Monte Carlo simulation evidence on the effect of the status-quo in choice experiment models for water supply Riccardo Scarpa University of York Kenneth G. Willis University of Newcastle Melinda Acutt Yorkshire Water and Silvia Ferrini University of Florence, Italy Presented at the Budapest conference of The European Association of Environmental and Resource Economists June 2004 Abstract 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 often unreported. We explore four different ways of addressing such systematic differences using data from two choice modelling exercises 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 choice-set complexity-based heteroskedastic logit specifications, along with a less usual error component analysis via mixed logit (kernel logit). The results are consistent with the hypothesis that alternatives offering changes from status-quo do not share the same preference structure as status-quo alternatives. 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 potential bias in conventional estimates is large, that alternative specific-constant specifications are efficient even if biased, and that the errors of ignoring complexity may be low. These findings have implications for practitioners and their stance towards the strategies for the econometric analysis of choice modelling data for the purpose of valuation. JEL classification: Q25, H41, D62, C42, C25 Keywords: choice-modelling, stated-preference, environmental valuation, status-quo bias, Monte Carlo simulations, water resources.