A Study of Banks’ Systemic Importance and Moral Hazard Behaviour: A Panel Threshold Regression Approach
Bibliographic record
Abstract
This study has two objectives, first, to investigate if the lending behaviour of banks exhibits moral hazard in the Indian Banking Industry, and second, to investigate whether banks’ moral hazard behaviour changes when the systemic importance of the banks is taken into consideration. We studied banks’ moral hazard behaviour by observing the impact of their level of Net Non-Performing Loans (NNPL) on their lending behaviour. This study used threshold panel regression by using 1 year lagged values of NNPL as the threshold variable to find its endogenously determined value that impacts the lending behaviour of the banks. The 1 year lagged value of the NNPL (threshold variable) has been used to depict the level of distress faced by a bank. Assuming that loans may turn bad any year after they are granted, a banks’ lending behaviour has been shown through the relationship between various lags of Loan Growth Rate (LGR) and the contemporaneous values of Net Non-Performing Loans (NNPL). As per our analysis, the loan growth ratio raises NPLs with a relatively higher value when banks are experiencing prior sizable loan losses as compared to when banks are relatively safe, indicating moral hazard behaviour in the Indian banking industry. However, when the systemic importance of the bank is considered, the systemically important banks are found to be engaged in risky lending irrespective of their level of distress, whereas the opposite results are found for the least important banks.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".