Design efficiency for non-market valuation with choice modelling: how to measure it, what to report and why*

By Riccardo Scarpa, John M. Rose. 2008

726 citations recorded by the source corpus.

We review the basic principles for the evaluation of design efficiency in discrete choice modelling with a focus on efficiency of WTP estimates from the multinomial logit model. The discussion is developed under the realistic assumption that researchers can plausibly define a prior belief on the range of values for the utility coefficients. D-, A-, B-, S- and C-errors are compared as measures of design performance in applied studies and their rationale is discussed. An empirical example based on the generation and comparison of fifteen separate designs from a common set of assumptions illustrates the relevant considerations to the context of non-market valuation, with particular emphasis placed on C-efficiency. Conclusions are drawn for the practice of reporting in non-market valuation and for future work on design research.

Subfields: Economics and Econometrics, General Decision Sciences

View Paper: https://www.semanticscholar.org/paper/491ebf8ef9c1f8429e6210a5d7687618f4a98a04

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Citations in the corpus

Experimental design influences on stated choice outputs: An empirical study in air travel choice (2009, background): “…but not limited to work undertaken by Bliemer et al. (2009), Burgess and Street (2003), Ferrini and Scarpa (2006), Kanninen (2002), Kessels et al. (2006), Sándor and Wedel (2001, 2002, 2005), Rose and Bliemer (2008), Scarpa and Rose (2008), Street and Burgess (2004) and Toner et al. (1999).”
Adoption of renewable heating systems: An empirical test of the diffusion of innovation theory (2017, methodology): “The combination of levels that appeared in each choice task was defined according to three different sub designs, namely near orthogonal, D-efficient [73], [74], [75], [76], and a serial design [77].”
Visual Attention and Attribute Attendance in Multi-Attribute Choice Experiments (2015, background): “Then, in conjunction with assumptions about the nature of utility functions the actual choice sets presented to individuals can be generated according to effi ciency criteria such as ‘d-optimality’and ‘balance’ (Scarpa and Rose, 2008).”
Spatial Heterogeneity of Willingness to Pay for Forest Management (2017, methodology): “The experimental design was optimized for Bayesian (median) d-efficiency of the MXL model (Sándor and Wedel 2001; Ferrini and Scarpa 2007; Bliemer et al. 2008; Scarpa and Rose 2008).”
Constructing Efficient Stated Choice Experimental Designs (2009, neutral): “, 2006 ; Ferrini and Scarpa, 2007 ; Rose and Scarpa, 2008 ).”
Constructing experimental designs for discrete-choice experiments: report of the ISPOR Conjoint Analysis Experimental Design Good Research Practices Task Force. (2013, neutral): “Bliemer, Rose, and various collaborators have developed a number of extensions and generalizations of previous experimentaldesign technologies.”
Non-attendance to attributes in environmental choice analysis: a latent class specification (2011, methodology): “The study employed a sequential experimental design with a Bayesian information structure to maximise the Db-optimal criterion (see Sándor and Wedel 2001, Rose and Bliemer 2007 and Scarpa and Rose 2008 for further details).”
Contemporary Guidance for Stated Preference Studies (2017, background): “Experimental design in these contexts, and particularly for CEs, is complex and evolving (Sándor and Wedel 2002; Johnson et al. 2006, 2013; Ferrini and Scarpa 2007; Rose et al. 2008; Scarpa and Rose 2008; Rose and Bliemer 2009, 2014; Bliemer and Rose 2010; Vermeulen et al. 2011).”
Farmers' willingness to provide ecosystem services and effects of their spatial distribution (2013, background): “See Scarpa and Rose (2008) for a discussion of these efficiency measures.”