Testing preference formation in learning design contingent valuation (LDCV) using advanced information and repetitivetreatments

Claudia Aravena (), W. George Hutchinson (), Fredrik Carlsson and David I Matthews ()
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Claudia Aravena: Queen's University Belfast
W. George Hutchinson: Queen's University Belfast
David I Matthews: Queen's University Belfast

No 619, Working Papers in Economics from University of Gothenburg, Department of Economics

Abstract: Policymakers have largely replaced Single Bounded Discrete Choice (SBDC) valuation by the more statistically efficient repetitive methods; Double Bounded Discrete Choice (DBDC) and Discrete Choice Experiments (DCE). Repetitive valuation permits classification into rational preferences: (i) a-priori well-formed; (ii) consistent non-arbitrary values “discovered” through repetition and experience; (Plott, 1996; List 2003) and irrational preferences; (iii) consistent but arbitrary values as “shaped” by preceding bid level (Tufano, 2010; Ariely et al., 2003) and (iv) inconsistent and arbitrary values. Policy valuations should demonstrate behaviorally rational preferences. We outline novel methods for testing this in DBDC applied to renewable energy premiums in Chile.

Keywords: Contingent valuation; double bounded discrete choice; repetitive learning; advanced information learning; bid dependency; theories of preference formation (search for similar items in EconPapers)
JEL-codes: D03 Q40 Q51 (search for similar items in EconPapers)
Pages: 44 pages
Date: 2015-04
New Economics Papers: this item is included in nep-dcm and nep-ene
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