Is NY's Supply-side Experiment Working? A Preliminary Analysis using County Unemployment Rates
Bibliographic record
Abstract
The State of New York recently enacted business tax reforms. The first legislative act launched the START-UP NY program in 2014. It created tax free enterprise zones throughout the state to incentivize business incubation within, or relocation of existing firms to, the State of New York. In that same year, the state lowered its corporate tax rate state-wide from 7.1% to 6.5% in 2016. We use a difference-in-differences (DID) methodology, evaluated using county-level data, to empirically test whether New York’s recent business tax reforms significantly reduce unemployment, beyond what would exist in the absence of the reforms. We fail to find significant evidence that START-UP NY affects unemployment during the period studied, 2014-2017. We do, however, find evidence suggesting that New York lowering its corporate tax rates in 2016 is associated with a large reduction in unemployment (by approximately 90,000 jobs) in 2016 and a smaller reduction (by approximately 25,000 jobs) in 2017.
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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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".