Bibliographic record
Abstract
This paper is built upon the predictions of the catering theory of dividends and examines how the different institutional environments impact catering effect. The focus of our analysis is the argument that when companies belong to different institutional environments and the nature of existing agency problems also differs, there will also be differences in the relationship between dividend policy and the catering effect. To achieve this aim, we propose a dividend model that incorporates a variable at a firm-level proxying for the catering effect. The results from the estimation of the model by using the GMM provide interesting results. Consistent with the predictions of the catering theory, we find that companies in Eurozone countries and the US, UK, Canada and Japan cater to their investors’ sentiments. More interesting, our findings show an interaction effect between catering and institutional factors, particularly the legal protection of investors, development of capital markets and the orientation of the financial systems, the effectiveness of the market for corporate control, the level of ownership concentration and the effectiveness of boards of directors. We find a substitute effect of external corporate governance mechanisms on catering dividends. Specifically, dividend payers with weak governance are significantly more likely to pay dividends than dividend payers with strong governance.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".