The Effect of China’s Two-Child Policy on the Child Sex Ratio: Evidence From Shanghai, China
Bibliographic record
Abstract
OBJECTIVE: The bias towards males at birth has resulted in a major imbalance in the Chinese sex ratio that is often attributed to China's one-child policy. Relaxation of the one-child policy has the potential to reduce the imbalance in the sex ratio away from males. In this study, we assessed whether the bias towards males in the child sex ratio was reduced as a result of the two-child policy in China. Medical records data from one large municipal-level obstetrics hospital in Shanghai, East China. DESIGN: Matching and difference-in-differences (MDID) techniques were used to investigate the effect of the two-child policy on the imbalance in the sex ratio at birth after matching for pregnancy status and socioeconomic factors. RESULTS: Analyzing 133,358 live births suggest that the relaxation of the one-child policy had a small, but statistically significant effect in reducing the imbalance in the male to female sex ratio at birth. CONCLUSION: The results demonstrate that relaxation of the one-child policy reduced the imbalance in the male to female sex ratio at birth from 1.10 to 1.05 over the study period at one of the major obstetrics and gynecology hospitals in China.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".