Unpublished
Edward R. Morey, Lodder Tymon S
1999 Jun
1999 Jun
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APA
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Morey, E. R., & Tymon S, L. (1999, June). Combining Responses to Actual and Hypothetical Offers to Estimate WTA for Highly-Polluting Clunkers : Ordered Probit with Distinct, Noisy and Biased Bounds .
Chicago/Turabian
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Morey, Edward R., and Lodder Tymon S. “Combining Responses to Actual and Hypothetical Offers to Estimate WTA for Highly-Polluting Clunkers : Ordered Probit with Distinct, Noisy and Biased Bounds ,” June 1999.
MLA
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Morey, Edward R., and Lodder Tymon S. Combining Responses to Actual and Hypothetical Offers to Estimate WTA for Highly-Polluting Clunkers : Ordered Probit with Distinct, Noisy and Biased Bounds . June 1999.
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@unpublished{edward1999a,
title = {Combining Responses to Actual and Hypothetical Offers to Estimate WTA for Highly-Polluting Clunkers : Ordered Probit with Distinct, Noisy and Biased Bounds },
year = {1999},
month = jun,
author = {Morey, Edward R. and Tymon S, Lodder},
month_numeric = {6}
}
Accelerated vehicle retirement programs (AVRP) are receiving increasing attention as a possible means of cost effectively reducing air pollution. Whether AVRPs are efficient depends on the cost of acquiring vehicles as a function of the amount of pollution they produce. This paper estimates an individual's willingness to accept (WTA) an offer price for his or her vehicle in an AVRP. Data was collected from a pilot program in Denver.
Available information includes: the owner's response to the AVRP's actual offer of $1000, and the owner's response(s) to hypothetical offers from the AVRP. The data is a combination of revealed and stated preferences. Our emphasis is on the information content of the responses to the hypothetical offers.
One model is a multiple-interval ordered probit model of WTA that assumes bounds generated by responses to actual offers are distinct and bounds generated by responses to hypothetical offers are noisy. A special case is the conventional all-bounds distinct. A more restrictive case is bounds generated by hypothetical offers are discarded.
The distinct/noisy model statistically dominates all-bounds distinct; both generate similar predictions. In contrast, discarding the bounds generated by the responses to the hypothetical responses generates significantly different predictions. Parameter estimates indicate that an individual's willingness to sell to an AVRP is a function of the characteristics of the vehicle, not the owner. A second model investigates the possibility that hypothetical responses are systematically biased.
While the application here is WTA for clunkers, the model could be used to estimate WTA or WTP (willingness to pay) with any data that provides bounds but where the responses that generate the bounds are thought to vary in informational content.
Available information includes: the owner's response to the AVRP's actual offer of $1000, and the owner's response(s) to hypothetical offers from the AVRP. The data is a combination of revealed and stated preferences. Our emphasis is on the information content of the responses to the hypothetical offers.
One model is a multiple-interval ordered probit model of WTA that assumes bounds generated by responses to actual offers are distinct and bounds generated by responses to hypothetical offers are noisy. A special case is the conventional all-bounds distinct. A more restrictive case is bounds generated by hypothetical offers are discarded.
The distinct/noisy model statistically dominates all-bounds distinct; both generate similar predictions. In contrast, discarding the bounds generated by the responses to the hypothetical responses generates significantly different predictions. Parameter estimates indicate that an individual's willingness to sell to an AVRP is a function of the characteristics of the vehicle, not the owner. A second model investigates the possibility that hypothetical responses are systematically biased.
While the application here is WTA for clunkers, the model could be used to estimate WTA or WTP (willingness to pay) with any data that provides bounds but where the responses that generate the bounds are thought to vary in informational content.
