Bibliographic record
Abstract
If fertility is not chosen in a socially optimal way, and if policies to directly target fertility are ineffective or politically infeasible, then public policies that affect fertility could have important welfare consequences through the fertility channel. We refer to these effects as population externalities, and in this paper we focus on one important variable that may have a causal impact on fertility: the education of potential parents. If increased education causes families to have fewer children, then a government would want to increase college tuition subsidies in the presence of environmental externalities such as climate change, to indirectly discourage families from having children who will generate future environmental costs. Alternatively, if fertility is inefficiently low, due to imperfect parental altruism for example, governments will want to lower tuition subsidies to encourage child-bearing. We present a simple model of the college enrollment decision and its fertility impacts, and show that such population externalities are quantitatively important: the optimal subsidy increases by about $5000 per year with climate change, and decreases by over $7000 per year with imperfect parental altruism. Our paper demonstrates how public economics can incorporate population externalities, and that such externalities can have significant impacts on optimal policy.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".