Can decision field theory enhance our understanding of health‐based choices? Evidence from risky health behaviors

David A. J. Meester, Stephane Hess, John Buckell and Thomas O. Hancock

Health Economics, 2023, vol. 32, issue 8, 1710-1732

Abstract: Discrete choice models are almost exclusively estimated assuming random utility maximization (RUM) is the decision rule applied by individuals. Recent studies indicate alternative behavioral assumptions may be more appropriate in health. Decision field theory (DFT) is a psychological theory of decision‐making, which has shown promise in transport research. This study introduces DFT to health economics, empirically comparing it to RUM and random regret minimization (RRM) in risky health settings, namely tobacco and vaccine choices. Model fit, parameter ratios, choice shares, and elasticities are compared between RUM, RRM and DFT. Test statistics for model differences are derived using bootstrap methods. Decision rule heterogeneity is investigated using latent class models, including novel latent class DFT models. Tobacco and vaccine choice data are better explained with DFT than with RUM or RRM. Parameter ratios, choice shares and elasticities differ significantly between models. Mixed results are found for the presence of decision rule heterogeneity. We conclude that DFT shows promise as a behavioral assumption that underpins the estimation of discrete choice models in health economics. The significant differences demonstrate that care should be taken when choosing a decision rule, but further evidence is needed for generalizability beyond risky health choices.

Date: 2023
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https://doi.org/10.1002/hec.4685

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Persistent link: https://EconPapers.repec.org/RePEc:wly:hlthec:v:32:y:2023:i:8:p:1710-1732

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