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  • 标题:Migration Incentives and Flows between Belarus, Moldova, Ukraine and the European Union: a Forecasting Model
  • 本地全文:下载
  • 作者:Peter Čajka ; Marta Jaroszewicz ; Wadim Strielkowski
  • 期刊名称:Economics & Sociology (Ternopil)
  • 印刷版ISSN:2071-789X
  • 电子版ISSN:2306-3459
  • 出版年度:2014
  • 卷号:7
  • 期号:4
  • DOI:10.14254/2071-789X.2014/7-4/1
  • 出版社:Centre of Sociological Research
  • 摘要:

    The main objective of this paper is to elaborate econometric model forecasting the stocks of migrants from the Eastern European states (EES) in the Visegrad group (V4) countries and the European Union Member States (EU MS) in case of visa abolition.

    We use the data span of 2008-2012 and the econometric techniques known as Seemingly Unrelated Regression (SUR), Panel data Least Squares (PLS) and General method of moments (GMM) to build three types of possible scenarios for migration from the Eastern European countries (Belarus, Moldova and Ukraine) to the V4 and to the EU as a whole in the next 35 years, i.e. until the year of 2050 with a simulated shock of visa abolition set at the year of 2015.

    Our results show that hypothetical visa abolition is not going to dramatically increase migration from the Eastern European countries in the EU Member States. Even though the immediate effect of visa abolition would probably result in the slight increase of migration stocks in the V4 and EU countries, the annual migration stocks comprised of residents of Belarus, Moldova and Ukraine in the EU MS in a long term might be around from one and a half to just above three – three and a half million people.

    Furthermore, a successful accession period with high growth and implementation of the reforms is actually leading to elimination of the migration pressures. More precisely, the citizens of Belarus, Moldova and Ukraine that had the strongest incentives to migrate have already done so long before the visas are eventually abolished

  • 关键词:international migration; transition economies; Belarus; Moldova; Ukraine; panel data; seemingly unrelated regressions
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