How does providing labour-market information to employers at the job-posting stage change job postings and hiring outcomes? Experimental evidence from French employers.

Last registered on September 29, 2025

Pre-Trial

General Information

Title

RCT ID

AEARCTR-0016840

Initial registration date

September 24, 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

September 29, 2025, 10:48 AM EDT

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

Locations

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Primary Investigator

Name

Mattis Gilbert

Affiliation

Sciences Po

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Other Primary Investigator(s)

PI Name

Roland Rathelot

PI Affiliation

ENSAE, Institut Polytechnique de Paris

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PI Name

Sebastian Butschek

PI Affiliation

Universität Innsbruck

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PI Name

Andreas Steinmayr

PI Affiliation

Universität Innsbruck

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PI Name

Marina Schwab

PI Affiliation

Universität Innsbruck

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Additional Trial Information

Status

In development

Start date

2025-09-25

End date

2026-03-31

Keywords

Labor

Additional Keywords

JEL code(s)

J31, J63, J64, C93, D83, J68

Secondary IDs

Prior work

This trial does not extend or rely on any prior RCTs.

Abstract

Many firms report difficulties in filling vacancies. This project investigates whether this may be due to posted wages being too low relative to what the supply side expects, and whether recruiters are misinformed about the prevailing wage distribution. In collaboration with a major French job board, we evaluate the results of a randomised experiment that provides recruiters with information on local labour market wages at the job-posting stage. The intervention relies on an interactive tool, developed by the job board, which displays a vacancy’s posted wage position within the distribution of posted wages in the same local labour market. The results of this experiment will shed light on the extent to which recruiter misinformation about the wage distribution distorts both vacancy postings and the job-matching process.

External Link(s)

Registration Citation

Citation

Butschek, Sebastian et al. 2025. "How does providing labour-market information to employers at the job-posting stage change job postings and hiring outcomes? Experimental evidence from French employers.." AEA RCT Registry. September 29. https://doi.org/10.1257/rct.16840-1.0

Sponsors & Partners

Experimental Details

Interventions

Intervention(s)

A major job board has created a software application to inform employers about the distribution of wages posted by other employers within the same local labour market. This application takes the form of a visually appealing widget that displays information based on a call to a specific API. The information underlying the widget is computed at the local labour market level.

Intervention Start Date

2025-09-25

Intervention End Date

2026-03-31

Primary Outcomes

Primary Outcomes (end points)

Our primary outcomes are:
- the (lower-bound) posted wage is non-missing (dummy),
- value of the (lower-bound) posted wage (in logs),
- absolute deviation of the (lower-bound) posted wage from the market-specific median wage,
- the vacancy has been filled (dummy).

Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)

• Characteristics of job ads:
– Dummy variable: completion of the job ad posting process
– Dummy variable: the posted wage first entered was changed during the ad posting process
– Experience required in the job
– Contract duration/type

• Application pool:
– Number of applications
– Number of clicks overall
– Applicant quality (as defined below)
– Number of clicks by unemployed vs. not unemployed users (data quality yet to be ascertained - we will remove this outcome if necessary)

• Hiring outcomes:
– Time required to hire.
– Realised wages (if successful hiring).
– Quality of hired candidate (if successful hiring).
– Job duration.

• Future postings:
– Number of job postings in the same occupation over the next 3/6/9/12 months

Secondary Outcomes (explanation)

We will proxy applicant quality with the predicted probability of being hired. To obtain this prediction, we will estimate a model of application success on candidate and job characteristics, using the set of applicants to a given job. To avoid using the same data for the estimation of applicant quality and treatment effects, we will rely on job advertisements posted prior to the experimental period.

Experimental Design

We will conduct a clustered randomised trial. Employers are the randomisation clusters. Job ads are the observation units.
Treated employers will be shown information about the distribution of posted wage, while control employers will not.

Experimental Design Details

Not available

Randomization Method

The pseudo-randomization is performed using a modulo 3 of the hashed recruiter account ID.

Randomization Unit

Randomization is at the recruiter account level.

Was the treatment clustered?

Yes

Experiment Characteristics

Sample size: planned number of clusters

47,000 employers.

Sample size: planned number of observations

180,000 job ads.

Sample size (or number of clusters) by treatment arms

15,667 control employers, 31,333 treated employers.

Minimum detectable effect size for main outcomes (accounting for sample design and clustering)

See PAP for detail.

Supporting Documents and Materials

IRB

Institutional Review Boards (IRBs)

IRB Name

IRB Approval Date

IRB Approval Number

Analysis Plan