Declining Quantity and Quality of Births in Chile amidst the COVID‐19 Pandemic
Bibliographic record
Abstract
Abstract Extensive demographic scholarship shows that the population‐level implications of mortality crises such as the COVID‐19 pandemic extend beyond mortality dynamics to affect fertility and family‐formation strategies. Using novel municipality‐level data from Chile covering all births that occurred between January 2017 and December 2021, this study explores trends in fertility and implications of the COVID‐19 pandemic for “quantum” and “quality” of births in the Chilean context. Building both a monthly and a yearly panel of 346 municipalities and leveraging fixed‐effects regression analyses, we focus on births and crude birth rates to measure quantum, while quality is assessed through the share of births that are low‐weight (LBW) and preterm (PTB). Our findings provide evidence of a significant drop in fertility in the wake of COVID—of the magnitude of a reduction of 1.3 live births per 1,000 individuals—which reaches a minimum around February 2021, followed by an incipient rebound in late 2021. Moreover, estimates on child health at birth suggest that the COVID period was associated with an increase in LBW and, foremost, PTB, by 1 and 2.2 percentage points, respectively. Findings from this study shed light on the role of policy interventions in the health arena and the linkages between short‐ and long‐run effects in relation to the various COVID‐19 waves in Chile.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".