How Does the Productivity and Economic Growth Performance of China and India Compare in the Post-Reform Era, 1981-2011?
Bibliographic record
Abstract
Applying an aggregate production possibility frontier (APPF) framework for growth accounting à la Jorgenson et al. to economy-wide Chinese and Indian industry productivity accounts, constructed in the spirit of the KLEMS principle, we estimate and compare growth and productivity performance in China and India over their post-reform period from 1981 to 2011. We show that during this period China grew over 50 per cent-faster than India in value added (9.4 versus 6.1 per cent per annum) but about 25 per cent-slower than India in TFP (0.83 versus 1.13 per cent per annum). The two economies also experienced very different growth and productivity performances over sub-periods distinguished by special policy regimes and governing systems. While both countries appeared to enjoy their best performances in the 2002-2007 period following China's WTO entry, China faltered much more in terms of total factor productivity growth in the wake of the global financial crisis.
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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 | 0.003 | 0.006 |
| 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) | 0.001 | 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".