Relationship Between Various Determinants and Dimensions of Financial Literacy Among Working Class
Bibliographic record
Abstract
The purpose of this empirical research work was to identify the relationship between various determinants of Financial Literacy among the working class of National Capital Region of India. It was a descriptive study based on the survey of 596 working class respondents. The data has been analyzed by factor analysis, correlation and regressions analysis. Based on the factor analysis, three factors have been found of financial knowledge, three factors of financial behaviour and in a similar manner four factors of financial attitude have been extracted. Further, based on the multiple regression models the contribution of financial attitude has been found highest in explaining the financial literacy and it has been followed by financial behaviour and financial knowledge. It means that both financial attitude and financial behaviour are better estimators of financial literacy in comparison to the financial knowledge. Therefore, the policy makers, financial system regulators and governments should do more efforts to improve the level of financial attitude and financial behaviour in comparison to the financial knowledge to improve the level of financial literacy because significant difference has been found in the level of financial attitude and financial behaviour across some of the demographic factors. The increased financial literacy would be helpful in improving the saving and investment behaviour of the public. This improved level of financial literacy of public will save the required level of capital for the capital formation for the targeted economic growth. Consequently, more employment opportunities will increase the social security in the society. Like every study, this study also have certain limitations like the universe of the study was limited to a particular geographical region i.e. National Capital Region of India, along with time and money constraints. In future similar study can be conducted by changing the target population and geographical area with a bigger sample.
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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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".