ICT use and total factor productivity growth: intangible capital or productive externalities?
Bibliographic record
Abstract
What accounts for the exceptional TFP growth performance in some ICT-using industries after the mid-1990s in the USA and some other OECD countries? Productivity gains in the production of ICT are given as the answer. But technical progress in upstream industries, in general, should not raise TFP growth in downstream industries. This article investigates two explanations for this apparent puzzle: the existence of intangible capital and the externalities of ICT investment. Using newly constructed comprehensive data covering 16 OECD countries for 24 industries for a period of 32 years, I find evidence of intangible capital accumulation, but no evidence of positive spillovers from ICT use. Results show that what would have considered as a perfect case of spillovers from ICT use under conventional method is the impact of R&D and other intangible capital. Once these two channels are accounted for in the model, neither domestic nor foreign ICT spillovers exist.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".