Portfolio challenges

Last registered on September 19, 2022
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Pre-Trial

Trial Information

General Information

Title
Portfolio challenges
RCT ID
AEARCTR-0010044
Initial registration date
September 08, 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 19, 2022, 2:59 PM EDT

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

Locations

Country
Poland
Region

Primary Investigator

Name
Emil Palikot
Affiliation
Stanford University
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Other Primary Investigator(s)

PI Name
Susan Athey
PI Affiliation
Stanford University
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Additional Trial Information

Status
Completed
Start date
2022-01-15
End date
2022-06-15
Keywords
Gender, Labor
Additional Keywords
Mentoring, Tech industry
JEL code(s)
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
The tech sector faces a challenge in improving gender balance. Various strategies of addressing it has been hypothesized, but we lack empirical evidence of their effectiveness. In this research, we study effectiveness of a targeted training program that helps develop portfolio items that signal skills and practical experience on the probability of finding a job in the tech industry.
External Link(s)

Registration Citation

Citation
Athey, Susan and Emil Palikot. 2022. "Portfolio challenges." AEA RCT Registry. September 19. https://doi.org/10.1257/rct.10044-1.0
Sponsors & Partners
Experimental Details

Interventions

Intervention(s)
In this experiment, we randomize access to the training program called Portfolio Challenge organized by DareIT. We first collect a pool of eligible applicants and then randomly selected applicants into the treatment and control groups. After the program we collect data on outcomes from LinkedIn profiles of subjects and through a survey.
Intervention Start Date
2022-01-15
Intervention End Date
2022-04-15

Primary Outcomes

Primary Outcomes (end points)
We are interested in labor market outcomes; specifically, we study whether a subject found a new tech job defined as a job in a tech company (excluding support jobs like HR or finance) or a tech role such as software development or IT support in a non-tech company after the start of the program. Additionally, we focus on subset of tech jobs defined as jobs in tech companies.
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
Individuals apply to the program through a dedicated website. Next, our partner, DareIT, screens applications for eligibility and applicants are randomly assigned into treatment or control group. After the end of the program, we collect outcomes through a survey and from LinkedIn profiles.
Experimental Design Details
Randomization Method
Randomization is done by a computer. Randomization is stratified on characteristics obtained from application form.
Randomization Unit
Individual applicant
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
400
Sample size: planned number of observations
400
Sample size (or number of clusters) by treatment arms
200 in treatment and 200 in control
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Supporting Documents and Materials
IRB

Institutional Review Boards (IRBs)

IRB Name
Research Compliance Office
IRB Approval Date
2021-10-18
IRB Approval Number
IRB-59983
Analysis Plan

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