Productivity Levels in Transport, Storage and Communication: A New ICOP 1997 Data Set
Bibliographic record
Abstract
This working paper provides industry-specific purchasing power parities for gross output in the transportation and communication sector. The calculation of these output PPPs builds on earlier work by the International Comparisons of Output and Productivity (ICOP) project in this field. The paper reviews the existing methods and develops a new system which takes full advantage of the improved data situation. The study captures the transportation and communication sectors of 32 countries (EU-25, Australia, Canada, Japan, Korea, New Zealand, Taiwan and United States). The second part of the paper applies the PPPs to productivity measures obtained from the EU KLEMS database and the 60-industry Database of the Groningen Growth and development Centre. This results in a consistent and comparable set of productivity levels at detailed industry level. We find that differences in productivity between the United States and other industrialized countries are only partly due to differences in industry structure. The United States especially outperform the EU-15 and Asia on productivity levels in land transport. Eastern European countries are still showing much lower productivity levels, except for land transport where they can become a though competitor for the former EU-15.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.028 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.006 |
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".