Utility in Willingness to Pay Space: A Tool to Address Confounding Random Scale Effects in Destination Choice to the Alps

By Riccardo Scarpa, M. Thiene, Kenneth Train. 2008

583 citations recorded by the source corpus.

We compare two approaches for estimating the distribution of consumers' willingness to pay (WTP) in discrete choice models. The usual procedure is to estimate the distribution of the utility coefficients and then derive the distribution of WTP, which is the ratio of coefficients. The alternative is to estimate the distribution of WTP directly. We apply both approaches to data on site choice in the Alps. We find that the alternative approach fits the data better, reduces the incidence of exceedingly large estimated WTP values, and provides the analyst with greater control in specifying and testing the distribution of WTP. Copyright 2008, Oxford University Press.

Subfields: Economics and Econometrics, Marketing

View Paper: https://www.semanticscholar.org/paper/5d53f04864bc7de97d76537fd432023a94fcdf81

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Citations in the corpus

Mixed logit with a flexible mixing distribution (2016, methodology): “…where the WTP's are specified to be jointly normal and the price/ scale coefficient s is lognormal.9 I first estimated the model by the hierarchical Bayes (HB) procedure in Train (2009) modified for WTP space as in Train and Weeks (2005, Ch. 12) and Scarpa et al. (2008) using my own Matlab codes.”
Measuring the consumer benefits of improving farm animal welfare to inform welfare labelling (2012, methodology): “Stated preference studies using this approach of parameterising the model in WTP space have been carried out by Cameron and James (1987), Train and Weeks (2005), Scarpa et al. (2008), and Balcombe et al. (2009a).”
Preferences for urban green spaces and peri-urban forests: An analysis of stated residential choices (2016, background): “However, Scarpa, Thiene, and Train (2008) eported that the specification in WTP space had a better fit than he model in preference space in their empirical study.”
Whatever the customer wants, the customer gets? Exploring the gap between consumer preferences and default electricity products in Germany (2013, background): “Revelt and Train (1999), Huber and Train (2001), and Scarpa et al. (2008) show that results from hierarchical Bayes estimation and mixed logit estimations (random coefficient estimations) are very similar.”
Can scale and coefficient heterogeneity be separated in random coefficients models? (2012, background): “(Scarpa et al., 2008, page 996) This discussion has shown that all three parameterisations are equivalent; a given specification for one parameterisation can be replicated with another one by making appropriate distributional assumptions.”
Contemporary Guidance for Stated Preference Studies (2017, background): “One increasingly used alternative is the estimation of indirect utility inWTP space, in which the distribution of the welfare measure is modeled directly (Cameron and James 1987; Train andWeeks 2005; Scarpa, Thiene, and Train 2008).”
Correlation and scale in mixed logit models (2017, background): “In choice among Alpine hiking sites, recreators who value warming huts at the site tend also to prefer sites with easier trails; and people who prefer difficult trails also tend to like having rope assists on the trails (Scarpa et al., 2008).”
Using revealed preferences to estimate the Value of Travel Time to recreation sites (2014, unknown): “Model (7) is a non-linear in parameters mixed logit model and its estimation can be implemented via Simulated Maximum Likelihood (SML) (Train, 2009, Scarpa et al., 2008).”