Modeling and Analysis of the Link between Accessibility and Employment Growth
Bibliographic record
Abstract
Various accessibility measures, differing in terms of their definitions and formulations, have been proposed over the past 40 years and been applied to a wide range of problems. This paper extends previous research by developing a new functional form to represent accessibility using real transportation data from the New York/New Jersey metropolitan area. The accessibility function is used as an input to develop an employment function in terms of several socioeconomic variables. Two main hypotheses are tested: (1) that improved accessibility, with all other factors remaining the same, will positively affect individuals' tendency to enter the labor market; and (2) that this effect will vary across employment types and industries. Both functions are estimated simultaneously with county-level data for the year 2000 using two-stage and three-stage least squares analysis (2SLS and 3SLS). Because the results of 3SLS were statistically more robust than 2SLS while the parameter estimates remained similar in magnitude and sign, the proposed model mainly used the 3SLS estimation results. Main results show that the changes in accessibility have a noticeable effect on employment in the studied area. Depending on skill requirements, offered wage rates, household's income, and children of specific age groups, participation in the employment sectors considered were proved to be responsive to accessibility improvements.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".