Macroeconomic, Market and Bank-Specific Determinants of the Net Interest Margin
Bibliographic record
Abstract
The objective of this article is to identify key determinants of the net interest margin (NIM) in the Austrian banking sector. In Austria, the NIM is one of the most important income drivers of banks given the importance of relationship banking, where interest income dominates other sources of revenue. However, the NIM differs substantially among Austrian banks. Drawing on a unique supervisory dataset for the Austrian banking sector of around 42,000 observations between the first quarter of 1996 and the second quarter of 2012, we analyze under which circumstances a bank has a relatively high or low NIM. We contribute to the empirical literature on the NIM by factoring in a bank’s business model in terms of its balance sheet structure and by accounting for the financial crisis from the third quarter of 2007 onward. Our estimation results suggest that not only the determinants identified in the existing empirical literature (different types of non-interest income and expenses, various risk measures, competition, macroeconomic environment) have a significant influence on the NIM, but also our two innovations (balance sheet structure, financial crisis).
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".