Rising home ownership among Chinese migrant workers: determinants and differences between cities
Bibliographic record
Abstract
Traditionally, Chinese migrant workers are housed in dormitories or in the private rental sector. In recent years, however, an increasing proportion of the migrant workers has managed to become home owners. This paper further analyzes this trend, which may signify a new phase in the Chinese urbanization process. After a review of the existing literature, we carry out a statistical analysis (binary regression modelling) on the China Migrants Dynamic Survey, thereby focusing on twenty cities in the Yangtze River delta urban region. For these cities, we determine the micro-level (characteristics of individual migrants) as well as of the city level (city size, local migration policies, housing market development) determinants of migrant home ownership, and we assess how these determinants have changed between 2012 and 2017. This will provide insight into changing housing pathways of Chinese migrant workers, and the interaction of these pathways with local policies .
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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; both teacher heads agree on what is shown here.
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".