Valuing Early Career Opportunities

Last registered on October 27, 2025
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Pre-Trial

Trial Information

General Information

Title
Valuing Early Career Opportunities
RCT ID
AEARCTR-0016906
Initial registration date
September 29, 2025

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
October 27, 2025, 6:15 AM EDT

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

Locations

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Primary Investigator

Name
Michelle Jiang
Affiliation
University of Massachusetts Boston
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Other Primary Investigator(s)

PI Name
Sydnee Caldwell
PI Affiliation
UC Berkeley
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Additional Trial Information

Status
In development
Start date
2025-10-27
End date
2026-08-31
Keywords
Labor
Additional Keywords
JEL code(s)
J24, J3
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
How do first-time workers make choices about jobs? We partner with the UMass Boston Career Center and the UC Berkeley Career Center to conduct a survey of the senior graduating Classes of 2025 and 2026. Using hypothetical firm-choice experiments, we examine how students trade off first-job salaries with other human capital investments.
External Link(s)

Registration Citation

Citation
Caldwell, Sydnee and Michelle Jiang. 2025. "Valuing Early Career Opportunities." AEA RCT Registry. October 27. https://doi.org/10.1257/rct.16906-1.0
Sponsors & Partners

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Experimental Details

Interventions

Intervention(s)
Intervention Start Date
2025-10-27
Intervention End Date
2025-11-30

Primary Outcomes

Primary Outcomes (end points)
Our endpoints of interest are (1) relative preferences over salary, human capital development, signaling, and future networks, measured via the revealed preference hypothetical firm-choice surveys, and (2) whether the salience of AI matters for these choices.
Primary Outcomes (explanation)
We run a regression on subjects' choices, with the randomized differences in salary, human capital, prestige, and networks as the covariates. We also do an interaction of this with the AI treatment.

Secondary Outcomes

Secondary Outcomes (end points)
We plan to test whether effects differ across the following heterogeneity splits: gender, race, socioeconomic status, parental education, family income, risk aversion, financial liquidity, impatience, STEM vs. non-STEM, beliefs about the importance of first jobs, and beliefs about the impacts of AI. Long-run, we hope to also match subjects' choices to their post-graduation outcomes.
Secondary Outcomes (explanation)
We suspect relative preferences will be different based on subjects' characteristics. Splitting our results across heterogeneity will allow us to observe this.

Experimental Design

Experimental Design
We partner with the UMass Boston Career Center and the UC Berkeley Career Center to conduct a survey of the senior graduating Classes of 2025 and 2026. Using hypothetical firm-choice experiments, we examine how students trade off first-job salaries with other human capital investments.
Experimental Design Details
Not available
Randomization Method
Our survey runs in Qualtrics, which allows us to randomize both the hypothetical-firm choice draws and whether a respondent is shown the AI treatment or not.
Randomization Unit
Randomization is done at the individual level. Each individual completes 18 firm-choice survey questions.
Was the treatment clustered?
Yes

Experiment Characteristics

Sample size: planned number of clusters
We plan to survey at least 1000 individuals, across 2 schools. If we do not attain enough individuals across the two schools, we are considering expanding the survey to other schools in the University of Massachusetts and University of California system.
Sample size: planned number of observations
1000 individuals
Sample size (or number of clusters) by treatment arms
1000 individuals, split 500/500 into the AI treatment vs. not.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
With 1000 individuals, assuming 80% power and 5% statistical significance, our minimum detectable effect size is 0.18 of a standard deviation.
Supporting Documents and Materials

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IRB

Institutional Review Boards (IRBs)

IRB Name
UMass Boston Institutional Review Board
IRB Approval Date
2025-10-15
IRB Approval Number
3900
Analysis Plan

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