Measuring Changes in the Bilateral Technology Gaps between China, India and the U.S. 1979 - 2008
Bibliographic record
Abstract
Popular literature suggests a rapid narrowing of the technology gap between China and the U.S. based on large percentage increases in Chinese patent applications, and equally large increases in college registrants and completed PhDs (especially in sciences) in China in recent years. Little literature attempts to measure the technology gap directly using estimates of country aggregate technologies. This gap is usually thought to be smaller than differences in GDP per capita since the later reflect both differing factor endowments and technology parameters. This paper assesses changes in China's technology gaps both with the U.S. and India between 1979 and 2008, comparing the technology level of these economies using a CES production framework in which the technology gap is reflected in the change of technology parameters. Our measure is related to but differs from the Malmquist index. We determine the parameter values for country technology by using calibration procedures. Our calculations suggest that the technology gap between China and the U.S. is significantly larger than that between India and the U.S. for the period before 2008. The pairwise gaps between the U.S. and China, and the U.S. and India remain large while narrowing at a slower rate than GDP per worker. Although China has a higher growth rate of total factor productivity than India over the period, the bilateral technology gap between China and India is still in India's favor. India had higher income per worker than China in the 1970's, and China's much more rapid physical and human capital accumulation has allowed China to move ahead, but a bilateral technology gap remains.
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How this classification was reachedexpand
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 | 0.024 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".