Corporate governance and earnings management in banks: An empirical evidence from India
Bibliographic record
Abstract
This paper aims to examine the role of corporate governance (CG) on earnings management (EM) in Indian commercial banks. In addition, the study examines the role of board gender diversity within the CG framework using data from 22 publicly traded commercial banks in India from 2010 to 2019. The study uses Principal Component Analysis (PCA) to develop a comprehensive CG measure. Using a Panel Corrected Standard Error (PCSE) approach, the study finds that CG has a significant negative impact on EM in Indian commercial banks. The findings further revealed a positive association between gender diversity of boards and EM, indicating that the lack of gender diversity on a bank’s board outweighs the benefits of gender-diverse boards. Our study shows that CG mechanisms are more effective when combined together than individual governance mechanisms. The study also provides new insight into the role of board gender diversity as a CG mechanism on EM in banks in the context of a developing country. The study provides practical implications for investors, managers, regulators and policymakers.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".