The Age Factor - Exploring Credit Allocation and Performance in Age-diverse Teams

Last registered on December 20, 2024

Pre-Trial

Trial Information

General Information

Title
The Age Factor - Exploring Credit Allocation and Performance in Age-diverse Teams
RCT ID
AEARCTR-0014972
Initial registration date
December 16, 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 20, 2024, 1:43 PM 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

Affiliation
Helmut-Schmidt-Universität

Other Primary Investigator(s)

PI Affiliation
Aarhus University

Additional Trial Information

Status
In development
Start date
2024-12-29
End date
2025-02-28
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
In this project, we investigate whether those in leadership roles consider the age of workers when assigning credit for group efforts, particularly when individual contributions are unobservable? How does the age of a supervisor or boss, and that of co-workers shape individual incentives among team members to contribute to a group effort? As collaborative work structures involving workers from different generations gain prominence in organizations, understanding fair credit allocation becomes pivotal for fostering inclusive and efficient workplaces. Evidence on how age heterogeneity in work teams impacts credit attribution and productivity is limited, emphasizing the need for this research to address a critical gap in knowledge.
External Link(s)

Registration Citation

Citation
Maaser , Nicola F. and Stefan Traub. 2024. "The Age Factor - Exploring Credit Allocation and Performance in Age-diverse Teams." AEA RCT Registry. December 20. https://doi.org/10.1257/rct.14972-1.0
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Sponsors

Experimental Details

Interventions

Intervention(s)
1. Trial: Participants are recruited as “managers” to assess the relative productivity of the older team member in "worker" teams of two who have to solve the Word Encryption Task.

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

In NoInfo, the managers receive information about the joint distribution of the performance F(X), but not about the fact that the two workers come from different age groups.
In 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.
In SepInfo, the managers receive information about the separate distributions of the performance F(X)_young and F(X)_old.

2. Trial
Participants are recruited as “workers” and have to solve the "Word Encryption Task" in a team, given information about the "managers" beliefs and different financial incentives.

There are five between-subjects treatments, a control treatment and 4 discrimination treatments that combine Bonus (fair, unfair) and discrimination based on performance information (AgeInfo, SepInfo). Note: Due to budget constraints and restrictions of the subject pool in the targeted older age group (52-59), the two Sep-Info treatments will only be implemented if the statistical power in terms of the minimum detectable effect size (MDE) in the AgeInfo treatments is sufficient.
Control: Each worker receives F’.
Fair-AgeInfo (FA): Each worker receives F’. The older worker is shown the managers’ expectations about the performance of the old workers from AgeInfo in the first Trial.
Fair-SepInfo (FS): Each worker receives F’. The older worker is shown the managers’ expectations about the performance of the old workers from SepInfo in the first Trial.
Unfair-AgeInfo (UA): The older worker is shown the managers’ expectations about the performance of the old workers from AgeInfo in the first Trial and receives only s_old|AgeInfo*2F’, whereas the younger worker receives (1-s_old|AgeInfo)*2F’.
Unfair-SepInfo (US): The older worker is shown the managers’ expectations about the performance of the old workers from SepInfo and the true s* in the first Trial and receives only s_old|SepInfo*2F’, whereas the younger worker receives (1-s_old|SepInfo)*2F’.

Intervention Start Date
2024-12-29
Intervention End Date
2025-02-28

Primary Outcomes

Primary Outcomes (end points)
1. Trial:
The key outcome variable is the managers' belief about the performance of the older team member s_old.
We assume that the managers have distorted beliefs about the performance of the two age groups and therefore discriminate against older workers (even though this reduces their own payoff). In NoInfo, there can be no age discrimination because the age information is not available to the managers. Therefore, on average, the estimated proportion of s_old should be 50% (H1); in JointInfo, the managers have information that one of the two works 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 we hypothesize that on average s_old<50% is estimated (H2); finally, in SepInfo, false beliefs about age-related performance can be corrected. If s_old is still underestimated in relation to the old workers’ true average performance s*, it is pure taste-based discrimination, which is costly to the manager. Formally, we have
H1 (neutrality): s_old|NoInfo=0.5
H2 (belief-based discrimination): s_old|AgeInfo<0.5
H3 (taste-based discrimination): s_old|SepInfo<s*
With regard to the difference between AgeInfo and SepInfo, we expect that beliefs-based discrimination is stronger than taste-based discrimination.
H4 (belief- vs. taste-based discrimination): s_old|AgeInfo/0.5 < s_old|SepInfo/s*
At the within-subjects level, the observation over 10 rounds also tests whether the strength of discrimination or misbelief 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. Formally, we expect
H5: d (s*-s_old)/dy>0.
Correlational Hypothesis:
We also hypothesize that bias against senior workers is more pronounced the younger the manager is. Formally, we expect that
H6: d(s*-s_old)/da<0, where a is the managers’ age.

