Signaling skills online and labor market outcomes: evidence from an adaptive experiment

Last registered on September 27, 2022


Trial Information

General Information

Signaling skills online and labor market outcomes: evidence from an adaptive experiment
Initial registration date
September 25, 2022

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 27, 2022, 11:54 AM EDT

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

Last updated
September 27, 2022, 12:08 PM 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)

PI Affiliation
PI Affiliation

Additional Trial Information

In development
Start date
End date
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Digital job tools promise to reduce frictions on the labor market. In this randomized controlled trial, we study an intervention seeking to increase the usage of a public online platform in France. The intervention, implemented with an adaptive design, consists in sending mails to job seekers, providing information, help and motivation to register or update their profiles on the platform. We focus primarily on discovering which treatments maximize uptake of the tool among the several type of incentives considered. We then analyze the impact of the platform's use on labor market outcomes.
External Link(s)

Registration Citation

Hoffmann, Morgane, Charly Marie and Bertille Picard. 2022. "Signaling skills online and labor market outcomes: evidence from an adaptive experiment." AEA RCT Registry. September 27.
Sponsors & Partners

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Experimental Details


The intervention aims at sending emails to registered job seekers in order to encourage them to engage with their profile on the public employment service's platform, i.e to fill the profile and potentially publish it to become visible to recruiters. We test several treatments that either provide information or reduce the cost of the tool's use, in combination with behavioral levers.
Intervention Start Date
Intervention End Date

Primary Outcomes

Primary Outcomes (end points)
- Online profile related outcomes
- Impact on the individual's usage of the platform
- Labor market outcomes
Primary Outcomes (explanation)
- Online profile related outcomes: (1) Whether the job seeker connected to his account, went to the profile page within a 2 days time window after sending emails and modified his/her profile (2) Whether the job seeker published his profile on the public employment service's website (3) Number of visits on the job seeker's profile by recruiters and or by caseworkers (4) An indicator of the degree of completion of the profile in %
- Y = 1 if the job seeker visits relevant pages on Pôle emploi's website after 2 days, 0 otherwise
- Labor market outcomes: (1) number of recruiter clicks on the individual's profile, (2) number and type of job ads recommended to the job seeker by the caseworker and by automatic suggestions (3) number and type of ads the job seeker clicked on or applied to (4) return to employment

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
We repeatedly follow these steps over the 300,000 individuals:
1. We sample 20 000 job seekers from the eligible population at time t.
2. We split them in two equal-sized samples: a control (who does not receive any incentive) and a treated group (who receives one of the incentives according to estimated individual probability of allocation).
3. For the treated group, an algorithm determines allocation probabilities for each individual; treatments are then assigned accordingly.
4. Individuals in the treated group are sent a message shortly after the assignment.
5. We observe whether individuals in the treated group clicked on relevant pages on Pôle emploi's website and this information is used by the algorithm to update the allocation probabilities.
Experimental Design Details
Not available
Randomization Method
The allocation to the control or treated groups is done by a computer with uniform probabilities. Within the treated group, the kind of intervention is chosen using a contextual bandits algorithm (i.e, randomization done by a machine learning algorithm with personalized sampling weights).
Randomization Unit
The unit of the randomization is the job seeker.
Was the treatment clustered?

Experiment Characteristics

Sample size: planned number of clusters
No cluster
Sample size: planned number of observations
~ 300,000 observations
Sample size (or number of clusters) by treatment arms
The treated group of size 150,000 is split between 16 treatment arms; the sample size for each arm is not known in advance (it is progressively adapted so that people are sent to the best treatments).
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)

Institutional Review Boards (IRBs)

IRB Name
Institutional Review Board - Paris School of Economics - Ecole d'Economie de Paris
IRB Approval Date
IRB Approval Number
2022 - 019