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

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  • Pedersen, Line Bjørnskov
  • Mørkbak, Morten Raun
  • Scarpa, Riccardo

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.

Suggested Citation

  • Pedersen, Line Bjørnskov & Mørkbak, Morten Raun & Scarpa, Riccardo, 2020. "Handling resolvable uncertainty from incomplete scenarios in future doctors' job choice – Probabilities vs discrete choices," Journal of choice modelling, Elsevier, vol. 34(C).
    • Handle: RePEc:eee:eejocm:v:34:y:2020:i:c:s1755534519301046
    DOI: 10.1016/j.jocm.2019.100199
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    Cited by:

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    2. Riccardo Scarpa & Claudia Bazzani & Diego Begalli & Roberta Capitello, 2021. "Resolvable and Near‐epistemic Uncertainty in Stated Preference for Olive Oil: An Empirical Exploration," Journal of Agricultural Economics, Wiley Blackwell, vol. 72(2), pages 335-369, June.
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    More about this item

    Keywords

    Discrete choice experiments; Elicited choice probabilities; Resolvable uncertainty; Rural general practice;
    All these keywords.

    JEL classification:

    • C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models
    • C35 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions
    • I11 - Health, Education, and Welfare - - Health - - - Analysis of Health Care Markets

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