Strategic avoidance of lying costs

Last registered on July 29, 2021

Pre-Trial

Trial Information

General Information

Title
Strategic avoidance of lying costs
RCT ID
AEARCTR-0006881
Initial registration date
December 14, 2020
Last updated
July 29, 2021, 9:35 AM EDT

Locations

Primary Investigator

Affiliation
WZB Berlin Social Science Center

Other Primary Investigator(s)

Additional Trial Information

Status
Completed
Start date
2020-12-18
End date
2020-12-31
Secondary IDs
Abstract
I study whether people take advantage of privileged positions to have someone lying on their behalf without explicit delegation. I use a sequential cheating game in dyads in which if at least one member of the dyad lie, both benefit from it. Consequently, the first-mover can avoid the cost of lying by reporting truthfully if he or she expects that the second-mover will lie. I use treatment variations to eliminate the belief of the second-mover lying, control for the positive externality of the first-mover lie, and control for simultaneous decisions.
External Link(s)

Registration Citation

Citation
Parra, Daniel. 2021. "Strategic avoidance of lying costs." AEA RCT Registry. July 29. https://doi.org/10.1257/rct.6881-1.3000000000000003
Sponsors & Partners

Sponsors

Experimental Details

Interventions

Intervention(s)
I study whether people take advantage of privileged positions to have someone lying on their behalf without explicit delegation.
Intervention Start Date
2020-12-18
Intervention End Date
2020-12-31

Primary Outcomes

Primary Outcomes (end points)
Whether participants lie or not.
Primary Outcomes (explanation)
They report a random draw, so I compare what they draw and what they report.

Secondary Outcomes

Secondary Outcomes (end points)
beliefs about the second player's report.
Secondary Outcomes (explanation)
I will elicit beliefs about the second mover report. I will use this information to understand whether participants understand that in some treatments the other participant has incentives to lie on their behalf.

Experimental Design

Experimental Design
I use a variation of the observed game presented by Gneezy et al. (2018). In my game, two participants play sequentially. In the beginning, the first-mover (P1) click on a box in the computer, and a color (Orange or Black) is revealed. The probability of drawing Orange is 0.2 and black 0.8. After the first-mover observes the draw, they are asked to report the observed color to the second-mover (P2). Once the second-mover learns this report, they are asked to click on a box in the computer that reveals a color; the second mover reports the observed color. The probabilities of drawing each color are the same as for the first-mover.

It is important to mention that the draw is only known by the participant and the experimenter, but not by other participants. Participants' payoffs depend on both the reports of the two members of a dyad. In particular, if any of the two participants report an Orange, both of them get 4 pounds. Otherwise, both gain 0.5 pounds. I use this payoff structure because I am interested in the extensive margin.

In the Baseline, I hypothesized that the first-movers will take advantage of their position and, therefore, will avoid the cost of lying. To attribute truth-telling to the possibility of having another participant lying on their behalf, I make the report of the second mover a random variable instead of a decision made by P2. Specifically, in the treatment No Avoidance, after the second-mover learns the report of P1, he or she clicks on a button that draws a color, and the computer reports the actual value automatically. Thus, in No Avoidance, the first-mover can not rely on second-mover incentives to lie.

In No Avoidance, the only thing that changes compared with the Baseline is the beliefs about the second-mover report. However, I am interested in knowing whether this result will hold even when dropping the positive externality generated by the first-mover’s report. With the previous treatments, I do not know whether the report in No Externalityis purely by the fact that it is not possible to use P2 as an implicit agent or because P1 is using the handy excuse of causing a benefit on P2. In other words, it may be the case that in the Baseline, the first-mover is not using the positive externality as a justification because both participants generate positive externalities and then take full advantage of their position. In contrast, in No Avoidancethe positive impact of P1’s report is more salient.

