First, using the aggregated-level data we investigate the compositional differences of Lithuanian population, focusing on the residents of the declining regions and those who have moved out from them. Labour market structure, degree of urbanization, population density and distance to major MAs are also among commonly reported contextual variables influencing migration (Nivalainen, 2004). Table 1 contains aggregated-level data and shows compositional differences between four groups of people living in Lithuania in 2011: all Lithuanians, residents of the MAs, residents of the rapidly declining regions and people who have moved out from the declining regions. We also found that distance to the major cities does not have an impact on migration behaviour, though the more declining regions are the more peripheral ones. Among its etymologies there are a derivation from the word Lietava, for a small river, a possible derivation from a word leičiai, but most probable is the name for union of Lithuanian ethnic tribes ('susilieti, lietis' means to unite and the word 'lietuva' means something which has been united). The dependent variable indicates if someone lives in a rapidly declining (losing 20% of the population or more between 2001 and 2011) region (1) or not (0). We did not find that those who are unemployed, uneducated and experiencing the worst living conditions were likely to move out from declining regions. This paper provides empirical evidence of selective migration and increasing regional disparities in Lithuania. The Organization for Economic Cooperation and Development, to which a member Lithuania became in July 2018, urged Lithuania to tackle emigration challenges as the Baltic country is losing many working-age people. Using individual-level data we are now able to investigate the directions and population structure of migration flows as well as the relationships between the individual characteristics which are affecting migration. Source: Based on the 2011 Lithuanian census. Generally, all migration studies using census data suffer from similar problems, also in other countries (Leetmaa & Tammaru, 2007; Nivalainen, 2004; Sjöberg & Tammaru, 1999; Tervo, 2000), but it is important to be aware of them when discussing study results. Moreover, those who work in public administration were less likely to move to ‘losing’ regions and those who work in industry were more likely to move to ‘winning’ regions. Between 2000 and 2010 they lost on average –3.7% of their population. In model 2 we included employment status variables. Our analysis is focused into 139,578 individuals of the rapidly declining regions, of whom 1605 have moved out of these regions between 2010 and 2011. Lastly, we controlled for (changes in) the unemployment rate of the origin region, which should reveal what the role is played by the regional context on the probability of leaving the shrinking regions and the destination of migration. Moreover, housing characteristics also play a role in migration behaviour. The results also showed (data not shown) that people who now have high-ranking positions in the labour market were more likely to migrate, especially to the ‘winning’ regions. Including labour market position in model 2 also affected the effects of age compared with model 1. The experiences of other countries have shown that the best strategy to cope with shrinkage is through the encouragement of local incentives and citizen participation. Summary statistics of the main variables included in the models can be found in Table 1. The study contributes to the existing literature by integrating population decline, migration and socio-spatial polarization, which are particularly common in post-socialist CEE countries. Table 1 also shows that those who moved out form the declining regions were younger, better educated and more qualified than the average of rapidly declining regions. The model also shows that in general Lithuanians were more likely to move than others, but when we take into account the direction of migration, Lithuanians were more likely to have moved into ‘losing’ regions while there are no differences between ethnic groups with regard to moving into ‘winning’ regions. The population decreased by 2,614 persons due to external migration and by 1,741 persons due to negative natural increase. Shortage, priority and urban growth: Towards a theory of urbanisation under central planning, Transitional statistics: Internal migration and urban growth in post-soviet Estonia, Uneven transformations: Space, economy and society 20 years after the collapse of state socialism, Demographic shifts in the Czech Republic after 1989: A second demographic transition view, Planning for shrinkage: Paradox or Paradigm, Demographic change as a future challenge for cities in east central Europe, Rural out-migration: Community consequences and individual migrant experiences, Suburban growth and suburbanisation under central planning: The case of soviet Estonia, The ethnic dimensions of suburbanisation in Estonia, Migration and labour market adjustment: Empirical evidence from Finland 1985‐90, EU integration & emigration consequences: The case of Lithuania, Lietuvos gyventojų vidaus migracijų teritoriniai ypatumai 21 a, Territorial features of internal migration of Lithuanian population in the 21st century, Ethno-political effects of suburbanization in the Vilnius urban region: An analysis of voting behavior, Shrinking regions in a shrinking country: The geography of population decline in Lithuania 2001-2011, Large social inequalities and low levels of socio-economic segregation in Vilnius, Planning urban systems in Soviet times and in the era of transition: The case of Estonia, Spatial mobility, family dynamics, and housing transitions. In a quarter of a century, since the early 1990s, Lithuania lost more than one-fifth of its residents (a rapid decrease from 3.7 million in 1989 to 2.9 million in … It has been shown that internal migration plays a major role in the processes of population redistribution and growing spatial imbalances (Ambinakudige & Parisi, 2015; Ubarevičienė, 2016). An important finding is that the probability of migration increases with higher unemployment rates in regions of origin and this probability is even higher if the region experienced an increase in unemployment rate over the last decade. The population of Lithuania had begun declining after the country regained its independence substantially as a result of emigration, concentrated among the country’s Russian minority. The largest share of residents' loss is due to emigration. Moreover, rural shrinkage is a natural response to structural changes in the economy, including increasing efficiency of the agricultural sector. There are three MAs in Lithuania, which contain cities and their suburban areas: Vilnius (635,480), Kaunas (392,313) and Klaipėda (210,635) (based on the 2011 Lithuanian census). By closing this message, you are consenting to our use of cookies. As in subsequent models, these models include only reference persons aged 18 years and older. Although our study has shortcomings, mainly related to data constraints, it is an important step on the path to a better understanding of internal migration and depopulation processes in post-socialist countries. Previously