China and India: Reforms and the Response: How Differently have the Economies Behaved
Bibliographic record
Abstract
The relative performance of China and India is compared using two different methods and they provide a very different picture of their relative performance.We compare the average absolute values of indictors for the decade of the 1980s, 1990s and the 2000s.We use indicators such as the current account balance (CAB), exports of goods and services (XGS), foreign direct investment inflow (FDI), gross domestic savings, gross fixed capital formation (GFCF), aid, private capital inflows (PrK) and workers' remittances, all as a percentage of GDP.We also look at the growth rate of per capita GDP, exports of goods and services and of gross fixed capital formation.Using a two tailed-test we find that China does better than India for most of these indicators.For instance, China has a higher growth rate of per capita income, XGS and GFCF as also a higher share of XGS, GFCF etc in GDP than does India.We also find that China usually has a lower CV, namely a more stable performance.But over the three decades the CV falls in India so it is approaching that in China, namely the two economies are becoming more similar.We also compare the dynamic performance of the two economies since their reforms.We form index numbers for the indicators.So for example, we from an index number for share of exports in GDP with year 1 of reform in China being 100, i.e. the index for the share in 1979 is 100.Year 2 would be the index number for 1980, namely the value of the share in 1980 with the share in 1979 being 100, etc.In the case of India year 1 would be 1992 once the reforms started, year 2 would be 1993 and so on, so the index would have 1992 as the base year.We find that the indices behave very similarly in the two economies for many of the indicators, namely the pattern of change in China after 1979 is the same as in India after 1992.
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".