The Age Factor II - Exploring How Age and Performance Information Influences Hiring Decisions

Last registered on November 26, 2025

Pre-Trial

Trial Information

General Information

Title
The Age Factor II - Exploring How Age and Performance Information Influences Hiring Decisions
RCT ID
AEARCTR-0017091
Initial registration date
November 24, 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
November 26, 2025, 7:02 AM EST

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

Locations

Primary Investigator

Affiliation
Helmut-Schmidt-Universität

Other Primary Investigator(s)

PI Affiliation
Aarhus University

Additional Trial Information

Status
In development
Start date
2025-12-01
End date
2026-02-01
Secondary IDs
Prior work
This trial is based on or builds upon one or more prior RCTs.
Abstract
In this project, we experimentally examine whether individuals in leadership positions consider the age of applicants when making hiring decisions if they only have statistical information about the past performance of a (mixed-age) team of applicants, but the individual contributions of the applicants are not directly observable. Second, we examine whether managers revise their hiring decisions when they learn about the actual performance of the applicants. Does this lead to taste-based discrimination, which results in material disadvantages for the manager? Is there also an interaction between the age of the manager and the influence of the age of the applicants on the hiring decision? As collaborative work structures involving employees of different generations become increasingly important in organizations, understanding fair, i.e., non-discriminatory, performance evaluation is critical to equal opportunity and the promotion of inclusive and efficient workplaces. There is little evidence on how statistical information about age-specific performance differences affects hiring decisions, underscoring the need for this research to fill a critical knowledge gap.
External Link(s)

Registration Citation

Citation
Maaser, Nicola F. and Stefan Traub. 2025. "The Age Factor II - Exploring How Age and Performance Information Influences Hiring Decisions." AEA RCT Registry. November 26. https://doi.org/10.1257/rct.17091-1.0
Sponsors & Partners

Sponsors

Experimental Details

Interventions

Intervention(s)
Participants are recruited as “managers” to hire one of two members of "worker" teams who have to solve the Word Encryption Task and who are the only candidates for an open position.

There are four information treatments (see also Experimental Design below):

In T2 AgeInfo, the managers receive information about the joint distribution of the performance F(X) and about the fact that the two workers come from different age groups. The age group to which the two workers belong is not revealed. The hiring decision only affects the managers' own payouts.
In T3 JointInfo, the managers receive information about the joint distribution of the performance F(X) and about the fact that the two workers come from different age groups. The age group to which the two workers belong is revealed. The hiring decision only affects the managers' own payouts.
In T4a SepInfo, the managers receive information about the separate distributions of the performance F(X)_young and F(X)_old. The age group to which the two workers belong is revealed. The hiring decision only affects the managers' own payouts.
In T4b SepInfo, the managers receive information about the separate distributions of the performance F(X)_young and F(X)_old. The age group to which the two workers belong is revealed. The hiring decision affects the managers' own payouts. In addition, managers know that their hiring decisions also have financial implications for workers.
Intervention (Hidden)
Intervention Start Date
2025-12-01
Intervention End Date
2026-02-01

Primary Outcomes

Primary Outcomes (end points)
The key outcome variable is the average probability that the manager will hire the older team member, p_old.

We know from the auxiliary experiment ("The Age Factor I" AEARCTR-0014972) that older workers perform worse on average than younger ones (however, the distribution of performance overlaps). It therefore maximizes expected profits to discriminate statistically against older workers.

From Experiment 1 ("The Age Factor I" AEARCTR-0014972), in which managers' beliefs were elicited, we know that managers in T3 JointInfo and T4 SepInfo statistically discriminate against older employees. However, managers in T3 still overestimate the average performance of older employees, while in T4, average performance is estimated almost exactly correctly.

In this experiment, we now examine how the beliefs in the different information treatments translate into actual behavior. Because T1 NoInfo offers no gain compared to T2 AgeInfo with respect to the hiring decision we are interested in, we do not conduct a hiring analogue of T1. Instead, we introduce a treatment T4b SepInfo, in which the manager's hiring decision influences not only his own payout but also the worker's payout, in order to control for social preferences.

In T2 AgeInfo, the managers have information that one of the two workers is older, but they do not know which. Therefore, on average, p_old should be 50% (H2).
In T3 JointInfo, the managers have information that one of the two workers is older, but they only know the joint distribution of the performance. So here, beliefs about F(X)_old and F(X)_young play a role and due to biased beliefs, we hypothesize that on average p_old=0% (belief based statistical discrimination). (H3).
Finally, in T4a and T4b SepInfo, false beliefs about age-related performance can be corrected. However, since the average performance of older worker is worse in the Word Encryption Task, this leads to perfect stochastic discrimination, p_old=0%. (H4a) In treatment 4b, social preferences could play a role, as workers' payouts are also affected by the hiring decision (H4b).

