University Dropouts and Firm Hiring

Last registered on March 06, 2024
View Trial History

Pre-Trial

Trial Information

General Information

Title
University Dropouts and Firm Hiring
RCT ID
AEARCTR-0013080
Initial registration date
February 26, 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
March 06, 2024, 3:33 PM EST

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

Locations

There is information in this trial unavailable to the public. Use the button below to request access.

Request Information

Primary Investigator

Name
Christian Gschwendt
Affiliation
University of Bern
Contact Primary Investigator

Other Primary Investigator(s)

PI Name
Stefan C. Wolter
PI Affiliation
University of Bern
Contact Investigator
PI Name
Andrea Diem
PI Affiliation
Swiss Coordination Centre for Reseach in Education
Contact Investigator

Additional Trial Information

Status
In development
Start date
2024-02-27
End date
2025-03-31
Keywords
Education, Firms & Productivity, Labor
Additional Keywords
dropouts, hiring, university, firms
JEL code(s)
J23, I24
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
How do university dropouts compare with high school graduates without any university education in a hiring process? We investigate this question in a large-scale discrete choice experiment (DCE) among roughly 5000 firms in Switzerland.
External Link(s)

Registration Citation

Citation
Diem, Andrea, Christian Gschwendt and Stefan C. Wolter. 2024. "University Dropouts and Firm Hiring." AEA RCT Registry. March 06. https://doi.org/10.1257/rct.13080-1.0
Sponsors & Partners
Experimental Details

Interventions

Intervention(s)
No intervention planned.
Intervention Start Date
2024-02-27
Intervention End Date
2024-09-30

Primary Outcomes

Primary Outcomes (end points)
The primary dependent variable is the choice variable of an alternative (the “candidate”) being chosen in the discrete choice experiment among two alternatives with four attributes each. The variable takes the value 1 for the chosen alternative and 0 for the alternative that was not chosen.
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Our mixed multinominal logit model allows for the estimation of heterogeneous preferences among respondents. Therefore, preference heterogeneity will be investigated depending on company characteristics, such as company size, industry, language region and further available company characteristics.
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
We examine a discrete choice experiment that is embedded into an online survey of roughly 5000 firms in Switzerland. The respondents are asked about their preferences in regards to filling a job position.
Experimental Design Details
Not available
Randomization Method
Randomization of the block of choice sets assignment to firms, the order of presented choice sets, and the order of alternatives within choice sets was done by computer using Stata.
Randomization Unit
Firm.
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
No clustering.
Sample size: planned number of observations
We expect roughly 5000 participating firms drawn by the Swiss Federal Statistics Office.
Sample size (or number of clusters) by treatment arms
No clustering or different treatments. However, in order to optimize our mixed logit DCE model and to keep the workload for respondents low, a total of 21 choice sets are subdivided into 7 blocks of 3 choice sets each are randomly assigned to firms, with expected 5000 firms participating, 714 firms per block.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Supporting Documents and Materials
IRB

Institutional Review Boards (IRBs)

IRB Name
IRB Approval Date
IRB Approval Number
Analysis Plan