Chapter 4
Labour Markets: Structural Characteristics and
the Impact of Two Crises
Martin Guzi and Michael Landesmann
Abstract This chapter covers labour market developments in the eight members of
the Emerging European Economies (EEE) in Central and Eastern Europe from the
ﬁnancial crisis in 2008-2009 up to the pandemic in 2020. The emphasis is on the
relevance of various structural characteristics (demography including gender and
age aspects, labour migration, skills and sectoral employment structures, regional
patterns, wage growth, income adequacy, etc.) which shaped how the economies
were aﬀected by the two crises.
4.1 Introduction
This chapter describes the labour market developments in the eight Emerging
European Economies (EEE) in Central and Eastern Europe over the period spanning
the beginning of the ﬁnancial crisis in 2008 and reﬂecting the changes during the
pandemic in 2020.1
The focus in this chapter is on a comparative assessment of the two crises, i.e., the
impact of the ﬁnancial crisis and the impact of the pandemic. Naturally, there is an
important diﬀerence: while we can follow the ﬁnancial crisis in all its phases starting
with the situation before the crisis, its immediate impact, and the subsequent phases
of recovery, the pandemic is still unfolding at the time of writing. Hence the strategy
followed in this chapter is to focus on longer-term structural characteristics of EEE
labour markets as they entered the two crises, compare their development in the
initial phase and ﬁnally to conjecture how the recovery from the Covid crisis might
Martin Guzi
Masaryck University, e-mail: Martin.Guzi@econ.muni.cz
Michael Landesmann
Vienna Institute for International Economic Studies, e-mail: landesmann@wiiw.ac.at
1 Martin Guzi acknowledges the support from the Czech Science Foundation (grant No. 20-31615S)
and from the Grant Agency of Masaryk University (MUNI/A/1650/2020).
155
156 Martin Guzi and Michael Landesmann
evolve. We highlight diﬀerences between the two crises in their starting positions, as
well as in their qualitative characteristics. Finally, we evaluate the policy challenges
the recovery from the current pandemic will pose.
Throughout this chapter we check on heterogeneity across the EEE as regards
possible diﬀerences in structural characteristics and policy interventions, plus - at
times - show comparisons with selected EU economies (Austria, Germany and Italy),
as well as the entire EU. Before starting the analysis, we sketch the ﬁve structural
characteristics that we cover in this chapter:
First, demographic developments are a deﬁning feature of EEE, including a strong
change in the age composition of their population. The transition from a planned to
a market economy after the fall of the Iron Curtain in 1989 was marked by rapid
political and societal changes. The economic hardship of the transition played a role
in making people more sensitive to economic risk, and also increased demand for
government protection, particularly in times of economic recession (Sirovátka, Guzi
& Saxonberg, 2019). The population born in the late 1970s shifted childbearing to
later years, which dramatically reduced fertility rates in the late 1990s (see Chapter
9). In addition to a ‘shock’ to the birth rate, the waves of net emigration also had a
strong impact on population sizes and age structure (as migration is biased towards
younger cohorts) in the EEE. Naturally, changes in population growth and in the
population’s age composition inﬂuence the ‘potential labour force’ and dependency
ratios, both of which are relevant for this chapter.
Second, labour market participation, i.e., the sections of the population that are
active participants in the labour market. There are several aspects relevant here:
retirement rates (Chapter 9), disparity in labour market prospects by educational
attainment, and - particularly important - gender diﬀerences in participation over the
life cycle.
Third, skills and educational attainment. It is well-known that labour market outcomes
(both activity rates and employment rates) are strongly dependent on skills
availability (measured traditionally by educational attainment levels). Tertiary attainment
and digital skills among young cohorts in the EEE are high relative to older
populations (Chapters 10 and 11). Hence, another dimension of the structural characteristics
of the labour market that will be analysed refers to the skills composition
of the labour force and how the groups of diﬀerent skills fared in the economies
during the diﬀerent phases of the crises and the recovery.
