The Impacts of TV News, Interest Rate, and Exchange Rate on Depositors: The Effects of Russia-Ukraine Conflict in Belarus
Bibliographic record
Abstract
The paper studies why Belarusian individuals didn’t withdraw deposits, but increased them at the beginning of the Russia-Ukraine military conflict. The correlation and regression methods are used to analyze daily data from 9214 individuals’ banking deposits on short notice in the Republic of Belarus from 01.09.2021–31.12.2022. As a result, the interest rate volatility had the highest impact on financial behavior in Belarus during the first months of the Russia-Ukraine military conflict. The rise of the interest rate of 1% increased the number and amount of deposits by 3.62–5.66%. The depositors from Minsk reacted by 1.2–1.89 pp. more actively than those from other regions of Belarus. The USD/BYN exchange rate volatility had a lower effect on the depositors. They converted their deposits from foreign currency into local currency under the influence of the interest rate hike rather than devaluation expectations. There was no evidence that the popularity rise of TV political news programs influenced the financial behavior of the Belarusian population in 2022.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".