The Role of Financial Incentives in Screening and Inducing Effort: Evidence from a Manufacturing Firm in China

Last registered on October 04, 2023
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Pre-Trial

Trial Information

General Information

Title
The Role of Financial Incentives in Screening and Inducing Effort: Evidence from a Manufacturing Firm in China
RCT ID
AEARCTR-0012140
Initial registration date
September 19, 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 04, 2023, 1:30 PM 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
Contact Investigator
PI Name
Sai Luo
PI Affiliation
Shanghai University of Finance and Economics
Contact Investigator

Additional Trial Information

Status
On going
Start date
2023-09-15
End date
2023-12-31
Keywords
Firms & Productivity, Labor
Additional Keywords
Screening, financial incentives, adverse selection, moral hazard, monitoring, efficiency wages
JEL code(s)
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
Economic theory suggests the potential role that wage setting can play in reducing turnover and increasing productivity. In this project, we explore two ways in which higher wages can affect productivity and turnover, through the selection of better job applicants (i.e. hidden characteristics) and by inducing worker effort (i.e. hidden action). First, to study selection, we will randomize workers into two groups at the recruitment stage, by varying the compensation (i.e. bonus) that they are offered prior to the job applicants accepting the job. Second, to test for moral hazard, within the first control group that does not receive the additional bonus at the first stage, we will have a surprise treatment where some workers will be surprised with a higher compensation bonus after they have already started at the firm. Finally, to test the trade-off between efficiency wages and increased monitoring, we will randomize the amount of monitoring done on the workers in our sample.
External Link(s)

Registration Citation

Citation
Cai, Jing, Sai Luo and Shing-Yi Wang. 2023. "The Role of Financial Incentives in Screening and Inducing Effort: Evidence from a Manufacturing Firm in China." AEA RCT Registry. October 04. https://doi.org/10.1257/rct.12140-1.0
Sponsors & Partners
Experimental Details

Interventions

Intervention(s)
We will randomize financial incentives for new hires at two points in time.

We will also randomize the subsequent monitoring of workers.
Intervention Start Date
2023-09-15
Intervention End Date
2023-11-30

Primary Outcomes

Primary Outcomes (end points)
Characteristics of new hires (education, age, experience, hometown, gender)

Retention/Turnover

Characteristics of those who stay versus leave (individual productivity and characteristics such as education, age, experience, hometown, gender)

Productivity outcomes from firm administrative data

Survey outcomes on work satisfaction, well-being, attitudes towards the firm and managers


Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Survey will also collect social network data to examine spillovers
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
We will randomize financial incentives for new hires at two points in time.

We will also randomize the subsequent monitoring of workers.
Experimental Design Details
There are three key experiments.

First, we will randomize workers into two groups at the recruitment stage, by varying the compensation (via a signing bonus) that they are offered prior to the job applicants accepting the job. This amount will be approximately half a month of earnings paid after one month of work with the condition that they continue to work at the company until the end of the year (or the signing bonus should be returned). One-third of this sample will be treated. We will randomize by hiring batch and the number of hiring batches depends on the number of hiring rounds that the firm does over this period. We estimate 12-24.

Second, within the control group that does not receive the additional bonus at the first stage, we will have a surprise treatment where some workers will be surprised with a higher compensation bonus AFTER they have already started at the firm. The amount of the bonus will be the same as for the first treatment group. Of this sample (i.e. the control group from the first randomization), one half will receive this treatment.

Third, among the stations that our sample works at, we will randomize whether the firm increases the level of monitoring happening over the workers. This will happen to one-half of the work stations.
Randomization Method
randomization done in office by a computer
Randomization Unit
For the first randomization on incentives, we will randomize at the individual level but in batches as workers are hired over this period. For the monitoring randomization, we will randomization at the station level.
Was the treatment clustered?
Yes

Experiment Characteristics

Sample size: planned number of clusters
For the monitoring experiment, the number of stations depends on the distribution of our workers across stations. We estimate 90-140 stations.
Sample size: planned number of observations
250-450 people, depending on the amount of hiring the firm does in this period
Sample size (or number of clusters) by treatment arms
For the first experiment on incentives, we will treat one-third of the sample.

For the second experiment on incentives, we will treat one-half of the relevant sample (control group on first experiment).

For the monitoring experiment, half of the stations will be chosen for treatment.
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 Pennsylvania
IRB Approval Date
2022-05-26
IRB Approval Number
851321
IRB Name
University of Maryland
IRB Approval Date
2022-05-27
IRB Approval Number
1912321-1
Analysis Plan

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