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Managing groundwater in a mining region: an opportunity to compare best‐worst and referendum data
Author(s) -
Hatton MacDonald Darla,
Rose John M.,
Johnston Robert J.,
Bark Rosalind H.,
Pritchard Jodie
Publication year - 2019
Publication title -
australian journal of agricultural and resource economics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.683
H-Index - 49
eISSN - 1467-8489
pISSN - 1364-985X
DOI - 10.1111/1467-8489.12326
Subject(s) - nonmarket forces , respondent , status quo , valuation (finance) , contingent valuation , referendum , welfare , willingness to pay , discrete choice , economics , actuarial science , econometrics , public economics , computer science , microeconomics , political science , market economy , finance , politics , factor market , law
In nonmarket valuation, practitioners must choose a format for the valuation questions. A common approach in discrete choice experiments is the ‘pick‐one’ format, often with two alternative policy proposals and a status quo from which the respondent selects. Other proposed formats, include best‐worst elicitation, where respondents are asked to indicate their most and least favoured alternative from a set. Although best‐worst formats can offer efficiency in data collection, they can also lead to responses that are difficult to reconcile with neoclassical welfare estimation. The current article explores methodological issues surrounding the use of pick‐one versus best‐worst data for nonmarket valuation, focusing on framing and status quo effects that may occur within three‐alternative discrete choice experiments. We illustrate these issues using a case study of surplus groundwater use from Western Australian mining. Results identify concerns that may render best‐worst data unsuitable for welfare estimation, including a prevalence of serial choices in which the status quo is universally chosen as the worst alternative, rendering part of the choice process deterministic. Asymmetry of preferences and serial choices can be obscured when models are estimated using ‘naively’ pooled best‐worst data. Results suggest that caution is warranted when using best‐worst data for valuation, even when pooled results appear satisfactory.

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