Forecasts for a post‐Roe America: The effects of increased travel distance on abortions and births
Bibliographic record
Abstract
Abstract I compile novel data measuring county‐level travel distances to abortion facilities and resident abortion rates from 2009 through 2020. Using these data, I implement a difference‐in‐difference research design measuring the effects of driving distance to the nearest abortion facility on abortions and births. The results indicate large but diminishing effects: an increase from 0 to 100 miles is estimated to reduce abortion rates by 19.4% and increase birth rates by 2.2%, while the next 100 miles reduces abortions by an additional 12.8% and increases births by an additional 1.6%. Based on this evidence, I forecast the effects of post‐Roe abortion bans on abortion rates by county, state, and region. In a scenario in which total abortion bans take effect in 24 states, about one‐quarter of residents seeking abortions are predicted to become trapped by distance and about three‐quarters of those who are trapped are predicted to give birth as a result.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".