Do Differences in School Quality Generate Heterogeneity in the Causal Returns to Education?
Bibliographic record
Abstract
Estimating the returns to education remains an active area of research amongst applied economists.Most studies that estimate the causal return to education exploit changes in schooling and/or labor laws to generate exogenous differences in education.An implicit assumption is that more time in school may translate into greater earnings potential.None of these studies, however, explicitly consider the quality of schooling to which impacted students are exposed.To extend this literature, we examine the interaction between school quality and policy-induced returns to schooling, using temporally-available school quality measures from Card and Krueger (1992).We find that additional compulsory schooling, via either schooling or labor laws, increases earnings only if educational inputs are of sufficiently high quality.In particular, we find a consistent role for teacher quality, as measured by relative teacher pay across states, in generating consistently positive returns to compulsory schooling.
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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 | 0.018 | 0.072 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".