Pedersen, L. B. and Mørkbak, M. R. and Scarpa, R. (2020) 'Handling resolvable uncertainty from incomplete scenarios in future doctors' job choice – probabilities vs discrete choices.', The journal of choice modelling., 34 . p. 100199.
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.
|Full text:||(AM) Accepted Manuscript|
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|Publisher Web site:||https://doi.org/10.1016/j.jocm.2019.100199|
|Publisher statement:||© 2019 This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/|
|Date accepted:||18 November 2019|
|Date deposited:||21 November 2019|
|Date of first online publication:||25 November 2019|
|Date first made open access:||25 May 2021|
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