Productivity – your countries need you!
Bibliographic record
Abstract
Increased global productivity could boost real wages, consumption, fiscal positions and alleviate fears of secular stagnation. But will it? Puzzles relate to the longer term global slowdown and to some countries' recent productivity‐less recoveries in jobs. We assess various explanations including mismeasurement, secular stagnation, financial sector malfunction and increased labour market flexibility. Our baseline forecast is for a moderate pro‐cyclical recovery in productivity but we show how downside risks imply it could be anaemic. Sustained weak productivity is a secular issue. Eight years after 2007, median productivity growth in OECD economies is less than Japan's was eight years into its lost decade. Aspects of secular stagnation and balance sheet adjustment have contributed. Measurement error may have played a role over the longer term. Recent experience divides recoveries into “haves” and “have nots” in terms of productivity and employment. The UK may finally be emerging from a “productivity‐less” recovery in employment after 2011; Spain and the Netherlands have experienced jobless recoveries in productivity; others, such as Canada and Sweden, have experienced pro‐cyclical (typically weak) recoveries in productivity; Italy hardly got going in either direction. Most theories provide, at best, a limited explanation for recent weak productivity performance. These include data mis‐measurement, increased labour market flexibility, financial sector malfunction and supply side secular stagnation. On balance, we think that a modest productivity bounce‐back could be imminent, caused by some demand recovery, tighter labour markets in major economies, higher real wages and firms deciding to invest more in capital, which enhances productivity and points the global economy towards normality. We also illustrate how global risk scenarios could dampen recovery. Negative skews imply mean G7 productivity growth across the scenarios would be an anaemic 1.1% in 2016, 0.5 percentage points (pp) lower than the baseline.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.015 | 0.012 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.207 | 0.175 |
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".