Canary in a Coal Mine: Infant Mortality, Property Values, and Tradeoffs Associated with Mid-20th Century Air Pollution
Bibliographic record
Abstract
Investments in local development and infrastructure projects often generate negative externalities such as pollution.Previous work has either focused on the potential for these investments to stimulate local economic activity or the health costs associated with air pollution.This paper examines the tradeoffs associated with the historical expansion in coal-fired electricity generation in the United States, which fueled local development but produced large amounts of unregulated air pollution.We focus on a highly responsive measure of health tradeoffs: the infant mortality rate.Our analysis leverages newly digitized data on all major coal-fired power plants for the period 1938-1962, and two complementary difference-in-differences strategies based on the opening of power plants and new generating units at existing sites.We find that coal-fired power plants imposed large negative health externalities, which were partially offset by the benefits from local electricity generation.We uncover substantial heterogeneity in these tradeoffs, both across counties and over time.Expansions in coal capacity led to increases in infant mortality in counties with high baseline access to electricity, but had no effect in low-access counties.Initial expansions in coal capacity led to decreases in infant mortality, but subsequent additions led to increases in infant mortality.These evolving tradeoffs highlight the importance of accounting for both current and future payoffs when designing environmental regulation.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".