Inequality Change in China and (Hukou) Labour Mobility Restrictions
Bibliographic record
Abstract
We analyze the Hukou system of permanent registration in China which many believe has supported growing relative inequality over the last 20 years by restraining labour migration both between the countryside and urban areas and between regions and cities.Our aim is to inject economic modelling into the debate on sources of inequality in China which thus far has been largely statistical.We first use a model with homogeneous labour in which wage inequality across various geographical divides in China is supported solely by quantity based migration restrictions (urban --rural areas, rich -poor regions, eastern coastal --central and western (noncoastal) zones, eastern and central --western development zones, eastern --central --western zones, more disaggregated 6 regional classifications, and an all 31 provincal classification).We calibrate this model to base case data and when we remove migration restrictions all wage and most income inequality disappears.Results from this model structure point to a significant role for Hukou restrictions in supporting inequality in China, and show how economic rather than statistical modelling can be used to decompose inequality change.We then modify the model to capture labour efficiency differences across regions, calibrating the modified model to estimates of both national and regional Gini coefficients.Removal of migration barriers is again inequality improving but now less so.Finally, we present a further model extension in which urban house price rises retard rural -urban migration.The impacts of removing of migration restrictions on inequality are smaller, but are still significant.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".