Unemployment Insurance Taxes and Labor Demand: Quasi-Experimental Evidence from Administrative Data
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
To finance unemployment insurance, states raise payroll tax rates on employers who engage in layoffs. Tax rates are, therefore, highest for firms after downturns, potentially hampering labor-market recovery. Using full-population, administrative records from Florida, I estimate the effect of these tax increases on firm behavior leveraging a regression kink design in the tax schedule. Tax hikes reduce hiring and employment substantially, with no effect on layoffs or wages. The results imply unanticipated costs of the financing regime which reduce the optimal benefit by a quarter and account for 12 percent of the unemployment in the wake of the Great Recession. (JEL D22, E24, H25, H32, H71, J23, J65)
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 it