controlled flows of internal migration changed direction, and many people moved towards the larger cities. … Although the general trend is very similar between the two directions of migration, those who move to ‘winning’ regions are relatively younger. The latter authors argue that skilled people benefit most from migration and they are most likely to relocate, while the sending regions lose their human capital and eventually also their economic potential. By knowing that migration tends to be selective by nature (Fratesi & Percoco, 2014; Tervo, 2000), it can be expected that the population leaving declining regions in Lithuania is very selective as well. However, our survey, which was completed right after the 2011 census, showed that 96% of people living in the declining regions are actually satisfied with their standard of living (Daugirdas et al., 2013). The highest rates of depopulation were recorded for the rural and peripheral areas of Lithuania; meanwhile, population increases could be observed in the regions directly surrounding the major cities. We distinguished between three categories of people: (1) reference category – persons who did not move from the rapidly declining regions6; (2) persons who moved into population-‘winning’ regions; and (3) persons who moved into population-‘losing’ regions. One-third of the population live in rural areas, with half of the … In total, there were 1605 reference persons who left declining regions between 2010 and 2011. However, the urban life-cycle theory says that urbanization is often accompanied by suburbanization and counterurbanization (van den Berg, Drewett, Klaassen, Rossi, & Vijverberg, 1982), because different locations are attractive to people with different individual characteristics, needs, opportunities and motives (Ambinakudige & Parisi, 2015). The probability to move over longer distances also highly depends on socio-economic status; it is much lower for individuals with a weaker labour market position (Fratesi & Percoco, 2014). Seniors in 2019 made up more than 16% of the U.S. population, compared to 13% in 2010. This paper describes the trends in suicide deaths in urban population in Lithuania by gender, dates and suicide method over the period 1984–2003. In 1904 and 1914 the urban inhabitants made up 12.8% and 13.64% of the population respectively. We will also investigate the effects of regional characteristics on population mobility. Since the registration of the residence is voluntary and the place of residence is not directly linked with other institutions (e.g., healthcare), not updating your address does not have any legal consequences. These debates largely portray declining regions as deteriorating and problematic and emphasize the need to counter the population decline. The war brought even bigger human losses. In addition, the results showed (data not shown) that the effect of migration distance is small when moving to ‘losing’ or ‘winning’ region, but people tend to migrate over longer distances when they move to the major cities (especially to Vilnius), and the shortest distances when the destination is a rural area. We found that in MAs the average age of the population has dropped and that the structure of the labour market became more oriented towards high-skilled jobs between 2001 and 2011. Moreover, according to census data, the number of internal migrants is very small in Lithuania, which might also be caused by the global financial crisis. Largest Cities in Lithuania CITY NAME This suggests that the most ‘successful’ people are leaving declining regions, which increases the socio-demographic and economic gap between the rapidly declining regions and the rest of the country. Several regional-level variables were added in model 4 to estimate the relationship between regional characteristics and the probability to live in a rapidly declining region. Cited by lists all citing articles based on Crossref citations.Articles with the Crossref icon will open in a new tab. VILNIUS, May 29 (Xinhua) -- Number of Lithuanian residents has been constantly decreasing in recent years, from 2,921,920 in 2015 to 2,810,100 in the beginning of … –2 Log-likelihood final = 7167.279. Now it has dipped below 2 million. Apart from population decline, an important feature of declining areas is ageing of the population. Moreover, the out-migration of the most ‘successful’ people and increasing concentration of the less ‘successful’ increases the gap of socio-demographic and economic differences between the rapidly declining regions and the rest of the country and leads to a spatially unbalanced development. This shows that the population decline in a number of regions of the Baltic country is as much as fifty per cent. While the focus is on Lithuania, the results of this study will also be of value for other CEE countries, many of which experienced similar trajectories during recent decades (Gentile, Tammaru, & van Kempen, 2012; Smith & Timar, 2010). The extreme population decline in some regions of Lithuania and the growth of population in others can be expected to result in increasing regional differences. The country’s population peaked in 1990, at 23.49 million. Aso erupts in Japan's Kumamoto Prefecture, More than 60 pct Chinese prefer digital reading: survey, Feature: Chinese builders expand metro network in Moscow, International conference dedicated to BRI opens in Belgrade. In a quarter of a century, since the early 1990s, Lithuania lost more than one-fifth of its residents (a rapid decrease from 3.7 million in 1989 to 2.9 million in 2015), which makes it one of the countries with the greatest population decline in … Latvia: The population is projected to drop from 1.9 million in 2020 to 1.5 million in 2050, a 21.6% … We did not find effects of distance to the bigger cities or effects of the labour market structure in the region of origin on migration behaviour. Despite that, these MAs are the only macro-regional centres that still have potential to grow in the rapidly shrinking country. Although it is not a fundamental rule, once population decline in an area has started, it is difficult to reverse it (Hudson, 2015; see also Myrdal, 1957). Population change in Lithuania, 2001–11. By 2030, the number of Lithuanian residents may shrink to 2.4 million or 2.58 million people, according to respectively pessimistic and optimistic scenarios. And third, we analyse the migration behaviour of individuals. In the long run, as the number of young people is decreasing due to emigration, this causes serious demographic problems – diminishing number of marriages, decreasing birth rate, lack of workforce. Since the 1990s, Lithuania lost almost a quarter of its population, and some regions within the country lost more than 50% of their residents. Urban development in Central and Eastern Europe – between peripheralization and centralization? Despite these shortcomings in the data, this study is an important step forward in better understanding internal migration and depopulation processes in the post-socialist countries. In other words, after migration from shrinking regions, most of the people do not own a house and often live in apartment buildings and more recently built dwellings. R2 (Nagelkerke) = 0.273. 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