This dissertation provides a thorough analysis of the costs associated with, and
efficacy of, sequential license buyback auctions. I use data from the Texas Shrimp
License Buyback Program - a sequential license buyback auction - to estimate the
effects of a repeated game set-up on bidding behavior. I develop a dynamic econometric
model to estimate parameters of the fisherman's optimal bidding function in this auction.
The model incorporates the learning that occurs when an agent is able to submit bids for
the same asset in multiple rounds and is capable of distinguishing between the
fisherman's underlying valuation of the license and the speculative premium induced by
the sequential auction. I show that bidders in the sequential auction do in fact inflate
bids above their true license valuation in response to the sequential auction format.
The results from our econometric model are used to simulate a hypothetical
buyback program for capacity reduction in the offshore shrimp fishery in the Gulf of
Mexico using two competing auction formats: the sequential auction and the one-time auction. I use this simulation analysis to compare the cost and effectiveness of
sequential license buyback program relative to one-time license buyback programs. I
find that one-time auctions, although they impose a greater up-front cost on the
management agency - are capable of retiring more fishing effort per dollar spent then
sequential license buyback programs. In particular, I find one-time license buyback
auctions to be more cost effective than sequential ones because they remove the
possibility for fishermen to learn about the agency's willingness to pay function and use
this information to extract sale prices in excess of the true license value.
| Identifer | oai:union.ndltd.org:tamu.edu/oai:repository.tamu.edu:1969.1/ETD-TAMU-2009-05-768 |
| Date | 16 January 2010 |
| Creators | Mamula, Aaron T. |
| Contributors | Woodward, Richard T., Griffin, Wade L. |
| Source Sets | Texas A and M University |
| Language | en_US |
| Detected Language | English |
| Type | Book, Thesis, Electronic Dissertation |
| Format | application/pdf |
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