2. Trial
The key outcome variable is the workers' performance in the Word Encryption Task s_old.
Hypotheses: We hypothesize that discrimination has a negative effect on the performance of older workers, with the discrimination effect being stronger for the unfair bonus and taste-based discrimination respectively.
H1 (bonus): s_old|C>s_old|U>s_old|F
H2 (info): s_old|C>s_old|A>s_old|S
H3 (interaction): s_old|FS<s_old|FA and s_old|US

Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
none
Secondary Outcomes (explanation)
none

Experimental Design

Experimental Design
Creating the performance data set

We recruit participants in age group 22-29 (young) and participants in age group 52-59 (old) through Prolific UK. Each age group contains about 50% men and 50% women. The participants have to solve as many “Word Encryption Tasks” as possible within seven minutes. Both age groups receive the same piece rate W and a fixed fee F. Since the task involves letters, we recruit only participants who report not to have any literacy problems or language-related disorders.

Thus, we create two performance data sets F(X)_young and F(X)_old. Optional after visual inspection of the data: To minimize the effects of extreme outliers in relation to the distribution, we trim the upper and lower 5% of observations by replacing the respective data with the values of the 5th and 95th percentile.

1. Trial Phase:
Subjects (>35 years) 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 estimate the older worker's relative contribution to the team’s overall performance: s_old=x_old/y.

There are three between-subjects treatments:
In NoInfo, the managers receive information about the joint distribution of the performance F(X), but not about the fact that the two workers come from different age groups.

In 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.

In SepInfo, the managers receive information about the separate distributions of the performance F(X)_young and F(X)_old.
The more accurately the managers estimate the proportion s_old on average, the higher their payoff.

2. Trial Phase:
Young (aged 22-29) and old (planned: aged 52-59) people are recruited as “workers” through Prolific UK. They solve as many “Word Encryption Tasks” as they can and want in a timeframe of seven minutes. Both know that they work in a team that consists of one old and one young worker. Both age groups receive the same piece rate W and the team as a whole receives, depending on the treatment, a bonus corresponding to two times a fixed fee F’.

There are five between-subjects treatments, a control treatment and 4 discrimination treatments that combine Bonus (fair, unfair) and discrimination based on performance information (AgeInfo, SepInfo). Note: Due to budget constraints and restrictions of the subject pool in the targeted older age group (52-59), the two Sep-Info treatments will only be implemented if the statistical power in terms of the minimum detectable effect size (MDE) in the AgeInfo treatments is sufficient.

Control: Each worker receives F’.

Fair-AgeInfo (FA): Each worker receives F’. The older worker is shown the managers’ expectations about the performance of the old workers from AgeInfo in the first Trial.

Fair-SepInfo (FS): Each worker receives F’. The older worker is shown the managers’ expectations about the performance of the old workers from SepInfo in the first Trial.
Unfair-AgeInfo (UA): The older worker is shown the managers’ expectations about the performance of the old workers from AgeInfo in the first Trial and receives only s_old|AgeInfo*2F’, whereas the younger worker receives (1-s_old|AgeInfo)*2F’.

Unfair-SepInfo (US): The older worker is shown the managers’ expectations about the performance of the old workers from SepInfo and the true s* in the first Trial and receives only s_old|SepInfo*2F’, whereas the younger worker receives (1-s_old|SepInfo)*2F’.
Experimental Design Details
Not available
Randomization Method
Randomization by a computer.
Randomization Unit
Individual randomization into a treatment; in trial 1 additionally individiual randomization into one of four sequences of 10 team performances.
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
no clustering
Sample size: planned number of observations
1200 participants (from different age groups, see below)
Sample size (or number of clusters) by treatment arms
For Trial Phase 1, we recruit 200 participants per treatment (600 in total). The sample will contain about 50% men and 50% women.
For Trial Phase 2, we recruit 120 participants per treatment (600 in total), of which 60 are from age group 22-29 (young) and 60 participants from age group old (planned: 52-59), respectively. The sample will contain about 50% men and 50% women. As noted above, depending on the MDE in the AgeInfo treatments, the number of participants in the AgeInfo treatments may be increased to 200 and the SepInfo treatments will not be carried out.
All samples will be recruited through Prolific UK.
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
2024-11-05
IRB Approval Number
jRxRozp9