DOES ICT GENERATE ECONOMIC GROWTH? A META‐REGRESSION ANALYSIS
Bibliographic record
Abstract
Abstract Despite phenomenal technological progress and exponential growth in computing power, economic growth remains comparative sluggish. In this paper, we investigate two core issues: (1) is there really no connection between ICT and national economic growth? and (2) what factors moderate the ICT–growth relationship? We apply meta‐regression analysis to 466 estimates drawn from 59 econometric studies that explore the Solow or Productivity Paradox that there is little impact of ICT on economic growth and productivity. We explore the differential impact of ICT on developed and developing countries and the differential impact of different types of ICT: landlines, cell phones, computer technology and Internet access. After accommodating potential econometric misspecification bias and publication selection bias, we detect evidence that ICT has indeed contributed positively to economic growth, at least on average. Both developed and developing countries benefit from landline and cell technologies, with cell technologies’ growth effect approximately twice as strong as landlines. However, developed countries gain significantly more from computing than do developing countries. In contrast, we find little evidence that the Internet has had a positive impact on growth.
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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 | 0.036 | 0.103 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.035 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| 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".