Air pollution, foetal mortality, and long-term health: Evidence from the Great London Smog
Bibliographic record
Abstract
This paper provides new evidence on the consequences of foetal exposure to high levels of pollution for the risk of stillbirth, and for the long-term health and labour market outcomes of those that survive. Variation in in utero exposure comes from a persistent weather system that affected London for five days in December 1952, preventing the dispersion of atmospheric pollution. This increased levels of total suspended particulate matter by around 300%. Unaffected counties in England and Wales are used in a differences-in-differences design to identify the short and long-term effects. Historical registrar data for the nine months following the smog show a 2% increase in reported stillbirths in London relative to national trends. As foetal deaths often go unreported, the exercise is then repeated for registered births. The data show around 1600 fewer live births then expected in London, or a reduction of 3% against national trends. Survivors are then identified by district and quarter of birth, and their health and labour market outcomes observed at fifty and sixty years old. Differences-in-differences estimates show that survivors are in general less healthy, less likely to have a formal qualification, and less likely to be employed than those unaffected by the smog.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 |
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".