The Role of Transit in the Upward Mobility of Low-Income Indianapolis Residents
Bibliographic record
Abstract
This study examines the extent to which transit may be able to assist with the upward mobility of low-income groups, specifically those making less than a living wage. Previous studies relating to job accessibility have examined the feasibility of reaching jobs using various modes of transportation, and some have factored educational requirements into the attainability of those jobs. However, no studies thus far have attempted to determine transit accessibility to jobs that can facilitate and enable upward mobility for low-income households. Employment data relevant to the labor force of Marion County, Indiana, is used to determine the earnings (mainly wages or salaries) associated with occupations, and which occupations require no more than a high school education. Those occupations are then paired with the various industries they are found in, and the earnings belonging to the industry’s highest-earning occupation is associated with that industry. The median household incomes of low-income Census block groups (CBGs) are then compared to the earnings of each transit-accessible industry to evaluate whether those earnings are large enough to induce upward mobility for those living in the CBGs. Bus routes and bus stops for the local transit system (IndyGo) along with workplace locations are mapped in ArcGIS to assess the low-income population’s accessibility to workplaces belonging to a select group of industries. Bus routes that serve both downtown Indianapolis and low-income CBGs were found to provide people living in those CBGs with access to some of the most lucrative jobs, particularly those found in the <i>Finance and Insurance</i> industries. Over half of Indianapolis’ transit-accessible industries have earnings amounts large enough to induce upward mobility for those living in all the low-income CBGs; this corresponds to 6,748 unique workplaces. Findings from this study suggest that low-income people would benefit from having access to transit routes that serve downtowns and other areas with high concentrations of white-collar jobs. Low-income Indianapolis residents informed of this study’s results may be motivated to explore the possibilities for better-paying jobs accessible to them by transit. Furthermore, methods used in this study can help in ranking different transit routes for accessibility to workplaces conducive to upward mobility. The rankings can be updated periodically to assist in addressing equity goals for transit planning.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.001 |
| 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".