Biased Childhood Sex Ratios and the Economic Status of the Family in Rural China
Bibliographic record
Abstract
While in most Western countries male births are slightly more frequent than female births (105 to 106 boys are born for every 100 girls), recent data for rural China show that sex ratios for Chinese children are much higher than 105 or 106. This phenomenon is often attributed to preference for sons and parental behavior aimed at producing more male children than biologically normal. Such behavior may include abortion of female fetuses as well as female infanticide. Parents may prefer boys because they perceive them to be investments with higher returns. It may also be that traditions emphasizing the dominant role and higher social status of men affect parental preferences. In this paper I try to shed light on the determinants of biased childhood sex ratios in rural China, with a focus on investigating whether the economic status of the parents affects the ratio of sons to daughters in the household. The data used in the paper come from the 1988 Chinese Household Income Project, a household survey with information on more than 10,000 families residing in China’s rural areas. I find that first-born surviving children of lower-income parents are significantly more likely to be boys. This result suggests that girls may be “luxuries” that higher-income parents are better able to afford. When looking at the effects of China’s well-known “one-child policy” I find no evidence that the policy has worsened the bias in sex ratios. In fact, the evidence points to more balanced sex ratios being achieved at the time when the one-child policy was enforced most strictly.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".