Religiously inspired baby boom: evidence from Georgia
Bibliographic record
Abstract
Abstract This study investigates the Georgian Orthodox Church’s response to declining fertility rates through a 2007 intervention, wherein the Patriarch personally baptized 3 $$^\text {rd}$$ rd and higher-parity children. Employing synthetic control and interrupted time series methods using macro data, we find suggestive evidence of increased fertility rates. Validating these findings with micro data from a representative sample of Georgian women, we use quasi-experimental variation generated by religion, ethnicity, and marital status of the women and the timing of the announcement to estimate the causal impact using the difference-in-differences estimator. We find a 17% increase (0.3 children per woman) in the national total fertility rate, a 42% increase in Georgian Orthodox women’s birth rate within marriage (an increase in annual hazard rate of 3.5 percentage points), and a 100% increase in their 3 $$^\text {rd}$$ rd and higher-order birth rate within marriage (1.3 percentage points higher annual hazard rate). The impact of the intervention also correlates with higher marriage rates and reduced reported abortions, aligning with the church’s goals. This research emphasizes the potential impact of non-economic factors such as religion and the influence of traditional authority figures on shifting fertility patterns in industrialized, educated, and low-fertility societies.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".