Land Security and Mobility Frictions
Bibliographic record
Abstract
Abstract Frictions that impede the mobility of workers across occupations and space are a prominent feature of developing countries. We disentangle the role of insecure property rights from other labor-mobility frictions for the reallocation of labor from agriculture to nonagriculture and from rural to urban areas. We combine rich household and individual-level panel data from China and an equilibrium quantitative framework featuring sorting of workers across locations and occupations. We explicitly model the farming household and the endogenous decisions of who operates the family farm and who potentially migrates, capturing an additional channel of selection in the household. We find that land insecurity has substantial negative effects on agricultural productivity and structural change, raising the share of rural households operating farms by over 40 percentage points and depressing agricultural productivity by more than 20%. Comparatively, these quantitative effects are as large as those from all residual labor-mobility frictions. We measure a sharp reduction in overall labor-mobility barriers over 2004–2018 in the Chinese economy, all accounted for by improved land security, consistent with reforms covering rural land in China during the period. JEL Codes: O11, O14, O4, E02, Q1.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.012 | 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".