Anchoring bias in markets
Last registered on October 22, 2018


Trial Information
General Information
Anchoring bias in markets
Initial registration date
October 16, 2018
Last updated
October 22, 2018 1:00 AM EDT

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Primary Investigator
University of Amsterdam
Other Primary Investigator(s)
PI Affiliation
University of Amsterdam
PI Affiliation
University of Amsterdam
Additional Trial Information
In development
Start date
End date
Secondary IDs
In a laboratory experiment, we investigate whether the information revealed during market participation reduces the anchoring bias observed in individual decision making. We control the amount of information available in the market using 3 different market settings: a double auction, bilateral bargaining, and a control treatment in which a good different from the anchored one is traded. Anchors are created randomly for each individual subject by rolling a 10-sided die twice; using a median split subjects are divided into a low anchor group and a high anchor group. The main question of interest is whether the difference in the willingness to pay between the low anchor and the high anchor group observed before market participation is eliminated after the market participation.
External Link(s)
Registration Citation
Ioannidis, Konstantinos, Theo Offerman and Randolph Sloof. 2018. "Anchoring bias in markets." AEA RCT Registry. October 22.
Experimental Details
Anchoring is attempted by the following Yes/No question: Would you sell the good back to the experimenter for X€? X is created randomly by rolling a 10-sided die twice. After the anchoring question, participants interact in a market: either a double auction, bilateral bargaining, or in a double auction for a different good (control treatment).
Intervention Start Date
Intervention End Date
Primary Outcomes
Primary Outcomes (end points)
Willingness To Accept (WTA) both before and after the market participation. The low and the high anchor group will be determined by a median split. We will also compare the difference in WTA before and after market experience between the highest quartile and lowest quartile of the anchor distribution.
Primary Outcomes (explanation)
Difference in WTA between the low and high anchor group, both before and after market participation.
Secondary Outcomes
Secondary Outcomes (end points)
Probability of a trade, Speed of a trade, Price of a trade
Secondary Outcomes (explanation)
Probability of a trade = ratio of agreed trades over all possible trades
Speed of a trade = seconds until trade is agreed (Only for realized trades)
Price of a trade = agreed price (Only for realized trades)
Experimental Design
Experimental Design
We anchor subjects with a random price and compare their WTA for a good before and after participating in a market. Three types of market settings are studied: a double auction, bilateral bargaining, and a double auction for a different good.
Experimental Design Details
Not available
Randomization Method
For each individual subject, the random anchor is obtained from two (10-sided) die rolls.
Randomization Unit
Subjects are randomly assigned treatment market setting before a session starts. Within each market setting, a median split on the random anchors defines the Low Anchor and the High Anchor groups.
Was the treatment clustered?
Experiment Characteristics
Sample size: planned number of clusters
480 participants from 30 sessions with 16 participants in each.
Sample size: planned number of observations
480 participants
Sample size (or number of clusters) by treatment arms
160 participants in the Double Auction treatment (80 in Low Anchor group and 80 in High Anchor Group)
160 participants in the Bilateral Bargaining treatment (80 in Low Anchor group and 80 in High Anchor Group)
160 participants in the Control treatment (80 in Low Anchor group and 80 in High Anchor Group)

(We may encounter problems with the size of our subject pool. If we cannot manage to have 480 subjects, we will have less in the Control treatment.)
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
With our sample and assuming a standard deviation of 3.5 (based on an in-classroom pilot session), the Minimum Detectable Effect size is 1€. Defining the effect size as the difference in means divided by the pooled standard deviation, the percentage is 28%.
IRB Name
Ethics Committee Economics and Business (EBEC), University of Amsterdam
IRB Approval Date
IRB Approval Number
EC 20180926020919