Regional Aspects of Demand and Supply Shocks: Economy of the Khabarovsk Territory During a Pandemic
Bibliographic record
Abstract
The article provides an overview of the short-term dynamics of macroeconomic indicators for the Khabarovsk Territory during the COVID-19 pandemic after the introduction of temporary restrictive measures in April and May 2020. The impact of these measures extended both to the elements of regional aggregate demand and aggregate supply. From the point of view of the theory of short-run economic fluctuations, aggregate supply and demand shocks, such as those that occurred in the initial period of the pandemic, lead to a reduction in aggregate output followed by an increase in actual and natural rate of unemployment. Expectations have an additional negative impact since the growth of uncertainty gives rise to an increase in savings and an additional reduction in consumer activity. In the case of Khabarovsk Territory it is shown that the most affected industries of the economy were retail trade and services. Both industries experienced a negative shock in April, but while the former began recovery as early as May, the latter returned to the growth trajectory only in June. Residents changed their income usage patterns due to the restrictions on the consumer market, as well as to increased uncertainty about their future income. The share of net savings and cash balances increased with a corresponding decrease in the share of spending on goods and services. A negative supply shock contributed to a sharp rise in unemployment up to 24.5 thousand unemployed in the third quarter of 2020. Starting from the fourth quarter unemployment began to decline rapidly, but it had not reached pre-pandemic level of less than 7 thousand unemployed by the second quarter of 2021. It is shown that the permanent population outmigration, which increased in 2020, is a specific feature of the regional labor market. It has slowed down the return of the regional aggregate supply to its pre-pandemic positions after the restrictions were cancelled
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".