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 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".