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Library Domestic Water Pricing with Household Surveys : A Study of Acceptability and Willingness to Pay in Chongqing, China

Domestic Water Pricing with Household Surveys : A Study of Acceptability and Willingness to Pay in Chongqing, China

Domestic Water Pricing with Household Surveys : A Study of Acceptability and Willingness to Pay in Chongqing, China

Resource information

Date of publication
mei 2012
Resource Language
ISBN / Resource ID
oai:openknowledge.worldbank.org:10986/6799

In determining domestic water prices,
policy makers often need to use information about the demand
side rather than only relying on information about the
supply side. Household surveys have frequently been employed
to collect demand-side information. This paper presents a
multiple bounded discrete choice household survey model. It
discusses how the model can be utilized to collect and
analyze information about the acceptability of different
water prices by different types of households, as well as
households' willingness to pay for water service
improvement. The results obtained from these surveys can be
directly utilized in the development of water pricing and
subsidy policies. The paper also presents an empirical
multiple bounded discrete choice study conducted in
Chongqing, China. In this case, domestic water service
quality was seriously inadequate, but financial resources
were insufficient to improve service quality. With a survey
of about 1,500 households in five suburban districts in
Chongqing Municipality, this study shows that a significant
increase in the water price is feasible as long as the
poorest households can be properly subsidized and certain
public awareness and accountability campaigns can be
conducted to make the price increase more acceptable to the
public. The analysis also indicates that the order in which
hypothetical prices are presented to respondents
systematically affects their answers, and should be taken
into account when designing survey instruments.

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Authors and Publishers

Author(s), editor(s), contributor(s)

Wang, Hua
Xie, Jian
Li, Honglin

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