A Panel Granger Causality Test of Investment in ICT Capital and Economic Growth: Evidence From Developed and Developing Countries
Bibliographic record
Abstract
This paper applies the Pairwise Panel Granger Causality test to examine the relationship between ICT (information and communication technology) expenditure and the rate of growth of GDP (gross domestic product) per capita. This is accomplished by using cross-country time-series data for a total of 70 developed and developing countries for the period from 2003 to 2008. The study reveals that the existence of causality and its direction differ across different income-group of countries and over the number of lags included. ICT investment expenditure as a percentage of GDP appears to cause the rate of growth of GDP per capita for the high income group and all income groups combined with lags higher than one year. However, for the upper-and lower-middle income groups, the study detects causality in neither direction. Also, when only one lag is included, the study suggests no causality in either direction for any of the income-groups of countries.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".