University Dropouts and Firm Hiring

Last registered on March 06, 2024


Trial Information

General Information

University Dropouts and Firm Hiring
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.


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Primary Investigator

University of Bern

Other Primary Investigator(s)

PI Affiliation
University of Bern
PI Affiliation
Swiss Coordination Centre for Reseach in Education

Additional Trial Information

In development
Start date
End date
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
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

Diem, Andrea, Christian Gschwendt and Stefan C. Wolter. 2024. "University Dropouts and Firm Hiring." AEA RCT Registry. March 06.
Experimental Details


No intervention planned.
Intervention Start Date
Intervention End Date

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
Was the treatment clustered?

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)

Institutional Review Boards (IRBs)

IRB Name
IRB Approval Date
IRB Approval Number