An Experimental Study of Complex-Offer Auctions: Payment Cost Minimization vs. Offer Cost Minimization
No 2007-13, Working papers from University of Connecticut, Department of Economics
A Payment Cost Minimization (PCM) auction has been proposed as an alternative to the Offer Cost Minimization (OCM) auction to be used in wholesale electric power markets with the intention to lower the procurement cost of electricity. Efficiency concerns about this proposal have relied on the assumption of true production cost revelation. Using an experimental approach, I compare the two auctions, strictly controlling for the level of unilateral market power. A specific feature of these complex-offer auctions is that the sellers submit not only the quantities and the minimum prices at which they are willing to sell, but also the start-up fees that are designed to reimburse the fixed start-up costs of the generation plants. I find that both auctions result in start-up fees that are significantly higher than the start-up costs. Overall, the two auctions perform similarly in terms of procurement cost and efficiency. Surprisingly, I do not find a substantial difference between less market power and more market power designs. Both designs result in similar inefficiencies and equally higher procurement costs over the competitive prediction. The PCM auction tends to have lower price volatility than the OCM auction when the market power is minimal but this property vanishes in the designs with market power. These findings lead me to conclude that both the PCM and the OCM auctions do not belong to the class of truth revealing mechanisms and do not easily elicit competitive behavior.
Keywords: strategic behavior; sealed-bid auction; complex offer auction; electricity; efficiency (search for similar items in EconPapers)
JEL-codes: C72 D4 D61 L94 (search for similar items in EconPapers)
Pages: 44 pages
New Economics Papers: this item is included in nep-ene, nep-exp and nep-gth
Note: The author would like to thank the National Science Foundation under grant SES 0648937, and the International Foundation for Research in Experimental Economics for financial support. The author is grateful to the Engineering and Economics faculty and students at the University of Connecticut working on the electricity project and the faculty and students at the Interdisciplinary Center for Economic Science at George Mason University for their helpful comments. The author would like to thank in particular Vicki Knoblauch and Bart Wilson for their valuable suggestions, Jeffrey Kirchner for writing the software for the experiments, Feng Zhao and William Blankson for explaining the optimization algorithms. Any mistakes are the responsibility of the author. The data is available upon request.
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