2010/2011

Table 2.2.3. Reasons of postponement of births in 2006
(% people aged 15–39).
Estonia Latvia Lithuania Poland Finland EU25
Financial situation 57 77 77 74 26 60
Mothers work 35 46 52 43 14 49
Housing conditions 69 72 77 71 38 57
Supportive partner 71 70 80 77 72 72
Fathers work 66 77 78 67 35 62
Childcare provision 59 78 64 37 28 46
Costs of children 56 74 69 61.5 13.5 50
Source: Testa 2006, author’s calculations
Lithuania even fell below 1.3. One explanation for these
developments was the broadening gap between desires
and the changes to fulfil them. Also, the absence of the
possibility to acquire a home and large increases in prices
for accommodation influenced childbearing behaviour
in Lithuania. According to a survey in 2001, concern
about the future of children was the most frequent
reason for not wanting the first child or subsequent children
(Stankuniene 2009). Part of this insecurity can also
be related to educational system reforms in Lithuania,
which did not make families feel certain about the fulfilment
of their expectations and about providing their
children with a desired education.
A special Eurobarometer survey from 2006 (Table
2.2.3) indicates the broader prevalence of various reasons
for the postponement of births in Lithuania. Difficulties
with partners, the work of the father, financial
situation and housing conditions were the most frequently
mentioned reasons for the postponement. Also,
fertility in Latvia was predominantly influenced by economic
barriers. The lack of child-care provision was the
second most important issue in Latvia. Estonia had the
lowest level of reported problems in total and this is also
reflected in the development of the total period fertility
rate (Figure 2.2.4), which was the highest in the Baltic
states. Analyses from Estonia revealed that the importance
of the economic aspect among the list of reasons
for postponement decreased in tandem with the
improvement of general economic situation in the country,
although new economic problems like housing loans
emerged as important fertility decision shapers (Oras &
Unt 2008). The increase in fertility in Estonia can also
be associated with some family policy measures, which
gave parents higher economic security during parental
leave and reduced poverty among families with children
(Ainsaar 2009).
Since 2006, Lithuania has also shown a speedy recovery
in fertility. The total fertility rate in Lithuania has
risen close to the Estonian rate. The main reasons for the
rise in fertility can be attributed to recuperation after the
postponement of fertility and some family policy efforts,
namely the introduction of high level parental leave coverage.
It is remarkable that fertility in Lithuania continued
to increase even during the recent economic recession,
during which Latvia and Estonia witnessed a decline.
All together, the natural increase in Estonia remained
remarkably higher than in Latvia and Lithuania due to
higher mortality and lower fertility in Latvia and Lithuania.
As a result of more consistent improvements in life expect-
ancy in Estonia throughout the 2000s, the country had the
highest life expectancy among Baltic states at the end of the
2000s. In Europe, the Baltic states together formed their
own Baltic mortality group with high, early and external
reasons leading to mortality (Niederlaender 2006).
External migration
Negative migration also persisted at the beginning of the
21st century. While negative outmigration was not seen
as a particular problem at the beginning of the 1990s,
by the 21st century it had already exhausted population
resources. Economic and unemployment factors were the
main reasons why the highest emigration readiness in
the EU persisted in all of the Baltic states (Krieger 2008).
The high emigration readiness is not surprising because
the Baltic states still remain the region with the lowest
incomes in the EU , despite a very quick increase in the
income levels of households (Krueger 2010).
Analyses of data from 1995–2009 show that net migration
was positively associated with wealth (p = 0.000) in all
Baltic states.
Migration became more positive together with the
rise of wealth and the decline of wealth was associated
with negative migration. The gap between in and outmigration
was most striking in Lithuania where key factors
accounting for migration were the high standard of living
and wages in receiving countries as well as the absence
of the protection of workers’ rights, criminal situation,
social vulnerability and limited career opportunities
(Sipavičienė 2009). Country comparative surveys (Mental
… 2006) demonstrated that people in Lithuania and
Latvia also had the highest work stress in Europe. Official
outmigration was in decline, when outmigration got
a new impulse following EU accession in 2005. The years
of the economic recession from 2008–2010 only increased
the migration gap.
The countries of the European Union, and in particular
Germany, the United Kingdom, Sweden and Ireland,
as well as the USA, became frequent destinations from the
second half of the 1990s. Finland also became an attractive
location for people conducted in Estonia. Although
public debate about migration was often focused on brain
drain issues, a survey form Estonia demonstrated, for
example, that the majority of emigrants were non-specialist
with secondary education level and working in the construction
area, agriculture, transportation sector, services,
etc. (Tur & Viilmann 2008). The probability of emigration
for manual workers was several times higher than that for
highly skilled professionals.
However, the official emigration figures are possibly
underestimated by up to 30–60% and real outmigration
at the beginning of the 21st century was much higher than
numbers from statistics demonstrate in all of Baltic states
(Stankuniene 2009, Tammur et al 2009, Eglite 2009). The
difference is caused by the fact that many people who left
the country did not register it as emigration; often, the
outmigration was not even perceived as final emigration,
although the stay abroad lasted long-term. Therefore, the
truth about external migration will be uncovered after the
2010–2011 population censuses and analyses of their results.
Social implications of demographic change
In the next subchapter, we will analyse the change of the
population structure during the transition period and the
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