Fourth, and related to the previous point, are longer-term changes in the demand
for diﬀerent skills, which are a function of technological change (digitisation,
automation, etc.) and linked with it in work-organisation (e.g., the move towards
tele-working) plus changes in ﬁnal demand structures (such as increased demand
for health and social care services, or for leisure activities; but also the impact of
lock-downs on shopping and restaurants during the recent epidemic). Hence, there
are both continuous and crisis-induced changes in the sectoral and occupational
structures of economies that characterise labour market dynamics.
Fifth, there is the intra-country regional dimension: in general, there is spatial
diﬀerentiation in the location and structure of economic activity within a country.
In the case of the EEE, there are some speciﬁc features that we intend to emphasise
4 Labour Markets: Structural Characteristics and the Impact of Two Crises 157
here: one is that the fall of the Iron Curtain in 1989 led to a signiﬁcant geographic reorganisation
of the economies, with a strong development of trade and other linkages
with Western European economies. Another relevant feature is strong agglomeration
processes in some economies where foreign direct investment (FDI) supported the
development of clusters of manufacturing (such as the car and supplier industries).
There is also the clustering of administrative, educational and business services in
bigger cities (in particular, in capital cities), or of tourism in other regions.
In summary, we shall demonstrate in this chapter how these various structural
features of the EEE were exposed to the impact of the two crises. We also bring
the policy dimension into discussion, i.e., the longer-term policy frameworks that
were/are in place in the diﬀerent economies, the way they adjusted, and the discretionary
measures used at diﬀerent points of the crises to impact labour market
developments.
4.2 Demography and Migration
Prior to 1989, any movement of citizens across borders in post-communist countries
was severely restricted. The collapse of communism also resulted in a substantial
population movement and triggered a wave of East-West migration from Eastern
Europe. Migration was driven by economic, political, and ethnic reasons. The reform
progress during the transformation period generated positive eﬀects on economic
growth, job prospects and returns to education in the long-run and diminished
incentives for emigration from the EEE (Guzi & Mikula, 2021).
Emigration from the EEE again intensiﬁed after EU enlargements in 2004 and
2007. Large economic disparities between countries triggered new waves of EastWest
and East-South migration (Kahanec, Pytlikova & Zimmermann, 2016; Kahanec
& Pytliková, 2017). For example in 2004, Poland’s GDP per capita was $5,632, that
is seven times lower than GDP in the United Kingdom ($38,813) and nine times
lower than GDP in Ireland ($49,678), which are two of the main destinations for
Polish workers. Similarly, in 2007 Romania’s GDP per capita of $8,061 was below
the levels in Spain ($32,460) and Italy ($38,237), the main migration destinations
for Romanians (Guzi, Kahanec & Ulceluse, 2021). The higher economic growth in
the EEE relative to Western economies positively contributed to wage convergence
between East and West, and hence somewhat reduced the beneﬁts of work migration.
Demographic development in the EEE signiﬁcantly impacts the size of workforce.
The population is younger relative to the EU average but population ageing in some
EEE is faster than in the EU as a whole (Chapter 9). The low number of births
throughout the late 1990s and early 2000s reduced the inﬂows of 20-year-olds to the
labour market seen over the last decade (Figure 4.1). Throughout the EEE the outﬂow
of workers over the age of 64 exceeds the number of 20-year-olds entering the labour
market (Lichner, Miklo˘sovi˘c, Radvanský & ˘Stefánik, 2018) . Before 2030, it is only
in Hungary and Romania that the inﬂow of 20-year-olds is projected to outnumber
158 Martin Guzi and Michael Landesmann
the outﬂow of 64-year-olds. The shrinking labour force will therefore contribute to
the labour shortages in the coming years.
Fig. 4.1: Projection of labour supply in the labour market until 2030 (in thousands)
Data: Eurostat (2021h)
Note: Baseline population projections are assumed.
60
70
80
90
100
80
100
120
140
40
45
50
55
60
100
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160
350
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450
500
550
150
200
250
300
20
25
30
50
60
70
80
2019 2030 2019 2030 2019 2030 2019 2030
2019 2030 2019 2030 2019 2030 2019 2030
BG CZ HR HU
PL RO SI SK
Inflow of labour supply (20¡years¡old)
Outflow of labour supply (64¡years¡old)
A well-designed immigration policy may be used as a tool to solve speciﬁc
labour market shortages (Kahanec & Guzi, 2017; Guzi, Kahanec & Kureková, 2018).
