Maybe Next Month? Temperature Shocks, Climate Change, and Dynamic Adjustments in Birth Rates
Bibliographic record
Abstract
Dynamic adjustments could be a useful strategy for mitigating the costs of acute environmental shocks when timing is not a strictly binding constraint. To investigate whether such adjustments could apply to fertility, we estimate the effects of temperature shocks on birth rates in the United States between 1931 and 2010. Our innovative approach allows for presumably random variation in the distribution of daily temperatures to affect birth rates up to 24 months into the future. We find that additional days above 80 F cause a large decline in birth rates approximately 8 to 10 months later. The initial decline is followed by a partial rebound in births over the next few months implying that populations can mitigate the fertility cost of temperature shocks by shifting conception month. This dynamic adjustment helps explain the observed decline in birth rates during the spring and subsequent increase during the summer. The lack of a full rebound suggests that increased temperatures due to climate change may reduce population growth rates in the coming century. As an added cost, climate change will shift even more births to the summer months when third trimester exposure to dangerously high temperatures increases. Based on our analysis of historical changes in the temperature-fertility relationship, we conclude air conditioning could be used to substantially offset the fertility costs of climate change.
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 | 0.001 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".