Bibliographic record
Abstract
The question of whether or how education affects income is a basic concern for economists and policy makers. The fact that education improves one's living perspectives is also a strong argument for undertaking substantial schooling investment in the developed and developing worlds. All these initiatives point to a more fundamental question: How do the better educated earn more? This study seeks to understand this question by drawing on the experience of policy reforms in rural China. In particular, I estimate the net profit function of rural households using China Household Income Project in 2002. I find strong support that education is rewarded through affecting households' allocation of labor and investments. It is estimated that an additional year of education is associated with 2.54 percent increase in net profits: 1.1 percent comes from better allocation of labor; 0.35 percent comes from better utilization of investment; 1.09 percent is due to the direct impact of education on earnings. The study has potentially important policy implications for completing China's economic reforms in that education is a crucial element. It also mirrors the experiences of other developing countries and shed light on how schooling should be financed: focusing on a few rather than universal provision may have a more profound impact on earnings.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".