Job rotation and worker performance

Last registered on October 17, 2023


Trial Information

General Information

Job rotation and worker performance
Initial registration date
October 13, 2023

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
October 17, 2023, 1:44 PM EDT

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


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Primary Investigator


Other Primary Investigator(s)

PI Affiliation
PI Affiliation

Additional Trial Information

On going
Start date
End date
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
How should firms allocate workers to jobs? A standard approach is for firms to assign workers to jobs and to train workers for one job. An alternative approach, called cross-training or cross-skilling, is for firms to rotate workers to different jobs while providing training for multiple jobs. Job rotation and cross-skilling may have several benefits. First, it may allow workers to better take advantage of comparative advantage, thus increasing productivity. Second, it may make workers happier, both because they get to choose a job which they enjoy more and because they appreciate the firm developing multiple capabilities. Third, cross-skilling may increase organizational agility and flexibility, as well as broad knowledge, allowing employees to better understand the needs of the full organization, and being able to step into different roles. Cross-skilling seems to be successfully used in several leading firms, such as the Spanish grocery chain Mercadona, but we have little rigorous evidence on the impact of cross-skilling.

We collaborate with a leading garment manufacturer in Southeast Asia to examine the impacts of job rotation. The experiment will involve all new workers and recently hired existing workers. In the control group, workers receive the standard approach of assignment and training for one job. In the treatment group, workers receive training for multiple jobs at the firm. After training, workers and managers state preferences regarding the assignment of workers to jobs, and workers are assigned to jobs using a version of the deferred acceptance algorithm. We examine how the treatment affects performance, employee turnover, work satisfaction, and employee skill.

External Link(s)

Registration Citation

Chotiputsilp, Ratchanon et al. 2023. "Job rotation and worker performance." AEA RCT Registry. October 17.
Experimental Details


Treatment workers will be randomly selected to join the company's rotation program. At the end of all the rotation cycles (2 other jobs for a week each), the firm and the pilot workers will work together to decide which job function fits the workers best in a way that takes into account both the preferences of the workers and the evaluations by managers (in a deferred acceptance algorithm used in two-sided matching). The control workers will continue to work in a job that was initially assigned by the firm.
Intervention Start Date
Intervention End Date

Primary Outcomes

Primary Outcomes (end points)
The first main outcome will be worker performance. This will be measured using the endline survey. It will also be measured using administrative data from the firm. The second main outcome will be employee turnover. This will be measured using administrative data. The third main outcome will be employee satisfaction. This will be measured using questions on the endline survey. It will include satisfaction with one's particular job, as well as general satisfaction at the firm. The fourth main outcome will be an employee's skill at their job. We aim to measure whether workers are more likely to find a job that they are good at in the treatment group. This will be measured using a skills assessment. Finally, we will measure whether workers change their preferences over various jobs after rotation.
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Probability of a worker being temporarily moved to a different task/team, violations and injury (data permitting)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
New workers and recently hired existing workers at the firm will be randomly assigned to control or treatment. Control is the standard approach of assignment and training for one job for new workers. For existing workers, the control condition means continuing with their current work.

Under the treatment, workers will go through a job rotation where they will try multiple jobs. Jobs can include sewing, heat transfer, embroidery, cutting, quality control, ironing/packing, warehouse/logistic, and material/stock. Which jobs are available will vary by factory and over time depending on work needs of the company. However, treatment workers will experience at least two jobs.

Experimental Design Details
Not available
Randomization Method
Randomization will occur in the office via a computer.
Randomization Unit
Randomization occurs at the individual worker level.
Was the treatment clustered?

Experiment Characteristics

Sample size: planned number of clusters
New hires will be batched by the factory-month that they started and individual-level randomization will happen at the batch level. We expect to obtain a sample of 500-1,000 employees.
Sample size: planned number of observations
New hires will be batched by the factory-month that they started and individual-level randomization will happen at the batch level. We expect to obtain a sample of 500-1,000 employees.
Sample size (or number of clusters) by treatment arms
We expect to obtain a sample of 500-1,000 employees.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
In our exploratory work at the partner firm, we collected data on managers’ evaluation of workers. Because this work did not involve a follow-up after rotation, our analysis on power and target sample size requires assumptions about the expected effects. With 0.8 power and 0.05 alpha, a 3 % effect requires 334 sample. Minimum detectable size of 5% requires half that sample and 1.5% requires 1324.

Institutional Review Boards (IRBs)

IRB Name
University of Toronto
IRB Approval Date
IRB Approval Number
IRB Name
Erasmus University
IRB Approval Date
IRB Approval Number
IRB Name
University of Pennsylvania
IRB Approval Date
IRB Approval Number