Mind the Gap! International Comparisons of Productivity in Services and Goods Production
Bibliographic record
Abstract
Abstract In this paper, we make a comparison of industry output, inputs and productivity growth and levels between seven advanced economies (Australia, Canada, France, Germany, the Netherlands, United Kingdom and United States). Our industry-level growth accounts make use of input data on labour quantity (hours) and composition (schooling levels), and distinguish between six different types of capital assets (including three information and communication technology (ICT) assets). The comparisons of levels rely on industry-specific purchasing power parities (PPPs) for output and inputs, within a consistent input-output framework for the year 1997. Our results show that differences in productivity growth and levels can be mainly traced to market services, not to goods-producing industries. Part of the strong productivity growth in market services in Anglo-Saxon countries, such as in Australia and Canada, may be related to relatively low productivity levels compared with the United States. In contrast, services productivity levels in continental European countries were on par with the United States in 1997, but growth in Europe was much weaker since then. In terms of factor input use, the United States is very different from all other countries, mostly because of the more intensive use of ICT capital in the United States.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".