Changing Complexity in the Productive Structure of Indian Economy
Bibliographic record
Abstract
The complexity in the productive structure of an economy may be measured by the nature and extent of association between the sales and the purchase profiles captured by its input-output table. In this paper we estimate the change in complexity in productive structure of the Indian economy during the period 1984 - 96, and identify its major sources, using two multivariate techniques viz. dummy variable regression technique and dual scaling technique. The analysis is carried out in two stages. In the first stage we use dummy variable regression technique with row and column dummies to identify input-output cells with high row-column dependence. Next we use dual scaling technique to decompose the row-column association into hierarchical loops. Our analysis reveals that i) there has been significant increase in the level of complexity and degree of integration in the productive structure of Indian economy during the period 1984 to1996. This increase is most pronounced during the nineties when the economic reforms, in consonance with globalisation, were initiated for the Indian economy, ii) the increase in the level of complexity and degree of integration in productive structure of Indian economy has not been gradual as there seems to have occurred a structural break in the productive structure of the Indian economy during the nineties iii) the changes in economic complexity of Indian economy have been accompanied with significant changes in technological and/or market conditions faced by different sectors of the Indian economy; Manufacturing, Allied Agriculture and Services being the sectors having undergone tremendous change in both the aspects and iv) in terms of maturity, as measured by the level of complexity and degree of integration, the Indian economy has started resembling the developed countries like USA and Canada.
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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 | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".