Will fast productivity growth persist
Bibliographic record
Abstract
Strong productivity growth is essential for improving living standards and can have an important impact on economic policy, yet economists are far from being experts at predicting when the trend of productivity growth might shift. In the 1960s, productivity growth boomed, growing at an average annual rate of 2-1/2%. It weakened in the early 1970s, and for the next two decades or so averaged an annual growth rate of only about 1-1/4%. Then, in the mid-1990s, productivity growth boomed again, averaging about a 3% annual rate from the last quarter of 1995 through the middle of 2004. These shifts were not predicted and were generally not widely recognized until years after they occurred. Considering that, since the middle of 2004, productivity growth has averaged only about 1-1/2% per year, it may be time to ask whether this is just a \\"pause\\" in the boom that started in the mid-1990s or a shift back to the growth rates seen in the 1970s and 1980s. This Economic Letter begins to answer this question by focusing on the factors that underlay the most recent productivity boom and what they may portend for the future.
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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 | 0.007 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.015 | 0.011 |
| Insufficient payload (model declined to judge) | 0.028 | 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".