Hence, in the treatment No Externality, I modify the payoff scheme to eliminate the positive externality generated by P1 in previous treatments. In No Externality, the game sequence and main game features, including the pecuniary payoffs for the first-mover, are identical to those in No Avoidance. However, the payoff for P2 depends only on the color chosen by the computer. Specifically, if Orange, he or she gets 4 pounds; otherwise, he or she gets 0.5 pounds.

In No Avoidance and No Externality, I control for different components that can explain why the first-mover decides to report truthfully in Baseline. However, there is another component that worth ruling out: the fact that participants play sequentially. In this treatment, Simultaneous, participants decide at the same time which color to report. The impact of this treatment on first-mover strategy is similar to No Avoidance. However, it allows me to understand the role of the moral signal that the first mover sends about whether it is correct to lie or not. Thus, this treatment makes it possible to confirm the first-mover’s willingness to avoid the cost of lying and the impact of the moral signal on second-mover behavior.

Experimental Design Details
Hypothesis 1(Strategic Cost Avoidance).In the Baseline, the first-mover will report their random draw truthfully.
Hypothesis 2 (No cost avoidance). The first-mover will lie more in No Avoidance compared with the Baseline.
Hypothesis 3 (Beliefs about second-mover). The first-mover will believe that the second-mover will report more Orange draws in Baseline than in No Avoidance.
Hypothesis 4 (Positive externality justification). The first-mover will report more Orange draws in No Avoidance compared with the ones reported in No Externality.
Hypothesis 5 (Delegation controlling for Externality). The difference between No Externality and the Baseline will be significant once we account for the difference between No Externality and No Avoidance.
Hypothesis 6 (No cost avoidance simultaneous). The first-mover will lie more in Simultaneous compared with the Baseline.
Hypothesis 7 (Moral signaling). The second-mover will lie more in Simultaneous compared with Baseline.
Randomization Method
The code of the experiment randomly assigns treatment.
Randomization Unit
Individual-level.
Was the treatment clustered?
Yes

Experiment Characteristics

Sample size: planned number of clusters
120 dyads for sequential treatments and 60 for simultaneous.

I assume that in simultaneous, given that participants decide without knowing what the other has decided, I can use the report of both participants in the group as identical. Then, I will only need to collect half of the dyads compared with other treatments.

There is the possibility that the second player left the game given that they can get tired of waiting for the first-mover decision. In these cases, a robot will take decisions on behalf of P2. I will not exclude these observations from the analysis, because arguably this does not change the decision because they only learn whether their partner dropped-out after making their decisions.
Sample size: planned number of observations
840 participants.
Sample size (or number of clusters) by treatment arms
120 dyads Baseline
120 dyads No avoidance
120 dyads No externality
60 dyads Simultaneous
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
I calculated the power using computer simulations. I used a minimum detectable effect size of 0.15 from people detected as liars. The power reached with my sample size of 120 observations by treatment is 0.8 when simulating 1500 Fisher tests.
IRB

Institutional Review Boards (IRBs)

IRB Name
WZB Research Ethics Review
IRB Approval Date
2020-11-26
IRB Approval Number
2020/3/98

Post-Trial

Post Trial Information

Study Withdrawal

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Intervention

Is the intervention completed?
Yes
Intervention Completion Date
December 22, 2020, 12:00 +00:00
Data Collection Complete
Yes
Data Collection Completion Date
December 22, 2020, 12:00 +00:00
Final Sample Size: Number of Clusters (Unit of Randomization)
The number of players in the role of P1 was 457.
Was attrition correlated with treatment status?
No
Final Sample Size: Total Number of Observations
899 participants which include P1 and P2.
Final Sample Size (or Number of Clusters) by Treatment Arms
The number of players in the role of P1 per treatment was: 129 in Avoid, 128 in No Avoid, 130 in No Externality, and 136 in Simultaneous.
Data Publication

Data Publication

Is public data available?
No

Program Files

Program Files
Reports, Papers & Other Materials

Relevant Paper(s)

Reports & Other Materials