The inﬂow of foreign populations to the EEE, however, remained low in the years
following the ﬁnancial crisis and accelerated only in the economically successful
years before the pandemic. For example, in Czechia the number of legally residing
immigrants reached a historic high of nearly 600,000 in 2019 (5.6% of the Czech
population). Immigrants are perceived to be highly entrepreneurial, contributing to
economic growth and innovation. The Czech labour market in 2019 was characterized
by low unemployment (the lowest in the EU for several years in a row) and a recordhigh
number of job vacancies. The inﬂow of the migrant workforce therefore helped
companies to ﬁll open vacancies and boost economic activity (Guzi, Macková &
Čech Valentová, 2021). In 2019, Czechia, Romania and Slovenia received more than
ten immigrants per 1000 inhabitants, which is above EU levels (see Figure 4.2). In
4 Labour Markets: Structural Characteristics and the Impact of Two Crises 159
contrast, the inﬂow to Slovakia remains the lowest among the EEE, staying below
1.5 immigrants per 1000 inhabitants over the last decade (Guzi & Fabo, 2021).
Fig. 4.2: Migration inﬂow per 1000 inhabitants
Data: Eurostat (2021c)
Note: Deﬁnition of a migrant is based on the country of birth.
0
5
10
15
2010 2019
EU BG CZ HR HU
PL RO SI SK
Most of the EEE countries are not typical destinations for immigrants, that is
documented by the low number of foreigners per population compared to EU-27
levels (see Figure 4.3). Czechia and Slovenia host the most foreigners per population
among the EEE, 55 and 75 foreigners per 1000 inhabitants, respectively. The migrant
population in other countries is below 20 foreigners per 1000 inhabitants. At the same
time, in 2015 the EEE were less aﬀected by humanitarian migration ﬂows relative
to Austria or Germany.
One tool to solve speciﬁc labour market shortages is the Blue Card. This is an
approved EU-wide program allowing highly qualiﬁed non-EU foreign nationals to
immigrate into the EU. To qualify for the EU Blue Card, immigrants have to prove
they have a university education, three years’ professional experience, an employment
contract for a minimum of one year, and a salary above a deﬁned threshold, typically
150% of the average income in the industry of their employment. During 2012-2019,
Germany was the most successful country as it attracted more than 80% of all EU
160 Martin Guzi and Michael Landesmann
Fig. 4.3: Migration stock per 1000 inhabitants
Data: Eurostat (2021g)
Note: Deﬁnition of a migrant is based on citizenship, i.e., migrants are not citizens
of the country in which they reside, including persons of unknown citizenship and
stateless persons.
0
20
40
60
80
2010 2019
EU BG CZ HR HU
PL RO SI SK
Blue Card holders in the EU. Austria, Bulgaria, Czechia and Poland are among the
top ten countries with most EU Blue Card holders per population (see Figure 4.4).
The rates for Hungary and Slovakia are among the lowest in the EU (Guzi & Fabo,
2021).
Many countries report diﬃculties related to the collection of data on emigrants
when migrants are not obliged to report their moves. For example, the number
of Slovaks living abroad is estimated from Slovak health insurance administrative
data. Rizman and Sacherová (2018) estimated that 300,000 residents (5.5 % of the
population) left Slovakia between 2002 and 2012. The chances to leave Slovakia
are higher among medical graduates, and people from the eastern regions where
job opportunities for university graduates are scarce. Rizman and Sacherová (2018)
ﬁnd that about half of those who left returned back home by 2015 and after 2016
more Slovaks returned home than left the country in the same year. Many Slovaks
return home for family reasons and the majority remain in Slovakia for the long term.
Interestingly, the Covid pandemic reversed the migration patterns in other countries
4 Labour Markets: Structural Characteristics and the Impact of Two Crises 161
Fig. 4.4: EU Blue Cards granted per million population, 2012-2019
Data: Eurostat (2021a)
Note: *For Germany, the ﬁgure shows the number of cards per 100,000 population.
0
50
100
150
200
AT BG CZ DE* HR HU IT PL RO SI SK
as well. Bulgaria, Poland and Romania have seen large number of nationals coming
back home in 2020 (Economist, 2021). Government schemes to facilitate the return
of skilled nationals may be therefore eﬀective.
