Impact of Bank Lending on Economic Growth – An Empirical Study in the Indian Context
Bibliographic record
Abstract
Background:In any country's growing economy, its banking system would play a crucial role in boosting GDP.The present study analyzed the effect of Indian banking borrowing on Indian economic growth.Purpose: The present study analyzed the effect of Indian banking borrowing on Indian economic growth.The study framed the exploratory research with secondary data from 2005 to 2020.The growth of any country's economy will depend on its banking system.The banking growth will depend on the lending system; the more robust the lending, the higher the country's economic growth. Research Design and Methodology:The study framed the exploratory research with secondary data from 2005 to 2020.The study classified it into two segments; food and non-food credits of the Indian banking lending.In the study, food and non-food credit are bank loans.The vector error correction model calculates the analysis of the relationship of banking borrowing to economic growth.The result showed that food credit and non-food credit had a short-term relationship with Indian GDP.The ordinary least square method was applied, and the result showed that food credit had a negative impact, but non-food credit had a negative impact.Findings: The result showed that food credit and non-food credit had a short-term relationship with Indian GDP.The ordinary least square method was applied, and the result showed that food credit had a negative impact, but nonfood credit had a negative impact.This study is helpful for bankers, regulators, and various government stakeholders.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| 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".