The Way People Lie in Markets

Last registered on September 19, 2019
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Pre-Trial

Trial Information

General Information

Title
The Way People Lie in Markets
RCT ID
AEARCTR-0004732
Initial registration date
September 19, 2019

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
September 19, 2019, 12:42 PM EDT

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

Locations

Country
France
Region

Primary Investigator

Name
Chloe Tergiman
Affiliation
PSU
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Other Primary Investigator(s)

PI Name
Marie Claire Villeval
PI Affiliation
Universite de Lyon, GATE CNRS
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Additional Trial Information

Status
Completed
Start date
2018-09-01
End date
2019-06-27
Keywords
Other
Additional Keywords
fraud, lying, markets
JEL code(s)
C91, D01, G41, M21
Secondary IDs
Abstract
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.
External Link(s)

Registration Citation

Citation
Tergiman, Chloe and Marie Claire Villeval. 2019. "The Way People Lie in Markets." AEA RCT Registry. September 19. https://doi.org/10.1257/rct.4732-1.0
Sponsors & Partners
Experimental Details

Interventions

Intervention(s)
We have 4 treatments, where competition and reputation are turned on and off independently.
Intervention Start Date
2018-09-01
Intervention End Date
2019-06-27

Primary Outcomes

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

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)

Experimental Design

Experimental Design
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
Randomization Method
subjects randomly show up for a given treatment.
Randomization Unit
experimental sessions.
Was the treatment clustered?
Yes

Experiment Characteristics

Sample size: planned number of clusters
about 35 observations for each type of subject.
Sample size: planned number of observations
400.
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
IRB

Institutional Review Boards (IRBs)

IRB Name
Office for Research Protections
IRB Approval Date
2018-07-25
IRB Approval Number
STUDY00010251
Analysis Plan

Post-Trial

Post Trial Information

Study Withdrawal

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Intervention

Is the intervention completed?
No
Data Collection Complete
Data Publication

Data Publication

Is public data available?
No

Program Files

Program Files
Reports, Papers & Other Materials

Relevant Paper(s)

Reports & Other Materials