Citation

Scarpa R, Willis KG. Transp. Rev. 2006; 26(4): 451-469.

Affiliation

Department of Economics, Waikato Management School, University of Waikato, Hamilton, New Zealand; School of Architecture, Planning and Landscape, University of Newcastle upon Tyne, Newcastle upon Tyne, UK

Copyright

(Copyright © 2006, Informa - Taylor and Francis Group)

DOI

10.1080/01441640600602252

PMID

unavailable

Abstract

The paper addresses the issue of consistency between two commonly employed stated preference data�referendum contingent valuation (CV) and discrete choice modelling (CM)�with respect to estimated distributions of individual willingness-to-pay (WTP) for non-market goods. The policy context is that of a local externality: effective speed reduction by means of traffic-calming in towns crossed by fast roads. In particular, data from two independent samples of the same population are contrasted. The findings show that both methods indicate that speed reduction via traffic-calming is valued in a polarized fashion. Results from both methods are consistent with the presence of two groups of preferences: a larger group holding positive values and a smaller one with non-positive values. While the estimates of the relative proportions of the two groups are similar across the two data sources, once the econometric analysis of the CM responses allows for polarized preferences the estimates of the distribution of individual WTP differ substantially. The results from the choice modelling survey indicate that residents are also willing to pay for other benefits from traffic-calming, such as noise reduction and a decreased waiting time for crossing, but preferences for these are also polarized, with WTP for aesthetic improvements being positive only for those supporting effective speed control. In comparing distributions of value estimates from CM and CV, surveys practitioners should account for the effects of taste heterogeneity over externalities and take advantage of the ability to derive individual-specific WTP estimates from panel estimation rather than simply deriving estimates for common features of the WTP distribution.