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A Spatial Probit Modeling Approach to Account for Spatial Spillover Effects in Dichotomous Choice Contingent Valuation Surveys

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  • Loomis, John B.
  • Mueller, Julie M.

Abstract

We present a demonstration of a Bayesian spatial probit model for a dichotomous choice contingent valuation method willingness-to-pay (WTP) questions. If voting behavior is spatially correlated, spatial interdependence exists within the data, and standard probit models will result in biased and inconsistent estimated nonbid coefficients. Adjusting sample WTP to population WTP requires unbiased estimates of the nonbid coefficients, and we find a $17 difference in population WTP per household in a standard vs. spatial model. We conclude that failure to correctly model spatial dependence can lead to differences in WTP estimates with potentially important policy ramifications.

Suggested Citation

  • Loomis, John B. & Mueller, Julie M., 2013. "A Spatial Probit Modeling Approach to Account for Spatial Spillover Effects in Dichotomous Choice Contingent Valuation Surveys," Journal of Agricultural and Applied Economics, Cambridge University Press, vol. 45(1), pages 53-63, February.
    • Handle: RePEc:cup:jagaec:v:45:y:2013:i:01:p:53-63_00
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    References listed on IDEAS

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    1. Pinkse, Joris & Slade, Margaret E., 1998. "Contracting in space: An application of spatial statistics to discrete-choice models," Journal of Econometrics, Elsevier, vol. 85(1), pages 125-154, July.
    2. Li, Hui & Jenkins-Smith, Hank C. & Silva, Carol L. & Berrens, Robert P. & Herron, Kerry G., 2009. "Public support for reducing US reliance on fossil fuels: Investigating household willingness-to-pay for energy research and development," Ecological Economics, Elsevier, vol. 68(3), pages 731-742, January.
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    4. Kelly Giraud & John B. Loomis Richard & L. Johnson, 1999. "Two valuation questions in one survey: is it a recipe for sequencing and instrument context effects?," Applied Economics, Taylor & Francis Journals, vol. 31(8), pages 957-964.
    5. Holloway, Garth & Shankar, Bhavani & Rahman, Sanzidur, 2002. "Bayesian spatial probit estimation: a primer and an application to HYV rice adoption," Agricultural Economics, Blackwell, vol. 27(3), pages 383-402, November.
    6. Seung-Hoon Yoo, 2004. "A Note on a Bayesian Approach to a Dichotomous Choice Environmental Valuation Model," Journal of Applied Statistics, Taylor & Francis Journals, vol. 31(10), pages 1203-1209.
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    8. Gregory L. Poe & Kelly L. Giraud & John B. Loomis, 2005. "Computational Methods for Measuring the Difference of Empirical Distributions," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 87(2), pages 353-365.
    9. Richardson, Leslie & Loomis, John, 2009. "The total economic value of threatened, endangered and rare species: An updated meta-analysis," Ecological Economics, Elsevier, vol. 68(5), pages 1535-1548, March.
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    Cited by:

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    4. Adjognon, Serge & Liverpool-Tasie, Lenis, 2015. "Spatial Neighborhood Effects in Agricultural Technology Adoption: Evidence from Nigeria," 2015 Conference, August 9-14, 2015, Milan, Italy 210934, International Association of Agricultural Economists.
    5. Adjognon, Serge & Liverpool-Tasie, Lenis Saweda O., 2014. "Spatial Dependence in the Adoption of the Urea Deep Placement for Rice Production in Niger State, Nigeria: A Bayesian Spatial Autoregressive Probit Estimation Approach," 2014 Annual Meeting, July 27-29, 2014, Minneapolis, Minnesota 170515, Agricultural and Applied Economics Association.

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    More about this item

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • Q51 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Valuation of Environmental Effects

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