Measuring ‘indirect’ investments in ICT in OECD countries
Bibliographic record
Abstract
ICT components, such as microprocessors, may be embodied in other capital goods not recorded as ICT in National Accounts. We name ‘indirect ICT investment’ the value of embodied ICT components in non-ICT investment. The paper provides estimates of ‘indirect ICT investment’ based on detailed and unpublished Supply-Use tables (SUT) in 12 OECD countries: Australia, Belgium, Canada, Chile, Czech Republic, Denmark, France, Germany, Japan, Israel, Mexico, New Zealand, the United Kingdom, and the United States.Our main finding is that ICT investment appears significantly higher when considering its indirect component, the average increase being about 35%. The inclusion of indirect ICT investment, excluding software (for which firms’ expenditures are difficult to measure), changes significantly the relative position of countries with respect to the ICT intensity of their investments. The inclusion of software further increases indirect ICT investment but the increase is smaller (in percentage) than without this inclusion. A final result, but concerning only three countries, it that the diagnosis of a stabilisation, or even a decrease, of ICT investment in percentage of GDP or of total investment, observed from the beginning of the century, is not modified if we take into account the indirect ICT investment.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".