From bust to boom? Birth and fertility responses to the COVID-19 pandemic
Bibliographic record
Abstract
Past economic, health and policy shocks were associated with a downturn in fertility. We use monthly birth data collected by the Human Fertility Database (Short-Term Fertility Fluctuations data series) to analyze the impact of the COVID-19 pandemic on birth trends until April 2022 in 37 highly developed countries. We also present estimates of monthly total fertility rate adjusted for seasonality. Overall, the coronavirus pandemic did not bring a lasting “baby bust” in most of the analyzed countries. On balance, many countries experienced an improvement in their birth dynamics compared with the pre-pandemic period. This was especially the case in the Nordic countries, German-speaking countries and Western Europe, alongside New Zealand, Israel and Quebec. However, this summary picture hides distinct short-term shifts during the pandemic. The initial pandemic shock resulted in a fall in births in most countries, with the sharpest drop in January 2021. Next, birth rates showed a surprising short-term recovery in March 2021, linked with the conceptions after the end of the first wave of the pandemic. Most countries then reported stable or slightly increasing numbers of births in the subsequent months, especially in Autumn 2021. Yet another downturn in births and fertility rates occurred in January-April 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 | 0.000 | 0.001 |
| 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".