Destination Choices of Permanent and Temporary Migrants in China, 1985–2005
Bibliographic record
Abstract
Abstract Previous studies on internal migration in China have failed to capture both the heterogeneity of migratory behaviours and migration processes and the rapidly changing migration circumstances. Using microdata from China's 1990 population census and the 1% population sample survey of 2005, this paper examines whether and how destination choices differ between permanent migrants (withhukouat the destination) and temporary migrants (withouthukouat the destination) and how such differentials change between 1985 and 2005. We use the conditional logit model to gauge the effect of the economic transition andhukoureforms and employ the mixed model to study howhukourestrictions are intertwined with migrants' socio‐economic status to influence the destination choices. Temporary migrants are found to be increasingly concentrated in southeast coastal provinces with better employment opportunities, whereas permanent migrants tend to move in the opposite direction, to south‐central and southwestern provinces with a low entry barrier and numerous return migrants. Modelling results reveal that over time, both types of migrants are increasingly responsive to interregional wage differentials, and that thehukousystem continues to matter in shaping destination choices. Moreover, the localisation of thehukouregulation and the commodification ofhukouin recent years have resulted in an increased concentration of highly skilled migrants relative to low‐skilled migrants in the most prosperous regions. Our findings suggest that state intervention is still intertwined with market mechanism to influence migration in reform‐era China, and that the state should not be seen as a unitary entity when understanding recenthukoureforms. Copyright © 2015 John Wiley & Sons, Ltd.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".