How does self-evaluations influence others' evaluations? An online experiment

Last registered on July 06, 2026
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Pre-Trial

Trial Information

General Information

Title
How does self-evaluations influence others' evaluations? An online experiment
RCT ID
AEARCTR-0017261
Initial registration date
June 29, 2026

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
July 06, 2026, 7:17 AM EDT

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

Locations

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Primary Investigator

Name
Qianyun Gao
Affiliation
University of Warwick
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Other Primary Investigator(s)

Additional Trial Information

Status
In development
Start date
2026-07-04
End date
2027-06-01
Keywords
Behavior, Labor
Additional Keywords
JEL code(s)
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
Differences in workplace outcomes can depend on factors beyond actual task performance. This project studies whether the way workers describe their own performance affects how others evaluate that performance. In an online experiment, participants are shown profiles of workers doing either a Raven-style logical reasoning test or a writing test and are asked to rate the workers on their test performance. Each profile contains either 1) the pieces of work with suggested solutions alongside self-evaluations of the worker describing their task performance on this task; or 2) the pieces of work with suggested solutions alongside some descriptive texts that match in length and positivity of language (control group), randomly shown to participants. I hypothesise that exposure to worker self-evaluations influences evaluators' assessments of worker performance. As a mechanism, I explore how the effect varies with aspects of self-evaluations, particularly its positivity.
External Link(s)

Registration Citation

Citation
Gao, Qianyun. 2026. "How does self-evaluations influence others' evaluations? An online experiment." AEA RCT Registry. July 06. https://doi.org/10.1257/rct.17261-1.0
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Experimental Details

Interventions

Intervention(s)
I show participants randomized profiles of workers doing certain tasks. While all profiles contain the worker's actual responses, some profiles also contain workers' self-evaluations.
Intervention Start Date
2026-07-04
Intervention End Date
2026-07-05

Primary Outcomes

Primary Outcomes (end points)
Rating on task performance
Primary Outcomes (explanation)
Rating on task performance will be a survey question with answers ranging from 0-10

Secondary Outcomes

Secondary Outcomes (end points)
The main mediator is positivity of self-evaluations. Additional exploratory variables include perception towards the worker (ie: good to work with); mechanism questions include perceived informativeness of the self-evaluations, and the cognitive load cognitive difficulty of making an assessment. I will also look at free-text responses on the evaluators' thinking-process, and other linguistic features of the self-evaluation text.
Secondary Outcomes (explanation)
Perception towards the qualities of the worker will be a set of questions that ask about qualities other than impression on direct performance

Experimental Design

Experimental Design
I plan to recruit participants from Prolific (UK-based working-age adults) as evaluators. Participants who serve as evaluators will be introduced to the tasks with suggested solutions and shown pieces of work, and rate the performances as well as how much they like the workers (on a scale of 0-10). Randomisation occurs at the evaluation level, where evaluators see slightly different versions of task responses. Collection of worker data was separately pre-registered.
Experimental Design Details
Not available
Randomization Method
Randomization will be implemented electronically through variables embedded in the survey software. Each evaluator will complete eight evaluations. Assignment will be balanced such that each evaluator sees four workers in the treatment condition and four workers in the control condition. Assignment will also be balanced across task types, with each evaluator evaluating four workers who completed the Raven-style task and four workers who completed the writing task.

Worker profiles will be stratified into low-, middle-, and high-performance groups based on objective task performance. Worker profiles will then be assigned such that approximately one-third of all evaluations involve workers from each performance group. Treatment assignment will be randomized within task and performance strata.
Randomization Unit
Unit of randomisation is each evaluation (evaluator-worker pair)
Was the treatment clustered?
Yes

Experiment Characteristics

Sample size: planned number of clusters
300 evaluators
Sample size: planned number of observations
2400 evaluations
Sample size (or number of clusters) by treatment arms
Each evaluator will be exposed to both the conditions. 1200 evaluations will be in control group, 1200 in treatment group.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Simulating from pilot data, my minimum detectable effect size for my main outcome is 0.19 points on the rating scale (0.09 standard deviations) at 80% power.
Supporting Documents and Materials
IRB

Institutional Review Boards (IRBs)

IRB Name
University of Warwick
IRB Approval Date
2026-01-13
IRB Approval Number
N/A
Analysis Plan