Political sentiment and corporate payouts
Bibliographic record
Abstract
We provide empirical evidence of the impact of firm-level political sentiment on dividend policy. Using a sample composed of over 34,000 firm years, we find that a high level of political sentiment is associated with a lower level of dividend payout. The evidence is robust and survives several tests that address potential endogeneity. Our results suggest that managers consider investors' political sentiment in setting dividend policy. When political sentiment is negative, they pay higher dividends to assuage investors' concerns regarding future prospects. Our results are also consistent with the view that firms pay higher dividends to address the agency cost issue that arises from the free cash flow problem during periods of negative political sentiment. • We find that a high level of political sentiment is associated with a lower level of dividend payout. • Our results suggest that managers consider investors' political sentiment in setting dividend policy. When political sentiment is negative, they pay higher dividends to assuage investors' concerns regarding future prospects. • Our results are also consistent with the view that firms pay higher dividends to address the agency cost issue that arises from the free cash flow problem during periods of negative political sentiment.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".