Labor cost of young firms

Last registered on October 02, 2023


Trial Information

General Information

Labor cost of young firms
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.

Last updated
October 02, 2023, 6:35 AM EDT

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


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Primary Investigator


Other Primary Investigator(s)

Additional Trial Information

In development
Start date
End date
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
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

Chotiputsilp, Brighton. 2023. "Labor cost of young firms ." AEA RCT Registry. October 02.
Experimental Details


There are two main interventions:
1. information provision about the exit rate of young firms in Thailand
Intervention Start Date
Intervention End Date

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
Experimental Design
I will invite 800 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.

Participants will be randomized into two treatment arms where actual young firms’ exit rates is shown to participants.
1. A randomly selected one-half will be in the active control group where information about the firms' exit rate in Thailand from the Townsend Thai data is shown.
2. A randomly selected one-half will be in the treatment group where information about the firms' exit rate in Thailand from the department of business development (estimated by the author) is shown

Additionally, I will include pure control where no information about firms exit rate is presented.
The information about firms’ exit rates will be first presented in a simple message about the true exit rate of young firms in Thailand with a breakdown of exit/survival rate of each year after the birth of the firm. 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. This is the incentive mechanism that would align participants interests to the reseacher's goal. In this stage, I may randomly allocate both ads that match the preferences and ads that don't match the preference (from the DCE estimation) depending on the feasibility of the project (the constraint of the platform)
Experimental Design Details
Not available
Randomization Method
randomization done in office by a computer
Randomization Unit
at job seekers level
Was the treatment clustered?

Experiment Characteristics

Sample size: planned number of clusters
Sample size: planned number of observations
800 subjects distributed equally among 2 treatment arms
Sample size (or number of clusters) by treatment arms
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Supporting Documents and Materials

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Institutional Review Boards (IRBs)

IRB Name
IRB Approval Date
IRB Approval Number