The Effect of Ownership Structure on Firm Profitability in India: A Panel Data Approach
Bibliographic record
Abstract
This paper attempts to analyze whether there is solid evidence to support the idea that variations across firms in observed ownership structures lead to systematic variations in observed firm performance. The current study examines this hypothesis by evaluating the impact of the ownership structure on corporate performance, measured by profitability, using data of BSE 100 Index companies. This study design ownership structure, first, as an exogenous variable and, second; we take into account four different measures of ownership structure showing different groups of shareholders with conflicting interests. Using panel data regression model in between all ownership structure measures and profitability measures, the empirical findings suggest that the non-promoters holding and non-promoters non institutional holding have a significant impact on EPS of the firm. In case of promoters holding and non-promoters institutional holding no effect is found on EPS of the firm. And in case of ROI promoters holding, non-promoters institutional holding and non-promoters non institutional holding have a significant impact on ROI of the firm. In case when non-promoters holding is taken as individual measure no effect is found on ROI of the firm. Further in case of PAT, it is found that the non-promoters holding and non-promoters non-institutional holding have a significant impact on PAT of the firm. In case of promoters holding and non-promoter institutional holding no effect is found on PAT of the firm. Presence of concentrated ownership is found in Indian firms.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".