Inference Effects in Discrimination Measurement

Last registered on July 13, 2026
View Trial History

Pre-Trial

Trial Information

General Information

Title
Inference Effects in Discrimination Measurement
RCT ID
AEARCTR-0019110
Initial registration date
July 06, 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 13, 2026, 7:32 AM EDT

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

Locations

There is information in this trial unavailable to the public. Use the button below to request access.

Request Information

Primary Investigator

Name
Tinghua Yu
Affiliation
Birkbeck, University of London
Contact Primary Investigator

Other Primary Investigator(s)

Additional Trial Information

Status
In development
Start date
2026-07-09
End date
2026-07-28
Keywords
Behavior, Gender
Additional Keywords
JEL code(s)
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
This study examines how people assess job candidates in a recruitment-style decision-making task. Participants will review a candidate’s response to a logical reasoning problem and evaluate the candidate’s performance and underlying ability. The study varies selected features of the recruitment context and the background information shown to participants before they make their evaluations. The findings will provide evidence on how information about the prior evaluation process affects judgments of candidate quality.


External Link(s)

Registration Citation

Citation
Yu, Tinghua. 2026. "Inference Effects in Discrimination Measurement." AEA RCT Registry. July 13. https://doi.org/10.1257/rct.19110-1.0
Sponsors & Partners
Experimental Details

Interventions

Intervention(s)
Participants take part in an online recruitment-style evaluation task. They are asked to assess a candidate who has completed a logical reasoning problem. Before making their assessment, participants are randomly assigned to receive one of several versions of the evaluation context. These versions vary the background information provided about the recruitment process and selected features of the candidate-evaluation scenario. Participants then provide ratings of the candidate’s performance based on the information shown to them.
Intervention Start Date
2026-07-09
Intervention End Date
2026-07-24

Primary Outcomes

Primary Outcomes (end points)
The first primary outcome is the participant’s estimate of the probability that the candidate’s answer to the logical reasoning problem is correct, after observing the candidate’s answer but before observing the candidate’s reasoning.
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
The second primary outcome is the participant’s estimate of the probability that the candidate’s answer is correct after observing both the candidate’s answer and the candidate’s reasoning.
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
This study is an online survey experiment in which participants complete a recruitment-style evaluation task. Participants are asked to review information about a job candidate who has completed a logical reasoning problem. They are randomly assigned to different versions of the evaluation context and candidate-evaluation scenario.

Participants first read background information about the recruitment setting. They then see a candidate profile and the candidate’s answer to a logical reasoning problem, and provide an initial assessment of the candidate’s performance. Participants subsequently receive additional information about the candidate’s reasoning and provide a second assessment. The study is designed to examine how information about the evaluation process affects judgments of candidate quality when participants observe different amounts of performance information.
Experimental Design Details
Not available
Randomization Method
Randomization is implemented in Qualtrics using the platform’s built-in randomizer. Participants are individually randomized to one of the experimental conditions with equal assignment probabilities across conditions.
Randomization Unit
The unit of randomization is the individual participant. All experimental conditions are assigned at the individual level, and there is no cluster-level randomization.
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
1,280 individual participants
Sample size: planned number of observations
1,280 individual participants
Sample size (or number of clusters) by treatment arms
The study has eight experimental arms, with approximately 160 individual participants assigned to each arm:

160 participants: Control information × human investment-banking evaluator × female-sounding candidate name

160 participants: Control information × human investment-banking evaluator × male-sounding candidate name

160 participants: Control information × algorithmic evaluator × female-sounding candidate name

160 participants: Control information × algorithmic evaluator × male-sounding candidate name

160 participants: Information treatment × human investment-banking evaluator × female-sounding candidate name

160 participants: Information treatment × human investment-banking evaluator × male-sounding candidate name

160 participants: Information treatment × algorithmic evaluator × female-sounding candidate name

160 participants: Information treatment × algorithmic evaluator × male-sounding candidate name
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
The planned sample size is 1,280 individual participants, with approximately 160 participants in each of the eight experimental cells. The outcome variables are measured as probability estimates on a 0–100 scale, so the unit of the minimum detectable effect is percentage points on this scale. Assuming 80% power, a two-sided 5% significance level, no clustering, equal cell sizes, and an outcome standard deviation of 20 points, the minimum detectable effect for a simple comparison between two equally sized experimental cells is approximately 0.31 standard deviations, or 6.2 percentage points. Because the main estimands involve interaction effects, the relevant minimum detectable effects are larger. Under the same assumptions, the approximate minimum detectable effect for the 2 × 2 candidate-gender-by-evaluator interaction within one information condition is approximately 0.44 standard deviations, or 8.8 percentage points. The approximate minimum detectable effect for the full information-by-candidate-gender-by-evaluator interaction is approximately 0.63 standard deviations, or 12.6 percentage points. These calculations are benchmarks; realized power will depend on observed outcome variance, realized cell balance, and the final analysis specification.
Supporting Documents and Materials

There is information in this trial unavailable to the public. Use the button below to request access.

Request Information
IRB

Institutional Review Boards (IRBs)

IRB Name
Faculty of Business and Law, Birkbeck, University of London
IRB Approval Date
2026-04-23
IRB Approval Number
N/A
Analysis Plan

There is information in this trial unavailable to the public. Use the button below to request access.

Request Information