| Abstract [eng] |
73 pages, 14 charts, 15 pictures, 73 references. The main purpose of this master thesis is to assess employment and demographic changes in rural areas since 2000, identify the main factors influencing employment and demographic processes and establish the relationship between employment levels and demographic processes. The work consists of four main parts: the analysis of literature, methodology of the empirical study, the research and its results, conclusion and recommendations. Literature analysis defines the concepts of employment and demography, identifies the factors influencing the level of employment and the size of the population, and shows how employment and demographic indicators have changed in rural Lithuania since 2000. After the literature analysis the author has carried out the study to determine what factors influence the level of rural employment and population in Lithuania. Using the Twostep clustering method in SPSS programme, 52 Lithuanian municipalities were divided into four clusters. Once the treatment groups were formed, panel vector autoregressive models were constructed for each group of municipalities using R studio software. The main objective of the panel VARs was to determine which factors influence the employment rate and population in each group. Maddala-Wu and Choi's unit root tests were used to determine whether variables are stationary and models stability condition was tested by calculating the modulus of each eigenvalue of the fitted model. The performed research revealed that the factors influencing employment rates and population differ between groups of municipalities. Population has a statistically significant positive effect on employment rates, except in the model for recreational areas. The employment rate has a statistically significant positive effect on population only in model for other areas. The conclusions and recommendations summarize the main concepts of literature analysis as well as the results of the performed research. The author believes that a more detailed study can be carried out by using rural employment rates in panel vector autoregressive models. |
