Impact of COVID-19 Pandemic on Insurance Demand in Russia: A Comparative Analysis with Global Markets
Bibliographic record
Abstract
The pandemic has exposed the Russian economy's weaknesses, particularly its insurance industry.In the study, the following qualitative data were utilized: an analysis of the impact of the COVID-19 pandemic on the insurance market in various countries, an assessment of the economic impact of the pandemic on the insurance sector, an examination of trends in the global insurance market, and an evaluation of the effectiveness of insurance companies across different nations.Quantitative data were employed, including the volume of insurance premiums in various countries, the number of insurance contracts, the amount of insurance compensation, the number of COVID-19 cases per 100,000 population, the Consumer Price Index (CPI), the Producer Price Index (PPI), and the Gross Domestic Product (GDP).The pandemic impact system was reproduced and built on the example of such countries as Russia, the USA, Canada, Australia, Japan, and many others.It has been proven that the development trend of this industry under the pandemic influence is an economic downturn with a decline in profits but an increase in requirements.In some nations, such as the United States and Canada, there was a slowdown in the life and disability insurance market, whereas in other countries, such as China and South Korea, a rapid market expansion was observed.In Russia, the insurance market maintained a positive trajectory in 2021, despite the pandemic's impact.The volume of insurance premiums in Russia increased to 1.5 billion rubles in 2021.Europe and Central Asia experienced diverse effects of the pandemic on insurance markets.In Poland, the Czech Republic, Slovakia, and Hungary, there was a decline in life insurance premiums, while Slovenia observed a positive growth trend.The study outlined the key issues that need to be addressed to reduce the repeated negative impact of pandemics to restore the global insurance market.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".