The Covid-19 Baby Bump: The Unexpected Increase in U.S. Fertility Rates in Response to the Pandemic
Bibliographic record
Abstract
We use restricted natality microdata covering the universe of U.S. births for 2015-2021 and California births from 2015 to August 2022 to examine the childbearing response to the COVID-19 pandemic. Although fertility rates declined in 2020, these declines appear to reflect reductions in travel to the U.S. Childbearing in the U.S. among foreign-born mothers declined immediately after lockdowns began-nine months too soon to reflect the pandemic's effects on conceptions. We also find that the COVID pandemic resulted in a small "baby bump" among U.S.-born mothers. The 2021 baby bump is the first major reversal in declining U.S. fertility rates since 2007 and was most pronounced for first births and women under age 25, which suggests the pandemic led some women to start their families earlier. Above age 25, the baby bump was also pronounced for women ages 30-34 and women with a college education, who were more likely to benefit from working from home. The data for California track the U.S. data closely and suggest that U.S. births remained elevated through the third quarter of 2022.
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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 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".