Performance of Error Component Models for Status-Quo Effects in Choice Experiments

By Riccardo Scarpa, Silvia Ferrini, K. Willis. 2005

354 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Marketing, Plant Science

View Paper: https://www.semanticscholar.org/paper/eb72df78f240fb6624a33b03eca38f9a3fe60499

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Economics and Econometrics

In Marketing

In Plant Science

Citations in the corpus

Sustainability labels on coffee: Consumer preferences, willingness-to-pay and visual attention to attributes (2015, background): “This is an important issue to account for in our data since the implemented design consists of two designed alternatives and one no-buy option, and hence, correlation across utilities may exist (Scarpa et al., 2005).”
Should Reference Alternatives in Pivot Design SC Surveys be Treated Differently? (2009, methodology): “In the case of pivot style data, a case can clearly be made that two hypothetical alternatives alternatives are more similar to each than a hypothetical alternative and the reference alternative, and as such could potentially have correlated error terms (cf. Scarpa et al., 2005).”
Designs with a priori information for nonmarket valuation with choice experiments: A Monte Carlo study (2007, methodology): “This specification is examined in some detail in [64] and it accounts for status quo effects in a more flexible fashion than the more commonly employed nested logit specification.”
Augmenting short Cheap Talk scripts with a repeated Opt-Out Reminder in Choice Experiment surveys (2014, background): “…deterministic part of the opt-out effects, the error component captures the stochastic part of the opt-out effects by estimating the covariance between the utilities of the two re-establishment alternatives relative to the utility of the opt-out alternative (Scarpa et al. 2005; Scarpa et al. 2008).”
European consumer preferences for beef with nutrition and health claims: A multi-country investigation using discrete choice experiments (2014, methodology): “A Monte Carlo study comparing the performance of different random utility models accounting for this effect showed that the EC model was more flexible and more robust to possible misspecification than other models (Scarpa et al., 2005).”
Handling respondent uncertainty in Choice Experiments: Evaluating recoding approaches against explicit modelling of uncertainty (2009, background): “Following Scarpa et al. (2005) an Alternative Specific Constant (ASC) is specified for the status quo alternative in order to capture the systematic component of a potential status quo effect.”
Status Quo Effect in Choice Experiments: Empirical Evidence on Attitudes and Choice Task Complexity (2009, background): “Scarpa, Willis, and Acutt (2007) stress that in a CE the alternative describing the SQ is experienced by the respondent.”
Valuing externalities from water supply: Status quo, choice complexity and individual random effects in panel kernel logit analysis of choice experiments (2007, background): “The resulting utility structure is: Uðc1 or c2Þ ¼ bxcjðnÞ þ 1ðc1 or c2Þmnj þ enj Usq ¼ bxsqðnÞ þ enj It is noted that in a Monte Carlo study Scarpa et al. (2005) show that this specification is sufficiently flexible to do as well as nested logit in deriving welfare estimates even when the true data…”
Valuing biodiversity enhancement in New Zealand's planted forests: Socioeconomic and spatial determinants of willingness-to-pay (2014, methodology): “This induces the correlation amongst the two designed alternatives as described in Scarpa et al. (2005).”
Incorporating Discontinuous Preferences into the Analysis of Discrete Choice Experiments (2008, background): “…and, ε is the Gumbel-distributed error.2 Note that this is analogous to the nested logit model in the sense that is allows for correlation of utilities across alternatives in the same nest, but different correlation across nests (Herriges and Phaneuf, 2002; Train, 2003; Scarpa et al. 2005).”
Choosing Between Internet and Mail Survey Modes for Choice Experiment Surveys Considering Non-Market Goods (2009, background): “(Scarpa et al. 2005; Scarpa et al. 2008).”
Benefit Estimates for Landscape Improvements: Sequential Bayesian Design and Respondents’ Rationality in a Choice Experiment (2005, background): “In a Monte Carlo study Scarpa, Ferrini, and Willis (2005) compare the performance of a number of standard random utility models addressing the status-quo effect.”