Education of Left-Behind Children and Return Decisions of Migrant Workers in China
Bibliographic record
Abstract
This paper uses a dynamic survey data of China labor force to explore the impacts of child education on their parents’ return decisions by means of constructing an empirical model. The migration situation of children is the basis for us to distinguish the sample migrant workers. And those migrants who migrate with their children and those who leave their children behind in their hometowns are the two types of migrants among this model which we will analyze in urban areas. The results show that the probability for migrant workers in urban areas to return to hometowns will significantly increase when their children are left behind at home. While these parents tend to stay in the cities which they work and live in when their left-behind children enter the school age. The data we use is from the China Labor Force Dynamics Survey and we establish a model to analyze the effects of left-behind children. The empirical results show that the probability for migrants to return to their hometowns will decrease by 20.8 % when their left-behind children enter the school age. To a large extent, the emergence of such a huge contrast may be the result of the optimal decision-making of migrant workers. And the phenomenon of large-scale “migrant worker shortage” caused by such mechanism has intensified in the labor market of coastal cities. And most of these cities have implemented relevant stringent admission policies for migrant children to receive education in urban public schools and this break the intentions of the immigrant parents who plan to take the left-behind children to the cities to receive education in local schools. And these immigrants choose to return in the case of decline of the family net income.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".