On the Distributional Outcomes of Policy-Induced Entrepreneurial Opportunities
Bibliographic record
Abstract
This analysis empirically evaluates the effectiveness of entrepreneurial policies using the number and distribution of firms as outcome variables. The analysis occurs within the context of a natural experiment: the START-UP NY program. Implemented in 2014, START-UP NY created enterprise development zones adjacent to publicly supported universities (i.e., SUNY and CUNY campuses) within the state. New business start-ups operating within these zones, and within a specific set of technology and health-related industries received tax incentives that substantially lowered tax rates for a 5-10 year period. In 2016, the State of New York substantially altered its corporate tax structure; a policy initiative affecting firms, business owners, and households in the state simultaneously, and may also induce entrepreneurship. The results suggest that START-UP NY had a positive effect on the growth of New York's micro and small-sized firms operating in professional, scientific, and technical industries. START-UP NY also negatively affected micro-sized manufacturing firms, while positively affecting small manufacturing firms. The latter finding suggests that START-UP NY is effective in incubating micro-sized manufacturing firms that eventually grow into small manufacturing firms.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.004 | 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".