Distributional effects of transport policies on inequalities in access to opportunities in Rio de Janeiro
Bibliographic record
Abstract
The evaluation of the social impacts of transport policies is attracting growing attention in recent years. Yet, this literature is still predominately focused on developed countries. The goal of this research is to investigate how investments in public transport networks can reshape social and geographical inequalities in access to opportunities in a developing country, using the city of Rio de Janeiro (Brazil) as a case study. Recent mega-events, including the 2014 Football World Cup and the 2016 Olympic Games, have triggered substantial investment in the city’s transport system. More recently, though, bus services in Rio have been rationalized and reduced as a response to a fiscal crisis and a drop in passenger demand, giving a unique opportunity to look at the distributional effects this cycle of investment and disinvestment have had on peoples’ access to educational and employment opportunities. Based on a before-and-after comparison of Rio’s public transport network, this study uses a spatial regression model and cluster analysis to estimate how accessibility gains vary across different income groups and areas of the city between April 2014 and March 2017. The results show that recent cuts in service levels have offset the potential benefits of newly added public transport infrastructure in Rio. Average access by public transport to jobs and public high-schools decreased approximately 4% and 6% in the period, respectively. Nonetheless, wealthier areas had on average small but statistically significant higher gains in access to schools and job opportunities than poorer areas. These findings suggest that, contrary to the official discourses of transport legacy, recent transport policies in Rio have exacerbated rather than reduced socio-spatial inequalities in access to opportunities. These results also suggest that future research should consider how the modifiable areal unit problem (MAUP) can influence the equity assessment of transport projects.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".