How Do the Better Educated Earn More? Evidence from Rural China
Why this work is in the frame
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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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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 it