Implementing Market Design in the Labor Market: Lessons and Experimental Evaluation

Last registered on January 02, 2024
View Trial History

Pre-Trial

Trial Information

General Information

Title
Implementing Market Design in the Labor Market: Lessons and Experimental Evaluation
RCT ID
AEARCTR-0012756
Initial registration date
January 02, 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
January 02, 2024, 11:25 AM EST

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

Locations

There is information in this trial unavailable to the public. Use the button below to request access.

Request Information

Primary Investigator

Name
Fuhito Kojima
Affiliation
University of Tokyo
Contact Primary Investigator

Other Primary Investigator(s)

PI Name
Shintaro Yamaguchi
PI Affiliation
University of Tokyo
PI Name
Yusuke Narita
PI Affiliation
Yale University
PI Name
Hiroaki Odahara
PI Affiliation
University of Tokyo
PI Name
Kan Kuno
PI Affiliation
University of Tokyo
PI Name
Akira Matsushita
PI Affiliation
University of Tokyo
PI Name
Kazuma Takakura
PI Affiliation
University of Tokyo

Additional Trial Information

Status
In development
Start date
2024-04-01
End date
2028-03-31
Keywords
Firms & Productivity
Additional Keywords
matching theory, human resource management, personnel assignment
JEL code(s)
D47, D82, J28, M5
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
We suggest implementing personnel assignment systems in organizations using matching theory. Furthermore, we propose using a randomized controlled trial to evaluate the impact of the systems by measuring performance indicators such as labor productivity and desirable personnel assignments.
External Link(s)

Registration Citation

Citation
Kojima, Fuhito et al. 2024. "Implementing Market Design in the Labor Market: Lessons and Experimental Evaluation." AEA RCT Registry. January 02. https://doi.org/10.1257/rct.12756-1.0
Sponsors & Partners
Experimental Details

Interventions

Intervention(s)
Intervention Start Date
2024-04-01
Intervention End Date
2028-03-31

Primary Outcomes

Primary Outcomes (end points)
Employee satisfaction with personnel assignments
Attrition rate
Match degree between position and skill
Performance or productivity in a match
Primary Outcomes (explanation)
Our main outcomes are selected to indirectly measure the personnel assignment from two perspectives: the behavior and psychology of employees and the production efficiency of the firm.
We will measure employee satisfaction and attrition rates to assess the effectiveness of our matching algorithm in Japan, where the attrition rate of new hires is as high as about 30% within the first three years.
In addition, we will evaluate labor productivity and the degree to which the employee's skills match the skills desired by the department to assess production efficiency. An important indicator is whether labor productivity has increased as a result of the matching algorithm adjusting the respective preferences of the employee and the department. Moreover, we will evaluate the extent to which the employee's skills align with the position requirements, as this is a factor in enhancing production efficiency.

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
We will evaluate the impact of matching via the deferred acceptance algorithm using a randomized controlled trial.
Experimental Design Details
Not available
Randomization Method
We will use a pseudo-random number generator to assign markets to the treatment or control group within pre-determined randomization blocks.
Randomization Unit
Firm
Was the treatment clustered?
Yes

Experiment Characteristics

Sample size: planned number of clusters
In order to ensure a sufficient sample size, we plan to invite approximately 20 firms to participate by 2025, one year before the final intervention wave.
Sample size: planned number of observations
In our experiments, it is impossible to predict in advance which firms will be subjects. Since all employees will be sampled due to the nature of the intervention, sample size cannot be determined a priori.
Sample size (or number of clusters) by treatment arms
We have two arms: treatment and control, each containing more than 10 firms.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Supporting Documents and Materials
IRB

Institutional Review Boards (IRBs)

IRB Name
The 1st DST Institutional Review Board
IRB Approval Date
2023-03-30
IRB Approval Number
23000035
IRB Name
The 2nd DST Institutional Review Board
IRB Approval Date
2023-04-06
IRB Approval Number
23000035
IRB Name
The 4th DST Institutional Review Board
IRB Approval Date
2023-08-14
IRB Approval Number
23000035
Analysis Plan