Bibliographic record
Abstract
Did the rapid expansion of Chinese exports between 1990 and 2010 contribute to the country's worsening environmental quality? We exploit variation in local industrial composition to gauge the effect on pollution and health outcomes of export expansion due to the decline in tariffs faced by Chinese exporters. In theory, rising exports can increase pollution and mortality due to increased output, but they may also raise local incomes, which can in turn promote better health and environmental quality. The paper teases out these competing effects by constructing two export shocks at the prefecture level: (i) the pollution content of export expansion and (ii) the export expansion in dollars per worker. We find that the pollution content of exports affects pollution and mortality: a one standard deviation increase in the shock increases infant mortality by 4.1 deaths per thousand live births, which is about 23% of the standard deviation of infant mortality change during the period. The dollar value of export expansion reduces mortality by 1.2 deaths, but the effect is not statistically significant. We show that the channel through which exports affect mortality is pollution concentration. We find a negative, but insignificant effect on pollution of the dollar-value export shocks, a potential “technique” effect whereby higher income drives demand for clean environment. Finally, 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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".