Handling resolvable uncertainty from incomplete scenarios in future doctors' job choice – probabilities vs discrete choices

Pedersen, L.B.; Mørkbak, M.R.; Scarpa, R.

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 milder preference distributional assumptions.

Citation

Pedersen, L., Mørkbak, M., & Scarpa, R. (2020). Handling resolvable uncertainty from incomplete scenarios in future doctors' job choice – probabilities vs discrete choices. Journal of Choice Modelling, 34, Article 100199. https://doi.org/10.1016/j.jocm.2019.100199

• Journal Article Type: Article
• Acceptance Date: Nov 18, 2019
• Online Publication Date: Nov 25, 2019
• Publication Date: Mar 31, 2020
• Deposit Date: Nov 19, 2019
• Publicly Available Date: May 25, 2021
• Journal: Journal of Choice Modelling
• Electronic ISSN: 1755-5345
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 34
• Article Number: 100199
• DOI: https://doi.org/10.1016/j.jocm.2019.100199
• Public URL: https://durham-repository.worktribe.com/output/1313555