Modelling attribute non-attendance in choice experiments for rural landscape valuation

By Riccardo Scarpa, Timothy J. Gilbride, D. Campbell, David A. Hensher. 2009

419 citations recorded by the source corpus.

Non-market effects of agriculture are often estimated using discrete choice models from stated preference surveys. In this context we propose two ways of modelling attribute non-attendance. The first involves constraining coefficients to zero in a latent class framework, whereas the second is based on stochastic attribute selection and grounded in Bayesian estimation. Their implications are explored in the context of a stated preference survey designed to value landscapes in Ireland. Taking account of attribute non-attendance with these data improves fit and tends to involve two attributes one of which is likely to be cost, thereby leading to substantive changes in derived welfare estimates. Oxford University Press and Foundation for the European Review of Agricultural Economics 2009; all rights reserved. For permissions, please email journals.permissions@oxfordjournals.org, Oxford University Press.

Subfields: Economics and Econometrics, General Decision Sciences

View Paper: https://www.semanticscholar.org/paper/23eb5942d5addff965bfd6f008c8e24b29c73671

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Citations in the corpus

Skew and attribute non-attendance within the bayesian mixed logit model (2011, neutral): “[21] and Campbell and Lorimer [5] who nd that those stating that they ignored attributes had not completely ignored them.”
Monitoring Choice Task Attribute Attendance in Nonmarket Valuation of Multiple Park Management Services: Does It Matter? (2010, methodology): “Each sample wave used a different WTPb-efficient fraction of the full factorial design (Ferrini and Scarpa 2007; Scarpa and Rose 2008), developed using Bayesian priors (hence the subscript ‘‘b’’) using information collected in all previous waves, following Scarpa, Campbell, and Hutchinson (2007),…”
Food miles or carbon emissions? Exploring labelling preference for food transport footprint with a stated choice study (2013, methodology): “For such inference, we use ECLC models for panel data (Hess and Rose 2007; Scarpa et al. 2009; Campbell et al. 2011).”
Bayesian optimal designs for discrete choice experiments with partial profiles (2011, background): “levels of just one attribute or a small subset of the attributes (see, e.g., Hensher 2006; Hensher and Rose 2009; Scarpa et al. 2009).”
Random regret minimization or random utility maximization: An exploratory analysis in the context of automobile fuel choice (2013, background): “Examples include attribute addition or parameter transfer under a common metric (e.g., Layton and Hensher 2010, Hensher and Layton 2010), attribute cut-off thresholds (e.g., Swait 2001), and attribute non-preservation (e.g., Scarpa et al. 2009, 2010).”
Attending to the Reasons for Attribute Non-attendance in Choice Experiments (2011, background): “Recently, several studies have focused on unveiling how respondents process attributes, and in particular how attribute non-attendance affects results and how it should be accounted for in CE surveys (e.g. Scarpa et al. 2010; Hensher et al. 2005a; Campbell et al. 2008; Carlsson et al. 2010; Kaye-bla”
It’s not that I don’t care, I just don’t care very much: confounding between attribute non-attendance and taste heterogeneity (2013, methodology): “A key role in this context was played by the early discussions in Hess and Rose (2007), who proposed the use of a latent class approach to accommodate attribute non-attendance, a method since adopted by numerous other studies (e.g. Hensher et al., 2011; Scarpa et al., 2009; Hensher and Greene, 2010;”
Non-attendance to attributes in environmental choice analysis: a latent class specification (2011, methodology): “The empirical evidence herein reinforces the evidence presented in Campbell et al. (2008), Hensher (2008), Hensher and Rose (2009), Scarpa et al. (2009) and Layton and Hensher (2010), that failure to identify and account for process heterogeneity tends to result in over- or under-estimates of the…”
Contemporary Guidance for Stated Preference Studies (2017, background): “…Veisten et al. 2004; Heberlein et al. 2005), sequencing effects (Bateman et al. 2004; Day and Prades 2010; Day et al. 2012), and attribute nonattendance (Campbell, Hutchinson, and Scarpa 2008; Scarpa et al. 2009, 2010; Cameron and DeShazo 2010; Campbell, Hensher, and Scarpa 2011), among others.”
Inferring attribute non-attendance from stated choice data: implications for willingness to pay estimates and a warning for stated choice experiment design (2012, background): “The biggest challenge for ongoing research is to find a way of accommodating estimates of VTTS where the time–cost trade-offs under specific attribute processing rules are not able to be revealed, a point also highlighted in Scarpa et al. (2009). This may require more careful consideration of the re”
Attribute Processing, Heuristics, and Preference Construction in Choice Analysis (2009, background): “…(in press), Hensher and Layton (2008), Hess and Hensher (2008), Puckett and Hensher (2008), Swait (2001), Cantillo et al. (2006), Cameron (2008), Scarpa et al. (2009,2009a), Beharry and Scarpa (2008), Cantillo and Ortúzar (2005), Carlsson et al. (2008), Caussade et al. (2005) and Hensher et al.…”
Latent class structures: taste heterogeneity and beyond (2014, methodology): “A key role in this area was played by the early discussions in Hess and Rose (2007), who proposed the use of a latent class approach to accommodate attribute non-attendance, a method since adopted by numerous other studies (e.g. Hensher et al., 2012; Scarpa et al., 2009; Hensher and Greene, 2010; Ho”