Impact of Population Migration Profiles on Financial Literacy: The Case of Bosnia and Herzegovina
Bibliographic record
Abstract
Financial literacy is becoming increasingly important in modern society, and its lack can have significant economic consequences. Global research indicates low rates of financial literacy, and population migrations additionally affect this dynamic. Migration flows in Europe have a special significance for financial literacy. The aim of this research is to determine the significance of the migration profile on financial literacy. Migration profiles such as immigrants, emigrants and residents without a migration background are analyzed, and an attempt is made to understand the connection between migration flows and financial literacy. The research was conducted on the basis of primary data collection, using the method of written (online) examination. As a form of data collection, a survey questionnaire was used, which was created on the basis of the OECD/INFE standardized questionnaire. The data was collected on a sample of 616 respondents in the first quarter of 2023. The results have showed that residents without a migration background have the highest level of financial literacy, while immigrants show the lowest level. The components of financial knowledge, behavior and attitudes and their connection with migration profiles were analyzed. The results indicate a negative impact of migration on financial literacy. Further analysis included control variables such as gender, age, education, work status and income. The model with control variables shows that immigrants (when the migration status is observed) and women (when the gender status of the respondents is observed) have lower financial literacy, while education and employment have a positive effect on it. The research highlights the importance of understanding the connection between migration and financial literacy, and the need to develop customized educational programs to improve the financial literacy of migrants. This analysis contributes to the understanding of the complex dynamics between migration and economic literacy, and can serve as a basis for future strategies to increase financial literacy in Bosnia and Herzegovina.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".