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Dealing with Heterogeneous Preferences Using Multilevel Mixed Models

  • Begoña A. Farizo
  • John Joyce
  • Mario Soliño

One of the main issues on the research agenda regarding stated preference methods concerns the heterogeneity of preferences either within or between individuals. We present a multilevel mixed model (MMM) to capture heterogeneity in deterministic utility components, instead of simply leaving them to random components. MMM captures heterogeneity at different levels: individuals, locations, and groups of individuals sharing other characteristics. The results show that individuals’ surroundings help to capture heterogeneity, and that can be controlled by specifying these aspects as predictors for this behavioral model. Therefore, MMM may contribute to the identification of the underlying structure affecting environmental decisions.

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File URL: http://le.uwpress.org/cgi/reprint/90/1/181
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Article provided by University of Wisconsin Press in its journal Land Economics.

Volume (Year): 90 (2014)
Issue (Month): 1 ()
Pages: 181-198

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Handle: RePEc:uwp:landec:v:90:y:2014:i:1:p:181-198
Contact details of provider: Web page: http://le.uwpress.org/

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  1. Soliño, Mario & Farizo, Begoña A. & Campos, Pablo, 2009. "The influence of home-site factors on residents' willingness to pay: An application for power generation from scrubland in Galicia, Spain," Energy Policy, Elsevier, vol. 37(10), pages 4055-4065, October.
  2. Dhar, Ravi, 1997. " Consumer Preference for a No-Choice Option," Journal of Consumer Research, University of Chicago Press, vol. 24(2), pages 215-31, September.
  3. Jeroen K. Vermunt, 2004. "An EM algorithm for the estimation of parametric and nonparametric hierarchical nonlinear models," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 58(2), pages 220-233.
  4. R. Bock & Murray Aitkin, 1981. "Marginal maximum likelihood estimation of item parameters: Application of an EM algorithm," Psychometrika, Springer, vol. 46(4), pages 443-459, December.
  5. Scarpa, Riccardo & Rose, John M., 2008. "Design efficiency for non-market valuation with choice modelling: how to measure it, what to report and why," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 52(3), September.
  6. Álvarez-Farizo, Begoña & Gil, José M. & Howard, B.J., 2009. "Impacts from restoration strategies: Assessment through valuation workshops," Ecological Economics, Elsevier, vol. 68(3), pages 787-797, January.
  7. Robert Johnston, 2007. "Choice experiments, site similarity and benefits transfer," Environmental & Resource Economics, European Association of Environmental and Resource Economists, vol. 38(3), pages 331-351, November.
  8. Kenneth E. Train, 1998. "Recreation Demand Models with Taste Differences over People," Land Economics, University of Wisconsin Press, vol. 74(2), pages 230-239.
  9. William Breffle & Edward Morey & Jennifer Thacher, 2011. "A Joint Latent-Class Model: Combining Likert-Scale Preference Statements With Choice Data to Harvest Preference Heterogeneity," Environmental & Resource Economics, European Association of Environmental and Resource Economists, vol. 50(1), pages 83-110, September.
  10. Bart Vermeulen & Peter Goos & Riccardo Scarpa & Martina Vandebroek, 2011. "Bayesian Conjoint Choice Designs for Measuring Willingness to Pay," Environmental & Resource Economics, European Association of Environmental and Resource Economists, vol. 48(1), pages 129-149, January.
  11. Edward Morey & Jennifer Thacher & William Breffle, 2006. "Using Angler Characteristics and Attitudinal Data to Identify Environmental Preference Classes: A Latent-Class Model," Environmental & Resource Economics, European Association of Environmental and Resource Economists, vol. 34(1), pages 91-115, 05.
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