Perceived Returns to Rest

Last registered on June 19, 2024


Trial Information

General Information

Perceived Returns to Rest
Initial registration date
April 18, 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
April 25, 2024, 11:59 AM EDT

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

Last updated
June 19, 2024, 6:23 AM EDT

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


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Primary Investigator

University of Zurich

Other Primary Investigator(s)

PI Affiliation
Massachusetts Institute of Technology

Additional Trial Information

In development
Start date
End date
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Notions of rest and self-care – be it meditation, napping, or a walk in nature – have become increasingly popular, and recent economic research suggests these activities can improve not only mental well-being but also performance overall. Nevertheless, uptake and engagement of these kinds of activities remain low – especially in stressful labor market situations. In the context of online labor markets, we explore workers’ beliefs on and decisions to invest in rest. We document the productivity effects of mandated rest periods during a strenuous work task. Furthermore, we look at who chooses to invest in rest when given the option and ask whether “break-skippers” and “break-takers” are maximizing their earnings.
External Link(s)

Registration Citation

Schubert, Alexandra and Jenny Wang. 2024. "Perceived Returns to Rest." AEA RCT Registry. June 19.
Experimental Details


Individuals will randomly be assigned to a break during the work period. During the break, participants listen to an audio script that is meant to help refocus.
Intervention Start Date
Intervention End Date

Primary Outcomes

Primary Outcomes (end points)
Work Performance:
- Overall experiment earnings
- Per-segment earnings (i.e. productivity)

Uptake of Break:
- Preference for break in middle of work period (for self)
- Preference for break vs work for randomly selected teammate whose earnings pay out to respondent

Financial optimality of break and heterogeneity:
- Effect of break on overall experiment earnings for those who select break vs those who do not
- Effect of break on per-segment earnings for those who select break vs those who do not
Primary Outcomes (explanation)
Bonus earnings are paid all-or-nothing for a given work segment if participants performance was above a pre-announced threshold. The per-segment bonus increases over time.
This threshold requires getting 75% of all images (critical/not-safe and non-critical/safe) correct and missing no more than 2 "critical/ not-safe" images.
Overall earnings constitute the sum of bonus earnings across all segments that a participant did.

Secondary Outcomes

Secondary Outcomes (end points)
Subjective well-being:
- [From main work exp] chronometric happiness, task satisfaction, and fatigue data (according to Liu & Netzer 2023)
- [From main work exp] Willingness to accept future work
- [From main work exp and short exp] Preferences over rest or work

- Correct/incorrectly evaluated images per segment (per type of stimuli)
- Fraction of correctly evaluated per image within segment

- Segments completed
- Number of images evaluated

- Workers beliefs about the effectiveness of breaks for self and others
- Beliefs about own performance (absolute and relative to others)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
Workers are hired to perform an incentivized attention-demanding task, and we allow them to voice a preference for resting or working. The choice will be randomly implemented for a subsample. Others will be assigned at random.
First, we study the effects of breaks on performance and well-being.
Second, we aim to understand what individuals prefer and how these preferences related to earnings – in both levels and effect sizes.

Furthermore, a separate sample of workers will be hired for a separate task that is designed to isolate their preferences over working and resting, holding earnings fixed.
Experimental Design Details
Not available
Randomization Method
Randomization done by a computer
Randomization Unit
The treatment is randomized at the subject level
Was the treatment clustered?

Experiment Characteristics

Sample size: planned number of clusters
N=1,000 individuals
Sample size: planned number of observations
N=1,000 individuals are included in the experiment. However, we will focus the analysis on 95% of the sample that were randomly assigned to the treatment. (For every individual, we observe 4 to 5 segments; and for every segment, we observe 300 image evaluations.) N=400 individuals are included in the additional preference-only experiment. We focus the analysis on lower-income individuals that we pre-define during a screener before data is collected.
Sample size (or number of clusters) by treatment arms
For the main work experiment: 5% of participants will receive their stated preference over work
95% of participants will be allocated randomly to a break or no-break treatment (with ½ to each). We expect this to be around N=475 subjects for each of the break and no-break arms.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)

Institutional Review Boards (IRBs)

IRB Name
Massachusetts Institute of Technology
IRB Approval Date
IRB Approval Number