Determinants of FDI Stock in Some Central European Countries
Bibliographic record
Abstract
Given the importance of foreign direct investment (FDI) in the economy, the purpose of this study is to identify and investigate the economic indicators that can explain the development of FDI in the economies of Central and Eastern European countries such as the Czech Republic, Poland, Hungary, and Slovenia throughout the period 1995–2020. When developing multiple linear regression models, the following explanatory variables were considered: exports, imports, import concentration and diversification indices, the balance of trade, the balance of payments, and different components of the economic freedom index. Therefore, it was shown that a rise in exports and imports has a beneficial impact on enhancing the flow of foreign direct investment (FDI) in each of the nations examined for this study. Furthermore, an increase in the value of the import diversification index is shown to have a beneficial effect on the levels of foreign direct investment (FDI) in the Czech Republic, Hungary, and Slovenia, as determined by this study. On the other hand, the import concentration index has been shown to benefit foreign direct investment in Poland. Furthermore, it was discovered that the balance of payments was a positive factor in the Hungarian economy. In contrast, the trade balance was shown to be a positive element in Poland and Slovenia. Both indicators have positively impacted foreign direct investment (FDI) flow.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".