These are valuable results, taking into account that many studies do not find a clear relationship between unemployment rates and out-migration. At present, the most populous population is aged 53-57 in Lithuania," Gruzevskis said. Including housing characteristics in model 3 reduced some of the effects of the other variables compared with model 2, but it increased the fit of the model considerably with a Nagelkerke pseudo-R2 of 0.234 in model 3. Compared to 1.03 % in 2012, the population decrease in 2013 comprised 1.10 %. Nazi Germany and local collaborators killed 95 percent of Lithuania's 220,000-strong Jewish community in 1941-1944. The country has now reached its lowest population in decades as more people emigrate to wealthier west European countries, particularly the United Kingdom. This paper will investigate the characteristics of those (1) who live in the rapidly declining regions and (2) those who are most likely to leave such regions. We used multinomial logistic regression to predict probabilities of the different possible outcomes of migration behaviour and migration directions; we compared persons who did not move from the rapidly declining regions with those who moved into ‘losing’ and ‘winning’ regions. The model also shows that the probability of migration was higher if the person was a student in 2011 (we do not know if he/she was a student before migration, however, universities are concentrated in the biggest cities). This is a spatial pattern common for all CEE countries, where urban expansion of the major cities is taking place due to the intense suburban development since the early 1990s (Kok & Kovács, 1999; Leetmaa & Tammaru, 2007; Nuissl & Rink, 2005; Ouředníček, 2007). 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. Model 1 also shows that single-person households and households with five or more members are more likely to live in declining regions than in other regions. According to the demographic report conducted by LDTI, Lithuania's population may decrease to 2.52 million by 2025 (pessimistic scenario) or 2.71 million (optimistic scenario). 4. LITERATURE REVIEW ON POPULATION DECLINE, MIGRATION AND REGIONAL DIFFERENCES, POPULATION DISTRIBUTION IN LITHUANIA: SHIFT FROM SOCIALIST TO POST-SOCIALIST PERIOD, https://doi.org/10.1080/21681376.2017.1313127, http://doi.wiley.com/10.1111/j.1549-0831.1996.tb00628.x, http://link.springer.com/10.1007/s12061-015-9171-1, http://apps.webofknowledge.com/full_record.do?product=WOS&search_mode=Refine&qid=7&SID=4E1ICj4J3p57 l@nD@bA&page=3&doc=27, http://www.tandfonline.com/doi/abs/10.1080/17421772.2010.540035, http://usj.sagepub.com/content/32/1/87.abstract, http://www.tandfonline.com/doi/abs/10.1080/02513625.2012.721611, http://www.sciencedirect.com/science/article/pii/S0743016714001065, http://ec.europa.eu/eurostat/web/population-demography-migration-projections/migration-and-citizenship-data/database, http://www.tandfonline.com/doi/abs/10.1080/00343404.2013.843162, http://doi.wiley.com/10.1111/j.1467-9663.2010.00597.x, http://eur.sagepub.com/cgi/doi/10.1177/0969776413481371, http://eur.sagepub.com/cgi/doi/10.1177/0969776413481985, http://www.palgrave-journals.com/doifinder/10.1057/udi.2012.29, http://dx.doi.org/10.1057/9781137415080_2, http://usj.sagepub.com/content/45/5-6/1055.full.pdf, http://www.mtafki.hu/konyvtar/hungeobull_64_3_2_en.html, http://link.springer.com/10.1007/s00148-003-0131-8, http://usj.sagepub.com/content/36/13/2217.short, http://www.ncbi.nlm.nih.gov/pubmed/12296066, http://eur.sagepub.com/cgi/doi/10.1177/0969776409358245, http://www.tandfonline.com/doi/abs/10.1080/09654313.2013.820082, http://osp.stat.gov.lt/web/guest/statistiniu-rodikliu-analize?epoch=ML, http://www.economist.com/news/europe/21714999-eus-newest-members-face-economic-decline-unless-they-woo-back-workers-or-recruit-immigrants, http://www.tandfonline.com/doi/full/10.1080/01629778.2015.1027935, http://www.hindawi.com/journals/usr/2016/5395379/, http://link.springer.com/10.1007/s11577-015-0327-4, Non-participating (over 65, housewives, disabled, missing), Unemployment rate (reference = below average), Change in unemployment rate (reference = decrease), Change in percentage jobs in primary sector, Change in percentage jobs in service sector. Previous studies have used aggregated-level data on municipality (LAU-1) or ward (LAU-2) level, and these studies could only investigate net migration. Latvia: The population is projected to drop from 1.9 million in 2020 to 1.5 million in 2050, a 21.6% … Of total population 917.1 thousand or 45.8% were males and 1084.4 thousand (54.2%) – females. We also found evidence of migration selectivity by migration destination: relatively younger and higher educated individuals and those who live in smaller households are more inclined to move into ‘winning’ regions than to the ‘losing’ ones. Most problems were associated with lack of employment opportunities and cultural entertainment. Central and Eastern European (CEE) countries1 are losing their populations at the national level, and at some of the highest rates in the world. 