The Impact of Migration on Financial Inclusion in Bosnia and Herzegovina
Bibliographic record
Abstract
Examining the impact of migration on financial inclusion in Bosnia and Herzegovina, the focus of this research is on the level of financial inclusion of immigrants, emigrants and residents without a migration background. Through a combination of quantitative and qualitative analyses, access to financial services among these groups is investigated, and possible obstacles they face are identified. The current active financial inclusion policies did not treat the migration phenomenon, and in this regard, they were not channeled towards more vulnerable categories, which is of great importance for its improvement. The innovativeness of the explained topic is precisely reflected in the development of awareness of the impact of migration on the level of financial inclusion with the aim of more efficient positive results in the end. The research analyzes the link between migration status and the level of financial inclusion, taking into account demographic characteristics such as gender, age, education, work status and income. The research was conducted on the basis of primary data collection, using the method of written (online) examination. An online survey was used as a data collection form, which was created based on the OECD/INFE (2011, 2015, 2018) standardized questionnaire. The data was collected from a sample of 616 respondents in the first quarter of 2023. The results indicate an unequal level of financial inclusion among different migration groups. Immigrants achieved the highest level of financial inclusion, and emigrants achieved the lowest. Regression models showed statistically significant relationships between migration status and financial inclusion, indicating the complexity of this relationship. Furthermore, socioeconomic characteristics such as education, work status and income have a positive influence on the level of financial inclusion, while gender and age are not significant factors. It is concluded that there is a need to improve financial inclusion in Bosnia and Herzegovina, with a focus on education, facilitating access to financial services and supporting the integration of migrants into the economic framework of the country. This paper provides the foundations for further research on the impact of migration on financial inclusion, emphasizing the complexity of the connections between these two phenomena.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".