Total Factor Productivity in Advanced Countries: A Longterm Perspective
Bibliographic record
Abstract
Changes in GDP during the 20th century have been mainly driven by total factor productivity (TFP). This article synthesizes results from our research based on the long period (1890-2015) productivity database we have constructed. In particular, we aim to refine our TFP measure by including the contribution of the improved quality of factor inputs and technology diffusion to TFP growth in four developed areas or countries: the United States, the euro area, the United Kingdom, and Japan. Two types of factor quality are considered: the average level of education and the average age of equipment. Two technological shocks corresponding to two general purpose technologies are investigated: electricity and information and communication technologies (ICT). However, even after these adjustments, long-term patterns of TFP growth do not change, with two major waves appearing over the past century and much of TFP growth remaining unaccounted for by quality-adjusted factors of production and technology diffusion. Our estimates show that the productivity impact of the recent ICT wave remains much smaller than that from the electricity wave, and that the post-1973 and the most recent slowdowns in TFP growth are confirmed.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.014 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".