TIMELY AND ADEQUATE MEASURES TO SUPPORT THE RUSSIAN ECONOMY AND POPULATION DURING THE PANDEMIC
Why this work is in the frame
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Bibliographic record
Abstract
The article considers measures of state support for the population and economy of Russia and summarizes the world experience of the budget support in some foreign countries affected by the coronavirus pandemic. The research and generalization performed in the following areas: applied forms of support, methods of financing, particularities in supporting the financial sector, manufacturing sector, small and medium business, population, social sphere, health care, support of regions. The study took into account two negative factors: the rapid spread of COVID-19 and its harmful impact on the global economy; the collapse in oil prices and the depreciation of the ruble. Under the influence of those factors, the Russian economy in the second quarter of 2020, according to Rosstat, declined by 8% year – on-year, and for the first half of the year-by 3.4%. The country’s GDP, according to the Ministry of economic development, decreased by 4.3% in annual terms, and for 8 months from the beginning of this year by 3.6%.Given the circumstances, the Government of the Russian Federation and the Bank of Russia developed a national plan for the recovery of the Russian economy in 2020–2021, which was adopted and approved by the Government on September 23, 2020. The consequences of COVID-19 have negatively affected the actions of most European companies in Russia. With more than half of them (56%), sales fell, and a third of companies (33%) had to cut their advertising and marketing research budgets (21%).The article assesses the economic situation of Russia against the global background. Although the economies of the US, UK, and EU countries have fallen much more deeply than the Russian one, they will recover sooner than we do, since these countries have invested significantly more money in supporting their economies than Russia. We have allocated no more than 3% of GDP to support the economy, while in developed countries at least 10%, and in Germany – 22%.The pandemic has hit small and medium-sized businesses the hardest for two reasons: a reduction in the number of consumers and increasing costs, and, first of all, rental rates. To restore small and medium-sized businesses, it is proposed to provide monetary support directly to the population in order to raise effective demand, or partially remove the tax and administrative burden on entrepreneurs. In conclusion, taking into account foreign experience and the real state of the economy, recommendations for its recovery are formulated.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it