The Effects of Foreign Direct Investment in the Insurance Industry in the Western Balkan Countries – A Panel Data Analysis
Bibliographic record
Abstract
The insurance industry is a solid cornerstone of the financial system, and by fostering an environment more favorable to investment, it significantly boosts economic growth.This is especially true for the Western Balkan countries, which are currently growing and joining the European Union.Developing countries can benefit from an additional avenue: the internationalization of insurance businesses through the reinsurance process.The aim of this research is to assess the potential impact of Foreign Direct Investment (FDI) on the insurance industry in the region.The research was carried out in six countries: Albania, Bosnia and Herzegovina, Montenegro, North Macedonia, Kosovo, and Serbia.Data from these countries were collected between 2004 and 2021, allowing for an analysis using panel data econometric models, namely the Fixed Effects and Random-Effects models (GLS).The findings of the economic analysis for the three independent variables indicate that FDI inflow positively affects Gross Written Premium (GWP), Insurance Assets (InsAsset), and Penetration Rate (PenetRate) at the α=0.05 significance level.Additionally, the models have demonstrated that the three variables have variations between the countries regarding the impact of FDI inflows using the Lagrange Multiplier (LM) Method -Breusch-Pagan test, at a confidence level of α=0.05.Since the econometric models for the three cases are based on the Random-Effects model (GLS Method), random effects are to blame for the variations in FDI influence between countries.The results have consequences for the insurance industry as well as regional policymakers, especially in Kosovo, who are deciding what measures to take to promote foreign direct investment.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".