand using a panel of Wisconsin municipalities

Abstract

A demand function of residential water consumption is developed from a 1997 to 2006 panel of 200 Wisconsin water utilities. A double-log functional form is assumed and parameters are estimated using a random effects model. The results suggest that the price is inelastic yet negative and statistically significant and this elasticity response grows stronger as the marginal price level is increased. Additionally, the model reveals water savings due to monthly billing and also the annual water savings from technology adoption.

Keywords:

• water demand
• water conservation
• billing frequency
• price elasticity of demand

JEL Classification:

• L95
• C23

Notes

¹ Electric Utility Week, ‘Water War in Georgia, Florida, Alabama boils over, frays relations and power supply’; 29 October 2007.

² Information for the status of each state in regard to ratifying the compact is available at http://www.cglg.org which is the website for the Council of Great Lakes Governors.

³ There are a few exceptions, for example, Madison Water Utility bills customers every 6 months.

⁴ An increasing block rate structure implies that as a consumer increases his/her volume of water consumption past a given tiered threshold, the volumetric price of water increases. A decreasing block rate functions in the opposite direction: as water consumption increases for a given customer the volumetric price decreases.

⁵ Thanks to Bruce Schmidt, water cost engineer, of the Wisconsin PSC who gathered the tariff data for this study.

⁶ Mainly due to more stringent Environmental Protection Agency (EPA) water regulations and replacement of capital infrastructure over this period.

⁷ Thanks to Edward J. Hopkins, PhD, who is an assistant state climatologist at the University of Wisconsin who gathered these data for this study.

⁸ At the time of this writing Madison Water Utility is requesting an 18% increase in water rates.

⁹ Data are available for the estimation of a cost function to determine the long-run marginal costs of customers and volumes given a utility-specific circumstance.

¹⁰Once again, this would require the estimation of a long-run cost function to determine if the cost savings outweigh the added expenses.

¹¹Expected changes in the other variables of the model should also be considered when forecasting demand such as price, income, weather, billing policy or conservation DSM programmes in addition to anticipated changes in industrial water load.