Bibliographic record
Abstract
Abstract We develop a spatial equilibrium model to quantify welfare losses from land market distortions in China. In the model, heterogeneous firms in various sectors choose their locations across regions with costly trade, frictional labour migration and land market distortions. We match land transaction and firm‐level survey data to estimate land market distortions for firms. Misallocation arises when similar firms are faced with land prices that effectively prevent productive firms from establishing in large cities where they can benefit from agglomeration forces and access higher productivity. Our framework incorporating land market distortions also sheds light on the mystery of China's undersized big cities, a phenomenon noted by Au and Henderson (2006) and Chauvin et al. (2017). Our estimates suggest large negative effects of land policies on the economic welfare in China. We end with a counterfactual exercise revealing that a coordinated land and labour migration reform would generate welfare gains and reduce regional inequality.
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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 | 0.002 | 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 | 0.001 | 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".