Bibliographic record
Abstract
In this paper I study the effects on jobless recovery of diminishing the power of an employer to fire an employee through Employment-At-Will Exceptions (EWEs). I do so by using a dynamic panel with quarterly data ranging from 1976 to 2010 for the 50 states in the United States. I test both changes in state unemployment rates and state-weighted GDP growth in single variable regressions and VAR regressions. My contribution to the literature is threefold. First, I show two of the three EWEs contribute significantly to jobless recovery in the U.S. The statistical tests in this paper show that Implied Contract Exceptions slow decreases in the unemployment rate during recovery from recession by between 0.025 and 0.033 percentage points per quarter, and Covenant of Good Faith and Fair Dealing Exceptions do so by between 0.039 and 0.055 percentage points per quarter. Second, I lend support to the predictions of theory that increased firing costs decrease the rate of hiring during recoveries. Third, I resolve differences in the various sources documenting the three types of EWEs in different states.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".