 Handling resolvable uncertainty from incomplete scenarios in future doctors' job choice – Probabilities vs discrete choicesLine Bjørnskov Pedersen, Morten Mørkbak and Riccardo Scarpa Journal of choice modelling, 2020, vol. 34, issue C Abstract: Health economists often use discrete choice experiments (DCEs) to predict behavior, as actual market data is often unavailable. Manski (1990) argues that due to the incompleteness of the hypothetical scenarios used in DCEs, substantial uncertainty surrounds stated choice. Uncertainty can be decomposed into “resolvable” and “unresolvable”; the former is expected to become resolved in actual choice, as individuals collect further information. To enable its identification, Manski suggests eliciting subjective choice probabilities (ECPs) rather than discrete choices. We introduce the ECP approach in health economics and explore its convergent validity. The context is future physicians’ stated choices of job in rural general practice in Denmark. Our results are mixed, but show remarkable similarities in forecasting abilities, despite the ECP models being less econometrically demanding and relying on different preference distributional assumptions. Keywords: Discrete choice experiments; Elicited choice probabilities; Resolvable uncertainty; Rural general practice (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:eejocm:v:34:y:2020:i:c:s1755534519301046 DOI: 10.1016/j.jocm.2019.100199 Access Statistics for this article Journal of choice modelling is currently edited by S. Hess and J.M. Rose More articles in Journal of choice modelling from Elsevier |
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