Crossing the Finish Line but Losing the Race? Socioeconomic Inequalities in the Labor Market Trajectories of Community College Graduates
Bibliographic record
Abstract
Abstract Despite decades and hundreds of billions of dollars of federal and state investment in policies to promote postsecondary educational attainment as a key lever for increasing the economic mobility of lower income populations, research continues to show large and meaningful differences in the mid-career earnings of students from families in the bottom and top income quintiles. Prior research has not disentangled whether these disparities are due to differential sorting into colleges and majors, or due to barriers that lower socioeconomic status (SES) graduates encounter during the college-to-career transition. Using linked individual-level higher education and Unemployment Insurance records for nearly a decade of students from the Virginia Community College System, we compare the labor market outcomes of higher and lower SES community college graduates within the same college, program, and academic performance level. Our analyses show that, conditional on employment, lower SES graduates earn nearly $500/quarter less than their higher SES peers one year after graduation, relative to higher SES graduate average of $10,846/quarter. The magnitude of this disparity persists through at least three years after graduation. Disparities are concentrated among non-nursing programs, in which gaps persist seven years from graduation. Our results highlight the importance of greater focus on the college-to-career transition.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".