Shallow Meritocracy

Last registered on June 22, 2022

Pre-Trial

Trial Information

General Information

Title
Shallow Meritocracy
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
June 22, 2022, 8:43 AM EDT

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
2021-08-30
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
Meritocracies aspire to reward effort and hard work but promise not to judge individuals by the circumstances they were born into. The choice to work hard is, however, often shaped by circumstances. This study investigates whether people's merit judgments are sensitive to this endogeneity of choice. In a series of incentivized experiments with a large, representative US sample, study participants judge how much money two workers deserve for the effort they exerted. In the treatment condition, unequal circumstances strongly discourage one of the workers from working hard.
External Link(s)

Registration Citation

Citation
Andre, Peter. 2022. "Shallow Meritocracy." AEA RCT Registry. June 22. https://doi.org/10.1257/rct.5811
Experimental Details

Interventions

Intervention(s)
Intervention Start Date
2020-06-01
Intervention End Date
2021-08-30

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

Documents

Document Name
Experimental Instructions
Document Type
survey_instrument
Document Description
Experimental instructions.
File
Experimental Instructions

MD5: 9a2fbc1339d4b073536a34d05a4fa8c7

SHA1: 5bbef0e2e4697e0601f9d82ffa6b6baf201c0222

Uploaded At: May 27, 2020

Document Name
Experimental Instructions (Work versus leisure experiment)
Document Type
survey_instrument
Document Description
Experimental instructions for the work versus leisure experiment.
File
Experimental Instructions (Work versus leisure experiment)

MD5: 668dfdc989d69161a4d6f94abdebb0b2

SHA1: 023b02b54aa90ffe54426d6fd68bad02f6bb175b

Uploaded At: June 22, 2022

Document Name
Experimental Instructions (Counterfactual Experiments)
Document Type
survey_instrument
Document Description
Experimental instructions for the counterfactual experiments.
File
Experimental Instructions (Counterfactual Experiments)

MD5: e6527a1fbedac08b57ef7e339140737b

SHA1: 9c6e5801c011fd3180ee277642be04a8edfbf5d9

Uploaded At: January 14, 2021

Document Name
Experimental Instructions (Robustness experiment: Reference dependence)
Document Type
survey_instrument
Document Description
Experimental instructions for the robustness experiment: reference dependence.
File
Experimental Instructions (Robustness experiment: Reference dependence)

MD5: d4474c9becbbdb392f3c78a252bba3b8

SHA1: 27a0bcf0e0dabc73ef1fdf3db409ca3534c5f9b7

Uploaded At: February 16, 2021

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 (Work versus leisure experiment)

MD5: 6052d79b2c572f087516919400119b1c

SHA1: 64b18ed96f2f01b9163fd847b5c720b82ba200bc

Uploaded At: June 22, 2022

Analysis plan (Counterfactual experiment with advantaged worker)

MD5: 71c88b0cf581325b49bea1933afca260

SHA1: 6eb6e947aee068f2d783df43c7abbd0f7e595c17

Uploaded At: June 22, 2022

Analysis plan (Counterfactual Experiments)

MD5: 7ac6f9ff0da126fd417193f5efb9650c

SHA1: 79e57a080c8a0c45f25bc04a0b6a411c36e2a003

Uploaded At: January 14, 2021

Analysis plan

MD5: 1895f325f8daa0149f410f3db5e61eb3

SHA1: 1fc76b81dbcf4c8cb0cb6be78ced20b361141899

Uploaded At: May 27, 2020

Analysis plan (Robustness experiment: Reference dependence)

MD5: 811811e976cf3ccc36deafa7ee291545

SHA1: 896524a117b572e1dc0110ff4cefb3b17707abe4

Uploaded At: February 16, 2021

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