From Divergence to Convergence: Reevaluating the History Behind China's Economic Boom
Bibliographic record
Abstract
China's long-term economic dynamics pose a formidable challenge to economic historians. The Qing Empire (1644–1911), the world's largest national economy before 1800, experienced a tripling of population during the seventeenth and eighteenth centuries with no signs of diminishing per capita income. While the timing remains in dispute, a vast gap emerged between newly rich industrial nations and China's lagging economy in the wake of the Industrial Revolution. Only with an unprecedented growth spurt beginning in the late 1970s did this great divergence separating China from the global leaders substantially diminish, allowing China to regain its former standing among the world's largest economies. This essay develops an integrated framework for understanding that entire history, including both the divergence and the recent convergent trend. We explain how deeply embedded political and economic institutions that contributed to a long process of extensive growth before 1800 subsequently prevented China from capturing the benefits associated with the Industrial Revolution. During the twentieth century, the gradual erosion of these historic constraints and of new obstacles erected by socialist planning eventually opened the door to China's current boom. Our analysis links China's recent development to important elements of its past, while using recent success to provide fresh perspectives on the critical obstacles undermining earlier modernization efforts, and their eventual removal. (JEL N15, N45, O11, O47, P21, P24, P26)
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".