Unwilling to reskill? Evidence from a survey experiment with Italian jobseekers

Last registered on August 10, 2023


Trial Information

General Information

Unwilling to reskill? Evidence from a survey experiment with Italian jobseekers
Initial registration date
August 04, 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
August 10, 2023, 1:26 PM EDT

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


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Primary Investigator

Bocconi University

Other Primary Investigator(s)

PI Affiliation
Harvard Business School
PI Affiliation
University of Milan
PI Affiliation
PI Affiliation
Queen Mary

Additional Trial Information

On going
Start date
End date
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
In recent decades, labor markets in OECD countries have experienced profound changes driven by digitalization, globalization and demographic shifts. Despite the widespread acknowledgment that training plays a vital role in supporting workers through these transformations, we have limited knowledge on the demand for training and reskilling among jobseekers. This project gathers new survey data and runs a discrete choice experiment to quantify the demand for training and reskilling among Italian jobseekers. Through simulations, we will also compare the effect of alternative policies on participation in training and reskilling. By identifying and understanding the barriers faced by our participants, we aim to propose effective measures tailored to their needs and to provide valuable insights into the design of policies that can foster the successful reallocation of workers into high-growth sectors.
We collect our survey on two different samples. The first sample is recruited through a survey company, which sent out our invitation to their registered users in order to have a sample representative of Italian unemployed. The second sample consists of jobseekers registered to job-centres in the metropolitan city of Milan.
External Link(s)

Registration Citation

Delfino, Alexia et al. 2023. "Unwilling to reskill? Evidence from a survey experiment with Italian jobseekers ." AEA RCT Registry. August 10. https://doi.org/10.1257/rct.11866-1.0
Sponsors & Partners

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Experimental Details


The survey instrument consisted of five parts:
1. Screening: To be eligible to participate, a respondent should be i) between 18 and 55 years old, ii) not working, iii) actively looking for a job and iv) not enrolled in any training course.
2. Working and training history: we asked respondents about their past training and work experience, whether they felt their past job was part of their identity, the skills needed in their previous job, expectations of wage, training effectiveness and job finding rates for the main job they are searching for. We also asked some information about their job search (e.g, hours spent searching, number of occupations).
3.Attitudes towards new occupation: we randomly assigned every survey respondent to a ``treatment occupation" (IT assistant or construction technician). We then asked expectations of employer's demand, wages, job finding rates, fit with own identity, confidence in skills and interest for this occupation.
4. Discrete choice experiment: respondents are asked to choose between six training options with randomized characteristics, as described in the attached PAP.
5. Demographics and unemployment: we collect further demographic information such as whether the person receives unemployment benefits, duration of unemployment, time spent in household chores and personality traits.
Intervention Start Date
Intervention End Date

Primary Outcomes

Primary Outcomes (end points)
Our primary outcomes are meant to capture interest in the two high-demand treatment occupations, and in undertaking specialized training programs to access these occupations (i.e., reskilling).
Thus we focus on two primary sets of outcomes: one for interest in treatment occupation, and one for interest in reskilling.

Interest in treatment occupation
The survey asked respondents to rate on a scale from 0 to 10 their interest in the treatment occupation. This is our main measure of interest for the treatment job. We will use the raw version of the survey variable, as well as a dummy for having interest above median in the sample.
We will also construct six additional versions of the variable for interest in the treatment job by residualizing the survey response to six different set of controls.

Interest in reskilling
We will define interest in reskilling by using respondents' answers to the six different discrete-choice scenarios they saw. Reskilling for us means preferring a vertical training (i.e., specific training preparing for an in-demand occupation) compared to a generalist training, or no training at all. First, for every respondent we will compute two variables of interest in reskilling: i) the share of scenarios (out of six) in which s/he picked a vertical course as first choice and ii) whether s/he ever picked a vertical course as first choice in any of the given scenarios. Second, we will also explore interest in reskilling by computing the average ranking given to the vertical training, horizontal training and opt-out by a respondent across scenarios. Third, we will quantify the increase in marginal utility and the willingness to pay for a vertical training course in our discrete-choice model.
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
We introduce two between-subjects randomized treatments in the surveys:

Treatment 1. After screening, the survey software randomly assigned respondents to one of two high-demand occupations: IT Assistant or Construction Technician. Assignment was stratified by gender. We refer to the occupation assigned to each respondent as ``treatment occupation".

Treatment 2. In order to study possible frictions to the demand for training, we introduced experimental variation within the survey right before the discrete choice experiment in part 4. Respondents are randomized into the following three groups:

1. Information (INFO): before asking for their training choices, respondents are provided with information on vacancies and average wage in the treatment occupation.
2. Role model and growth mindset (MINDSET): before asking for their training choices, respondents are shown a “success story” of a person who succeeded in reinventing herself after a training and learn about the concept of “growth mindset”.
3. Control group (CONTROL): no additional information is provided before training choices.

The discrete choice experiment in part 4 of the experiment also contained randomised variation within and between subjects. Within subjects, we randomised the characteristics of training programmes. Between subjects, we randomised the block of choices seen by a person. A person could be randomly assigned to 9 different blocks, which differed in the training options (3 versions) and in the order of visualisation of the training features within each option (3 versions). See the attached PAP for details.
Experimental Design Details
Not available
Randomization Method
Randomization done by the survey software.
Randomization Unit
Individual randomization, with stratification by gender.
The assignment of the INFO/MINDSET/CONTROL treatment was also stratified by treatment occupation.
The assignment of a block of training choices was stratified by gender, treatment occupation and INFO/MINDSET/CONTROL treatment.

For all treatments, we also stratify by respondents' sample.
Was the treatment clustered?

Experiment Characteristics

Sample size: planned number of clusters
Survey panel sample: 600 respondents
Jobcentres sample: between 400 and 800 respondents.
Sample size: planned number of observations
Same a planned number of clusters
Sample size (or number of clusters) by treatment arms
Within each gender, even split in six arms that combine a treatment occupation (IT or Construction) with an information treatment (INFO/MINDSET/CONTROL).
Within each arm, we also evenly assigned the block of training choices.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)

Institutional Review Boards (IRBs)

IRB Name
Bocconi Ethics Committee
IRB Approval Date
IRB Approval Number
IRB Name
Bocconi Ethics Committee
IRB Approval Date
IRB Approval Number
Analysis Plan

Analysis Plan Documents

Pre-Analysis Plan

MD5: 68095b1cbea40b851422cfbdc409402f

SHA1: 40c9c5fcc5c23a9820a66f6fb51403befe8cc058

Uploaded At: August 04, 2023

Survey Company - Document about Data Sharing

MD5: fd4e181d429d9281b33dc3b68a79622e

SHA1: c2bb2e6b88a2465c0967e18ed0a0f385baf14db3

Uploaded At: August 04, 2023