China's Growth Slowdown and Prospects for Becoming a High-Income Developed Economy
Bibliographic record
Abstract
After decades of hyper growth, China's economy has slowed significantly in recent years, causing widespread anxiety both within and outside the country. Although economists have not reached a consensus about China's growth potential, it is undeniable that the country has switched gears toward a “new normal” of moderate growth amidst ongoing structural change. To assess China's growth performance and prospects, this study modifies Masahiko Aoki's analytical framework of a unified growth theory into a multi-sector model and applies it to identify the sources of China's per capita income growth in recent decades. The analysis confirms Aoki's early observation that China entered the so-called “Kuznets phase” of development in the 1980s, which then became overlapped by the H-phase, in which human capital–based growth is characterized by high labor productivity growth. This study provides evidence that China's labor productivity growth has been predominantly driven by fixed capital formation. It also reveals that the Kuznets effect (with its labor reallocation effect) has now passed its peak and is fading away. The most alarming finding is that net total factor productivity (TFP) growth in the latest period has slowed to a near halt. This trend is particularly worrisome given that China has exhausted its past demographic dividend and its industrial structure has evolved to the end of industrialization stage. Meanwhile, demographic projections clearly indicate that China has entered what Aoki defined as the development phase of “post demographic transition.” Whether China can reverse the downward trend of TFP growth will determine how soon it can achieve the goal of becoming a high-income developed economy.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 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".