Comparison of second-child fertility intentions between local and migrant women in urban China: a Blinder–Oaxaca decomposition
Bibliographic record
Abstract
With China's termination of the longstanding one-child policy and its implementation of a universal two-child policy since 2016, it remains an open empirical question whether the Chinese, including more than 200 million rural-to-urban migrants, are willing to have a second child. Using the 2017 China Migrants Dynamic Survey data, this study compares the intention of having a second child between urban local women and rural-to-urban migrant women in Chinese cities. We find significantly lower second-child fertility intentions among migrant women, despite their younger average age than local women. Employing the Blinder–Oaxaca decomposition technique borrowed from labour economics, we reveal that education and son preference both play particularly prominent roles in explaining the lower second-child fertility intentions among rural migrants. First, more education is found to promote second-child fertility intentions in urban China. Rural migrants’ fertility intentions are depressed by their lower educational levels. Second, in urban China, when the first child is a boy, a couple tends to have a lower intention to have a second child. This fertility-depressing effect of already having a son is particularly pronounced among rural migrants, and moreover, compared with urban locals, a higher percentage of rural migrants’ first child is a son.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".