Firm Births, Access to Transit, and Agglomeration in Portland, Oregon, and Dallas, Texas
Bibliographic record
Abstract
The formation of new firms is one process by which economies grow and innovate. Public transportation services may facilitate the birth of new firms by both providing better access and causing local densification that leads to agglomeration economies. In this study firm births are investigated to determine how they are related to newly provided light rail transit service in two metropolitan areas in the United States. A geocoded time-series database of firm establishments in Dallas, Texas, and Portland, Oregon, from 1991 through 2008 is used. The data set allows the study of spatial patterns by industry and the analysis of the relationship of firm births to rail station proximity, accessibility, and local agglomeration while controlling for a number of potentially confounding factors. Positive, large, and statistically significant relationships are found in Portland between rail station proximity and firm births. The rail proximity results in Dallas are also generally positive, though not as large; this finding is consistent with the smaller accessibility value of rail in Dallas, as well as policies encouraging commercial development near rail in Portland. Rail proximity increases firm births across almost all industrial sectors in both of these metropolitan areas when controlling for the negative effects on firm births of local own-industry employment. Local block-level agglomeration and generalized accessibility are also highly significant but appear to work independently of rail access. These results imply that passenger rail service increases firm births near rail stations by expanding access to the labor market but not by increasing information spillovers or increasing face-to-face interactions.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.004 | 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 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".