Measuring Financial Inclusion in Southeast European Countries Using Multidimensional Index of Financial Inclusion
Bibliographic record
Abstract
This paper provides an insight into measuring financial inclusion through a multidimensional index of financial inclusion in Southeast European countries (SEE). We used a two-stage principal component analysis to extract dimensions of financial inclusion. Data were obtained from two sources, the World Bank Global Findex Survey (WB-GFS) data base and the International Monetary Fund Financial Access Survey data (IMF-FAS), for twelve SEE countries for the years 2011, 2014, 2017, and 2021. The research confirms that financial inclusion can be measured using two dimensions in terms of access as one factor and usage and availability as the second factor. Practical implications of this research are in ensuring an adequate measure of the level of financial inclusion for SEE countries that can be used for further research related to understanding the underlying factors contributing to financial inclusion, barriers to financial inclusion as well as the impact of financial inclusion on economic growth and poverty alleviation.
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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 | 0.005 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".