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Examining spatially heterogeneous preferences for coastal ecosystem restoration with Bayesian spatial probit approaches

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  • Kei Kabaya

    (Rikkyo University)

Abstract

Preferences for environmental goods often display spatial heterogeneity. Nevertheless, spatial discrete choice models have received minimal attention in the stated-preference literature to date. The present study examined three alternative Bayesian spatial probit models to investigate the various implications of space on respondents’ voting patterns. The case study was a dichotomous choice contingent valuation of coastal ecosystem restoration in Yokohama City, Japan. A non-spatial probit model first showed that spatial observable variables, such as distance and landscapes, failed to explain spatially motivated voting patterns. A spatial autoregressive probit model then revealed significantly negative spatial feedback effects on respondents’ votes and demonstrated insignificant but potentially policy-relevant difference in willingness-to-pay estimates. A spatial error probit model also unveiled significant spatial heterogeneity based on latent factors. Finally, a spatially structured effects probit model with a more flexible boundary setting enabled the researcher to identify some regional voting patterns. These results suggest that the application of spatial probit models to a contingent valuation study can provide additional insights on the spatial aspects of respondents’ preferences.

Suggested Citation

  • Kei Kabaya, 2022. "Examining spatially heterogeneous preferences for coastal ecosystem restoration with Bayesian spatial probit approaches," Letters in Spatial and Resource Sciences, Springer, vol. 15(1), pages 99-112, April.
    • Handle: RePEc:spr:lsprsc:v:15:y:2022:i:1:d:10.1007_s12076-022-00301-z
    DOI: 10.1007/s12076-022-00301-z
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    More about this item

    Keywords

    Bayesian estimation; Contingent valuation; Spatial autoregressive probit model; Spatial error probit model; Spatially structured effect probit model; Willingness-to-pay;
    All these keywords.

    JEL classification:

    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities
    • Q51 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Valuation of Environmental Effects
    • Q57 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Ecological Economics

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