Suchergebnisse

AbstractThe main characteristics of intra-EU labour mobility are well documented. There is less focus, however, on the pattern of mobility of the East European (EU-13) EU-mobile citizens. This group constitutes more than half (57%) of all the EU movers and show, to some extent, other features than the rest of the EU mobile citizens (EU-15). The first part of this paper gives a brief overview of some key demographic and labour market characteristics of the East European mobile citizens in the most important destination countries. The perspectives of the sending countries are not analysed frequently enough, and thus the second part of the paper focuses on this issue in the case of Hungary, by asking to what extent the serious labour shortages, ensuing from the outflow of Hungarians, could be compensated by the recent increase of immigration of third country nationals. Using OECD data, the paper quantifies the balance of labour gains and losses for Hungary and compares this with Czechia, Poland, and Slovakia. The analysis concludes that despite the substantial recent inflow of third country nationals into Hungary, it remains to be seen whether this has a real substitution effect for the lost domestic labour force.

Effective decision making uses various databases including both micro and macro level datasets. In many cases it is a big challenge to ensure the consistency of the two levels. Different types of problems can occur and several methods can be used to solve them. The paper concentrates on the input alignment of the households' income for microsimulation, which means refers to improving the elements of a micro data survey (EU-SILC) by using macro data from administrative sources. We use a combined micro-macro model called ECONS-TAX for this improvement. We also produced model projections until 2015 which is important because the official EU-SILC micro database will only be available in Hungary in the summer of 2017. The paper presents our estimations about the dynamics of income elements and the changes in income inequalities. Results show that the aligned data provides a different level of income inequality, but does not affect the direction of change from year to year. However, when we analyzed policy change, the use of aligned data caused larger differences both in income levels and in their dynamics.