The External Economic Environment as an Attractive Factor for Foreign Direct Investment in Kosovo: An Econometric Analysis
Bibliographic record
Abstract
This study aims to analyze the impact of key economic factors on the motivation of foreign investors in Kosovo, focusing on indicators such as exchange rate (ER), inflation (INF), interest rates (IR), access to credit (AC), competition (CO), banking services and e-banking (E-BBS), economic growth (EGr), and taxes (Tax).Primary data were collected through a structured questionnaire randomly distributed to a sample of 148 foreign investors.The reliability and validity of the data were assessed through statistical tests such as Cronbach's Alpha, KMO and Bartlett's Test, as well as Principal Component Analysis (PCA) and Collinearity Statistics.For data analysis, multiple linear regression and Kendall's Tau-b analysis were applied using the SPSS 25 statistical software package.The findings highlight that Economic Growth, strongest positive impact (B=6.497,p<0.001), indicating that GDP growth enhances investor confidence.ER, significant positive effect (B=2.584, p =0.009), highlighting the importance of currency stability.INF Rate, notable positive influence (B=2.734,p=0.019), suggesting that predictable INF is acceptable to investors.AC, strong negative effect (B=-5.575, p<0.001), emphasizing that limited credit access deters investment.IR, marginal negative impact (B=-3.144,p=0.055), implying that higher IR may reduce investment motivation.The model explains 30.8% of the variability in investor motivation, implying that other factors also deserve further analysis.Kendall's tau-b analysis confirms a strong correlation between ER and AC with investor motivation, while IR and banking services show a weak correlation.In line with Dunning's theory, location advantages-such as the ER and economic growth-play a vital role in attracting investments, while internalization advantages -such as AC-also contribute significantly.This research challenges traditional views by revealing a positive relationship between INF and foreign direct investment (FDI), offering new perspectives for policymakers.It recommends focusing on ER stability, improving AC, and undertaking structural reforms as mechanisms for boosting foreign investment in Kosovo.The original contribution of this study lies in identifying a positive link between INF and FDI, as well as highlighting less-studied factors such as ER and credit access as key elements.Additionally, it emphasizes the impact of competition and banking services, the challenges to economic growth, and the role of structural reforms and fiscal incentives in attracting new investors.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".