Is the Recent Productivity Boom Over?
Bibliographic record
Abstract
Productivity growth has been quite strong over the past 2 years, despite a drop in the second quarter of 2010. Many analysts believe that productivity growth must slow sharply in order for the labor market to recover robustly. However, looking at the observable factors underlying recent productivity growth and the patterns of productivity over past recessions and recoveries, a sharp slowdown appears unlikely. Labor productivity, defined as output per hour of labor, unexpectedly stalled in the second quarter of 2010, falling by a 1.1 % annual rate in the total business sector based on data available through the end of August. This follows 2 years of generally strong productivity growth, which started when the recession began at the end of 2007. In fact, the annualized 2.5 % pace of labor productivity growth during the latest recession, which appears to have ended in mid-2009, was the fourth strongest of the 11 recessions since World War II. Post-recession, from the third quarter of 2009 to the second quarter of 2010, productivity grew at an even faster annual pace of 2.8%, even with the second-quarter drop. This strong growth is one reason for the scant downward movement in the unemployment rate despite moderate GDP gains. Businesses have been able to meet demand for their products and services without hiring new workers or increasing the hours of current staff because they are managing to get more from each hour of labor.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.022 | 0.005 |
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".