Contribution of Insurance on Economic Growth in India: An Econometric Approach
Bibliographic record
Abstract
Insurance is an important part in the financial sector that contributes significantly to the economy of a country. Insurance market contributes hugely to the economic growth and also helps in managing risk more effectively. This piece of research work made an attempt to examine the relationship between insurance and economic growth in India considering the state level data and contributing to the existing literature. The data is collected for twenty-five states of India and covers the time period for 1995 to 2015. Endogenous growth model is used. Fixed effect model. Pooled ordinary least square generalized moment method (GMM) estimation techniques are used to establish the relationship between insurance and economic growth. This result infers that the insurance policies which can improve the insurance penetration in different states of India should be promoted. The relationship between physical capital and economic growth indicates that more investments should be made on infrastructure policies like health facilities, road etc. This research work could contribute largely to the insurance growth and economic growth and thus is beneficial to the Insurance sector and the Government of India.
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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) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".