The labour market prospects of migrant workers in the EEE are favourable. In
general migrants are more educated relative to the native population in the EEE
and their participation rates are relative to natives (see Table 4.1). Migrant workers
from EU countries are typically more employed in the high-skilled occupations than
native workers in the EEE. In Hungary and Slovakia migrants of non-EU origin
have higher incidence of high-skilled occupations relative to native workers. Guzi
and Fabo (2021) ﬁnd that migrants from EU countries are typically older, while
migrants from countries outside the EU are younger, that may explain the success
of EU migrants. For example, in Slovakia young migrants (below 30 years old) earn
more than young Slovaks. This is because many young Slovaks seek better paid jobs
abroad and their positions are ﬁlled with young migrants from countries outside the
EU (Guzi & Fabo, 2021). In Austria, Germany and Italy, the educational attainment
and labour market participation of migrants are lower relative to natives. In contrast
162 Martin Guzi and Michael Landesmann
to the EEE, migrants in Austria, Germany and Italy are less likely to have high-skilled
occupations.
Table 4.1: Characteristics of migrants relative to natives, 2019
Data: Own elaboration based on Eurostat Labour Force Survey database
Note: The ratio of tertiary education, participation rate and the incidence of
high-skilled occupation respectively of immigrants of diﬀerent origins relative to
native population. The high-skilled occupation is deﬁned by ISCO 1-3
classiﬁcation. The migrant origin is deﬁned by country of birth. Data do not
provide reasonably reliable information about foreign-born populations in Bulgaria
and Romania, which are not shown.
(the blue colour indicates the higher level for migrants, the green colour for natives
and the white colour indicates only a narrow diﬀerence between natives and
migrants)
Country Tertiary Education Participation Rate High-skilled Occupation
EU non-EU EU non-EU EU non-EU
CZ 1.26 1.57 1.02 0.97 1.08 0.89
HR 1.16 0.72 1.03 0.94 1.41 0.78
HU 1.25 1.50 1.07 0.94 1.11 1.26
PL 2.03 1.56 1.03 1.05 1.75 0.90
SI 0.94 0.31 0.98 0.92 1.00 0.36
SK 1.32 1.84 1.07 0.98 1.06 1.21
AT 1.03 0.86 0.97 0.90 0.62 0.69
DE 0.86 0.89 1.00 0.86 0.58 0.66
IT 0.50 0.70 1.03 1.00 0.31 0.33
4.3 Employment Situation
The ﬁnancial crisis reduced employment rates and increased unemployment in all
EU countries. In most of the EEE, employment rates had been falling after 2008 and
returned to pre-crisis levels by 2015 (see Figure 4.5). Employment grew strongly
in the years before the pandemic, with employment rates in Bulgaria, Czechia,
Hungary, and Slovenia climbing above EU-27 levels (68%). In 2019, employment
rates in the EEE exceeded those in 2008. Overall, the general labour market situation
was favourable before the Covid-19 pandemic.
In the EEE, workers with tertiary education coped better during the ﬁnancial
crisis, and their employment levels fell only minimally, while workers with lower
4 Labour Markets: Structural Characteristics and the Impact of Two Crises 163
education fared worse. This is in contrast to the situation in Austria, Germany or
Italy, where employment rates remained fairly ﬂat throughout the period (see Figure
4.6). In the EEE, the incidence of employment for both men and women varies with
the educational attainment more than in the EU. In the EEE, low-educated women
are at least twice less likely to be in employment than tertiary-educated women.
In Italy as well, we see a similarly big diﬀerence in employment rates by education
among women. The economically successful years before the pandemic pulled up the
employment rates of the least educated, particularly in Bulgaria, Croatia, Czechia,
and Hungary.
