The Causal Effects of Urban-to-Urban Migration on Left-behind Children’s Well-Being in China
Bibliographic record
Abstract
As China's urbanization process deepens, more and more residents of small and medium-sized cities are moving to large cities, and the number of left-behind children is increasing. In this paper, using data from the China Education Panel Survey (CEPS), a nationally representative survey sample, we examine the well-being of left-behind children with urban household registration at the junior high school level and the causal effects of parental migration on their well-being. Research findings indicate that children who are left behind in urban areas are at a disadvantage in most aspects of their well-being compared to urban non-left-behind children. We examine the determinants of urban household registration for left-behind children. Children in families with lower socioeconomic status, more siblings, and poorer health were more likely to be left behind. In addition, our counterfactual framework reveals that, on average, staying behind negatively impacts the well-being of urban children, based on the propensity score matching (PSM) method. Compared to non-migrant children, left-behind children had significantly lower physical health, mental health, cognitive ability, academic performance, school affiliation, and relationships with their parents.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".