The ICOP Manufacturing Database: International Comparisons of Productivity Levels
Bibliographic record
Abstract
International productivity comparisons have traditionally focused on productivity growth rates. International productivity level comparisons are much more complex, requiring comparable industry data and estimates of purchasing power at a detailed industry level. The International Comparisons of Output and Productivity (ICOP) project established at the University of Groningen in the Netherlands in 1983 has pioneered the development of international estimates of productivity levels by industry. In this article Bart van Ark and Marcel Timmer, two economists from the University of Groningen, provide an overview of the ICOP manufacturing database. They note that the novelty of the ICOP approach is the derivation and use of industry-specific purchasing power parities based on producer output data instead of final expenditure information. A key finding that emerges from their research is the difference between labour productivity levels measured in terms of output per person employed and per hour. By the former measure, the United States has by a wide margin the highest level of labour productivity in manufacturing. But when the more appropriate output per hour measure of productivity is used, the United States is no longer the manufacturing productivity leader, being surpassed by the Netherlands and Belgium. The much greater number of annual hours worked in the United States accounts for this discrepancy.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.033 | 0.077 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.035 | 0.023 |
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".