Handling resolvable uncertainty from incomplete scenarios in future doctors' job choice: Probabilities vs discrete choices

Line Bjørnskov Pedersen*, Morten Raun Mørkbak, Riccardo Scarpa

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

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.

Original languageEnglish
Article number100199
JournalJournal of Choice Modelling
Volume34
Number of pages14
ISSN1755-5345
DOIs
Publication statusPublished - Mar 2020

Keywords

  • Discrete choice experiments
  • Elicited choice probabilities
  • Resolvable uncertainty
  • Rural general practice

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