Institutional Investor Heterogeneity and Corporate Response to the Covid‐19 Pandemic
Bibliographic record
Abstract
Abstract We examine the role of institutional investors in determining firms’ decisions whether to reduce dividends and share buybacks during the Covid‐19 pandemic. Our simple model predicts that the probability of cuts in payouts is linked to the holdings and types of institutions. We link our model to the attention‐based theories of the firm. We posit that the highlyproximatenature of the pandemic may encourage greater risk aversion in organizations. Consequently, the presence of institutions that actively engage with managers results in a reduction in shareholders’ payouts during the pandemic to enable firms to deal with increased uncertainty, while institutions that seek short‐term value releases reduce the probability of cuts. We test our hypotheses using novel hand‐collected data on shareholders’ payout cuts in the UK during the Covid‐19 lockdown. We find that in firms with larger institutional holdings, shareholders’ payouts are more likely to be reduced as a response to the pandemic. However, institutional heterogeneity matters as institutions with a view to improve firms’ long‐term growth are more likely to affect corporate payout decisions. In contrast, institutions that focus on regular income (e.g. pension funds) seem to resist cuts even in the aftermath of a severe exogenous shock like the Covid‐19 pandemic.
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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 | 0.013 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".