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. In Lithuania, the population decline was –10.4% (United Nations, 2015). In 2019, Lithuania's population decreased by 0.53 percent, compared to the previous year. The distribution of migrants according to population change in the destination regions is shown in Figure 2. The total population decline unevenly affected regions within Lithuania and as a result regional differences increased. This is similar to what Tervo (2000) found in the case of Finland. This latter effect has been confirmed by other research (Albrecht & Albrecht, 1996; Rogers, 1996). 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. So the effects of time-varying variables such as education, occupation and household status should be interpreted with caution. The population of the entire EU increased by 1.1 million people (+2.1‰) during 2018 with the population increasing in eighteen EU Member States and decreasing in ten. These debates largely portray declining regions as deteriorating and problematic and emphasize the need to counter the population decline. The results show that the effect of unemployment was small, although people who were unemployed in 2011 were less likely to have moved to ‘winning’ regions. The average change in population between 2001 and 2011 at the LAU-2 level was –14.1%, so we defined ‘winning’ regions as those that lost less than the average, and ‘losing’ if they lost more than the average. Lithuania's population currently amounts to 2.79 million people, official statistics show. The extreme population decline, and the growing regional disparities in Lithuania of the last decades, can be largely considered as the outcome of Soviet planning principles. For instance, Bulgarian population contracted from 9 … It is interesting that unemployed residents, compared with those non-participating in the labour market, were less likely to live in the declining regions. The aim of this paper is to obtain more insight into the composition of the population in the rapidly declining regions in Lithuania and the composition of the flows out of these regions, as well as to understand to what extent the Soviet-made settlement system contributed to extreme population decline and population redistribution in Lithuania. On the other hand, although Stockdale (2004) identifies rural out-migration as a ‘loss of human and social capital’, she emphasizes the positive side of migration as mobile individuals can enjoy opportunities that would otherwise not be available to them. The aggregated-level data gave some first insights into the population composition of the regions of Lithuania. Source: Based on the 2011 Lithuanian census. This work was supported by the Marie Curie programme under the European Union’s Seventh Framework Programme (FP/2007-2013)/Career Integration Grant [grant number PCIG10-GA-2011-303728] (CIG Grant NBHCHOICE, Neighbourhood choice, neighbourhood sorting, and neighbourhood effects). Dependent variable = population change (1 = regions that lost more than 20% of population, 0 = the rest). Since women are overrepresented among the reference persons in the declining regions, the interpretation of the gender differences in the models would be biased, and only serves as a control variable. The results show that those with a job in the primary sector had the highest odds of living in rapidly declining regions, and those working in the service sector and students had the lowest odds. OTB – Research for the Built Environment, Faculty of Architecture and the Built Environment, Delft University of Technology, Delft, the Netherlands; Institute of Human Geography and Demography, Lithuanian Social Research Centre, Vilnius, Lithuania, OTB – Research for the Built Environment, Faculty of Architecture and the Built Environment, Delft University of Technology, Delft, the Netherlands; School of Geography and Geosciences, University of St Andrews, St Andrews, UK, Family structure among urban, rural and farm populations: Classic sociological theory revisited, A spatiotemporal analysis of inter-county migration patterns in the United States. Other indicators visualized on maps: (In English only, for now) Adolescent fertility rate (births per 1,000 women ages 15-19) We use cookies to improve your website experience. By the end of 2016, Ukraine's population had decreased by about 9.5 million from its 1993 peak of 52,244,100—a net 18 percent drop. Cited by lists all citing articles based on Crossref citations.Articles with the Crossref icon will open in a new tab. 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. To learn about our use of cookies and how you can manage your cookie settings, please see our Cookie Policy. It means that relatively more people moved into ‘winning’ regions, but the majority of residents moved into the areas that have negative population change. The inherited Soviet urban system did not meet the needs of the post-socialist society. The extreme population decline in some regions of Lithuania and the growth … An even spread of population was the aim of the communist planning doctrine, and it was intended to achieve that through the spatial distribution of human and economic resources (Bertaud & Renaud, 1997). According to official statistics, since 1990 the number of residents living in Lithuania has dropped by 24 percent of the entire population. 