THE INFLUENCE OF COVID-19 PANDEMIC ON CROATIAN LIFE INSURANCE MARKET
Bibliographic record
Abstract
The novel coronavirus pandemic has had numerous negative consequences on different aspects of human life and the economy. Therefore, the authors wanted to see how the closing of social and economic activities and imposed measures have affected insurance companies' activities conducting life insurance business in Croatia. For this purpose, quarterly panel data for the first three quarters of 2020, 2019, and 2018 are employed. To test how the COVID-19 outbreak has affected the Croatian life insurance market, we have employed quarterly year-on-year gross written premium growth expressed as a percentage, insurance density, and insurance depth that serves as dependent variables level of development of the insurance market. Moreover, independent variables comprise several COVID-19 confirmed cases, number of COVID-19 death cases, coronavirus dummy variable, and year-on-year quarterly GDP growth rate. After conducting static panel analysis, the results reveal that coronavirus dummy variable, taking value one if confirmed cases of COVID-19 disease every quarter are reported and 0 otherwise, negatively affects the level of life insurance market development.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".