The Performance of Banks in Post-war Lebanon
Bibliographic record
Abstract
This paper analyzes the performance and balance-sheet characteristics of banks in post-war Lebanon for the years 1993 to 2000. Although we find that Lebanese banks are profitable, most of them had accounting return on assets (ROA) greater than one percent over most of our test period, they are not as profitable as a control group of banks from five other countries located in the Middle East. Bank safety and soundness in Lebanon has increased as leverage was reduced (capital adequacy improved) and a risk index indicates lower probabilities of book-value insolvency. We attribute this improved bank performance and safety to better management and to three external factors: political (cessation of war), economic (lower inflation), and regulatory (BIS capital requirements). We employ regression models that relate bank profitability ratios to various explanatory variables. We find, for example, that ROA is positively associated with lagged growth in real GDP, spread or net interest margin, and holdings of Lebanese T-bills but negatively related to bank size as measured by the natural log of total assets. As a policy implication, we recommend that Lebanese banks increase their lending to the private sector to achieve a more efficient allocation of resources and to stimulate economic growth. To help achieve this objective, Banque du Liban, the central bank, should abandon its practice of setting T-bill rates above market levels, which provides a disincentive to bank lending.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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; 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".