The Effects of Information Technology Achievement and Diffusion on Foreign Direct Investment
Bibliographic record
Abstract
Abstract Foreign direct investment (FDI) and the new information and communications technology (ICT) have gained significant grounds in many parts of the world in somewhat parallel fashion. The objective of this paper is to assess the proposition that the level of technological achievement and diffusion is a determining factor in attracting FDI in high-income countries. A sample of technologically advanced countries was chosen on the basis of the technological achievement index (TAI). Crosscountry data for the period of 1994 to 1998 were used to estimate a fixed effects model. The empirical results obtained provide strong evidence that technology diffusion of new instruments of ICT, such as mobile phones and Internet hosts, are major pull factors of FDI. The results also provide evidence that robust economic environment, low unit cost, and high degree of openness are other essential determinants of FDI. We conclude that in order to retain and attract FDI, countries should create opportunities for useful innovations to be created and diffused, as well as maintain flexible, competitive and dynamic economic environments. The main policy implication for countries lagging in terms of attracting foreign investment is to build on reforms that emphasize creation and diffusion of ideas and products, as well as maintain a high degree of openness to new investors, especially in ICT.
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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 | 0.002 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".