Little to Lose: Exit Options and Attitudes towards Automation in Chinese Manufacturing
Bibliographic record
Abstract
Abstract Recent discussions on the future of work emphasize the negative effects of labour-replacing technology on employment and wages. However, original surveys and field research show that Chinese manufacturing workers currently consider themselves the beneficiaries of technological upgrading. This paper presents quantitative and qualitative evidence from two original surveys of over 2,400 workers and 600 companies in the manufacturing sector, interviews with firm managers and workers from 76 companies, and 34 factory visits in 19 cities in southern China. It finds that insofar as labourers experience automation anxiety, local workers are more likely than internal migrant workers to worry about technological displacement and are more pessimistic about their prospects of securing comparable employment after displacement. Owing to the features and consequences of the household registration system, internal migrants have a larger set of acceptable exit options that are no worse than their status quo, contributing to their lower anxiety about automation compared to locals. These findings suggest that automation susceptibility does not directly translate into automation opposition as previously assumed; institutions can shape technological receptiveness among people who face similar threats of automation by altering their exit options.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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; a candidate call from one teacher head, 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".