Productivity, Technology and Economic Growth: What is the Relationship?
Bibliographic record
Abstract
The relationship between productivity, technology and economic growth has been debated extensively in the endogenous growth, growth accounting, New Economy and policy literature. This paper briefly surveys the literature on total factor productivity (TFP) calculations – the various techniques and problems associated with it. We argue that TFP is not a measure of technological change and only under ideal conditions does it measure the supernormal profits associated with technological change. The critical driving force of economic growth is not the super normal profits that technological change generates but rather the continuous creation of opportunities for further technological development. Six illustrations of when TFP fails to correctly measure these super normal profits are provided. A version Carlaw and Lipsey’s (2003b) model of endogenous general purpose technology‐ driven growth is then utilized to make some progress toward answering Prescott’s (1998) call for a theory of TFP. The model is used to simulate artificial data and connect theoretical assumptions of returns to scale and resource costs to the conditions under which TFP miss‐measures the actual growth of technological knowledge.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.011 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.008 | 0.018 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".