Theory and Empirics of Economic Inequality Influencing Economic Growth: A Study of Major Indian States
Bibliographic record
Abstract
Unlike the conventional approach, this paper theoretically shows that when median voter’s income is much below the mean level, higher inequality of income increases redistribution in favor of median voter, and thereby influencing higher economic growth provided that major share of tax-financed capital is allocated in public education which benefits all. In the empirical findings this study suggests that despite continuous increase in consumption inequality in major Indian states, redistribution in real social expenditure by Centre and States continues to increase in real per capita terms including median voter during post-reform period. Although inequality of consumption expenditure induces an increase in economic growth for about 50 per cent of major Indian states and the regression coefficients are almost insignificant, such tax-financed public education might act as externality to everybody if major tax financed resources are allocated on education. This might lead to a positive and significant impact into the growth process provided that the large proportion of working population of major Indian states get employment in the service sector. However the empirics of Indian states during the current years also show that service sector of Indian economy, which depends completely on stepping up of educational level to the working population, acts as the major contributor to growth.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".