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Working Paper Series

Muffled Price Signals:  Household Water Demands Under Increasing-Block Prices

Authors

  • Michael W. Hanemann, Goldman School of Public Policy, University of California, Berkeley
  • Robert N. Stavins, University of California, Berkeley
  • Sheila M. Cavanagh, Harvard University

History

  • Goldman School of Public Policy Working Paper (February 2002)

Abstract

In many areas of the world, including large parts of the United States, scarce
water supplies are a serious resource and environmental concern. The possibility
exists that water is being used at rates that exceed what would be dictated by
efficiency criteria, particularly when externalities are taken into account. Because
of this, much attention has been paid by policy makers and others to the use of
demand management techniques, including requirements for the adoption of specific
technologies and restrictions on particular uses. A natural question for economists
to ask is whether price would be a more cost-effective instrument to facilitate water
demand management.
As a first step in such an investigation, this paper draws upon a newly-available
set of detailed data to estimate econometrically the demand function for household
use of urban water supplies. We analyze cross-sectional time-series data that track
1,082 single-family households served by 16 water utilities in 11 urban areas in the
United States and Canada. Because of the diverse multiple-block pricing structures
that abound, estimating the effects of price and price structure on residential water
demand poses some challenging and interesting problems.
We find that the sensitivity of residential water demand to price is quite low,
and that the effect of price structure may be more influential than the magnitude
of marginal price itself. The household-level data we use allow us to assess the
influences on residential water demand of climate, sociodemographic factors, and characteristics of housing stock, including home vintage. Our results indicate substantial heterogeneity in likely household responses to utility demand-management

policies.

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Last updated on 06/07/2013