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 the world (The Economist, 2017; United Nations, 2015). And third, we analyse the migration behaviour of individuals. Distribution of migrants according to the population change in the region of destination. Pe… "What is the most worrying, it is the decreasing number of people of reproductive age. Lithuania has been facing demographic issues for more than a decade, with emigration challenges particularly pronounced in the country. Therefore, we can state that migration flows from the declining regions which are highly selective, with relatively more ‘successful’ people showing a higher probability to move out. Despite that, there is not much attention for regional development in Lithuania and there is no policy dealing with declining regions. In the Baltic States, which had a major role as suppliers of agricultural production to the Soviet Union, residents were encouraged to live and work in rural settlements where they were provided with housing facilities and income, often at a higher standard than in the cities (Tammaru, 2001). The latter results are not surprising and they coincide with the descriptive data in Table 1. According to Fratesi and Percoco (2014), persistent population decline, especially when it accompanies an ageing population and brain drain, is the most harmful and difficult to reverse, and leads to imbalances between regions, and may hinder economic growth. As regards migration policy, first of all, emigration should be reduced and the immigration policy should be focused on promoting re-emigration to Lithuania, Gruzevskis noted. It means that skilled people benefit the most from migration, which is consistent with what was found in the literature review. Some urban areas of the major cities also lost a significant share of their population (partly due to suburbanization), but these were not included in this study due to the different processes underlying rural and urban decline. First, the model shows that the probability of living in declining regions strongly increases with age. The demographic projections were presented at a conference on Lithuania's demographic policy held by Lithuania's Employment Service (UZT) and Lithuanian Confederation of Industrialists (LPK). This shows evidence of an increasing polarization within Lithuania. Between 2000 and 2010 they lost on average –3.7% of their population. 3099067 The results show that there is a positive effect of homeownership on the probability of living in the declining region, while the effect of living in an apartment building or more recently built dwelling was negative. The Black population grew by almost 12% over the decade, and the white population increased by 4.3%. During this period, population movement was regulated between the communist states and even within national borders. This suggests that part of the original age effect on living in a declining area is actually an employment status effect. 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 … Some studies show that ethnic minorities are less likely to move than those belonging to majority populations (Sjöberg & Tammaru, 1999; Tammaru, van Ham, Leetmaa, Kährik, & Kamenik, 2013), but some studies also find the opposite (Bonvalet, Carpenter, & White, 1995; Finney & Simpson, 2008). In model 3 we added housing characteristics. Fourth, census data do not contain any information on the reasons or motives of internal migration. The term ‘Central and Eastern Europe (CEE)’ is used here for countries that were part of the Soviet communist block from 1945/50 to 1989/91, and which now are European Union member states: Croatia, the Czech Republic, Bulgaria, Estonia, Hungary, Latvia, Lithuania, Poland, Slovakia, Slovenia, Romania and East Germany. 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). Two-thirds of respondents said that the prospects for the young are poor in the declining regions, and 15% of the residents were considering leaving their current place of residence in the near future, with the main reasons being employment related. Lithuania is losing population at increasing rates since the political reforms of the early Nineteen Nineties, and it is now among the fastest shrinking countries in the world. Since the 1990s, Lithuania lost almost one-quarter of its population, and some regions within the country lost more than 50% of their residents. Third, census data in Lithuania do not provide information on intra-urban or intra-rural migration; only those moves when the boundary of the city municipality or ward (LAU-2 region) was crossed have been recorded. The dependent variable is according to the average population change in the country between 2001 and 2011 (–14.1%). At the same time income inequality also increased as not all groups benefitted equally from the new market economic system (Kährik & Tammaru, 2008; Valatka, Burneika, & Ubarevičienė, 2016). The regions with the sharpest decline in population (–20% and more) covered 44% of the countries’ territory and inhabited around 330,000 or 11% of the total population in 2011 (urban areas of the major cities excluded). In Lithuania, the population decline was –10.4% (United Nations, 2015). This means that those who should have the greatest motivation to leave remain in the declining regions, thus reducing the attractiveness of such regions and increasing the burden on social support structures. It is also the first study to explore internal migration and population change using individual data from Lithuania, so there is little prior knowledge of the underlying processes in Lithuania. 