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Biased estimates in discrete choice models: the appropriate inclusion of psychometric data into the valuation of recycled wastewater

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  • Gibson, Fiona L.
  • Burton, Michael P.

Abstract

The introduction of measurement bias in parameter estimates into non-linear discrete choice models, as a result of using factor analysis, was identified by Train et al. (1987). They found that the inclusion of factor scores, used to represent relationships amongst like variables, into a subsequent discrete choice models introduced measurement bias as the measurement error associated with each factor score is excluded. This is an issue for non-market valuation given the increase in popularity of including psychometric data, such as primitive beliefs, attitudes and motivations, in willingness to pay estimates. This study explores the relationship between willingness to pay and primitive beliefs through a case study eliciting Perth community values for drinking recycled wastewater. The standard discrete decision model, with sequential inclusion of factor scores, is compared to an equivalent discrete decision model, which corrects for the measurement bias by simultaneously estimating the underlying latent variables using a measurement model. Previous research has focused on the issue of biased parameters. Here we also consider the implications for willingness to pay estimates.

Suggested Citation

  • Gibson, Fiona L. & Burton, Michael P., 2009. "Biased estimates in discrete choice models: the appropriate inclusion of psychometric data into the valuation of recycled wastewater," 2009 Conference (53rd), February 11-13, 2009, Cairns, Australia 47943, Australian Agricultural and Resource Economics Society.
  • Handle: RePEc:ags:aare09:47943
    DOI: 10.22004/ag.econ.47943
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    References listed on IDEAS

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    Cited by:

    1. Ricardo Daziano & Denis Bolduc, 2013. "Covariance, identification, and finite-sample performance of the MSL and Bayes estimators of a logit model with latent attributes," Transportation, Springer, vol. 40(3), pages 647-670, May.
    2. Xuemei Fu & Zhicai Juan, 2017. "Estimation of multinomial probit-kernel integrated choice and latent variable model: comparison on one sequential and two simultaneous approaches," Transportation, Springer, vol. 44(1), pages 91-116, January.
    3. Madjid Bouzit & Sukanya Das & Lise Cary, 2018. "Valuing Treated Wastewater and Reuse: Preliminary Implications From a Meta-Analysis," Water Economics and Policy (WEP), World Scientific Publishing Co. Pte. Ltd., vol. 4(02), pages 1-26, April.

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