Choice complexity in a Stated Choice Experiment: valuing environmental resources in Chile

Abstract

The psychological literature has emphasized that choice complexity and other contextual factors affect how people make decisions. However, empirical economic choice models generally do not consider the complexity of different scenarios when estimating preferences from contingent valuation or stated choice models. Recently Swait and Adamowicz (2001b) propose and estimate a conditional logit model that takes into account choice complexity in making inferences from individual data. Choice complexity is modeled as an entropy index that measures how close choice alternatives are in preference space. This definition implies that choice complexity is a function of the same parameters as the utility function and they must be estimated simultaneously. We apply this framework to a stated choice experiment conducted in Chile to value the environmental impacts caused by hydroelectric projects, namely, the destruction of native forests and the relocation of indigenous communities. The survey contains close to 3.000 observations, which makes it an ideal data set to apply the estimation strategy proposed by Swait and Adamowicz (2001b). The results of this paper show that taking into account choice complexity in the modelling of individual decisionmaking increases the average valuation of the environmental resources under study. This evidence implies that valuation studies based on choice surveys that do not take into account choice complexity may lead to biased results.