THE CONTRIBUTION OF HUMAN CAPITAL TO CHINA'S ECONOMIC GROWTH
Bibliographic record
Abstract
This paper develops a human capital measure in the sense of Schultz (1960) and then reevaluates the contribution of human capital to China's economic growth.The results indicate that human capital plays a much more important role in China's economic growth than available literature suggests, 38.1% of economic growth over 1978-2008, and even higher for 1999-2008.In addition, because human capital formation accelerated following the major educational expansion increases after 1999 (college enrollment in China increased nearly fivefold between 1997 and 2007) while growth rates of GDP are little changed over the period after 1999, total factor productivity increases fall if human capital is used in growth accounting as we suggest.TFP, by our calculations, contributes 16.92% of growth between 1978 and 2008, but this contribution is -7.03% between 1999 and 2008.Negative TFP growth along with the high contribution of physical and human capital to economic growth seem to suggest that there have been decreased in the efficiency of inputs usage in China or worsened misallocation of physical and human capital in recent years.These results underscore the importance of efficient use of human capital, as well as the volume of human capital creation, in China's growth strategy.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".