Of Yeast and Mushrooms: Patterns of Industry-Level Productivity Growth
Bibliographic record
Abstract
Abstract In this paper we analyse labour productivity growth in the United States, four European countries (France, Germany, the Netherlands and United Kingdom), Australia and Canada between 1987 and 2003 from an industry perspective. Rather than analysing broad industry groups, we compare the pattern of growth in all industries through Harberger diagrams. We introduce new summary measures, which indicate the pervasiveness of growth patterns. These indicators show that investment in both information and communication technology (ICT) and non-ICT capital is fairly balanced or ‘yeasty’, driven by overall macro-economic conditions. However, growth of total factor productivity (TFP) is much more localized or ‘mushroom-like’. In particular we find a clear distinction between countries in continental Europe, in which TFP is decelerating after 1995 and becoming more localized, and Anglo-Saxon countries in which TFP growth is accelerating and becoming more broad-based, especially after 2000. The increased breadth of Anglo-Saxon TFP growth is consistent with delayed effects of intangible investments that are complementary to ICT investments.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".