Hot Spots, Cold Feet, and Warm Glow : Identifying Spatial Heterogeneity in Willingness to Pay

We propose a novel extension of existing semi-parametric approaches to examine spatial patterns of willingness to pay (WTP) and status quo effects, including tests for global spatial autocorrelation, spatial interpolation techniques, and local hotspot analysis. We formally account for the fact that observed WTP values are estimates and incorporate the statistical precision of those estimates into our spatial analyses. We demonstrate our two-step methodology using data from a stated preference survey that elicited values for improvements in water quality in the Chesapeake Bay and lakes in the surrounding watershed. Our methodology offers a flexible way to identify potential spatial patterns of welfare impacts, with the ultimate goal of facilitating more accurate benefit-cost and distributional analyses, both in terms of defining the appropriate extent of the market and in interpolating values within that market