А Quantitative Assessment of the Impact of Credit on Economic Growth in Russia
Bibliographic record
Abstract
The Russian credit market reflects the significant range of problems faced by the national economy. Among them are structural and regional imbalances; restrictions that prevent the free movement of funds in the interbank market; uncertainty that leads to a slowdown in the rates of attraction and placement of credit resources. As a result, the question arises as to how effective the credit market in Russia is and whether it has a stimulating effect on the national economy. The purpose of this article is to assess the impact of credit on economic growth as one of the criteria for the effectiveness of the credit market in the national economy. Growth rates of real quarterly GDP levels cleared of seasonality as well as quarterly growth rates of real household consumption in Russia cleared of seasonality are viewed as indicators of economic growth. Indicators of the credit market include quarterly growth rates of real loans to households and quarterly growth rates of real loans to non-financial organizations. In addition, such events as the global economic crisis of 2008 – 2009 and its impact, Western sanctions and the increase of crude oil prices were taken into account. As a result of the study conducted by the authors using an open econometric model of vector autoregression, the conclusion was drawn that loans to households and non-financial organizations in the long term have a stimulating effect on the Russian economy.
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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 | 0.002 | 0.002 |
| 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) | 0.001 | 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 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".