Fig. 4.5: The share of 15-64 year-olds in employment, 2008-2020
Data: Eurostat (2021e)
50
55
60
65
70
75
2008 2020
EU BG CZ HR HU
PL RO SI SK
The ﬁnancial crisis impacted workers in all countries, leading to higher unemployment,
and many jobs have not recovered since. Unemployment in the EEE reached
its peak around 2012 (see Figure 4.7). Workers with tertiary education were the least
aﬀected by the economic shock in 2008, with their unemployment rate remaining
below 7%, except for Croatia where it rose to above 11%. The unemployment rate
increased most for workers with lower than secondary education and reached levels
of above 20% everywhere, while it remained below 10% in Romania. Interestingly,
diﬀerences in unemployment rates are very small by education level in Romania (the
164 Martin Guzi and Michael Landesmann
Fig. 4.6: Employment rates of workers with tertiary education relative to
employment rates of workers with lower or medium educational attainment by
gender, 2008-2020
Data: Eurostat (2021e)
Note: Education categories: low (ISCED 0-2), medium (ISCED 3-4), and high
(ISCED 5-6)
1
2
1
2
3
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2008 2020 2008 2020 2008 2020
2008 2020 2008 2020 2008 2020
2008 2020 2008 2020 2008 2020
2008 2020 2008 2020 2008 2020
AT BG CZ
DE EU HR
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RO SI SK
Females: high/low edu Females: high/medium edu
Males: high/low edu Males: high/medium edu
relatively large share of the rural population where oﬃcial ﬁgures traditionally record
low rates of unemployment might account for this). By 2015, the unemployment rate
had moved back to pre-crisis levels in most countries, although this happened much
later in Bulgaria, Czechia and Croatia, and in Slovenia unemployment remained
above pre-crisis levels.
Structural problems in the labour market are illustrated by the rate of the young
population (15-24 year-olds) not in employment, education, or training (NEET) in
Figure 4.8, and the share of the active population unemployed for more than 12
months in Figure 4.9. Both indicators have followed an inverted U-shape pattern
since 2008, indicating the worsening of the situation in the labour market after the
ﬁnancial crisis, and a full recovery in the years before the pandemic. The labour
market situation in the EEE was more favourable in 2019 than in 2008, except in
one case (Romania had higher NEET in 2019 than in 2008). Relative to the EU-27,
the shares of the long-term unemployed were higher in several members of the EEE,
4 Labour Markets: Structural Characteristics and the Impact of Two Crises 165
Fig. 4.7: Unemployment by educational attainment and total unemployment
Data: Eurostat (2021e)
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2008 2020 2008 2020 2008 2020
2008 2020 2008 2020 2008 2020
2008 2020 2008 2020 2008 2020
2008 2020 2008 2020 2008 2020
AT BG CZ
DE EU HR
HU IT PL
RO SI SK
Total Low edu Medium edu High edu
but by 2020 they had largely decreased and fallen below EU-27 levels (except for
Slovakia).
166 Martin Guzi and Michael Landesmann
Fig. 4.8: The share of young people (15-24 year-olds) neither in employment nor in
education and training (NEET)
Data: Eurostat (2021e)
5
10
15
20
25
2008 2020
EU BG CZ HR HU
PL RO SI SK
Fig. 4.9: The share of persons (20-64 year-olds) unemployed for 12 months or more
in the total active population in the labour market
Data: Eurostat (2021e)
0
5
10
2008 2020
EU BG CZ HR HU
PL RO SI SK
Unemployment statistics might underestimate the total unmet demand for employment,
also called the ‘labour market slack’, which includes unemployed, discouraged,
and underemployed workers. Discouraged workers (e.g., persons seeking work but
4 Labour Markets: Structural Characteristics and the Impact of Two Crises 167
not immediately available, or persons available but not seeking work) are close to
unemployment but are not included in unemployment statistics. Underemployment
includes involuntary part-time workers who wish to work more hours. Figure 4.10
conﬁrms that the labour market slack decreased in the EEE between 2008 and 2019,
and except for Croatia, the levels are below the EU-27 average. This conﬁrms that
labour markets in the EEE were in a better condition in 2019 than in 2008 also in
comparison to Austria or Italy. Also gender diﬀerences in the labour market slack
became narrower in 2019, although the slack remains higher for women, except in
Bulgaria and Romania. In all countries, the slack attains highest levels among young
people (15-24 year-olds). Czechia ranks as the country with the lowest unemployment
rate and labour market slack, while Croatia has the highest values.
