Growth Of Employment and Its Determinants in Microfinance Institutions in India: An Application of Error Correction Mechanism
Bibliographic record
Abstract
Microfinance Institutions (MFIs) play a vital role in India in respect of employment generation. MFIs provide credits to Micro, Small and Medium Enterprises (MSMEs), Joint Liability Groups (JLGs) and individuals to undertake commercial activities, thereby creates opportunities of employment generation. On the other hand, these MFIs recruits skilled and educated youths as employees in their organizations. This has emerged as a source of employment to the educated youths of India. In this study, effort has been made to see the relationship between the number of employees and three explanatory variables, namely, number of branches, number of clients and Gross Loan Portfolio (GLP) of the MFIs. Time series da has been used in this study ranging from first quarter of 2012-13 to last quarter of 2024-25. Engel-Granger cointegration test and Error Correction Model (ECM) are used to establish the above relationship. The empirical results show a strong and significant relationship between the number of employees and other explanatory variables, implying effective intervention on the part of the government and the monetary authority.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".