2013-1 The Changing Determinants of High School Attainment in Rural China
Bibliographic record
Abstract
In recent years China has experienced a substantial increase in rural schooling levels and contemporaneous reforms in rural educational policies, including the nationwide adoption of free nine-year compulsory education, the two exempt and one compensation program, and the school consolidation policy. These developments point to the possibility that the determinants of rural education have changed. In this paper we examine empirically the determinants of rural high school attainment between 2002 and 2007. Using data from a nationwide household survey and a multilevel regression model with and without instrumental variables, we estimate the relationship between rural high school attainment and individual, family, and community level variables. We find that the size and significance of household income and other individual and household variables declined, while community characteristics and local public expenditures on schools continued to have a significant impact in both years. When we carry out the estimation using instrumental variables, the coefficients on parental education are no longer significant. We conclude that policy changes plus rapid income growth in rural China has brought about substantial change in the determinants of high school attainment, and that the widely observed correlation between parental and child education may be due to unobserved characteristics.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".