In markets where suppliers experience learning by doing over time or, more generally, economies of scale in production, buyers are auctioning off longer-term contracts with an eroding price policy. Under an eroding price contract, the buyer initially competitively awards production to the lowest-bid supplier via an auction. Before
the auction takes place, the buyer makes it clear to the suppliers that, if chosen, a sequence of price reductions will be mandatory in
subsequent periods.
In this thesis, we mainly study the design of the optimal eroding price contract in a two period setting under three different model settings : (1) Every supplier faces a new cost in each period (NLI model), (2) The supplier who wins the auction in the first period locks-in his cost for the future, and the buyer makes the future payment based on the winning supplier's current bid (LI1), and (3) The supplier who wins the auction in the first period locks-in his
cost for the future, and the buyer makes the future payment based on the winning supplier's actual cost (LI1). Under NLI setting, the magnitude of the cost reduction due to learning by doing is common knowledge, while the magnitude is uncertain under LI1 and LI2
settings. We also study the optimal reserve prices in sequential independent auctions under NLI setting.
We go on to compare the performance of the eroding price policy against sequential independent auctions (without or with the optimal
reserve prices) under the above model settings. Via analytical and numerical comparisons, we find that even in the presence of learning
by doing/economies of scale in production, a buyer is often better off running sequential auctions with a reserve price, rather than
limiting competition and contracting with a single supplier in the hopes of extracting a better future price.
| Identifer | oai:union.ndltd.org:GATECH/oai:smartech.gatech.edu:1853/7525 |
| Date | 22 November 2005 |
| Creators | Oh, Se-Kyoung |
| Publisher | Georgia Institute of Technology |
| Source Sets | Georgia Tech Electronic Thesis and Dissertation Archive |
| Language | en_US |
| Detected Language | English |
| Type | Dissertation |
| Format | 861537 bytes, application/pdf |
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