Factors Influencing the Settlement Intentions of Chinese Migrants in Cities: An Analysis of Air Quality and Higher Income Opportunity as Predictors
Bibliographic record
Abstract
With rapid urbanization, the air pollution issue is becoming an increasingly serious issue given that people are strongly swayed in their location choice to settle down in a growing urban area where most job opportunities have been created. This study investigated the influences of both air quality and income on the settlement intentions of Chinese migrants by using microlevel samples of the China Migrants Dynamic Survey (CMDS) data from 2017 and the annual average concentration of PM2.5 (particles with diameter ≤ 2.5 μm in the air) to measure a city's air quality. The results showed that the settlement decisions of Chinese migrants involved a trade-off between income and air quality. Poorer air quality could significantly decrease the settlement intention, while a higher income could significantly increase the settlement intention of Chinese migrants. However, as the migrants' income opportunity increased at a location, the negative influence of poorer air quality on the settlement intention at that location gradually declined. Specifically, when deciding whether to settle down in cities, the migrants with a non-agricultural "hukou" (household registration) tended to pay more attention to air quality than the migrants with an agricultural "hukou," and migrants who moved farther away in geographic distance tended to pay more attention to income. It was concluded that the influences of air quality and income on the settlement intentions of the migrants were robust and consistent after using different estimation methods and considering the issue of endogeneity.
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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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".