Bibliographic record
Abstract
In this paper, we study the effects of education on the total factor productivity (TFP) of a large number of countries. We estimate TFP using a variant of augmented Solow growth model in which health capital is one of the factors of production. We find that quantity of education significantly and positively affects TFP. This result is in contrast to the findings of the previous literature, that suggest that either the quantity of education does not matter for growth (e.g. Benhabib and Spiegel 1994, Caselli et al. 1996) or only the quality of education matters for growth (e.g. Hanushek and Kimko 2000). We also find that TFP differences explain about 1/3rd of per-capita real income differences across countries. This estimate is substantially lower than the existing estimates (e.g. Klenow and Rogriguez-Clare 1997, Hall and Jones 1999) which suggest that TFP differences are the dominant source of per-capita real income differences across countries.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 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) | 0.007 | 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".