The effects of environmentalism on fertility in the United States: changing trends and causality
Bibliographic record
Abstract
Abstract Environmental issues such as climate change have become major contemporary concerns that affect beliefs and, in some cases, behaviors. We examine whether and how environmentalism may have contributed to the second demographic transition that has driven declining fertility in some parts of the world since the 1970s. Data come from the 2006–2014 and 2016–2020 General Social Survey rolling panels in the USA. We fit Bayesian linear mixed-effects models to estimate the effects of ones’ environmental support, as measured by their support for the government’s environmental spending, on their actual childbearing and beliefs about ideal family size. We found that ones’ environmental support was negatively associated with both outcomes. Specifically, the negative association between environmental support and childbearing was stronger among younger cohorts. Among reproductive-aged women (ages 18–45), their environmental support was negatively associated with the likelihood of new childbearing contemporaneously, but did not predict future childbearing. These findings suggest that environmentalism has largely contributed to the second demographic transition in the USA, while also raising another mechanism that parents may deprioritize the environment to cope with expected childrearing responsibilities.
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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 | 0.002 | 0.004 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".