Framing and adapting suggestions of labor search strategies to job seekers’ biases

Last registered on October 04, 2023

Pre-Trial

Trial Information

General Information

Title
Framing and adapting suggestions of labor search strategies to job seekers’ biases
RCT ID
AEARCTR-0012218
Initial registration date
October 01, 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
October 04, 2023, 4:54 PM EDT

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

Locations

Region

Primary Investigator

Affiliation
University of Oxford

Other Primary Investigator(s)

PI Affiliation
CREST
PI Affiliation
CREST

Additional Trial Information

Status
In development
Start date
2023-10-02
End date
2024-05-20
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
Defining a job search strategy is a complex task that requires the processing of a large amount of labor market information in order to choose several parameters, such as the level and allocation of effort or the reservation wage and mobility. The literature and our previous work suggest that these perceptions and decisions are subject to important biases that are highly heterogeneous across the population and that may delay the return to employment or deteriorate its quality. In this study, we take advantage of ex-ante knowledge about different types of job search biases, characterized by the use of a rich panel of subjective expectations, to test the impact of formulating and adapting job search strategy suggestions to job seekers' biases on job search behavior and job acquisition. Using two experimental surveys, we first measure baseline labor market beliefs, expectations, and preferences, and then provide personalized job search strategy recommendations that also vary in their framing. We observe the impact of these recommendations on updated beliefs and search parameters using a follow-up survey, and on search behavior and return to employment outcomes using administrative data.
External Link(s)

Registration Citation

Citation
Crépon, Bruno, Aurélien Frot and Christophe Gaillac. 2023. "Framing and adapting suggestions of labor search strategies to job seekers’ biases." AEA RCT Registry. October 04. https://doi.org/10.1257/rct.12218-1.0
Experimental Details

Interventions

Intervention(s)
Intervention Start Date
2023-10-02
Intervention End Date
2024-01-20

Primary Outcomes

Primary Outcomes (end points)
- Jobseekers' perceptions of the labor market, job search parameters and beliefs (follow-up surveys on expectations)
- Search behavior: effort, reservation wage, number of applications and characteristics of the targeted job openings, clicks on job openings.
- Research outcomes: unemployment duration, type of contract, wage.
Primary Outcomes (explanation)
The treatments aim at providing adapted information about the labor market, some suggesting changes in job search parameters. This could influence beliefs about the possibilities of applying and modify search behavior. Ultimately, this could affect the research outcomes listed.

Secondary Outcomes

Secondary Outcomes (end points)
- Research outcomes: satisfaction with the job found (follow-up survey) and entrance in training programs.
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
Jobseekers are randomly assigned to individualized information treatments designed to improve their search behavior and search outcomes. As shown in previous work, jobseekers' biases about their unemployment duration provide a strong signal of their knowledge about key labor market parameters as well as their search behavior. Individuals' treatments are thus designed to meet their specific needs as inferred from their predicted biases. Informational treatments take the form of personalized advice to help jobseekers determine their search strategy, in particular with respect to their reservation wage or the way they diversify their applications across sectors.
Experimental Design Details
We first select micro-markets that will be used in the experiment. They will be selected so that the restriction to these job x geographical areas allows to build reliable recommendation for all the treatments.

Next, we classify jobseekers registered in these markets at the French public employment service (Pôle Emploi, hereafter, the PES) according to their predicted bias w.r.t their job finding probability. We use a model trained on a rich panel about expectations and the available administrative data useful for the prediction. We then build 4 groups gathering jobseekers that are predicted to share the same type of bias: pessimistic, rational, optimistic and over-optimistic. We then design interventions that aim at improving job search, distinguishing between the pessimistic and the others.

For the pessimists (S1): the suggestions made to this group of jobseekers therefore relate to the fact that they could be more selective about the salary of the vacancies chosen, while at the same time intensifying their search efforts.

For the others (S2): the suggestions made to this group relate to the extension of searches to other sectors of activity and to the benefits of such searches on the return to employment.

The suggestions are formulated in three ways:
- T1 ("direct" direct suggestion),
- T2 ("peer" suggestion referring to the behavior of claimants with the same profile),
- T3 ("explanatory" explanation of the different biases in perceptions of the labor market leading to the adoption of a sub-optimal search strategy).

Then we sample 390,000 jobseekers from the selected micro-markets and randomly allocate them into 8 initial treatment and control groups:
- Super control: do not receive the questionnaire;
- Survey control: they receive the questionnaire but no recommendation at the end of it;
- S1 and T1
- S1 and T2
- S1 and T3
- S2 and T1
- S2 and T2
- S2 and T3

Rational, optimistic and over-optimistic claimants were treated only with S2-type recommendations, and were divided into 5 arms:
- Super control: they do not receive the questionnaire;
- Survey control: they receive the questionnaire but no recommendation at the end of it;
- S2 and T1
- S2 and T2
- S2 and T3.

For pessimistic allocated to S1, but whose reservation wage is above the suggested wage for the main job they are looking for, the S1 treatment is proposed without this precise suggestion. The following table sums up the different treatment each bias group potentially receives.

Bias group Wage level S1 S1 – without wage suggestion S2
Pessimists Lower than suggested X X
Higher than suggested X X
Others X

Treatment will then be adapted according to one answer of the survey about the subjective job-finding probability. Depending on the provided answer and more accurate model for the biases, the previous classification into pessimistic, rational, optimistic and over-optimistic is refined. At the end of the questionnaire, all groups but the super control receive an email with at least a link to the PES repository of job postings, and a reminder of the suggestions if any. This reminder is also sent two weeks later by email.

Taking into account the two steps randomization, the 390,000 job seekers will thus be randomly assign into 13 groups. The survey control group receive the survey collecting the expectations but no treatment suggestions. The control group receives no survey at all. We expect to have a 10% response rate to the survey and first stage stratification will be done to target 3000 respondents per treatment arm. We intend to estimate intention to treat (ITT) effects as well as average treatment effects (ATE) on the compliers of the survey. We also intend to estimate conditional average treatment effects (CATE), in particular with respect to previous unemployment experience, search behavior, and predicted group status.

To evaluate the impact on job search behavior and job finding outcomes we use the data made available through our partnership with the PES. This allows to characterize the job applications decisions (job, wage, type of contract, distance, required experience) as well as the results of these and the characteristics of the potential job (contract date, wage, type of contract, location). A follow-up survey one month later will provide more details on the impact on subjective outcomes such as the representation of the labor market, as well as the updated set of job search parameters. The super control group will help us controlling for potential market equilibrium effects and provide a more robust analysis.
Randomization Method
Stratified randomization based on individuals characteristics (unemployment duration, micro-market, gender, unemployment duration, as well as ex-ante predictions of the biases). Randomization will be done on a computer in the offices of the French Public Employment Service.
Randomization Unit
Jobseeker
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
390,000, 13 treatment arms including super control groups.

Sample size: planned number of observations
390,000
Sample size (or number of clusters) by treatment arms
30,000
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
IRB

Institutional Review Boards (IRBs)

IRB Name
Paris School of Economics IRB
IRB Approval Date
2023-07-17
IRB Approval Number
2023-021

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