Fertility decline in the later phase of the COVID-19 pandemic: The role of policy interventions, vaccination programmes, and economic uncertainty
Bibliographic record
Abstract
Abstract BACKGROUND During the COVID-19 pandemic, birth rates in most higher-income countries first briefly declined and then shortly recovered, showing no common trends afterwards until early 2022, when they unexpectedly dropped. STUDY FOCUS We analyse monthly changes in total fertility rates in higher-income countries during the COVID-19 pandemic, with a special focus on 2022, when birth rates declined in most countries. We consider three broader sets of explanatory factors: economic uncertainty, policy interventions restricting mobility and social activities outside the home, and the role of vaccination programmes. STUDY DESIGN, DATA This study uses population-wide data on monthly total fertility rates adjusted for seasonality and calendar effects provided in the Human Fertility Database (HFD, 2023). Births taking place between November 2020 and October 2022 correspond to conceptions occurring between February 2020 and January 2022, i.e., after the onset of the pandemic but prior to the Russian invasion of Ukraine. The data cover 26 countries, including 21 countries in Europe, the United States, Canada, Israel, Japan and the Republic of Korea. METHODS First, we provide a descriptive analysis of the monthly changes in the total fertility rate (TFR). Second, we estimate the effects of the explanatory factors on the observed fertility swings using linear fixed effects (within) regression models. MAIN RESULTS We find that birth trends during the COVID-19 pandemic were associated with economic uncertainty, as measured by increased inflation, the stringency of pandemic policy interventions, and the progression of the COVID-19 vaccination campaign, whereas unemployment did not show any link to fertility during the pandemic. LIMITATIONS, REASONS FOR CAUTION Our research is restricted to higher-income countries with relatively strong social support policies provided by the government as well as wide access to modern contraception. Our data do not allow analysing fertility trends by key characteristics, such as age, birth order and social status. WIDER IMPLICATIONS OF THE FINDINGS This is the first multi-country study of the drivers of birth trends in a later phase of the COVID-19 pandemic. In the past, periods following epidemics and health crises were typically associated with a recovery in fertility. In contrast, our results show that the gradual phasing out of pandemic containment measures, allowing increased mobility and a return to more normal work and social life, contributed to declining birth rates in most countries. In addition, our analysis indicates that some women avoided pregnancy during the initial vaccination roll-out.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".