An Analysis of the Employment Effects of the Washington High Technology Business and Occupation (B and O) Tax Credit: Technical Report
Bibliographic record
Abstract
This paper estimates the effects of an R&D tax credit in the state of Washington on job creation. The research uses micro-data on the job creation and tax credits received by individual firms in the state of Washington from 2004 to 2009. We correct for the endogeneity of R&D tax credits received by individual firms by using instrumental variables based in part on national industry factor shares for R&D. We estimate that this tax credit created jobs, but at a high cost. The cost per job-year created is estimated to be between $40,000 and $50,000. The credit was so high cost in part because the credit was non-refundable. As a result, about one-quarter of the firms receiving credits were maxed out on credit eligibility, so that the credit provided no marginal incentive for additional R&D spending or job creation.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 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".