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The Way People Lie in Markets
Last registered on September 19, 2019
View Trial History
The Way People Lie in Markets
Initial registration date
September 19, 2019
September 19, 2019 12:42 PM EDT
Contact Primary Investigator
Other Primary Investigator(s)
Marie Claire Villeval
Universite de Lyon, GATE CNRS
Additional Trial Information
The goal of this paper is to investigate how people lie in the context of markets with asymmetric information and when lying has varrying intensities in terms of probability of getting caught. Some lies are detectable ex post with certainty, but others guarantee plausible deniability. We look at which market mechanisms affect the amount and types of lies.
Tergiman, Chloe and Marie Claire Villeval. 2019. "The Way People Lie in Markets." AEA RCT Registry. September 19.
Sponsors & Partners
We have 4 treatments, where competition and reputation are turned on and off independently.
Intervention Start Date
Intervention End Date
Primary Outcomes (end points)
The frequency of extreme lies (detected ex post with certainty), high risk lies (detected ex post with 66% chance), low risk lies (detected ex post with 33% chance) and deniable lies (undetectable).
Primary Outcomes (explanation)
no transformed data.
Secondary Outcomes (end points)
We will also use individual characterisitics (Machiavellian scores as well as gender and how much people value efficiency) to show the robustness of our results.
Secondary Outcomes (explanation)
Fixed Pair treatment (baseline): In each period, a project manager (PM, hereafter) is matched with an investor and pairs are randomly rematched at the beginning of each
new period. We now describe the timing of each period.
(i) First, Nature randomly draws a set of three cards for each PM. The cards convey information on the quality of the PM's projects. Each card has a (independent) 0.5 probability of displaying a star, which indicates a successful project. If the card does not display a star, it is
blank. Only the PM can observe his three cards and see how many of display a star. The total number of stars is then in the (0; 1; 2; 3) set.
(ii) Then, the PM sends a cheap-talk message m to the investor regarding the number of cards that display a star, where m can be 0; 1; 2; 3.
(iii) The investor receives an endowment of 100 tokens. After observing m, the investor decides on an action a in (Invest;Not Invest). If a= Invest, the entire endowment has to be invested.
(iv) Nature randomly draws one of the PM's three cards, which determines the quality of the selected project. If the selected card displays a star, the investment is a success; if the card is blank, it fails.
(v) Finally, both the PM and the investor learn whether a star was on the card nature drew, regardless of whether the investor invested
or not. A history box is also displayed on the subjects' screen. This history box lists (m, a, whether a star was drawn by nature)
for each past period.
We have 3 other treatments:
Fixed Pairs: PM and investor are in a fixed match for all 27 periods
Random Triplets: 2PMs and 1 investor are randomly rematched in each period.
Fixed Triplets: 2PMs and 1 investor are in a fixed match throughout the 27 periods.
Experimental Design Details
subjects randomly show up for a given treatment.
Was the treatment clustered?
Sample size: planned number of clusters
about 35 observations for each type of subject.
Sample size: planned number of observations
Sample size (or number of clusters) by treatment arms
at least 35 observations for each type of subject.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Supporting Documents and Materials
INSTITUTIONAL REVIEW BOARDS (IRBs)
Office for Research Protections
IRB Approval Date
IRB Approval Number
Post Trial Information
Is the intervention completed?
Is data collection complete?
Is public data available?
Reports and Papers