Evaluating the effects of investment in information and communication technology
Bibliographic record
Abstract
Most of the studies on the consequences of information and communication technology (ICT) have been focused on US aggregate data. In contrast to these studies, this paper empirically assesses the industrial effect of ICT investment on three key variables – real output, employment, and labour productivity – in some European Union-15 (EU-15) countries and the USA using panel-vector autoregression models. An increase in ICT investment is positive for the economies of these countries, giving rise to larger growth in real output, employment, and labour productivity at the industrial level. The pattern of responses to changes in ICT investment is quantitatively diverse across most of the EU-15 countries studied and in the two types of industries considered (i.e. ICT-intensive and less intensive industries). Moreover, the positive impact on labour productivity in ICT-intensive industries is larger after the mid-1990s, with the USA being the most positively affected country.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".