Markets, Human Capital and Inequality: Evidence from Rural China
Bibliographic record
Abstract
Beginning in the 1980s, almost all of the socialist countries replaced their planned economies with economic systems that relied heavily on market forces to determine the production and allocation of goods and services. This transformation has affected the lives of nearly two billion people. Historically, the two main arguments in favour of planned economies were that they are more productive in the long run (because they avoided the inherent instability of market forces, and were able to mobilize more resources for investment than a decentralized system), and that they provide a more equitable distribution of income. The experience of both socialist and market economies in the twentieth century decisively rejects the first argument; it would be hard to find observers of almost any persuasion who claim that planned economies are more productive or more efficient than market economies. Yet the second argument may well be valid; planned economies may indeed be more equitable than market economies. This raises the possibility that some societies may wish to retain at least some of the policies of planned economies, despite their inefficiencies, to maintain a more equitable distribution of income. Consequently, for countries that abandoned planning in favour of the market an important policy issue is the extent to which this policy shift has increased inequality.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".