Does Federal Student Aid Raise Tuition? New Evidence on For-Profit Colleges
Bibliographic record
Abstract
We use administrative data from five states to provide the first comprehensive estimates of the size of the for-profit higher education sector in the U.S. Our estimates include schools that are not currently eligible to participate in federal student aid programs under Title IV of the Higher Education Act and are therefore missed in official counts. We find that the number of for-profit institutions is double the official count and the number of students enrolled during the year is between one-quarter and one-third greater. Many for-profit institutions that are not Title IV eligible offer certificate (non-degree) programs that are similar, if not identical, to those given by institutions that are Title IV eligible. We find that the Title IV institutions charge tuition that is about 78 percent higher than that charged by comparable institutions whose students cannot apply for federal financial aid. The dollar value of the premium is about equal to the amount of grant aid and loan subsidy received by students in eligible institutions, lending some credence to a variant of the "Bennett hypothesis" that aid-eligible for-profit institutions capture a large part of the federal student aid subsidy.
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 | 0.004 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".