An Overview of Laboratory Research on
Conservation Auctions
Steven Schilizzi
Steven.Schilizzi@uwa.edu.au
Centre for Environmental Economics and Policy (CEEP)
First landmarks
• First theoretical paper on C.A. in AJAE, 1997
• First experimental paper on C.A. in JEEM, 2003
• Just over 5 years between them
• Both in top ranked journals
• Slow beginning… but recent acceleration!
Laboratory studies of conservation
auctions in the last decade
(cumulative)
Laboratory studies of conservation
auctions in the last decade
(per year)
Main publishing journals (by frequency)
• Land Economics
• AmJAE
• Ecol Econ
> 25% of
papers yet
unpublished
in 2013
Also the following journals:
• JEEM
• EaRE
• AJARE
This overview
not just
published
papers
Organization of overview
I.
Issues and questions addressed
II. Key insights and contributions
III. Pending questions and unresolved problems
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
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I/ Issues and questions addressed
• Auction institution design
Choice of auction format
Auction implementation rules
• Bidders
 Bidder characteristics
 Bidder behaviour
• Auction performance evaluation
 Evaluation criteria
 Performance evaluations
Auction institution design (1)
Choice of auction format, in relation to
 to bidder behaviour:
•
•
Indirectly : adverse selection; competition intensity; sequential entry
Directly : individual / group bids; LDCs / DCs; crowding out potential
 to the bids themselves:
•
Sequential auctions; iterated bidding; multi-unit bids; combo bids;
agglomeration bonuses; P vs Q bids
 to the distribution of information:
•
•
In multi-round, number of rounds known ex-ante or not
Neutral vs contextualized auction setting
 to payment rules:
•
DP vs UP; BC vs TC; linking payments to outcomes
Auction institution design (2)
Auction implementation rules, in relation to
 The distribution of information
• Communication between bidders;
• type/amount of info provided by auctioneer
 Payment rules (once format is chosen)
•
•
•
•
If UP, first rejected vs last accepted price
In iterative bidding, use of lock-in rule
Auctioneer’s reserve price
Imposed payment rule or ex-ante chosen by bidders
 Transaction costs
Bidders
Bidder characteristics
 Bidder heterogeneity
 Endowment bias
Bidder behaviour
 Bidder learning (but not auctioneer’s learning)
•
effects on DP vs UP, BC vs TC, P vs Q performance
 Bidder risk aversion
•




Effects on outcome-linked payments
Bid shading (or rent-seeking)
Imperfect compliance & moral hazard
Role of social networks & group leaders in belief format’n
Trust, reciprocity & non-selfish prefs in repeated auctions
II/ Key insights and contributions
Auction format (1)
•
DP vs UP
 DP > UP in ‘standard’ lab settings, but:
 Not robust to
•
•
•
rate of bid shading,
bidder heterogeneity (in costs & risk aversion),
post-contracting compliance (moral hazard),…
 UP > DP if goal is to learn about compliance costs
•
BC > TC in repeated auctions (less bidder learning)
•
Q > P bids in repeated auctions (less learning)
•
‘Dynamic entry’  more efficient scoring metrics…
(insights, cont’d)
Auction format (2)
• Linking payments to uncertain outcomes
 Either do so without auctioning, or auction but not do so
 But srongly depends on what performance metric is used
• Iterated bidding
 Benefit: Greater efficiency in earlier rounds (then eroded by learning)
Can permit better coordination at landscape level
 Cost:
Greater transaction costs for auctioneer
• Capturing site-synergies
 Strongly depend on indiv vs group bids, communic.
 Agglom bonus  better offers, spatial targeting  best parcels selected
• Potential for crowding out = serious !
(insights, cont’d)
Implementation rules (1)
• Information provision (by auctioneer)
 on envir. benefits, on aggreg/contiguity benefits
 improves auction’s cost-effic.
 on the (implicit) reserve price
 bidder confusion and ‘auction failure’ (target not met)
 less info (uncertainty) on bidders’ own opport. costs
 reduced participation; bids & auct perf more volatile
• Info transmission (between bidders)
 strong ‘demand’ for communic’n by bidders
  collusion but also  sharing of benefits ( cooperat’n)
• Feedback in iterative bid  collusion & cooperat’n
(insights, cont’d)
Implementation rules (2)
• Feedback in iterative bid  collusion & cooperat’n
• Group leaders and social networks:
 leaders  beliefs, collusion, more rents
 networks  opposite effect if group-perf’ce incentives
(insights, cont’d)
Bidder characteristics
• Cost heterogeneity  impacts DP vs UP
• Steep cost curves  UP > DP , but…
• Very steep curves  auction not best approach (?) - (nor very flat curves)
• Productivity heterogeneity (for outcome pay’ts)
 Knowledge of type affects participation and bidding
 But interacts in complex ways with risk aversion
• Endowment bias
 WTA for A does depend on how much of B a bidder has
(insights, cont’d)
Bidder behaviour
• Learning (in repeated auctions)
 Bidder learning is greater in TC and P than in BC and Q …
 but knowledge of auction format (e.g. DP vs UP) matters
 Can erode auction perf’ce to < {fixed price scheme}
• Bid shading: theoretical predictions confirmed, but
 Over-bid-shading by low cost bidders not constant
E.g. hockey stick in agriculture but logit shape in fisheries
 Neuro-economic studies have started to investigate
• Risk aversion impacts (in complex ways) on
 DP vs UP performance
 Participation, bidding and resulting auction performance
• Imperfect compliance impacts DP vs UP (DP = more adv selectn)
III/ Pending questions
and unresolved problems
Auction performance evaluation
• Role of experimental parameterizations in results
• Multiplicity of evaluation criteria  trade-offs
• Performance (outcome) metrics: problematic, imprecise
knowledge of which can affect bidding behaviour
• Weight of transaction costs (often not measured)
 Value of (framed) field experiments
(Pending questions, cont’d)
Auction format and implementation rules
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Role of contextualization (vs. neutral framing)
(Potential) political hurdles for UP auctions
Impact of duration or permanence of conserv’n contract
Role of multiple rounds in iterated-bidding auctions
Role of different types of uncertainty (costs, competition…)
Presence of common-value elements (winner’s curse)
Site synergies with more realistic configurations
More realistic auctions with outcome-linked payments
Specific impacts of different shapes of cost curve
Specific impacts by type of information provided/withheld
Auctions distributional outcomes and equity preferences
(Pending questions, cont’d)
Bidder characteristics and behaviour
• Impacts of bidder heterogeneity, with some realism
 In costs, risk attitudes, equity prefs, endowments…
• Using students for field extrapolations (lab vs artefactual)
• Little understanding of how bidders form expectations
• What exactly is ‘bidder learning’? Different types?
• Auctioneer’s learning and choice of auction format etc.
• Conservation auctions run by non-government agents?
• Role of social networking; pre-existing vs formed groups
• Role of group- or joint bidding (& combinatorial bids)
Conclusion : taming conservation
auctions via an epistemological reversal?
• Conservation auctions are very complex!
• We may never understand them completely…
…if we hold on to the old analytical view
• “Context” (auction’s environment) may be the key
Vary context systematically and observe outcomes …
… specifying institutional & regulatory ‘implementation conditions’
• Field studies to inform on the “implementation conditions”
• = Complete reversal of traditional approach
• Not just for conservation auctions – for much of economics
Danke für Ihre Aufmerksamkeit
Thank you for your attention