Testing preference formation in learning design contingent valuation (LDCV) using advanced information and repetitive treatments
Data(s) |
01/04/2015
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Resumo |
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. |
Identificador | |
Idioma(s) |
eng |
Direitos |
info:eu-repo/semantics/restrictedAccess |
Fonte |
Aravena , C , Hutchinson , W G , Carlsson , F & Matthews , D I 2015 , Testing preference formation in learning design contingent valuation (LDCV) using advanced information and repetitive treatments . in School of Business, Economics and Law, Goeteborg University. Working Papers in Economics . vol. No. 619 , Working Papers in Economics , vol. No. 615 , Gothenburg , pp. 1-43 . |
Palavras-Chave | #Contingent valuation; #double bounded discrete choice #repetitive learning. #advanced information learning #bid dependency. #theories of preference formation |
Tipo |
contributionToPeriodical |