Thus, our working hypothesis in comparing the treatments is that as soon as the age of the applicants is identifiable, their chances of being hired drops to zero (or close to zero). Furthermore, in this case (comparison of JointInfo vs. SepInfo), it does not matter whether the managers know that older workers perform actually worse on average or only believe this to be the case.

At the within-subjects level, the observation over 10 rounds also tests whether the strength of hiring discrimination correlates with the level of overall performance, i.e., whether the older worker of stronger teams is systematically attributed an even smaller share of success and therefore has a lower hiring probability.

As explained in more detail below in the experimental design, after completing the 10 rounds, managers are presented with two of the hiring decisions again. The actual performance of both workers in a team is revealed, with the older worker performing better in one team and the younger worker performing better in the other. Managers can now revise their original hiring decision or stick with it. Four possible scenarios are conceivable.
1) The manager always hires only the better candidate. => No taste based discrimination (profit-maximization and meritocratic motive go hand in hand).
2) The manager always hires only the younger candidate. => Taste based discrimination against older workers (costly for the manager in the situation where younger worker performed worse).
3) The manager always hires only the older candidate. => Taste based discrimination against younger workers (costly for the manager in the situation where olderr worker performed worse).
4) The manager always hires only the worker who performed worse. => May indicate social preferences, anti-meritocratic attitudes, etc.
Our exploratory hypothesis is that all four outcomes occur, but that overall there is little taste-based discrimination. Comparing T4a and T4b, we hypothesize p_old|T4a < p_old |T4b.

Correlational Hypothesis:
We also hypothesize that both stochstical and taste based discrimination against older workers is more pronounced the younger the manager is.
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
This RCT uses the "performance data set" of a previous RCT (AEARCTR-0014972)

In order to collect the "performance data set", we recruited participants in age group 22-29 (young) and participants in age group 52-59 (old) through Prolific UK. Each age group contained about 50% men and 50% women. The participants had to solve as many “Word Encryption Tasks” as possible/as they choose to within seven minutes. Both age groups received the same piece rate W and a fixed fee F. Since the task involved letters, we recruited only participants who report not to have any literacy problems or language-related disorders.
Thus, we created two performance data sets F(X)_young and F(X)_old.

Experimental Design of this RCT

Subjects (35 years to 46 years old) are recruited at Prolific UK and assigned the role of “managers”. In 10 rounds, the managers are each given information about the overall performance y=x_young+x_old of a “worker” team of two workers, one randomly selected from F(X)_young and one from F(X)_old. In order to establish comparability between the treatments and subjects, 4 sequences of 10 teams are drawn at random before the start of the experiment and presented to a quarter of the managers in each case.

The managers' task is to hire one of the two workers, who are the only candidates for an open position.
There are four between-subjects treatments:
(Note that T1 NoInfo of the previous RCT (AEARCTR-0014972) offers no information gain with respect to our research question compared to T2 AgeInfo and is therefore omitted.)
In T2 AgeInfo, the managers receive information about the joint distribution of the performance F(X) and about the fact that the two workers come from different age groups. The age group to which the two workers belong is not revealed. The hiring decision only affects the managers' own payouts.
In T3 JointInfo, the managers receive information about the joint distribution of the performance F(X) and about the fact that the two workers come from different age groups. The age group to which the two workers belong is revealed. The hiring decision only affects the managers' own payouts.
In T4a SepInfo, the managers receive information about the separate distributions of the performance F(X)_young and F(X)_old. The age group to which the two workers belong is revealed. The hiring decision only affects the managers' own payouts.
In T4b SepInfo, the managers receive information about the separate distributions of the performance F(X)_young and F(X)_old. The age group to which the two workers belong is revealed. The hiring decision affects the managers' own payouts. In addition, managers know that their hiring decisions also have financial implications for workers.

Each manager is presented with 10 team results from teams consisting of one younger and one older worker. The managers must choose one worker from each team. After completing the 10 rounds, managers are presented with two of the hiring decisions again. The actual performance of both workers is revealed, with the older worker performing better in one team and the younger worker performing better in the other. Managers can now revise their original hiring decision or stick with it.

The manager is then paid according to the actual performance of the 10 workers hired. In treatment 4b, the hired worker receives a bonus.
Experimental Design Details
Randomization Method
Randomization by a computer.
Randomization Unit
Individual randomization into a treatment; additionally individiual randomization into one of four sequences of 10 team performances (and two decisions with the possibility of revision). The arrangement of decision-relevant information on the screen is randomized and balanced (left/right).
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
None.
Sample size: planned number of observations
800 click-workers on Prolific UK
Sample size (or number of clusters) by treatment arms
We recruit 200 participants per treatment (800 in total). The sample will contain about 50% men and 50% women.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
IRB

Institutional Review Boards (IRBs)

IRB Name
German Association for Experimental Economic Research e.V
IRB Approval Date
2025-10-23
IRB Approval Number
pFhtgCas

Post-Trial

Post Trial Information

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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