Do Earnings Announcements Affect Employee Spending? Evidence from Transaction Data*
Bibliographic record
Abstract
ABSTRACT Leveraging micro‐level data on individual employees’ bank and credit card transactions, we examine the impact of earnings announcement (EA) news on employee spending. Utilizing an event study methodology, we find strong evidence that EA news elicits significant reactions in employee spending. These reactions are stronger for employees located in the firm's headquarters state, with longer tenure, possessing investment experience, or earning higher wages, consistent with these employees being more likely to attend to their firm's EAs. The reactions are also stronger for the fourth fiscal quarter than interim quarters, suggesting that year‐end results garner greater employee attention. Furthermore, consistent with media facilitating employee processing of EA news, the reactions are stronger for EAs covered by a larger number of news articles. Finally, in line with the notion that EAs contain information about employees’ future cash flows, we find that EA news predicts changes in employee wages and that employees with higher past wage‐to‐EA news sensitivity exhibit stronger spending reactions. Overall, our findings provide evidence of the role of financial reporting in employees’ spending decisions.
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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 | 0.011 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.007 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".