Bibliographic record
Abstract
Has the expansion in exports affected pollution and health outcomes across different prefectures in China in the two decades between 1990 and 2010?We exploit variation in the initial industrial composition to gauge the effect of export expansion due to the decline in tariffs faced by Chinese exporters.We construct two export shocks at the prefecture level: (i) PollutionExportShock represents the pollution content of export expansion and is measured in pounds of pollutants per worker; (ii) ExportShock measures export expansion in dollars per worker.The two measures differ because prefectures specialize in different products: while two prefectures may experience the same shock in dollar terms, the one specializing in the dirty sector has a larger PollutionExportShock.We instrument export shocks using the change in tariffs faced by Chinese producers exporting to the rest of the world.We find that the pollution content of export affected pollution and mortality.A one standard deviation increase in PollutionExportShock increases infant mortality by 2.2 deaths per thousand live births, which is about 13% of the standard deviation of infant mortality change during the period.The dollar value of export expansion tends to reduce mortality, but is not always statistically significant.We show that the channel through which exports affect mortality is pollution concentration: a one standard deviation increase in PollutionExportShock increases SO2 concentration by 5.4 micrograms per cubic meter (the average is around 60).We find a negative, but insignificant effect on pollution of the dollarvalue export shocks, a potential "technique" effect whereby higher income drives demand for clean environment.We find that only infant mortality related to cardio-respiratory conditions responds to exports shocks, while deaths due to accidents and other causes are not affected.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".