Impact of Elimination of Dividend Distribution Tax on Indian Corporate Firms Amid COVID Disruptions
Bibliographic record
Abstract
Economic fallouts from COVID-19 have been unprecedented across all industries, with a handful of exceptions. The present study attempts to capture the impact of dividend distribution tax elimination, introduced through the Indian Finance Act 2020, on corporate dividend behavior in India. It explores the determinants of dividend payouts, changing payout decisions, dividend behavior of regular payers, and the prevalence of factors associated with changing payouts. Out of the top 1000 firms, based on their market capitalization at the Bombay Stock Exchange, 509 non-financial firms pursuing consistent dividend payments from 2015 to 2019 are analyzed. The study also examines the dividend behavior of regular payers exhibiting a stable or step-up payout from 2015 to 2019. COVID’s impact on the firm’s financial performance and sentiments seems to dominate, suppressing investors’ expectations of enhanced payouts associated with dividend distribution tax advantages, with considerable reductions in payouts and omissions shown by regular and irregular payers in 2020 and 2021 vis-à-vis the preceding years. The findings signify that the dividend payouts of sample firms are positively associated with the firms’ size, MBV ratio, and past dividends, and negatively allied with free cash flows and the EBITDA margin. Regular payers are observed to be more sensitive to past dividends. The study lends credence to the conservatism and prevalence of signaling and catering theories in the dividend behavior of Indian corporate 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 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".