Does ownership structure affect firm performance? Evidence of Indian bank efficiency before and after the Global Financial Crisis
Bibliographic record
Abstract
Abstract This paper investigates the relationship between bank ownership and efficiency before and after the 2008–2009 Global Financial Crisis. Using a sample of 58 Indian commercial banks from 2005 to 2017, we examine the interconnection between these factors in a dynamic data envelopment analysis (DEA) framework. We use an innovative modeling strategy based on a two‐stage dynamic DEA framework. The first stage employs the Dynamic Slack‐Based Measure model to measure bank efficiency, explicitly considering the effects of desirable and undesirable carry‐overs between consecutive periods. In a second stage, we perform a regression of the efficiency scores on bank ownership types and several other contextual factors, while also accounting for the presence of endogenous relationships between the variables involved. Our results show that foreign banks outperform their domestic competitors, with bank size and profitability being the main drivers. We also find that while foreign and state‐owned banks were more efficient than their rivals during the Global Financial Crisis, private banks recovered quickly, reaching the efficiency standards of state‐owned banks by 2017. The latter faced a prolonged decrease in efficiency, gradually losing their initial advantage over their private domestic counterparts. Our results are robust to alternative regression specifications, especially those aimed at addressing potential endogeneity issues. The applied methodology and the findings of our research should be of interest to scholars, bank managers, and policymakers. The latter, in particular, should be concerned about the medium‐term effects of reforms that unevenly affect banks with different ownership structures, especially in terms of bank resilience to aggregate shocks.
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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 | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".