Political Uncertainty and the Timing of Mass Layoffs
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This study examines the relation between political uncertainty arising from state-level election cycles and the timing of employee dismissal and plant closure notices filed by US firms under the Worker Adjustment and Retraining Notification (WARN) Act of 1988 (hereafter, WARN notices). We appeal to a real options framework to predict that firms delay layoff decisions and the issuance of WARN notices until the resolution of political uncertainty. Using establishment-level data on layoffs disclosed in WARN notices and state elections occurring between 1994 and 2022, we document that the likelihood of issuing WARN notices declines during the election quarter but increases in the subsequent quarter. Cross-sectional findings show that political uncertainty plays a significant role in the timing of WARN notices during election periods while other factors, including partisanship, economic conditions, union strength, and firm visibility, may also play a role. Further, firms that delay WARN notices do not experience a significant deterioration in their medium-term financial performance. Overall, our findings provide evidence that firms delay labor adjustment decisions and the announcements of such decisions in response to political uncertainty.
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.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 it