Focal Points in Experimental All-pay and Winner-pay Pooled Auctions with Similar Items

Last registered on March 06, 2024

Pre-Trial

Trial Information

General Information

Title
Focal Points in Experimental All-pay and Winner-pay Pooled Auctions with Similar Items
RCT ID
AEARCTR-0013079
Initial registration date
February 22, 2024

Initial registration date is when the trial was registered.

It corresponds to when the registration was submitted to the Registry to be reviewed for publication.

First published
March 06, 2024, 3:10 PM EST

First published corresponds to when the trial was first made public on the Registry after being reviewed.

Locations

Primary Investigator

Affiliation
National Taiwan University

Other Primary Investigator(s)

PI Affiliation
National Taiwan University

Additional Trial Information

Status
In development
Start date
2024-03-01
End date
2025-03-01
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
We plan to compare first-price and all-pay auctions with two units sold, and observe the effect of focal points on bidding, revenue and efficiency. Specifically, we plan to conduct laboratory experiments, in which each session has 6 bidders drawing private values independently (for each period) and competing to win one of two prizes, with the second prize valued at a fraction (80%, 40%, 20% etc.) of the first prize. The risk neutral Bayesian Nash equilibrium (RNBNE) predicts revenue equivalence and both formats being efficient. However, experimental results would likely be affected by the fraction (80%, 40%, 20% etc.), becoming bidding focal points. If this occurs, the first-price auction would yield higher revenue compared to the all-pay auction, and have efficiency levels closer to 100%. In contrast, the single-unit, first-price auction does not have such focal points, and would likely result in lower (first-prize) revenue and efficiency than all-pay, since bidders tend to overbid in all-pay auctions.
External Link(s)

Registration Citation

Citation
Hsu, Dexter and Joseph Tao-yi Wang. 2024. "Focal Points in Experimental All-pay and Winner-pay Pooled Auctions with Similar Items." AEA RCT Registry. March 06. https://doi.org/10.1257/rct.13079-1.0
Experimental Details

Interventions

Intervention(s)
Intervention Start Date
2024-03-02
Intervention End Date
2025-02-27

Primary Outcomes

Primary Outcomes (end points)
Subjects' bids, seller revenue, and efficiency
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
We adopt a focal point set Λ = {0, 0.2, 0.4, 0.8, 1}, creating a total of 5 experimental treatments.
For each treatment, we recruit 36 subjects, randomly distributed across 6 experimental groups.
Among the six groups, three groups experience all-pay auctions first (“All-Pay × Winner-Pay”),
and the other three experience winner-pay auctions first (“Winner-Pay × All-Pay”). This design
allows us to investigate how the magnitude of λ affects participants’ bidding behavior in winner-
pay and all-pay auctions. Additionally, it facilitates an exploration of the potential order effect.
Experimental Design Details
Not available
Randomization Method
Subjects are recruited by ORSEE. Group assignment randomized one the fly using experimental software zTree. Experimental parameters drawn randomly prior to the experiment using zTree.
Randomization Unit
individual
Was the treatment clustered?
Yes

Experiment Characteristics

Sample size: planned number of clusters
180 Subjects
Sample size: planned number of observations
10,800 bids, 1800 auctions
Sample size (or number of clusters) by treatment arms
36 subjects in each of the five treatments
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
See attachment.
IRB

Institutional Review Boards (IRBs)

IRB Name
Ethical Review Approval
IRB Approval Date
2024-08-01
IRB Approval Number
202401HS007
IRB Name
Ethical Review Approval
IRB Approval Date
2023-10-23
IRB Approval Number
202101HS002
Analysis Plan

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