Determinants of country-level investment in information technology
Bibliographic record
Abstract
Investment in information technology (IT) is an important driver of economic growth and productivity in the United States and other developed countries, but as yet it is not shown to be a significant driver in developing countries. Previous research suggests that IT investment and complementary assets are insufficient for developing countries to realize economic benefits. This research note examines the factors that influence IT investment in developed and developing countries to determine how greater investment might be stimulated to achieve productivity gains. We use the flexible accelerator model of investment and find that it is a good predictor of country-level IT investment. We also extend the model to include country-level variables, and find a negative relationship between IT investment and interest rates, but positive and significant relationships between investment, openness to trade, and telecommunications infrastructure. When we include interaction effects between national income levels and country variables, we find that the impacts of interest rates, size of the financial sector, teledensity, and intellectual property rights are strongest in shaping IT investment for developed countries. In contrast, we find that the impact of openness to trade is greater for developing countries, as is the size of government and education levels. © 2007 INFORMS.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".