Performance of Error Component Models for Status-Quo Effects in Choice Experiments
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Environmental economists have advocated the use of choice modelling in environmental valuation. Standard approaches employ choice sets including one alternative depicting the status-quo, yet the effects of explicitly accounting for systematic differences in preferences for non status-quo alternatives in the econometric models are not well understood. We explore three different ways of addressing such systematic differences using data from two choice modelling studies designed to value the provision of environmental goods. Preferences for change versus status-quo are explored with standard conditional logit with alternative-specific constant for status-quo, nested logit and a less usual mixed logit error component specification (kernel logit). Our empirical results are consistent with the hypothesis that alternatives offering changes from status-quo do not share the same preference structure as status-quo alternatives, as found by others in the marketing literature, in the environmental economic literature and in food preference studies. To further explore the empirical consequences of such mis-specification we report on a series of Monte Carlo experiments. Evidence from the experiments indicates that the expected bias in estimates ignoring the status-quo effect is substantial, and—more interestingly—that error component specifications with status-quo alternative specific-constant are efficient even when biased. These findings have significant implications for practitioners and their stance towards the strategies for the econometric analysis of choice modelling data for the purpose of valuation.
Keywordschoice-modelling stated-preference environmental valuation status-quo bias Monte Carlo simulations water resources
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