Higher Education Expansion and Return to Education in China: Evidence from CGSS2005 and CGSS2013
Bibliographic record
Abstract
We conducted an empirical study to estimate the private internal rate of return to years of schooling (IRR) in China during the period after the implementation of higher education expansion policy using data from the Chinese General Social Survey data conducted in 2006 and 2014 (CGSS2005, CGSS2013). The major conclusions are as follows: first, from 2005 to 2013, IRR decreased from 8.6% to 7.8% for the whole sample, IRR decreased from 8.3% to 7.4% for men, and IRR decreased from 9.0% to 8.2% for women. Second, IRR values among various education category groups are different. IRR is greater for the high-level education group than that for the middle and low-level education groups in both 2005 and 2013. Third, to consider the impact of the higher education expansion policy on IRR, the IRR of the university graduates decreased from 15.4% (2005) to 11.2% (2013), whereas the IRR of the graduate school graduates rose from 10.1% (2005) to 19.0% (2013). The effect of the policy on IRR differs between the university and graduate school graduates. Fourth, the IRR is higher for women than for men. There is a gender disparity for IRR; IRR is different by ownership types, registration system types, industrial and regional groups in both 2005 and 2013.
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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 | 0.003 | 0.004 |
| 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) | 0.002 | 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".