Decision uncertainty in multi-attribute stated preference studies

Dekker, Thijs; Hess, Stephane; Brouwer, Roy; Hofkes, M.W.

doi:10.1016/j.reseneeco.2015.11.002

Cited by 40 publications

(35 citation statements)

References 43 publications

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“…WTP to reduce the EV charging time by an hour ranges from $96 (GCM-t) to $60 (GCM-N). The higher WTP estimates of GCM-t are aligned with the finding of the study by Dekker et al (2016), who also observed an increase in WTP for flood risk reductions after controlling for the behavioral responses to decision uncertainty in an integrated choice and latent variable model. Finally, because vehicle purchase and parking costs happen at different times, the annual subjective discount rate of parking cost is estimated at 10.7% (GCM-t) and 9.3% (GCM-N), which is slightly above market interest rates in the automotive industry.…”

Section: [Insert Figure 2 Here]supporting

confidence: 82%

“…Such choice behaviors cannot be modeled using standard logit and probit links, even when accounting for preference heterogeneity. Also, neglecting decision uncertainty results into underestimation of welfare measures (Dekker et al, 2016). A few studies have quantified the decision uncertainty by asking follow-up questions after each choice task and incorporating these self-reported responses as explanatory variables or in other structural forms (Lundhede et al, 2009;Olsen et al, 2011;Beck et al, 2013;Börger, 2016).…”

Section: Behavioral Implicationsmentioning

confidence: 99%

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A Generalized Continuous-Multinomial Response Model with a t-distributed Error Kernel

Dubey

Bansal

Daziano

et al. 2020

Transportation Research Part B: Methodological

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In multinomial response models, idiosyncratic variations in the indirect utility are generally modeled using Gumbel or normal distributions. This study makes a strong case to substitute these thin-tailed distributions with a t-distribution. First, we demonstrate that a model with a t-distributed error kernel better estimates and predicts preferences, especially in class-imbalanced datasets. Our proposed specification also implicitly accounts for decision-uncertainty behavior, i.e. the degree of certainty that decision-makers hold in their choices relative to the variation in the indirect utility of any alternative. Second -after applying a t-distributed error kernel in a multinomial response model for the first time -we extend this specification to a generalized continuous-multinomial (GCM) model and derive its full-information maximum likelihood estimation procedure. The likelihood involves an open-form expression of the cumulative density function of the multivariate t-distribution, which we propose to compute using a combination of the composite marginal likelihood method and the separation-of-variables approach. Third, we establish finite sample properties of the GCM model with a t-distributed error kernel (GCM-t) and highlight its superiority over the GCM model with a normally-distributed error kernel (GCM-N) in a Monte Carlo study. Finally, we compare GCM-t and GCM-N in an empirical setting related to preferences for electric vehicles (EVs). We observe that accounting for decision-uncertainty behavior in GCM-t results in lower elasticity estimates and a higher willingness to pay for improving the EV attributes than those of the GCM-N model. These differences are relevant in making policies to expedite the adoption of EVs.

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Section: [Insert Figure 2 Here]supporting

confidence: 82%

Section: Behavioral Implicationsmentioning

confidence: 99%

A Generalized Continuous-Multinomial Response Model with a t-distributed Error Kernel

Dubey

Bansal

Daziano

et al. 2020

Transportation Research Part B: Methodological

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“…The combination of endogeneity and potential measurement error in auxiliary data has received limited attention in the SP literature (Train et al 1987;Hess and Beharry-Borg 2012;Mariel, Meyerhoff, and Hess 2015). Recent advances in hybrid choice models such as the integrated choice and latent variable (ICLV) model are designed to address such concerns (e.g., Ben-Akiva et al 1999;Hess and Beharry-Borg 2012;Dekker et al 2016;Czajkowski et al 2017).…”

Section: Using Data From Auxiliary and Supporting Questionsmentioning

confidence: 99%

Contemporary Guidance for Stated Preference Studies

Johnston

Boyle

Adamowicz

et al. 2017

Journal of the Association of Environmental and Resource Econom

Self Cite

777

701

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This article proposes contemporary best-practice recommendations for stated preference (SP) studies used to inform decision making, grounded in the accumulated body of peer-reviewed literature. These recommendations consider the use of SP methods to estimate both use and non-use (passive-use) values, and cover the broad SP domain, including contingent valuation and discrete choice experiments. We focus on applications to public goods in the context of the environment and human health but also consider ways in which the proposed recommendations might apply to other common areas of application. The recommendations recognize that SP results may be used and reused (benefit transfers) by governmental agencies and nongovernmental organizations, and that all such applications must be considered. The intended result is a set of guidelines for SP studies that is more comprehensive than that of the original National Oceanic and Atmospheric Administration (NOAA) Blue Ribbon Panel on contingent valuation, is more germane to contemporary applications, and reflects the two decades of research since that time. We also distinguish between practices for which accumulated research is sufficient to support recommendations and those for which greater uncertainty remains. The goal of this article is to raise the quality of SP studies used to support decision making and promote research that will further enhance the practice of these studies worldwide.

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“…The inclusion of self-reported choice certainty in choice models could arguably result in potential endogeneity bias when modelling choice behavior (e.g. Dekker et al 2016). Less certain respondents are expected to make more random choices and choose more frequently the opt-out alternative.…”

Section: Choice Consistencymentioning

confidence: 99%

Choice Consistency and Preference Stability in Test-Retests of Discrete Choice Experiment and Open-Ended Willingness to Pay Elicitation Formats

Brouwer

Logar

Sheremet

2016

Environ Resource Econ

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This study tests the temporal stability of preferences, choices and willingness to pay (WTP) values using both discrete choice experiment (DCE) and open-ended (OE) WTP elicitation formats. The same sample is surveyed three times over the course of two years using each time the same choice sets. Choice consistency is positively correlated with choice certainty and choice complexity. The impact of choice complexity fades away in time, most likely as a result of learning and preference refinement. Although the OE WTP values remain stable over a time period of 2 years as in previous stated preference studies, DCE based WTP measures differ significantly, suggesting their use in benefits transfer may be limited.

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Decision uncertainty in multi-attribute stated preference studies

Cited by 40 publications

References 43 publications

A Generalized Continuous-Multinomial Response Model with a t-distributed Error Kernel

A Generalized Continuous-Multinomial Response Model with a t-distributed Error Kernel

Contemporary Guidance for Stated Preference Studies

Choice Consistency and Preference Stability in Test-Retests of Discrete Choice Experiment and Open-Ended Willingness to Pay Elicitation Formats

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