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Labor cost of young firms

Last registered on September 14, 2023
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Pre-Trial

Trial Information

General Information

Title
Labor cost of young firms
RCT ID
AEARCTR-0009651
Initial registration date
September 04, 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
September 14, 2023, 12:39 PM EDT

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

Locations

Country
Thailand
Region

Primary Investigator

Name
Brighton Chotiputsilp
Affiliation
RSM
Contact Primary Investigator

Other Primary Investigator(s)

Additional Trial Information

Status
In development
Start date
2023-10-01
End date
2023-12-31
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
According to survey findings, active job seekers significantly overestimate young
firms’ exit rates—64 percent (belief) vs. 20 percent (actual). Do young firms pay
for workers’ incorrect beliefs? Analyzing the resume data, young firms are matched
with a weaker pool of candidates, yet wages at young firms are higher for the observationally equivalent workers. This hiring pattern is largely driven by the labor supply as evidence from job ads does not show any difference between old and young firms’ labor
demands. In this paper, I will run a series of experiments to show 1. Job candidates
have incorrect beliefs about exit rate and performance of young firms 2. Job candidates
demand a higher premium from this incorrect belief 3. Simple information provision
about small young firms’ performance can help improve hiring in young firms.
External Link(s)

Registration Citation

Citation
Chotiputsilp, Brighton. 2023. "Labor cost of young firms ." AEA RCT Registry. September 14. https://doi.org/10.1257/rct.9651-1.0
Sponsors & Partners
Experimental Details

Interventions

Intervention(s)
There are two main interventions:
1. information provision about the exit rate of young firms in Thailand
Intervention Start Date
2023-10-01
Intervention End Date
2023-12-01

Primary Outcomes

Primary Outcomes (end points)
1. WTP/WTA estimate from a discrete choice experiment (for young firms)
2. Measure of interest in ads. Eg. clickthrough rate for the job ad, time spent reading the ad, application rate.
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Via post-treatment survey.
we will collect;
1. number of position applied (and where)
2. #call backs as well as characteristics of firms
3. eventual salary
4. general opinion about young firms.
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
I will invite 1000 job seekers who recently created or updated their resumes to take part in an incentivized survey. The survey will be framed as a pilot tool to help the platform tailor job openings to job seekers. We will inform the participants that the more accurate the answer they give the better the outcome of the job recommendation the platform can cater to them. We will first collect their resumes which include baseline characteristics such as demographics, past employment, expected salary for their next job, etc. After collecting baseline information, all participants will be entered into two modules where the sequence is random. The first module is the belief elicitation, and the second module is the discrete choice experiment.

Participants will be randomized into four treatment arms where I randomize the order of the modules and when information about young firms’ exit rates is shown to participants.
1. A randomly selected one-fourth will be in the control group 1 where we elicit the belief about young firms before they enter the DCE experiment. This arm will not receive a true distribution of young firms’ exit rates
2. A randomly selected one-fourth will be in the control group 2 where we elicit the belief about young firms after they finish the DCE experiment. This arm will not receive information about the true distribution of young firms’ exit rates.
3. A randomly selected one-fourth will be in treatment group 1 where we elicit the belief about young firms before they enter the DCE experiment. This arm will receive information about the true distribution of young firms’ exit rates after they finish the DCE experiment.
4. A randomly selected one-fourth will be in treatment group 2 where we elicit the belief about young firms before they enter the DCE experiment. This arm will receive information about the true distribution of young firms’ exit rates immediately after they finish the survey model and before they enter the DCE experiment.
The information about firms’ exit rates will be first presented in a simple message about the true exit rate of young firms in Thailand. Additionally, we will show dashboard information about firms’ exit rates in Thailand where participants can choose different parameters such as the firm’s location, industry, initial registered capital, etc. I will also send this information—or link to the dashboard—to their emails where they can always come back and see this information throughout the study period.


One week after they finish the survey, I will send out a list of real job ads that match the preferences of the DCE to job seekers. In this part of the experiment, treatment arm 1 and treatment arm 2 where no information about young firms’ exit rate will be our control group and treatment arm 3 and 4 where the information provision is given will be our treatment group. The recommendation list in one email will contain 10 job adverts: 4 young firms and 4 old firms that match the preferences extracted from the DCE and the other 2 random ads. The purpose of 2 random job ads is to test the validity of our DCE experiment.

During the email experiment phase, I will introduce another variation in the 2X2 design where I will include (public) accounting information about firms such as sale growth, interest coverage ratio, debt ratio, profitability, etc. Job ads that will be sent to participants will be randomized and stratified such that across treatment groups job ads are similar at the same time match the individual preferences of the job seekers.
Experimental Design Details
Randomization Method
randomization done in office by a computer
Randomization Unit
at job seekers level
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
n/a
Sample size: planned number of observations
1000 subjects distributed equally among 4 treatment arms
Sample size (or number of clusters) by treatment arms
500
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Supporting Documents and Materials

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IRB

Institutional Review Boards (IRBs)

IRB Name
IRB Approval Date
IRB Approval Number
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