Ageism Unveiled: Understanding Discrimination Against the Elderly

Last registered on April 10, 2025

Pre-Trial

Trial Information

General Information

Title
Ageism Unveiled: Understanding Discrimination Against the Elderly
RCT ID
AEARCTR-0015742
Initial registration date
April 04, 2025

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
April 10, 2025, 7:26 AM EDT

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

Locations

Region

Primary Investigator

Affiliation
University of Zurich

Other Primary Investigator(s)

Additional Trial Information

Status
In development
Start date
2025-04-07
End date
2025-04-21
Secondary IDs
Prior work
This trial is based on or builds upon one or more prior RCTs.
Abstract
This is a follow-up experiment to the previously registered study “Ageism Unveiled: Understanding Discrimination Against the Elderly.” The previous experiment investigated the presence of age discrimination in different types of labor markets: those requiring fluid intelligence and those relying on crystallized intelligence. The current experiment examines whether workers anticipate age discrimination and how such anticipation affects their labor force participation decisions. Additionally, the experiment explores heterogeneity in these expectations across age, gender, and actual ability in the tasks.
External Link(s)

Registration Citation

Citation
Feng, Jiajing. 2025. "Ageism Unveiled: Understanding Discrimination Against the Elderly." AEA RCT Registry. April 10. https://doi.org/10.1257/rct.15742-1.0
Experimental Details

Interventions

Intervention(s)
There are two types of random assignments in this experiment:

Task assignment across subjects: Participants will be randomly assigned to complete one of two tasks. Half of the participants will complete the Raven's Matrices task, and the other half will complete the vocabulary task.

Competing candidate profiles within subjects: Each participant will be shown four different competing candidate profiles: one identical to their own profile, one differing in gender, one differing in age, and one differing in both age and gender. The order in which these profiles are presented will be randomized.
Intervention (Hidden)
Intervention Start Date
2025-04-07
Intervention End Date
2025-04-21

Primary Outcomes

Primary Outcomes (end points)
1. Belief about anticipation of being discriminated (they need to estimate how many employers out of 100 would hire them and how many would hire the competing candidate to do the task).
2. Willingness to participate in the labor market through a multiple price list.
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
This experiment contains two sections. In the first section, the workers complete a task: either the raven's matrices task, which measures fluid intelligence, or the vocabulary task, which measures crystalized intelligence. The task will be randomized across subjects.

The second section elicits anticipation about discrimination and willingness to participate in the labor market. The workers will compete with four other candidates in a labor market to get hired for the task. Each worker will see 4 cases, and in each case, they will compete with a different candidate (varying age and gender). For each case, they will complete two tasks:

Task 1:
Workers will be asked to predict how typical employers (average Americans aged 36 to 55) made hiring decisions when choosing between two worker profiles. Specifically, they need to estimate how many employers out of 100 would hire them and how many would hire the competing candidate to do the task. If their guess is within 5 percentage points of the actual result (based on decisions made in previous sessions with the same profiles), they will earn a bonus of $0.25.

Task 2:
In this task, workers need to choose between fixed payment or entering the labor market and compete to be hired by employers. If they enter the labor market and get hired, they earn a $1 bonus. If they enter the labor market but are not hired, they receive no bonus for this section.
They will make this choice 11 times per case, with the fixed payment amount varying each time. At the end of the experiment, one case and one of the 11 choices will be randomly selected. Their decision in that round will determine their payoff. If they chose the fixed payment, they receive the corresponding amount. If they choose to enter the labor market, the hiring outcome will be based on average decisions from previous sessions: if employers on average preferred hiring someone with their profile over the competitor’s profile, they will be hired; if employers on average preferred hiring someone with the competing profile, they will not be hired.

At the end of the experiment, workers' risk preferences and willingness to compete will be elicited.
Experimental Design Details
Randomization Method
There are two types of random assignments in this experiment:

Task assignment across subjects: Participants will be randomly assigned to complete one of two tasks. Half of the participants will complete the Raven's Matrices task, and the other half will complete the vocabulary task.

Competing candidate profiles within subjects: Each participant will be shown four different competing candidate profiles: one identical to their own profile, one differing in gender, one differing in age, and one differing in both age and gender. The order in which these profiles are presented will be randomized.

The task will be randomized across subjects. The randomization will be done through Prolific. The competing workers' profiles will be randomized within subjects. This will be done through the randomization function (loop and merge) on Qualtrics.
Randomization Unit
The task will be randomized across subjects. The randomization will be done through Prolific. The competing workers' profiles will be randomized within subjects. This will be done through the randomization function (loop and merge) on Qualtrics.
Was the treatment clustered?
Yes

Experiment Characteristics

Sample size: planned number of clusters
For this experiment, I will hire 300 older subjects (aged 56-65) and 300 younger subjects (aged 26-35).
Sample size: planned number of observations
For this experiment, I will hire 300 older subjects (aged 56-65) and 300 younger subjects (aged 26-35). Half of each group will complete the Raven's matrices task, the other half will complete the vocabulary task. Competing candidate profiles within subjects: Each participant will be shown four different competing candidate profiles varying age and gender. The total number of observations (in long format) would be 600*4 = 2400
Sample size (or number of clusters) by treatment arms
For this experiment, I will hire 300 older subjects (aged 56-65) and 300 younger subjects (aged 26-35). Half of each group will complete the Raven's matrices task, the other half will complete the vocabulary task.

Competing candidate profiles within subjects: Each participant will be shown four different competing candidate profiles: one identical to their own profile, one differing in gender, one differing in age, and one differing in both age and gender. The order in which these profiles are presented will be randomized.

In each task, there will be 300 subjects, 300*4=1200 observations.
For each competing profile, there will be 600 subjects (summing up two tasks) making the labor market participation decision.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
IRB

Institutional Review Boards (IRBs)

IRB Name
Human Subjects Committee of the Faculty of Economics, Business Administration, and Information Technology, University of Zurich
IRB Approval Date
2024-09-16
IRB Approval Number
OEC IRB # 2024-079

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