Matriculation in U.S. Economics Ph.D. Programs: How Many Accepted Americans Do Not Enroll?
Bibliographic record
Abstract
Using a sample of 26 U.S. economics Ph.D. programs in Fall 2003, we estimate that only about 12 percent of the U.S. and Canadian students accepted for doctoral study did not enroll in any U.S. economics Ph.D. program in Fall 2003 or Fall 2004. It is not possible to increase the supply of new Ph.D. economists substantially by "closing the sale" on accepted applicants: additional qualified applicants are needed. Nonmatriculants are remarkably similar to enrollees in demographics, prior education, test scores, and fields of special interest, but express less interest in economic research and are less likely to have been offered financial aid. An expected financial aid deficiency was also the most-cited reason for deciding not to matriculate, followed by how long it takes to earn an economics Ph.D., and the expectation of higher lifetime earnings in a career other than economics. Most who decided against an economics Ph.D. enrolled in an alternative graduate program.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".