Who Rides Out the Storm? The Immediate Post-College Transition and its Role in Socioeconomic Earnings Gaps
Bibliographic record
Abstract
Despite a large earnings premium for bachelor's degree completion in general, graduates from lowincome families earn substantially less than graduates from high-income families.While prior research has documented the role of college quality and major choice in explaining these gaps, we examine undermatching on a different margin: the first (post-college) job transition.The transition from college to the labor market can be challenging to navigate, and students with financial, informational, or other disadvantages during the job search may be more likely to "undermatch" to their first job.Using administrative data from a large, urban, public college system, we document large gaps in earnings five years after graduation by SES (proxied by financial aid receipt) that remain unexplained even after controlling for GPA, college, field of study, and other pregraduation characteristics.We then examine how features of the initial job transition relate to longer-term earnings, and to what extent differences in the first job transition can explain later SES earnings gaps.Our results show that first job transitions are rocky for many graduates, strongly predict earnings at Year 5, and are a substantial mediator of socioeconomic gaps in earnings five years after college graduation-reducing the unexplained gap by almost two-thirds.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".