Market concentration and foreign direct investment in the financial leasing sector of the Republic of Serbia
Bibliographic record
Abstract
The financial leasing market in previous years is characterised by a growth that is also expected in the coming period. Besides, developing countries are striving to attract as much foreign direct investment (FDI) as possible to accelerate economic growth and achieve macroeconomic stability. The aim of this paper is to determine whether there is a relationship between FDI and the level of market concentration in the financial leasing sector of the Republic of Serbia and to determine whether this relationship is long-term or short-term. Quarterly data from the first quarter of 2006 to the first quarter of 2019 were used. Autoregressive Distributed Lag approach (ARDL) and bounds test were used for data analysis. The results showed that there is a negative relationship between FDI and the level of market concentration in the financial leasing sector of the Republic of Serbia in the long run, while there is no statistically significant relationship between FDI and the level of market concentration in the short run.
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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 | 0.000 | 0.001 |
| 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 | 0.002 | 0.001 |
| 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".