What Drives Reskilling Decisions? Evidence from a Discrete Choice Experiment with Unemployed Jobseekers

Last registered on November 19, 2024

Pre-Trial

Trial Information

General Information

Title
What Drives Reskilling Decisions? Evidence from a Discrete Choice Experiment with Unemployed Jobseekers
RCT ID
AEARCTR-0014829
Initial registration date
November 17, 2024

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
November 19, 2024, 3:56 PM EST

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

Locations

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Primary Investigator

Affiliation
Erasmus University Rotterdam

Other Primary Investigator(s)

PI Affiliation
Université libre de Bruxelles
PI Affiliation
Université libre de Bruxelles
PI Affiliation
Bocconi University
PI Affiliation
Harvard Business School

Additional Trial Information

Status
In development
Start date
2024-11-18
End date
2027-06-30
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
Rapidly evolving labor markets have led to deteriorating employment opportunities for workers with few or obsolete skills, and increasing labor and skills shortages for employers. This has led to a growing mismatch between the available jobs and the qualifications of workers seeking employment. As a result, reskilling the workforce is becoming an essential strategy to address these challenges.

Our project investigates whether jobseekers are willing to reskill and, if so, what this decision depends on. We will explore this question with a discrete choice experiment implemented with the Public Employment Service (PES) operating in Wallonia, Belgium. Using responses from approx. 3,000 Belgian jobseekers, we will explore their willingness to enroll in demand-driven occupational training programs, assess their willingness-to-pay (WTP) for different training features, and compare these preferences with those for job-related characteristics. Our study will thus focus on the importance of key policy-relevant factors, as well as the importance of working conditions in the target occupation, in explaining the decision to reskill. Moreover, we will examine whether jobseekers' willingness to reskill (and their WTP for different training and job features) depends on: their interest in and beliefs about the target occupation, the distance between their target occupation and previous work experience, as well as other personal characteristics. By linking survey data with administrative records, we will be able to relate jobseekers' stated preferences to their actual training decisions.
External Link(s)

Registration Citation

Citation
Dejardin, Benjamine et al. 2024. "What Drives Reskilling Decisions? Evidence from a Discrete Choice Experiment with Unemployed Jobseekers." AEA RCT Registry. November 19. https://doi.org/10.1257/rct.14829-1.0
Experimental Details

Interventions

Intervention(s)
This is a survey experiment consisting of five parts:
1. Occupational preferences
2. Training decisions under different scenarios with varying training characteristics
3. Beliefs about preferred occupations
4. Training decisions under different scenarios with varying job-related characteristics
5. Additional questions
The experimental design is described in detail in the pre-analysis plan.
Intervention Start Date
2024-11-18
Intervention End Date
2025-06-30

Primary Outcomes

Primary Outcomes (end points)
Our primary outcomes are meant to capture interest in undertaking occupational training programs for in-demand occupations (interest in reskilling). We want to explore how interest in reskilling varies depending on i) the features of the training program and/or ii) the characteristics of the job the training prepares participants for, and/or iii) jobseekers demographics, work history and beliefs.
Primary Outcomes (explanation)
We will define interest in reskilling by using respondents' answers to the different discrete-choice scenarios they see.

First, we will compute two main variables of interest in reskilling: (i) the share of scenarios in which a respondent picked a training course in a shortage occupation as first choice, and (ii) whether they ever picked a training course in a shortage occupation as first choice in any of the given scenarios. As the choice scenarios are split into two sets - one of 5 choice scenarios (which vary the occupation targeted by the training) and one of 3 choice scenarios (which keep the targeted occupation) - we will define these reskilling variables by pooling together all the choices, as well as separately for the two sets of scenarios.

Second, we will also consider an alternative outcome encompassing “interest in trainings” more broadly. This measure will be defined as: (i) the share of scenarios in which a respondent picked any training course, and (ii) the respondent having ever picked any training course as first choice in any of the given scenarios. However, Given the hypothetical nature of our experiment, we expect fewer people to pick the no training option compared to reality, and thus training interest to be biased upward. We will therefore pay particular attention to estimates pertaining to interest in reskilling, and consider interest in training as rather exploratory.

Third, in our discrete choice model pertaining to training characteristics (for the set of 5 scenarios), we will quantify the increase in marginal utility and the willingness to pay for reskilling by considering as the "reskilling" option the training course which targets a respondent's (less preferred) shortage occupation.

Finally, in our discrete choice model pertaining to job-related characteristics (for the set of 3 scenarios), we will fix the occupation to the second-preferred, and estimate the willingness-to-pay for a set of job-related characteristics. The estimates will be benchmarked against our WTP measures for reskilling found in the previous paragraph.

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
A. Random variation in information on labor shortage
In order to study possible frictions to the demand for training, we introduced experimental variation within the survey right before the discrete choice experiment. Respondents are randomized into the following two groups:
1. Shortage Information Group: before asking for their training choices, respondents are informed that the occupation for training B (one of the training options) is in shortage.
2. Shortage Control group: no additional information is provided before training choices.

B. Random variation in training (and job-related) characteristics
The discrete choice experiment contains random variation within and between subjects. Within subjects, we randomized the characteristics of training programs. Between subjects, we randomized the block of choices seen by a person.

C. Random variation in information on employer involvement in trainings
At the end of the survey, jobseekers receive information on trainings that seem to match their preferences. For a randomly selected half of respondents, we mention that some of these trainings are organized in collaboration with employers.
1. Employer Involvement Information Group: respondents are told that some of these trainings are organized with employers.
2. Employer Involvement Control group: respondents are not told that some of these trainings are organized with employers.
Experimental Design Details
Not available
Randomization Method
Randomization is done within the Qualtrics survey.
Randomization Unit
Individual respondents.
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
Target= 3,000 individual respondents.
Sample size: planned number of observations
Target= 3,000 individual respondents.
Sample size (or number of clusters) by treatment arms
Half of respondents will be in the shortage information treatment (target: 1,500 treated, 1,500 controls).
An orthogonal half of respondents will be allocated to the employer involvement information treatment (target: 1,500 treated, 1,500 controls).
In the discrete choice with varying training (resp. job-related) characteristics, respondents are allocated to one of three (resp. four) blocs of 5 scenarios with given sets of characteristics.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
See pre-analysis plan.
IRB

Institutional Review Boards (IRBs)

IRB Name
Erasmus School of Economics (ESE) Internal Review Board
IRB Approval Date
2024-09-17
IRB Approval Number
ETH2425-0275
Analysis Plan

Analysis Plan Documents

PAP_SurveyExperiment-Forem.pdf

MD5: de33ba7ad59ecf4bef2b1cf137af492c

SHA1: 024924b177da8d8aec4df433d8a64f259eae4542

Uploaded At: November 14, 2024