International Choice Modelling Conference, International Choice Modelling Conference 2017

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Handling resolvable uncertainty from incomplete choice set scenarios - choice probabilities versus discrete choices
Morten Raun Mørkbak, Line Bjørnskov Pedersen, Riccardo Scarpa

Last modified: 28 March 2017

Abstract


Forecasting choice behavior for new health care, environmental or transportation programs and services is challenging because actual data is often unavailable. In order to derive estimates of the demand for such programs and services researchers often must resort to data derived from hypothetical market scenarios. An increasing popular way of doing this is by means of hypothetical (Discrete) Choice Experiments (DCE). Respondents participating in a hypothetical discrete choice experiment are likely to be provided with only a subset of the information deemed relevant or even necessary for conducting a real life choice. Manski (1990) argues that even under best case hypothesis, intentions stated during DCE survey will not be good predictors of future behavior, since scenario descriptors will always be at least in part “incomplete”. Such unavoidable incompleteness will be at least in part resolved in a real choice context, which gives rise to a component of uncertainty referred to as “resolvable” because once faced with a real choice scenario subjects will have some uncertainty resolved. Cognizant of this fact analysts are faced by an extrapolation problem in which assumptions are likely to be crucial and hence matter. However, eliciting choice probabilities (ECP) instead of stated choices could potentially overcome this issue, by allowing respondents to explicitly state uncertainty about their stated choice. It turns out that this approach might afford the additional advantage of being less econometrically demanding. In the present paper we compare the elicited subjective choice probabilities approach with the standard DCE approach using a split sample design in a health care context. The very preliminary results show large differences with respect to willingness-to-pay estimates, but remarkable similarities with respect to forecasting abilities, suggesting the validity of the far less econometrically demanding ECP approach, would seem to be at least as good as the usual more econometrically demanding DCE approach. Furthermore, we extend the model of the ECP approach by distinguishing between those with at least some resolvable uncertainty and those with only unresolved uncertainty by using separate simultaneous equations related to the choice attributes. This is done by fitting a logit distribution to the two extreme probability processes (zero and one) and a Beta distribution to the intermediate process.


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