How Does the Productivity and Economic Growth Performance of China and India Compare in the Post-Reform Era, 1981-2011?
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it