Labor Market Returns to MBAs From Less‐Selective Universities: Evidence From a Field Experiment During COVID‐19
Bibliographic record
Abstract
Abstract Master's degree enrollment and debt have increased substantially in recent years, raising important questions about the labor market value of these credentials. Using a field experiment featuring 9,480 job applications submitted during the early months of the COVID‐19 pandemic, I examine employers’ responses to job candidates with a Master of Business Administration (MBA), which represents one‐quarter of all master's degrees in the United States. I focus on MBAs from three types of less‐selective institutions that collectively enroll the vast majority of master's students: for‐profit, online, and regional universities. Despite the substantial time and expense required for these degrees, job candidates with MBAs from all three types of institutions received positive responses from employers at the same rate as candidates who only had a bachelor's degree—even for positions that listed a preference for a master's degree. Additionally, applicants with names suggesting they were Black men received 30 percent fewer positive responses than otherwise equivalent applicants whose names suggested they were White men or women, providing further evidence of racial discrimination in hiring practices.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".