Where in America Are the Tech Firms Going and Why: An Exploratory Analysis of Site Selection Trends in the Information Technology Sector Based on Incentive Packages from 1980 to 2018
Bibliographic record
Abstract
This paper tracks the location trends of information technology (IT) firms in the United States for the last 4 decades to identify commonalities in place-based recruitment subsidy policies and strategies. Utilizing the Good Jobs First Subsidy Tracker database, examined are: a) specific subsidy amounts; b) the type of subsidy, based on the different federal, state, and local options and c) the source of the subsidy funds, be it state, local or federal. Using ArcGIS programming, the analysis maps out the spatial clustering for new location deals of 421 IT facilities from 1981 to 2018. The trends in location choice are used to offer a typology of sub-industry relocation classifications, based on NAICS codes. These relocation flows are then evaluated for job creation outcomes. The findings indicate that fairly remote locations seem to consistently have lower number of jobs created at much higher dollar amounts spent per new job, as compared to metro areas. A clear trend of moving away from Silicon Valley emerges, where most new jobs are created in the Northeast and Canada, as a function of the most generous subsidy packages.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".