Job rotation and worker performance

Last registered on March 21, 2024

Pre-Trial

Trial Information

General Information

Title
Job rotation and worker performance
RCT ID
AEARCTR-0012136
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.

Last updated
March 21, 2024, 3:07 PM EDT

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

Locations

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Primary Investigator

Affiliation

Other Primary Investigator(s)

PI Affiliation
PI Affiliation

Additional Trial Information

Status
On going
Start date
2023-08-01
End date
2025-02-15
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
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

Citation
Chotiputsilp, Ratchanon et al. 2024. "Job rotation and worker performance." AEA RCT Registry. March 21. https://doi.org/10.1257/rct.12136-1.1
Experimental Details

Interventions

Intervention(s)
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
2023-08-15
Intervention End Date
2024-08-15

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?
No

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)
The unit of analysis in this project is at the individual level. We aim to recruit between 500 and 1000 new hires for this project. We use numbers from the manager evaluations obtained from the pilot work in May 2023 and made assumptions on the expected effect size. 16 workers were evaluated in up to 4 different jobs on a scale of 1 to 4. We use the overall ratings of the worker in 59 observations to do power calculations below. The average score is 2.712 and the standard deviation is 0.696. We also assume a compliance rate of 80%. Given our target sample size, the minimum effect size we should be able to detect in the best-case scenario lies around 1/4 of the standard deviation. Ideally, we would like to have as many observations as possible to enable power for the heterogeneity analyses. In practice, the partner company incurs costs from this project, both explicitly (such as the wage bills of dedicated personnel) and implicitly (such as the opportunity costs of deviating from the default of job assignments). We maintain that a sample size between 500 and 1000 new hires should provide us with confidence in detecting the effect of the program.
IRB

Institutional Review Boards (IRBs)

IRB Name
University of Toronto
IRB Approval Date
2023-06-01
IRB Approval Number
44754
IRB Name
Erasmus University
IRB Approval Date
2023-03-24
IRB Approval Number
ETH2223-0548
IRB Name
University of Pennsylvania
IRB Approval Date
2023-07-18
IRB Approval Number
853902
Analysis Plan

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