The Effects of COVID-19 on Public Transit in the City of Calgary: Fiscal Impacts and Equity Considerations
Bibliographic record
Abstract
Using monthly public transit revenue data, we investigate the effect of the SARS-CoV-2 (COVID-19) pandemic on public transit revenues in the large urban municipality of Calgary in Alberta, Canada. Calgary provides a poignant environment in which to examine the effect of COVID-19 on transit fare revenues. First, City Council has set a general policy whereby 50% of Calgary Transit operating costs are to be recovered through operating revenue, including transit fare revenue. Second, Calgary Transit has a unique program—the fare entry program—that heavily discounts public transit fares to qualifying low-income Calgarians. The fact that these fare products exist at not only the level of the local transit authority but also that the revenues from these products are tracked separately by the transit authority allows for an equity analysis of the impact of COVID-19 on public transit use that would not be possible in other jurisdictions. As a result, we can consider the impact on different user groups over the pandemic, paying particular attention to the inequitable impacts on transit users. Finally, Calgary was hit hard by COVID-19 caseloads, spread, and public health orders that resulted in the immediate reduction of public transit use. We find that COVID had the largest (statistically significant) impact on adult transit fare revenue, a smaller impact on youth fares, and almost no impact on low-income fares suggesting that youth and low-income transit pass users were less able to substitute away from or forgo public transit during the COVID shock, unlike adults. Reductions in transit services that occurred at the same time were more likely borne by youth and low-income transit users. To minimize service reductions and their inequitable effects, we argue that given municipalities have little financial power and flexibility, higher orders of government should provide transit operating funding during times of transit fare shocks.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".