COVID-19 Wage Subsidy Disclosure and Firms' Contemporaneous Dividend Payouts
Bibliographic record
Abstract
The Canada emergency wage subsidy (CEWS) was designed as a bailout for employees who had been sidelined from employment during COVID-19. However, the eligibility rules for the wage subsidy suggest that it was not restricted to jobs that would otherwise have been lost. CEWS recipients also did not have to demonstrate the need for cash, so the cash received from the subsidy, based on a decline in monthly revenue, could be used for other purposes if annual revenues did not end up declining. This article examines characteristics of publicly listed firms that voluntarily disclosed the wage subsidy they received and whether such disclosure was associated with increases in contemporaneous dividend payouts. The authors hypothesize and show that firms may have been reluctant to disclose their CEWS if they increased their dividend payouts in the same year. This finding is moderated by firms' cash holdings, reported losses, lower accounting earnings (compared to the prior year), and the extent to which firms managed their accounting earnings. The results hold under endogeneity tests using a two-stage least-square regression.
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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 | 0.002 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".