Trends in household income and expenditure, and the pace of credit activity in Georgia
Bibliographic record
Abstract
Georgia's financial system remains stable and continues to support economic lending. As of April 2025, the banking sector maintains healthy levels of capital and liquidity. Compared to the previous year, the pace of credit activity has gradually normalized, and in April 2025, the annual growth of loans—excluding the exchange rate effect—stood at 15.5%. This growth was largely driven by an increase in business loans. According to data from the first quarter of 2025, the loan-to-GDP ratio remains below the long-term trend. On one hand, strong economic growth recorded in the first quarter and, on the other, the normalization of credit activity have led to a widening of the negative gap between loans and GDP. The committee estimates that, given the current dynamics of credit activity and the gradual normalization of economic growth, this negative gap will close in 2025. Accordingly, at this stage, there is no need to adjust the cyclical component of the countercyclical capital buffer. Commercial banks continue the phased accumulation of the neutral component of the countercyclical capital buffer. In January–May 2025, the volume of new loans issued by commercial banks (24 billion GEL) increased by 7% compared to the same period of the previous year (January–May 2024 – 22.4 billion GEL). For comparison, loan growth in the first five months of 2024 was 18.5%, in 2023 – 17.4%, and in 2022 – 12%. In summary: the growth rate of new loans issued by commercial banks in January–May 2025 has significantly slowed down.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".