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Shallow Meritocracy

Last registered on January 14, 2021

Pre-Trial

Trial Information

General Information

Title
Redistributive Behavior When Circumstances Shape Choices
RCT ID
AEARCTR-0005811
Initial registration date
May 27, 2020

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
May 27, 2020, 12:20 PM EDT

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

Last updated
January 14, 2021, 3:21 AM EST

Last updated is the most recent time when changes to the trial's registration were published.

Locations

Region

Primary Investigator

Affiliation
briq - Institute on Behavior & Inequality

Other Primary Investigator(s)

Additional Trial Information

Status
Completed
Start date
2020-06-01
End date
2020-12-31
Secondary IDs
Abstract
The common distinction between fair inequality based on choices and unfair inequality based on circumstances neglects that choices and circumstances are inherently related: Choices typically depend strongly on circumstances. In an experiment with a large representative sample from the US, I study how the effect of circumstances on choices influences redistributive behavior and positive fairness views. I explore and contrast two important mechanisms: inferences about the role of circumstances and redistributive preferences.
External Link(s)

Registration Citation

Citation
Andre, Peter. 2021. "Redistributive Behavior When Circumstances Shape Choices." AEA RCT Registry. January 14. https://doi.org/10.1257/rct.5811
Experimental Details

Interventions

Intervention(s)
Intervention Start Date
2020-06-01
Intervention End Date
2020-12-31

Primary Outcomes

Primary Outcomes (end points)
The redistributive behavior of spectators in the first seven redistribution scenarios. See the analysis plan.
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
I use the paradigmatic spectator-worker design to (i) create an experimentally controlled situation of inequality between two workers and (ii) observe how spectators redistribute money between them. The focus of this study is on the redistribution decisions of the spectators in different experimentally created inequality situations between workers.
Experimental Design Details
#### GENERAL INTRODUCTION TO THE EXPERIMENTAL DESIGN ####

I use the paradigmatic spectator-worker design to (i) create an experimentally controlled situation of inequality between two workers and (ii) observe how spectators redistribute money between them. The focus of this study is on the redistribution decisions of the spectators in different experimentally created inequality situations between workers.

[WORKERS| I hire 100 US workers on Amazon's online labor market Mechanical Turk for an email collection task. Each worker k earns a piece-rate pi_k and can freely choose how many tasks e_k to complete. Afterward, workers are assigned to pairs. I frequently refer to the two workers in a pair as worker A and worker B.

[SPECTATORS] I invite participants from the general US population, also referred to as spectators, to an online experiment. Spectators can redistribute the worker's earnings.

The central feature of the design is a between-subject comparison of redistributive behavior in two types of inequality situations:

Situation type (a): The circumstances to which worker A and worker B react are identical. That is, they have the same piece-rate expectations. Ultimately, worker A receives piece-rate pi_A, and worker B receives pi_B.

Situation type (b): Worker A reacts to different circumstances than worker B. That is, they have different piece-rate expectations. However, eventually, workers receive the same piece-rates as in situation type (a).

Thus, the design systematically varies the expected circumstances to which workers react but keeps constant which piece-rate they ultimately earn. If workers react to the same circumstances, their effort choices are directly comparable. If workers, however, react to different circumstances, circumstances exert a differential impact on their choices. Contrasting redistributive behavior across these two situation types illustrate whether or not spectators take this into account.

Differences in redistributive behavior in these treatment comparisons jointly derive from two different mechanisms. First, they depend on the beliefs of spectators about the effect of circumstances on effort choices. If, for instance, spectators do not understand that expected circumstances affect choice, treatments 1 and 3 (or 2 and 4) appear identical to them, and no change in redistributive behavior is to be expected. If this is not the case, the second factor, fairness preferences, becomes critical: Is inequality due to choices that derive from randomly assigned circumstances considered fair?

To distinguish between these two mechanisms, two additional treatments exogenously manipulate the beliefs of participants. To do so, I include a new page to the instructions on which I inform spectators that effort choices in the task are strongly context-dependent; i.e., workers typically react to the piece-rate they expect to receive.

See the analysis plan for further details.
Randomization Method
Randomization done by a computer in an online survey.
Randomization Unit
Individual
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
About 1800 respondents

The sample ought to be representative of the US general population in terms of gender, age, income, education, and region. If required, a few additional observations may be collected to improve the match to US census data. This can happen if, for instance, the initial sample contains too few female respondents.
Sample size: planned number of observations
About 1800 respondents The sample ought to be representative of the US general population in terms of gender, age, income, education, and region. If required, a few additional observations may be collected to improve the match to US census data. This can happen if, for instance, the initial sample contains too few female respondents.
Sample size (or number of clusters) by treatment arms
About 300 respondents in each of the six treatments
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Supporting Documents and Materials

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IRB

Institutional Review Boards (IRBs)

IRB Name
Gesellschaft für experimentelle Wirtschaftsforschung e.V.
IRB Approval Date
2019-11-12
IRB Approval Number
HyegJqzx
Analysis Plan

Analysis Plan Documents

Analysis plan (Counterfactual Experiments)

MD5: 7ac6f9ff0da126fd417193f5efb9650c

SHA1: 79e57a080c8a0c45f25bc04a0b6a411c36e2a003

Uploaded At: January 14, 2021

Analysis plan

MD5: 1895f325f8daa0149f410f3db5e61eb3

SHA1: 1fc76b81dbcf4c8cb0cb6be78ced20b361141899

Uploaded At: May 27, 2020

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