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Impact of Valuation Ranking Information on Bidding in First-Price Auctions: A Laboratory Study


  • Alexander Elbittar

    () (Centro de Investigacion Economica (CIE), Instituto Tecnologico Autonomo de Mexico (ITAM))


Landsberger, et al. (2001) have identified optimal bidder behavior in first-price private-value auctions when the ranking of valuations is common knowledge, and derived comparative-statics predictions regarding the auctioneer's expected revenue and the efficiency of the allocation. The experiment reported here tests the behavioral components of these comparative-statics predictions using the dual-market bidding procedure, which permits very powerful tests. The results support the predictions that buyers are inclined to bid more aggressively when they learn they have the low value. Contrary to theory, buyers are inclined to bid less when they learn they have the high value. Once information is revealed, bidders tend to move toward better responses, exploiting new economic opportunities. Consistent with theory, the overall proportion of efficient allocations is lower than in the first-price auction before information is revealed. But as a result of high-value bidders decreasing their bids, the expected revenue does not increase on a regular basis, contrary to the theory's predictions.

Suggested Citation

  • Alexander Elbittar, 2003. "Impact of Valuation Ranking Information on Bidding in First-Price Auctions: A Laboratory Study," Working Papers 0304, Centro de Investigacion Economica, ITAM.
  • Handle: RePEc:cie:wpaper:0304

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    References listed on IDEAS

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    More about this item


    Asymmetric auctions; laboratory experiments; affiliation and economics of information;

    JEL classification:

    • C92 - Mathematical and Quantitative Methods - - Design of Experiments - - - Laboratory, Group Behavior
    • D44 - Microeconomics - - Market Structure, Pricing, and Design - - - Auctions
    • D82 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Asymmetric and Private Information; Mechanism Design


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