| Title |
Tarptautinės migracijos srautams įtaką darančių veiksnių analizė / |
| Translation of Title |
Analysis of factors influencing international migration flows. |
| Authors |
Pamarnackaitė, Gintarė |
| Full Text |
|
| Pages |
91 |
| Keywords [eng] |
principal component analysis ; nonparametric regression ; RStudio ; international migration ; migration flows |
| Abstract [eng] |
The final master thesis deals with an issue that is relevant to a modern society – factors affecting international migration flows. Migration flows are growing every year and this phenomenon is even more magnified by refugee flows from the Middle East and Africa. According to Federal Minister of the Interior T. de Maiziere (2018): “migration and refugee flows will be one of the main challenges for our society in the coming decades”. The first part of this thesis discusses scientific researches on analysis of migration flows and influential factors. Software tools and analytical measures employed for the research are defined. The second part of this thesis contains methodological information for analysing factors affecting international migration flows. Preparation of data matrix is described and models for organizing countries into homogenous groups, identifying statistically important factors affecting migration flows and nonparametric regression analysis are developed. A new and original model for identifying and analysing factors affecting migration flows and forecasting migration flows is created. In the third part of the thesis, using the developed model, the analysis of migration flows in the EU member states is performed and insights are presented. The study revealed that 28 member states, studied in terms of migration flows and GDP per capita, can be clustered into 3 groups. More than half of these countries are classified to the category of “The poorest countries”. One of the member states is distinguished by a particularly high number of immigrants and GDP per capita. Factors of economic welfare are only relevant for the poorest countries. Developed prognostic models for quantitative assessment of migration flows distinguish 34 significant factors for the group of poor countries and 4 statistically significant factors for the group of rich countries. These models explain data dissemination respectively by 81.12% and 65.79%. |
| Dissertation Institution |
Kauno technologijos universitetas. |
| Type |
Master thesis |
| Language |
Lithuanian |
| Publication date |
2018 |
