Productivity trends from 1890 to 2012 in advanced countries
Bibliographic record
Abstract
In order to examine innovation diffusion and convergence processes, we study productivity trends, trend breaks and levels for 13 advanced countries over 1890-2012. We highlight two productivity waves, a big one following the second industrial revolution and a small one following the ICT revolution. The first big wave was staggered across countries, hitting the US first in the Interwar years and the rest of the world after World War II. It came long after the actual innovation could be implemented, emphasizing a long diffusion process. The productivity leader changed during the period under study, the Australian and UK leadership becoming a US one during the first part of the XXth century and, for very particular reasons, also a Norwegian, Dutch and French one at least for some years at the end of the XXth century. The convergence process has been erratic, halted by inappropriate institutions, technology shocks, financial crises but above all by wars, which led to major productivity level leaps, downwards for countries experiencing war on their soil, upwards for other countries. Productivity trend breaks are detected following wars, global financial crises, global supply shocks (such as the oil price shocks) and major policy changes (such as structural reforms in Canada or Sweden). The upward trend break for the US in the mid-1990s is confirmed, as well as the downward trend break for the Euro Area in the same period. The downward trend break observed as early as the mid-2000s for the US leads one to question the future contribution of the ICT revolution to productivity enhancement.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.008 | 0.013 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".