The Restraining Effect of Human Capital Mismatch and Structure Distortion on Economic Growth -- A New Structural Economics Analysis
Bibliographic record
Abstract
Based on the theory of new structural economics, this paper investigates the restraining effect of human capital mismatch and structural distortion on economic growth. First of all, this paper puts forward the core hypothesis through theoretical analysis, and then analyzes it by mathematical model, which shows that the human capital mismatch between monopoly departments and competitive departments will inhibit innovation efficiency, and then inhibit economic growth. The structural distortion of human capital in the two sectors will make industrial structure deviate from the comparative advantage, and then restrain economic growth. Secondly, using the provincial panel data of China from 2006 to 2019, this paper establishes the index of human capital mismatch and structural distortion to conducts empirical tests on the theoretical model. The conclusion shows that the uneven distribution of human capital among industries in China will restrain economic growth through human capital mismatch and structural distortion, while the latter has a greater impact, so structural distortion is the main reason why human capital accumulation cannot effectively promote economic growth, which is consistent with the basic theory of new structural economics.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".