The Returns to Education: A Review of the Macro-Economic Literature
Bibliographic record
Abstract
authors and do not necessarily reflect the views of the Department of Education and Employment. All errors and omissions remain the authors. This work is from a forthcoming publication in the Department of Education and Employment Research Series. EXECUTIVE SUMMARY Over the last two decades there has been an outpouring of empirical work exploring the impact of ‘human capital ’ – a concept of worker quality and skills generally measured by formal education – on the level and growth of productivity. In this report, we review the empirical macro-econometric literature on productivity and education with a particular focus on UK policy. We detail over twenty studies, giving both summaries and critiques, as well as attempting to put all the studies in a common quantitative form The idea of positive educational externalities is that the benefits from education have the potential to spill-over to other individuals. The new growth theory emphasises the higher rate of innovation that can be generated by having more educated workers generating new ideas. There are also other types of education-related externalities that may have an effect on the level of GDP per capita (like lower unemployment, lower crime, etc.).
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.012 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| 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".