What Determines the Success of Financial Inclusion? An Empirical Analysis of Demand Side Factors
Bibliographic record
Abstract
A financially well included society can have an extremely positive effect on the economy. Financial Inclusion is impacted by demand side and supply side factors. It is important to understand the factors affecting financial inclusion from the demand side which can be truly identified by conducting a survey. While extant literature highlights the importance of the supply side factors, there has been little study to appreciate the challenges that people in emerging economies face with respect to banking. Given the backdrop of the new policies with respect to financial inclusion, we attempted to understand the demand side perception of respondents to identify the causes of inclusion/exclusion. The techniques used for analysis are Exploratory Factor Analysis and Confirmatory Factor Analysis. Some of the main factors that were found include technological factors, the benefits that bank accounts offer, banking outreach, and demographic factors. Banks need to focus on such dimensions more effectively to achieve the Government¡¯s target of making the economy completely inclusive.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".