“Uncertain Competition in the Ultimatum Game”

Last registered on October 05, 2021

View Trial History

Pre-Trial

Trial Information

General Information

Title

“Uncertain Competition in the Ultimatum Game”

RCT ID

AEARCTR-0008323

Initial registration date

October 02, 2021

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

October 05, 2021, 2:10 PM EDT

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

Locations

Country

United States of America

Region

Primary Investigator

Name

Connor Magnuson

Affiliation

University of Wyoming

Contact Primary Investigator

Other Primary Investigator(s)

PI Name

Linda Thunstrom

PI Affiliation

University of Wyoming

Contact Investigator

PI Name

Todd Cherry

PI Affiliation

University of Wyoming

Contact Investigator

PI Name

Chian Jones Ritten

PI Affiliation

University of Wyoming

Contact Investigator

Additional Trial Information

Status

On going

Start date

2021-09-13

End date

2021-10-30

Keywords

Behavior, Labor

Additional Keywords

JEL code(s)

Secondary IDs

Prior work

This trial does not extend or rely on any prior RCTs.

Abstract

This study seeks to identify how uncertainty about competition shape labor market outcomes. This paper will use a variant of the ultimatum game that corresponds to posted-offer hiring to identify how uncertain competition alter labor market outcomes. Our ultimatum game will incorporate competition through multiple respondents for a single proposer and will incorporate uncertainty by hiding the true number of respondents from other respondents.

External Link(s)

Registration Citation

Citation

Cherry, Todd et al. 2021. "“Uncertain Competition in the Ultimatum Game”." AEA RCT Registry. October 05. https://doi.org/10.1257/rct.8323-1.0

Sponsors & Partners

Experimental Details

Interventions

Intervention(s)

We vary the structure of the ultamatum game to capture the effects of uncertainty and competition on outcomes.

Intervention Start Date

2021-09-20

Intervention End Date

2021-10-30

Primary Outcomes

Primary Outcomes (end points)

Ultimatum Game Split, Offer acceptance

Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)

Secondary Outcomes (explanation)

Experimental Design

We will run treatment groups through Qualtrics. The first batch of subjects will be assigned the role of proposer and will be randomly assigned to one of three treatments: standard ultimatum game, competitive ultimatum game, and uncertain
competitive ultimatum game. Once proposers have been assigned to their treatment, they will then decide how to split $10. After making their offer, they will complete a demographic survey and then exit the survey.

The second batch of experiments will be assigned the role of respondent and will be randomly assigned to a treatment group. Respondents are then matched to an offer that was made by a proposer. They then decide to accept or reject the offer. After making this descision, they complete a demographic survey and exit the survey.

We will use a 3x1 design: standard ultimatum game, competitive ultimatum game, and uncertain
competitive ultimatum game. The standard ultimatum game and the competitive ultimatum game will
allow us to replicate Fischbacher et al. 2009. We observe the proposer’s offer and the responder’s
decision to accept or reject the offer. After the experiment is over, we can create a probability of
acceptance for each potential split.

Subjects within the standard ultimatum treatment will be told that they will play the ultimatum game with
one other partner. Subjects within the competitive ultimatum treatment will be told that there will be one
proposer and three respondents in each group. If more than one respondent accepts an offer in a round,
one randomly determined respondent will receive the entire payment. In the uncertain competitive
ultimatum treatment, subjects will be told that there will be a 50% chance that there will only be one
respondent and a 50% chance that there will be three respondents in the group.

Experimental Design Details

Randomization Method

Randomization done by computer

Randomization Unit

Individual randomization

Was the treatment clustered?

Experiment Characteristics

Sample size: planned number of clusters

600 total subjects (200 offers and 400 responses)

Sample size: planned number of observations

600 total subjects (200 offers and 400 responses)

Sample size (or number of clusters) by treatment arms

50 proposers control, 50 proposer competition, 50 proposers uncertain control, 50 proposers uncertain competition
50 respondents control, 150 respondents competition, 50 respondents uncertain control, 150 respondents uncertain competition

Minimum detectable effect size for main outcomes (accounting for sample design and clustering)

Supporting Documents and Materials

IRB