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

Published online by Cambridge University Press:  26 January 2015

John B. Loomis  and
Julie M. Mueller
John B. Loomis
Affiliation:
Department of Agricultural and Resource Economics, Colorado State University, Fort Collins, Colorado
Julie M. Mueller
Affiliation:
The W.A. Franke College of Business, Northern Arizona University, Flagstaff, Arizona
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Extract

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.

Keywords

Bayesian estimationcontingent valuationspatial probitwillingness to payC11Q51
Type
Research Article
Information
Journal of Agricultural and Applied Economics , Volume 45 , Issue 1 , February 2013 , pp. 53 - 63
Copyright
Copyright © Southern Agricultural Economics Association 2013

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References

Carson, R.T., Mitchell, R.C., Hanemann, M., Kopp, R.J., Presser, S., and Rudd, P.A.. “Contingent Valuation and Lost Passive Use: Damages from the Exxon Valdez Oil Spill.” Environmental and Resource Economics 25(2003):257–86.10.1023/A:1024486702104Google Scholar
Dillman, D. Mail and Internet Surveys: The Tailored Design Method. 2nd ed. New York, NY: John Wiley & Sons, 2000.Google Scholar
Franzese, R.J., Hays, J.C., and Schaffer, L.M.. Spatial, Temporal, and Spatiotemporal Auto-regressive Probit Models of Binary Outcomes: Estimation, Interpretation, and Presentation, APSA 2010 Annual Meeting Paper. Internet site: http://ssrn.com/abstract=1643867. 2010.Google Scholar
Gelfland, A.E., Hills, S., Racine-Poon, A., Smith, A.F.M.. “Illustration of Bayesian Inference in Normal Data Using Gibbs Sampling.” Journal of the American Statistical Association 85(1990):972–85.10.1080/01621459.1990.10474968Google Scholar
Gelman, A., Carlin, J.B., Stern, H.S., and Rubin, D.B., eds. Bayesian Data Analysis. New York, NY: Chapman and Hall, 1995.Google Scholar
Giraud, K., Loomis, J., and Johnson, R.. “Two Valuation Questions in One Survey: Is It a Recipe for Sequencing and Instrument Context Effects?” Applied Economics 31(1999):957–64.10.1080/000368499323670Google Scholar
Hanneman, W.M. “Welfare Evaluations in Contingent Valuation Experiments with Discrete Responses.” American Journal of Agricultural Economics 66(1984):332–401.10.2307/1240800Google Scholar
Holloway, G., Shankar, B., and Rahman, S.. “Bayesian Spatial Probit Estimation: A Primer and an Application to HYW Rice Adoption.” Agricultural Economics 27(2002):383–402.10.1111/j.1574-0862.2002.tb00127.xGoogle Scholar
Krinsky, I., and Robb, A.L.. “On Approximating the Statistical Properties of Elasticities.” The Review of Economics and Statistics 68(1986):715–19.10.2307/1924536Google Scholar
LeSage, J., and Pace, R.K., eds. Introduction to Spatial Econometrics. Boca Raton, FL: CRC Press, 2009.Google Scholar
Li, H., Jenkins-Smith, H.C., Silva, C.L., Berrens, R.P., and Herron, K.G.. “Public Support for Reducing U.S. Reliance on Fossil Fuels: Investigating Household Willingness-to-Pay for Energy Research and Development.” Ecological Economics 68(2009):731–42.10.1016/j.ecolecon.2008.06.005Google Scholar
Loomis, J., and Gonzalez-Caban, A.. “Forest Service Use of Nonmarket Valuation in Fire Economics: Past, Present, and Future.” Journal of Forestry 108(2010):389–96.Google Scholar
Loomis, J.B. “Expanding Contingent Value Sample Estimates to Aggregate Benefit Estimates: Current Practices and Proposed Solutions.” Land Economics 63(1987):396–402.10.2307/3146296Google Scholar
Loomis, J.B. “Vertically Summing Public Good Demand Curves: An Empirical Comparison of Economic Versus Political Jurisdiction.” Land Economics 78(2000):312–21.Google Scholar
McMillen, D.P. “Probit with Spatial Autocorrelation.” Journal of Regional Science 32(1992):335–48.10.1111/j.1467-9787.1992.tb00190.xGoogle Scholar
Mueller, J.M., and Loomis, J.B.. “Bayesians in Space: Using Bayesian Estimation to Inform Choice of Spatial Weights in Hedonic Property Analyses.” The Review of Regional Studies 40(2010):245–55.Google Scholar
Murdoch, J. Sandler, CT., and Vijverberg, W.P.M.. “The Participation Decision versus the Level of Participation in an Environmental Treaty: A Spatial Probit Analysis.” Journal of Public Economics 87(2003):337–62.10.1016/S0047-2727(01)00152-9Google Scholar
Pinkse, J., and Slade, M.E.. “Contracting in Space: An Application of Spatial Statistics to Discrete Choice Models.” Journal of Econometrics 85(1998):125–54.10.1016/S0304-4076(97)00097-3Google Scholar
Pinkse, J. “The Future of Spatial Econometrics.” Journal of Regional Science 50(2010):103–17.10.1111/j.1467-9787.2009.00645.xGoogle Scholar
Poe, G., Giraud, K.L., and Loomis, J.B.. “Computational Methods for Measuring the Difference of Empirical Distributions.” American Journal of Agricultural Economics 87(2005):353–65.10.1111/j.1467-8276.2005.00727.xGoogle Scholar
Richardson, L., and Loomis, J.B.. “The Total Economic Value of Threatened, Endangered and Rare Species: An Updated Meta- Analysis.” Ecological Economics 68(2009):1535–48.10.1016/j.ecolecon.2008.10.016Google Scholar
Smith, T.E., and LeSage, J.P.. “A Bayesian Probit Model with Spatial Dependencies.” Spatial and Spatiotemporal Econometrics: Advances in Econometrics. Vol 18. Amsterdam: Elsevier: Emerald Group Publishing Limited, 2004, pp. 127–60.10.1016/S0731-9053(04)18004-3Google Scholar
Yoo, S. “A Note on a Bayesian Approach to a Dichotomous Choice Environmental Model.” Journal of Applied Statistics 31 (2004):1203–09.10.1080/0266476042000285558Google Scholar