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

Last registered on September 27, 2022

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Pre-Trial

Trial Information

General Information

Title

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

RCT ID

AEARCTR-0010085

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.

Locations

Country

France

Region

Primary Investigator

Name

Bertille Picard

Affiliation

Contact Primary Investigator

Other Primary Investigator(s)

PI Name

PI Affiliation

PI Name

PI Affiliation

Additional Trial Information

Status

In development

Start date

2022-09-26

End date

2022-12-31

Keywords

Behavior, Labor

Additional Keywords

Adaptive experiment, contextual bandits, online matching, employment platform

JEL code(s)

J23, J24, C93

Secondary IDs

Prior work

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

Abstract

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

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. https://doi.org/10.1257/rct.10085-2.0

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Experimental Details

Interventions

Intervention(s)

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

2022-09-26

Intervention End Date

2022-12-31

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

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

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)

Supporting Documents and Materials

IRB