The efficiency of educational production: A comparison of Denmark with other OECD countries
Bibliographic record
Abstract
Denmark, Norway, New Zealand, Canada and the USA are the OECD countries that spend most on education, measured in relation to GDP. Focusing in particular on upper secondary education, this paper examines whether the heavy expenditure on education in Denmark is matched by high output from the educational sector, both in terms of a large number of students enrolled in educational programmes and a high completion rate. The methodology used is to compare (benchmark) Denmark with a relevant group of countries and to calculate how much cheaper Denmark could teach the same number of students and maintain the same graduation/completion rates as today if the country could achieve the same level of cost effectiveness as its most efficient counterparts. Comparing Denmark to a group of the richest OECD countries reveals that potential savings lie between 12 and 34 percent. Figures that fall to between zero and nine percent when a comparison is made between Denmark and other Northern European countries. On the input side, the slightly weaker academic level among young people in Denmark on completion of lower secondary education – as measured by the PISA scores of the various countries – goes some way to explain the higher costs of upper secondary education. On the output side, if earnings and levels of employment among those who complete their education are taken into account, then Denmark is in fact found to be efficient. However, this high level of efficiency has become less clear-cut in recent years, since expected earnings (multiplied by rate of employment) is currently falling in comparison with the expected earnings in the peer countries. This might be an indication that Denmark’s current position is not stable, unless the present situation is entirely attributable to the economic downturn in the wake of the financial crisis.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".