The Impact of Pay Structure on Employee Learning and Performance

Last registered on April 17, 2025
View Trial History

Pre-Trial

Trial Information

General Information

Title
The Impact of Pay Structure on Employee Learning and Performance
RCT ID
AEARCTR-0015765
Initial registration date
April 08, 2025

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 17, 2025, 6:25 AM EDT

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

Locations

Country
China
Region

Primary Investigator

Name
Shing-Yi Wang
Affiliation
Contact Primary Investigator

Other Primary Investigator(s)

PI Name
Jing Cai
PI Affiliation
University of Maryland
PI Name
Tianqi Gan
PI Affiliation
Renmin University

Additional Trial Information

Status
In development
Start date
2025-04-08
End date
2025-07-15
Keywords
Firms & Productivity, Labor
Additional Keywords
JEL code(s)
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
We are partnering with a manufacturing firm in China that compensates sewing workers entirely through piece-rate pay. We will randomly assign half of the workers to a new pay scheme in which 25% of their pay will be converted to a base rate pay scheme, with the remaining 75% continuing to be piece-rate. Our primary focus is to understand how this changes workers’ willingness to switch to take on new tasks that require some learning. Additionally, we will examine the on worker outcomes such as performance, earnings, hours, absenteeism, retention, well-being, job satisfaction and willingness to help coworkers. Finally, we will also examine their actual choices between the pay structures after our period of random assignment is over.
External Link(s)

Registration Citation

Citation
Cai, Jing, Tianqi Gan and Shing-Yi Wang. 2025. "The Impact of Pay Structure on Employee Learning and Performance." AEA RCT Registry. April 17. https://doi.org/10.1257/rct.15765-1.0
Sponsors & Partners
Experimental Details

Interventions

Intervention(s)
Intervention (Hidden)

Intervention Start Date
2025-04-25
Intervention End Date
2025-06-30

Primary Outcomes

Primary Outcomes (end points)
o Worker adoption of new assignments
o Quit rates
o Worker productivity outcomes including performance, earnings, hours, absenteeism
o Worker satisfaction and well-being
o Worker choice of piece rate versus base rate pay in the last month
Primary Outcomes (explanation)
We are interested in whether workers change their behavior in response to having some insurance in the form of base rate pay. Specifically, we aim to examine whether they become more willing to switch to new tasks that may involve a learning period during which their initial productivity is lower.

Secondary Outcomes

Secondary Outcomes (end points)
o Helping other workers (i.e. team work)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
We partner with a manufacturing firm in China that currently uses a 100% piece-rate pay structure. We randomly select half of workers to be converted to a new structure where, on average, 75% of their compensation remains piece rate and 25% is converted to a base wage. The goal is to ensure that, in the absence of changes in effort, average earnings will be the same across to the two payment schemes.
Experimental Design Details
We will ask the firm to pre-determine the order of teams to be offered new task assignments coming in.

In the treatment group, we plan to send biweekly text message reminders about their pay structure and what they are on track to earn. In half of the control group, we will send text message reminders to them as well in order to separate the effects of the text messages alone from the change in pay structure.

In the treatment group, the piece rate for these workers will be 75% of the standard piece rate. We will determine the base rates to offer based on salaries in three months prior to the launch of our intervention.
Randomization Method
The randomization that we implement will be done in office by a computer.

Randomization Unit
Workers
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
400-500 workers
Sample size: planned number of observations
400-500 workers, depending on firm size and their hiring
Sample size (or number of clusters) by treatment arms
50% of the sample in treatment so estimated 200-250 workers
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Supporting Documents and Materials
IRB

Institutional Review Boards (IRBs)

IRB Name
University of Maryland
IRB Approval Date
2025-03-24
IRB Approval Number
2299018-1
Analysis Plan

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Post-Trial

Post Trial Information

Study Withdrawal

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Intervention

Is the intervention completed?
No
Data Collection Complete
Data Publication

Data Publication

Is public data available?
No

Program Files

Program Files
Reports, Papers & Other Materials

Relevant Paper(s)

Reports & Other Materials