As I’ve mentioned before, I am teaching behavioral economics this semester. So far, I’ve introduced the concept of prospect theory and loss aversion- specifically, how people how people are made more unhappy by a loss than they are made happy by what they see as an equivalent gain- and we discussed how loss aversion can lead to anomalies in behavior in financial markets such as the disposition effect, i.e. the tendency fro investors to be more willing to sell securities that have gained in value since they were purchased than to sell securities that have lost in value.
Read the rest of the post for a nice teaching example.
And note that the lazy NBER cites continue:
Achieving socially efficient outcomes when there are production externalities requires
reliable empirical estimates of these external costs. Given the challenges associated with using stated-preferences techniques to value non-market amenities (see e.g. Hausman 2012), a substantial fraction of existing work in valuation relies on hedonic analysis of housing markets. (p. 25)
Source: Currie, J., Davis, L., Greenstone, M., & Walker, R. (2013). Do Housing Prices Reflect Environmental Health Risks? Evidence from More than 1600 Toxic Plant Openings and Closings (No. w18700). National Bureau of Economic Research.
In other words, "CVM sucks (Hausman 2012), so [insert author's preferred method] is awesome." Q.E.D.
This approach is unfortunate since, as a number of people have recognized, revealed preference and stated preference approaches are complements and not substitutes. Here is a recent example:
Combining Revealed and Stated Preference Data to Estimate Preferences for Residential Amenities: A GMM Approach
Daniel J. Phaneuf, Laura O. Taylor, and John B. Braden
We show how stated preference information obtained from a choice experiment, and revealed preference information based on housing market transactions, can be combined via generalized method of moments (GMM) estimation. Specifically, we use a moment condition matching the predicted marginal willingness to pay (WTP) from a first-stage hedonic model to the marginal WTP formula implied by the choice experiment utility function. This is coupled with other moments from the choice experiment to produce GMM-based estimates of parameters that reflect the strengths of each data source. Our application values remediation of a contaminated site in Buffalo, New York, and we find evidence in support of estimates arising from our approach. (JEL Q51, Q53)
Land Economics, February 1, 2013 vol. 89 no. 1 30-52