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. The results also show that living in a region with a high share of jobs in the primary sector and a low share of jobs in the service sector, and living in a region with an increase in the primary sector and a drop in the service sector jobs, is associated with living in a rapidly declining region. It is also the first study to use individual-level Lithuanian census data to analyse migration, while very few studies have been done in other CEE countries, making this study of wider interest. At the same time, birth rates dropped sharply, which happened so suddenly that some demographers have called it the ‘demographic shock’ (Eberstadt, 1994; Rychtaříková, 1999; Sobotka, Zeman, & Kantorová, 2003; Steinführer & Haase, 2007). 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). The population decreased by 2,614 persons due to external migration and by 1,741 persons due to negative natural increase. In 1904 and 1914 the urban inhabitants made up 12.8% and 13.64% of the population respectively. This study is one of the first to use individual-level Lithuanian census data from 2001 and 2011, and is also the first to explore internal migration and population change at the individual-level in Lithuania. The average size of the population and the population density of the declining regions have changed dramatically during the inter-census period (2001–11). It could also mean that anyone who has the potential to leave has already left and the population is decreasing due to negative natural change. Individuals who now live in apartment buildings as well as in the more recently built dwellings were more likely to have left, and this probability is higher for those who moved into the ‘winning regions’. In addition to the census data, we also report some results from a survey among residents (N = 602) of the sparsely populated regions in Lithuania. A high unemployment rate is also an important factor related to the decisions to leave rapidly declining regions. In model 2 we included employment status variables. Logistic regression models were used to investigate the differences between the residents of the rapidly declining regions2 and the rest of the country as well as to gain an understanding of the individual differences in migration behaviour among the residents of the rapidly declining regions. The population of Lithuania increasingly concentrates in the major MAs,5 although the total population of these areas also dropped by 6.2% between 2001 and 2011. Although all the factors (natural population change, internal and outward migration) contribute to the population drop and changing population composition, it has been shown that internal migration is the most effective in redistributing population from rural to urban areas (Ambinakudige & Parisi, 2015; Ubarevičienė, 2016). Population decline often starts with economic decline, but then becomes part of a vicious circle, causing a downward spiral of the local economy, declining tax revenues, a decline in service provision and social infrastructure, and more and more abandoned homes and factories (Elshof, van Wissen, & Mulder, 2014). The results presented in Table 1 also imply that the demographic and economic capacity is running low in the rapidly declining regions, which could lead to a declining quality of life. that the population leaving declining regions in Lithuania is very selective as well. No other country has had a more precipitous fall in population — 18.2 percent according to U.N. statistics. 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. This paper seeks to obtain more insight into the recent processes of socio-spatial change in Lithuania, with specific attention for the role of selective migration. Register to receive personalised research and resources by email. These differences might endanger the stability of society and the economy. The results of the survey lead to the conclusion that the most sparsely populated and declining regions will continue to lose population in future. Despite the general population decline in Lithuania, an increasing concentration of population is observed in the major city-regions, albeit the population is dropping in the inner cities themselves (Ubarevičienė et al., 2016). 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. All our findings suggest that human, social and economic capital is running low in the rapidly declining regions. Seniors in 2019 made up more than 16% of the U.S. population, compared to 13% in 2010. The results also showed that those having low-skilled jobs had the highest odds of living in declining regions and those having high-skilled jobs had the lowest odds (not included into the models due to overlap with the variables education and labour market position). –2 Log-likelihood final = 7167.279. Population change in Lithuania, 2001–11. 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