CEO Gender and Beliefs about Firm Performance: an experimental approach

Last registered on January 07, 2025
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Pre-Trial

Trial Information

General Information

Title
CEO Gender and Beliefs about Firm Performance: an experimental approach
RCT ID
AEARCTR-0014832
Initial registration date
November 20, 2024

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
December 02, 2024, 11:02 AM EST

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

Last updated
January 07, 2025, 5:01 AM EST

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

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Primary Investigator

Name
Beatrice Ferrario
Affiliation
Harvard University
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Other Primary Investigator(s)

PI Name
Marcela Mello
PI Affiliation
Harvard Business School
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Additional Trial Information

Status
In development
Start date
2024-12-01
End date
2025-01-31
Keywords
Firms & Productivity, Gender
Additional Keywords
JEL code(s)
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
Does CEO gender influence how we perceive firm performance? While previous literature has explored the wealth of observational data on US public companies to document a correlation between beliefs about firm performance and CEO gender, establishing the causal link between these variables remains a challenge. In this paper, we take an experimental approach to address this issue. We design an experiment where the assignment of CEO gender is randomized and measure the extent to which gender affects participants’ forecasts of firm profits. In the experiment, participants predict the profits of three real US public companies, one of which has the gender of its CEO (disclosed via a picture) altered. For each company, participants first receive real data about its past financial performance, along with experts’ forecasts for future profits and form predictions for profits in the next two quarters. Then, they receive information about the actual profit for the first quarter and revise their prediction for the second quarter. The design includes a “good news” (profit realization higher than experts’ forecasts) versus “bad news” (profit realization lower than experts’ forecasts) treatment that allows us to test whether the effect of CEO gender on beliefs is intermediated through the sign of the news received. Finally, the study also includes questions aimed at determining the mechanism behind the dependency of beliefs about firm performance on CEO gender – e.g. do participants think that female CEOs are less skilled than their male peers?
External Link(s)

Registration Citation

Citation
Ferrario, Beatrice and Marcela Mello. 2025. "CEO Gender and Beliefs about Firm Performance: an experimental approach." AEA RCT Registry. January 07. https://doi.org/10.1257/rct.14832-2.0
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Experimental Details

Interventions

Intervention(s)
Intervention Start Date
2024-12-01
Intervention End Date
2025-01-31

Primary Outcomes

Primary Outcomes (end points)
The forecast (errors) of firms' profits
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
Participants will be asked to review information about and make predictions for the profits of three real US companies. For two out of the three companies, all the information provided will be true. But for one randomly chosen company out of the three, there’ll be a fictional component. This fictional component will be the identity of the CEO, which will be female in case the real CEO is male, or vice-versa. For each participant, the three companies will be randomly selected out of a population of 48 company-quarter pairs – 24 whose actual CEO in a given time period is a woman and 24 whose CEO is a male.

For each of the three companies, there will be six steps that participants will be asked to complete:
1. Read information about the company and the CEO.
2. Observe a graph with past profits and two expert forecasts for profits in the next quarter.
3. Make predictions for profits in the next two quarters.
4. Build a confidence interval around the prediction for the first quarter.
5. Learn the actual profit in the first quarter. Make new prediction for the second quarter.
6. Answer some questions regarding one’s personal opinion.
For each company there is a “good news” versus “bad news” treatment which will be randomly assigned. Under the “good news” treatment, the participant will observe two expert forecasts in step 2 that are both below the actual profit in that period. Hence, in step 5, this participant will receive “good news” because the actual profits will be above what experts expected. Under the “bad news” treatment, the two expert forecasts will be both above the actual profit in that period so that there will be “bad news” in step 5. This allows me to understand if the gender-treatment effects depend on whether good or bad news is observed. Note that under both treatments – good news or bad news – participants are observing true information that has been curated to that specific case. In the database of analysts’ forecasts, the companies in this experiment are among those that have at least four analyst forecasts: at least two forecasts above and at least two forecasts below the realization for a given quarter. After completing the 6 steps for each of the three companies, participants will be asked to answer standard demographic and financial literacy questions.
Experimental Design Details
Not available
Randomization Method
Randomization coded in the software (i.e., done by a computer).
Randomization Unit
We randomize at the individual level
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
Around 200 individuals
Sample size: planned number of observations
Around 200 individuals
Sample size (or number of clusters) by treatment arms
Each individual sees three firms. We manipulate the gender of the CEO that the individual observes for only one out of the three firms. Hence, depending on the firm considered, one individual can either be in the treatment or control group.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Supporting Documents and Materials
IRB

Institutional Review Boards (IRBs)

IRB Name
Harvard University Area
IRB Approval Date
2024-07-25
IRB Approval Number
IRB24-0851
Analysis Plan