Discrimination against older workers in training and hiring

Last registered on February 20, 2025
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Pre-Trial

Trial Information

General Information

Title
Discrimination against older workers in training and hiring
RCT ID
AEARCTR-0015146
Initial registration date
February 17, 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
February 20, 2025, 5:31 AM EST

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
Arne Uhlendorff
Affiliation
CREST
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Other Primary Investigator(s)

PI Name
Pierre Cahuc
PI Affiliation
Science Po
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PI Name
Jérémy Hervelin
PI Affiliation
ZEW Mannheim
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Additional Trial Information

Status
In development
Start date
2025-01-12
End date
2026-03-31
Keywords
Labor
Additional Keywords
JEL code(s)
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
Globalization and technological change are rapidly transforming the workplace, generating demand for new skills while rendering other skills obsolete. In this context, having access to training is essential for workers. This study seeks to analyze the influence of age on the likelihood of being accepted into vocational training programs and the impact of this training on hiring probabilities. We hypothesize that age-based discrimination may exist, supported by existing evidence of hiring biases against older workers. Studies reveal recruiters often have negative perceptions of older workers’ cognitive abilities, social and technical skills, flexibility, and salary expectations, leading to an overall negative view of their productivity.

To test this, we will send fictitious applications to vocational programs. The applications will vary by age of the applicant and will target common low-skilled training programs. In a second step, we will send unsolicited job applications from profiles matching those who applied for the training. Some of these fictitious applicants will have completed the training programs, while others will not. This will help us assess how age affects training outcomes and explore potential reasons why older applicants may face exclusion from vocational programs.
External Link(s)

Registration Citation

Citation
Cahuc, Pierre, Jérémy Hervelin and Arne Uhlendorff. 2025. "Discrimination against older workers in training and hiring." AEA RCT Registry. February 20. https://doi.org/10.1257/rct.15146-1.0
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Experimental Details

Interventions

Intervention(s)
The study uses a correspondence test approach where fictitious CVs and cover letters are sent to vocational training programs and employers in the corresponding occupational fields. These applications will be similar except for the age of the applicant in case of the applications to vocational programs. The unsolicited applications to potential employers will be similar except for the age and previous training of the applicants.
Intervention Start Date
2025-02-24
Intervention End Date
2025-12-31

Primary Outcomes

Primary Outcomes (end points)
Our key outcomes are call back rates of the training providers and of the employers.
Primary Outcomes (explanation)
A reply from a training provider or employer stating that the application was not selected is classified as a negative callback, like the absence of any callback.

We will use two definitions for a positive callback. In the first definition, we consider any reply by phone or email which indicates interest in the application as a positive outcome. This includes a request for a call back from the applicant, requests on the applicant’s background, availability and motivation, an invitation for an interview or meeting, and the acceptance in the program for training programs or a job proposal for job applications.

In the second and more narrow definition, we only consider invitations for an interview or meeting and an acceptance in the program or a job proposal as a positive outcome.

Further, we will investigate effect heterogeneity depending on the occupation specific local labor market tightness.

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
We will send out fictitious CVs and cover letters to vocational training programs and employers. These applications relate to six low-skilled trades: care assistant, secretary, forklift operator, employees in restaurants, sales clerk and truck driver. We will use six age categories (35, 40, 45, 50, 55, and 60). For each occupation, we will create two types of profiles. One will represent a rather highly educated profile (at least high school) and one a rather low educated profile (below high school diploma). By observing average differences in the rate of positive responses according to the variation introduced, we can identify the causal effect of this variable on the response rate.

In the first part (applications to training providers), we send two applications to the same training program. The two applicants differ by age. Further, we make sure that the two applications also differ in other dimensions which are not related to the age of the applicants (e.g. name, template of the CV, content of the email message), to avoid potential detection by the training provider. In the second part (applications to potential employers), we send one or two applications to each potential employer. The applications differ by age and by the fact whether the applicant has completed a training. Again, we make sure that the applications also differ in other dimensions which are not related to the age of the applicants (same dimensions as in the applications to training providers), to avoid potential detection by the employer.

Our design allows us to measure (i) the effect of the age of the applicant on the probability of a positive response from the training center and (ii) the age-specific effect of vocational training on the probability of a candidate receiving a positive response to his or her application.
Experimental Design Details
Not available
Randomization Method
randomization done in office by a computer
Randomization Unit
individual application
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
5400 applications to training programs
10800 applications to potential employers
Sample size: planned number of observations
5400 applications to training programs 10800 applications to potential employers
Sample size (or number of clusters) by treatment arms
For the training program: 900 observations per age group
For the job applications: 900 with and 900 CVs without a certified training per age group.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
When we compare the call back rates between two age groups, the MDE for applications sent to training programs is 5.3 percentage points for a baseline outcome of 20%. When we compare job applications within the same age group with and without a training certificate, the MDE is 3.4 percentage points assuming a baseline outcome for the callback rate of 7%.
Supporting Documents and Materials
IRB

Institutional Review Boards (IRBs)

IRB Name
Research Ethics Committee of the Paris Institute of Political Studies (CDR)
IRB Approval Date
2025-02-13
IRB Approval Number
2025-059
Analysis Plan