Fairness and Excuse Seeking-Behavior, part 2

Last registered on October 04, 2023

Pre-Trial

Trial Information

General Information

Title
Fairness and Excuse Seeking-Behavior, part 2
RCT ID
AEARCTR-0012181
Initial registration date
September 25, 2023

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 04, 2023, 1:55 PM EDT

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

Locations

Region

Primary Investigator

Affiliation
University of Pittsburgh

Other Primary Investigator(s)

Additional Trial Information

Status
On going
Start date
2023-09-25
End date
2024-03-31
Secondary IDs
Prior work
This trial is based on or builds upon one or more prior RCTs.
Abstract
Understanding preferences for distribution and inequality acceptance in environments with uncertainty about the cause of inequality is key to designing redistributive policies. This is an extension to a previous RCT registered under the ID AEARCTR-0011312 where I use a laboratory experiment to study how uncertainty about the role luck and effort play in determining income affects redistribution, and how people use this uncertainty to excuse behavior not aligned with their fairness views in favor of self-interest.



External Link(s)

Registration Citation

Citation
Ahumada, Beatriz. 2023. "Fairness and Excuse Seeking-Behavior, part 2." AEA RCT Registry. October 04. https://doi.org/10.1257/rct.12181-1.0
Experimental Details

Interventions

Intervention(s)
Intervention (Hidden)
Intervention Start Date
2023-09-26
Intervention End Date
2023-11-30

Primary Outcomes

Primary Outcomes (end points)
I collect the following primary outcomes:
- Distribution to himself and to partner
- Beliefs about the probability of getting high and low piece rate for self and partner.
- Beliefs about the probability of getting high and low piece rate for participants from previous sessions.
- Implemented inequality
- Effort share conditional on beliefs
Primary Outcomes (explanation)
Below we outline how we will use our primary outcomes and the key hypothesis.

Hypothesis 1 (Motivated Beliefs): Participants will report a higher probability that their partner got the high piece rate in comparison to the belief they'll report about participants from previous sessions and a lower probability that they got the high piece rate than what they say for someone else (partner and another participant).

Hypothesis 2 (Acceptance of Inequality): Inequality implemented will be higher for decision makers who have the higher earnings of the pair and have more motivated beliefs, and inequality will be lower when the decision maker is the low earning of the pair and has more motivated beliefs.

Distribution to himself and to partner will be measured both in absolute and relative terms.

Implemented inequality will be measured as the relative difference in earnings after redistribution for each decision.

Effort share conditional on beliefs will be constructed using the beliefs about piece rate for themselves and their partner and their earnings.

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
Participants perform a task and the money they earn depends on effort and luck. Then they are paired and distribute the sum of earnings between themselves. Participants do not know the role luck and effort had in determining their earnings.
Experimental Design Details
The experiment will be conducted at the Pittsburgh Experimental Economic Laboratory. Subjects will participate in one session of an hour that consists of three parts, and they cannot have participated in the previous study from April 2023.

In the first part of the experiment, participants perform the counting zero task of Abeler et al. (2011), where they see a 15x10 table filled with 0 or 1, and they have to count how many 0 are; after they submit an answer, a new table appears. Participants will work on this task for 25 minutes. Participants are paid according to a piece rate, where there are two possible piece rates with 50% each. In the high piece rate, participants get 50 cents for each table counted correctly, and in the low piece rate, they get 50 cents for 3 correctly counted tables. At the moment of performing the task, participants do not know which piece rate they have. While they are performing the task, they do not know if their answer is correct or incorrect.

Before starting the task, participants are told the payment earned in this part is provisional and the payment from the experiment will depend on the decisions made by them or others from the second part. They answer comprehension questions to make sure they know this.

In the second part, participants will be shown the earnings from the real effort task of participants who participated in the session from April 2023. Participants will be asked their beliefs about the piece rate these participants got. The beliefs are incentivized using the binarized scoring rule and following the recommendations of Danz et al. (2022).

In the third part, the participants are paired and have to decide how to distribute the sum of their and their partner's earnings between each other. Both participants of the pair will be making this decision, and one of them will be implemented.

Participants will not know the piece rate each of them got and neither the number of correctly counted tables. They will only know the earnings of each of them. Before making the distribution decision, participants will be asked their beliefs about the piece rate they got and the piece rate their partner got. The elicit beliefs will be compared with the beliefs from the second part.

In this part, participants will be making distributive decisions and belief elicitations for 11 different scenarios. 10 of these scenarios are hypothetical and one is the real one that corresponds to the information of the partner. The participants do not know which scenario is the real one, in the survey at the end of the experiment, they are asked which scenario they thought is the real one. The 10 hypothetical situations are the same for every participant. To determine the effort for the hypothetical situations, I used the data from the sessions run on April 2023.

Finally, the experiment finishes with a demographic survey.

These sessions are a variation of the partial information treatment from study AEARCTR-0011312. It differs first, by the scenarios participants will see. For the previous study, the scenarios were constructed following the data from Abeler et al. (2011) and Zimmerman (2020), but the effort performed by participants was lower on average, which made the Bayesian posterior of each situation near to 1 or 0 in most of the scenarios.

Second, in this study, there is an additional part, where beliefs about the payment rate of participants from previous sessions, are elicited. This is done just after the real effort task and before the distributive decisions. Participants will not know at this moment, that there is going to be a distribution of earnings after. These beliefs are going to be used as a benchmark for the beliefs elicited in the third part.

References:
Abeler, Johannes, Armin Falk, Lorenz Goette, and David Huffman. 2011. "Reference Points and Effort Provision." American Economic Review, 101 (2): 470-92.
Danz, David, Lise Vesterlund, and Alistair J. Wilson. 2022. "Belief Elicitation and Behavioral Incentive Compatibility." American Economic Review, 112 (9): 2851-83.
Zimmermann, Florian. 2020. "The Dynamics of Motivated Beliefs." American Economic Review, 110 (2): 337-61.
Randomization Method
Randomization of piece rate is done via the computer, in oTree.
Randomization Unit
The randomization of the piece rate is done by individual.
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
No Clusters
Sample size: planned number of observations
I expect to have 200 participants
Sample size (or number of clusters) by treatment arms
200 participants, each of them makes decisions for the same 10 situations.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
IRB

Institutional Review Boards (IRBs)

IRB Name
BA - Decision Making Study
IRB Approval Date
2022-10-13
IRB Approval Number
STUDY22100006

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