Estimates of the Productivity Trend Using Time-Varying Parameter Techniques
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Bibliographic record
Abstract
In the second half of the 1990s, U.S. productivity growth moved up to rates not seen in several decades. In this paper, I use time-varying parameter techniques to isolate trend from cyclical movements in productivity and to obtain an estimate of the trend rate of productivity growth. I examine models both with and without an explicit role for capital accumulation. I find that in the models without an explicit role for capital accumulation, trend productivity growth is estimated to have moved up from around 1-1/2 percent in the period from the early 1970s to the mid 1990s, to about 2-1/2 percent by the final observation used in this paper, the second quarter of 2000. I find that if I allow for an explicit role for capital accumulation, the recent pace of trend productivity growth is even higher, at around 3 percent.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it