External Migrants under Mainland China’s Informal Welfare Regime: Risk Shifts, Resource Environments, and the Urban Employees’ Social Insurance
Bibliographic record
Abstract
Social insurance in mainland China long catered to populations that were assumed to remain in one place permanently. In recent decades, however, internal and transnational labour migration has been on the rise. Building on existing research about internal migrants' social security, this study asks how different groups of external labour migrants cope with the social risk shifts induced by mobility. It focuses on documented migrants from UN member countries; from Taiwan, Hong Kong, and Macao; and on undocumented migrants. It employs the resource environment approach, which integrates a transnational perspective and acknowledges informal sources of security. Focusing on healthcare, the study argues that informal practices affect the majority of external migrants irrespective of nationality or migration status, protecting expatriates from double coverage, causing low-income migrants to fall through the gaps, but also enabling access to healthcare for undocumented migrants. Despite mandatory participation, effective migrant coverage of the Urban Employees' Social Insurance (UESI) remains low. The system is highly decentralized with incomplete internal and external portability, and cities have considerable leeway over their own migration and welfare regimes. Migrants from more socio-economically developed areas tend to have a greater reliance on public services and security from the sending areas, or on high-end private alternatives. Conversely, as the example of Nigerian traders illustrates, undocumented migrants piece together their protective arrangements from individual networks and community institutions. Religious organizations from the Global South also reach out transnationally and provide informal protections to migrant communities. This study employs a mix of ethnographic fieldwork, document analysis, and descriptive statistics.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".