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A latent segmentation approach to a Kuhn-Tucker model: An application to recreation demand

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  • Kuriyama, Koichi
  • Michael Hanemann, W.
  • Hilger, James R.

Abstract

In this paper, we extend the latent segmentation approach to the Kuhn-Tucker (KT) model. The proposed approach models heterogeneity in preferences for recreational behavior, using a utility theoretical framework to simultaneously model participation and site selection decisions. Estimation of the latent segmentation KT model with standard maximum likelihood techniques is numerically difficult because of the large number of parameters in the segment membership functions and the utility function for each latent segment. To address this problem, we propose the expectation-maximization (EM) algorithm to estimate the model. In the empirical section, we implement the EM latent segmentation KT approach to analyze a Southern California beach recreation data set. Our empirical analysis suggests that three groups exist in the sample. Using the model to analyze two hypothetical beach management policy scenarios illustrates different welfare impacts across groups.

Suggested Citation

  • Kuriyama, Koichi & Michael Hanemann, W. & Hilger, James R., 2010. "A latent segmentation approach to a Kuhn-Tucker model: An application to recreation demand," Journal of Environmental Economics and Management, Elsevier, vol. 60(3), pages 209-220, November.
  • Handle: RePEc:eee:jeeman:v:60:y:2010:i:3:p:209-220
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    References listed on IDEAS

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    1. Daniel J. Phaneuf & Catherine L. Kling & Joseph A. Herriges, 2000. "Estimation and Welfare Calculations in a Generalized Corner Solution Model with an Application to Recreation Demand," The Review of Economics and Statistics, MIT Press, vol. 82(1), pages 83-92, February.
    2. Stephen Hynes & Nick Hanley & Riccardo Scarpa, 2008. "Effects on Welfare Measures of Alternative Means of Accounting for Preference Heterogeneity in Recreational Demand Models," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 90(4), pages 1011-1027.
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    12. Phaneuf, Daniel J. & Smith, V. Kerry, 2006. "Recreation Demand Models," Handbook of Environmental Economics,in: K. G. Mäler & J. R. Vincent (ed.), Handbook of Environmental Economics, edition 1, volume 2, chapter 15, pages 671-761 Elsevier.
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    1. repec:eee:jeeman:v:88:y:2018:i:c:p:159-179 is not listed on IDEAS
    2. Sobhani, Anae & Eluru, Naveen & Faghih-Imani, Ahmadreza, 2013. "A latent segmentation based multiple discrete continuous extreme value model," Transportation Research Part B: Methodological, Elsevier, vol. 58(C), pages 154-169.
    3. Taro Ohdoko & Kentaro Yoshida, 2012. "Public preferences for forest ecosystem management in Japan with emphasis on species diversity," Environmental Economics and Policy Studies, Springer;Society for Environmental Economics and Policy Studies - SEEPS, vol. 14(2), pages 147-169, April.
    4. Koichi Kuriyama & James Hilger & Michael Hanemann, 2013. "A Random Parameter Model with Onsite Sampling for Recreation Site Choice: An Application to Southern California Shoreline Sportfishing," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 56(4), pages 481-497, December.
    5. repec:kap:enreec:v:68:y:2017:i:4:d:10.1007_s10640-016-0060-0 is not listed on IDEAS
    6. Chandra R. Bhat & Subodh K. Dubey & Mohammad Jobair Bin Alam & Waleed H. Khushefati, 2015. "A New Spatial Multiple Discrete-Continuous Modeling Approach To Land Use Change Analysis," Journal of Regional Science, Wiley Blackwell, vol. 55(5), pages 801-841, November.
    7. Abdul Pinjari & Chandra Bhat & David S. Bunch, 2013. "Workshop report: recent advances on modeling multiple discrete-continuous choices," Chapters,in: Choice Modelling, chapter 3, pages 73-90 Edward Elgar Publishing.

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