Influence of migrant remittances on socio-economic indicators of the country.
Bibliographic record
Abstract
The ever-increasing global migration is an important phenomenon affecting countries' socio-economic performance. The impact of migration on the country's socio-economy depends on the age and education of the people who left the country. The most common consequences of migration are an ageing society, a brain drain and declining activity. Both the migration and their remittances have an impact on the country's socio-economic performance. Migrants send remittances for various reasons, but the most common motive is altruistic. Migrant remittances are seen as a stable, non-declining source of finance for receiving families, even in crisis times. Remittances are also important from a macroeconomic perspective: in a quarter of the world, remittances account for more than 4 % of GDP. The impact of migrant remittances on the country's socio-economic indicators is increasingly being studied due to the lack of information on this topic in the scientific literature. Migrants' growing trust in official remittance channels allows for increasingly accurate results in the new research. The growth of migration is observed in the EU member states and the rest of the world. An increase in remittances in the EU has been observed after the expansion of the EU in 2004. This study aims to examine the impact of remittances on the socio-economic indicators in the countries that joined the EU in 2004. The objective is to study the impact of migrant remittances on the country's socio-economic indicators. The research reveals the problems and presents theoretical solutions to the effects of remittances on socio-economic indicators. The methodology developed is used to assess the impact and evaluate the results of migrant remittances on the country's socio-economic indicators in the EU10. The impact of migrant remittances on the country's socio-economic indicators is mixed in the scientific literature. Researchers say rising remittances boost the country's GDP and reduce poverty, but there is also a contrary view among researchers. This disagreement defines the research problem: what impact do migrant remittances have on a country's economy based on socio-economic indicators? Regression and correlation analyses were performed to explain the effects of remittances. An empirical study of the EU10 countries has shown that migrant remittances positively impact GDP in the short run. Impacts have been identified in Estonia, Cyprus, Latvia, Poland, Lithuania and Slovenia. This result confirms the opinion of most researchers. Different effects of remittances have been identified on the S80/S20 income distribution indicator. Slovakia and Cyprus have seen a narrowing of the S80/S20 income distribution ratio, while in Malta, it has increased. The results of the S80/S20 analysis of income distribution are in line with the different findings in the scientific literature on the impact on income inequality. Both the literature and this study have found positive and negative effects of remittances. The study results show that remittances positively impact employment rate in Poland, i.e. as remittances increase, employment rate increases. This conclusion is not in line with the opinion of scientists about the negative impact of remittances on the country's employment rate.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".