Study of the Influence Factors of the Attraction of FDI in the Economy of the Recipient State - Example of Russia
Bibliographic record
Abstract
This article focuses on the formation of new scientific decisions regarding the study of factors that influence the attraction of FDI in the economy of the host state using Russia as an example. The study focuses on the need for stimulation of FDI in the Russian economic system so as to overcome the recession and the impact of economic sanctions. Based on the analysis of the dynamics of FDI per capita, inflows, outflows, FDI balances in Russia, as well as a three-year forecast, a conclusion about the critical state of attracting such investments to the economy of the recipient country is made. Separately, on the basis of critical literary analysis, the concept of identifying the factors influencing the attraction of FDI in the host economy is selected, and seventeen of the main factors of such influence for the Russian Federation are identified. Based on a quantitative analysis of the identified factors influencing the attraction of FDI in the Russian economy, the directions of their influence (positive or negative) are identified. It is noted that only three of the selected influence factors have a positive effect on attracting FDI into the economy of the Russian Federation and ten identified factors have a negative effect, which is a critically negative aspect for the host state selected for research within the framework of its competitiveness in the market of investment resources. It is indicated that Russia needs to change the strategy for attracting FDI into the national economic system based on the development and implementation of a set of long-term measures. Also, based on the use of a comparative assessment, significant differences between the factors influencing the attraction of FDI in developed and developing countries is proved.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".