The long-term effects of financial aid and career education: Evidence from a randomized experiment
Bibliographic record
Abstract
I study the effects of the Future to Discover Project, a randomized experiment in which Canadian high school students were either invited to participate in career planning workshops or were made eligible for an $8,000 college grant. By matching the experimental data to post-secondary institution records and income tax files, I am able to examine the effects of the interventions on college enrollment, graduation, and earnings in adulthood. I show that the career education intervention greatly improved students' outcomes in the long run by improving academic matching. In contrast, the college grant had no long-term monetary benefits despite increasing college enrollment, which is consistent with classical models of human capital investment in the absence of credit constraints. My findings suggest that informational frictions and behavioral obstacles-rather than financial constraints-represent the primary barrier to four-year college enrollment faced by low-income students. And that they explain a large part of the gap in four-year college enrollment between high- and low-income students.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".