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.
|Title of host publication||School of Business, Economics and Law, Goeteborg University. Working Papers in Economics|
|Place of Publication||Gothenburg|
|Number of pages||44|
|Publication status||Published - Apr 2015|
|Name||Working Papers in Economics|
|Publisher||University of Gothenberg|
Bibliographical noteNo. 619 in a monographic series
- Contingent valuation;
- double bounded discrete choice
- repetitive learning.
- advanced information learning
- bid dependency.
- theories of preference formation
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, pp. 1-43). (Working Papers in Economics; Vol. No. 615).. https://gupea.ub.gu.se/handle/2077/38652