Recent developments in financing and bank lending to the non-financial private sector. Second half of 2022
Bibliographic record
Abstract
Rationale. To analyse, owing to their macroeconomic implications, the conditions and volume of funding raised by households and firms and to quantify the credit risk taken on by deposit institutions via loans to these two sectors. Takeaways. • Financing conditions continued to tighten in the second half of 2022 and the transmission of market rate rises to the cost of lending accelerated. This has led to a decrease in the flow of new funding. • The bank loan stock to the resident private sector in 2022 Q3 saw a slight decrease compared with the same quarter in recent years, mainly owing to trends in the stock of loans to business activities. Non-performing and Stage 2 loans continued to decline, except in some portfolios, such as those with ICO-backed loans. • Exposures to the energy sector have a limited weight in the bank credit business in Spain, although their quality has worsened throughout 2022 and somewhat tighter credit standards have been observed.
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How this classification was reachedexpand
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 | 0.001 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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; both teacher heads agree on what is shown here.
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".