Information Networks and Conservation Auctions: Evidence from Laboratory Experiments

  • Author / Creator
    Zhang, Daiwei
  • Conservation auctions have become a popular mechanism for gathering information about farmers' willingness to accept (WTA) compensation for the adoption of beneficial management practices (BMPs), allowing governments and environmental authorities to foster the adoption of these practices. In real life, farmers that participate in such auctions may not only know their own adoption costs but also those of other socially connected participants (e.g. neighbors). However, there is very limited literature on how information networks influence farmers' behaviors in conservation auction. This thesis tries to answer this question by conducting laboratory experiments containing multiple bidding rounds. We find that: i) learning exists in multiple bidding rounds auction and may lead to efficiency loss; ii) networks in general may decrease auction efficiency as low-cost participants who are more likely to win try to increase bids when information about other participants' costs is available; iii) specific network structures, such as regular lattice and Erdos-Renyi, help reduce information rents gained by participants; iv) participants are only influenced by connections within two degrees of separation; and v) auction efficiency is expected to increase in auctions with information networks in which high-cost participants are highly connected and low-cost participants are isolated. These findings provide guidelines and suggestions for conservation auction design and policy decisions.

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  • Graduation date
  • Type of Item
  • Degree
    Master of Science
  • DOI
  • License
    This thesis is made available by the University of Alberta Libraries with permission of the copyright owner solely for non-commercial purposes. This thesis, or any portion thereof, may not otherwise be copied or reproduced without the written consent of the copyright owner, except to the extent permitted by Canadian copyright law.