Fig. 4.10: Labour market slack (percentage of extended labour force)
Data: Eurostat (2021e)
Note: The labour market slack is deﬁned by discouragement and underemployment.
Plotted are shares of 20-64 year-olds in the extended labour force.
0
5
10
15
20
CZ HU PL SI SK RO DE BG AT HR IT EU
2008 2019
Figure 4.11 contrasts the relative importance of discouraged and underemployed
workers with the number of unemployed. First, it follows that the unemployed make
up a larger share of the labour force than the other two groups, except in Croatia.
Second, the number of discouraged workers is typically larger than the group of
168 Martin Guzi and Michael Landesmann
involuntary part-time workers (underemployment), except for Romania and Slovenia.
Third, except for Croatia, labour market slack in the EEE remains largely below the
EU-27 average.
Fig. 4.11: Unemployment, discouragement, and underemployment (as a percentage
of extended labour force), 2019
Data: Eurostat (2021e)
0
5
10
15
20
CZ DE HU PL RO BG AT SI SK HR IT EU
Unemployed Discouragement Underemployment
During a crisis, unemployment increases and job vacancies are scarce. The labour
market thus becomes tighter, which is expressed by the number of jobs that ﬁrms are
seeking to ﬁll relative to the number of potential job applicants. The labour market
tightness, deﬁned as the ratio of vacancies to number of unemployed, illustrates the
shortage of workers in the labour market. The statistics on the number of vacancies
may underestimate the total demand for workers, since companies searching for more
qualiﬁed employees may not approach the employment oﬃce. Figure 4.12 documents
that since 2015 the number of posted vacancies has been sharply increasing in all
countries. The rise was very steep in 2017 and 2018 throughout the EEE, and the
labour market was tightest in Czechia (there were more than two vacancies for one
job applicant).
4 Labour Markets: Structural Characteristics and the Impact of Two Crises 169
Fig. 4.12: Labour market tightness, 2009-2021
Data: Eurostat (2021e, 2021d)
Note: Labour market tightness is the ratio of vacancies to the number of
unemployed.
0.1
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2009q1 2021q1 2009q1 2021q1 2009q1 2021q1
2009q1 2021q1 2009q1 2021q1 2009q1 2021q1
2009q1 2021q1 2009q1 2021q1 2009q1 2021q1
BG CZ DE
HR HU PL
RO SI SK
In all EU member states, young women (25-34 year-olds) have higher tertiary
attainment levels than young men. Female attachment to the labour force is lower
at childbearing age but long career breaks contribute to a higher gender wage gap
and have subsequent impacts on pension rights. Countries where a high share of
women permanently withdraw from the labour force after childbirth have higher
gender diﬀerences in the labour market (Bičáková, 2016).
The gender gap in employment opens at childbearing age the most in Czechia,
Hungary, Poland and Slovakia (see Figure 4.13). Later, around the age of 50, the
gender gap in employment almost closes in Bulgaria, Croatia, Czechia, Hungary,
Poland, Slovakia and Slovenia. In addition, in Czechia and Slovakia, the maternal
employment rate of tertiary educated women is among the lowest in the OECD
(OECD, 2019). In comparison, a drop in female employment at childbearing age is
much lower in Austria and Germany.
When skilled labour is in short supply, taking advantage of female labour may
provide a considerable boost to the economy. This would require adjusting family
policies and institutions that aﬀect mothers’ transition from maternity leave to work,
170 Martin Guzi and Michael Landesmann
and facilitating the family-work balance. For a start, reducing maximum parental
leave, and expanding aﬀordable and quality childcare may do the job in some of the
EEE (OECD, 2019).
Fig. 4.13: Employment rates over the lifetime by gender, 2019
Data: Eurostat (2021e)
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100
20 30 40 50 60 20 30 40 50 60 20 30 40 50 60
AT BG CZ
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Females Males
Age
4.4 GDP and Employment Comparison
A comparison of GDP and employment developments over the period 2005q1 to
2020q4, i.e., covering the two crises and the recovery period in between (see Figure
4.14), gives rise to the following ﬁve observations:
– Firstly, there are two groups of countries amongst the EEE that diﬀer with respect
to patterns of recovery. One group, comprising Croatia, Hungary and Slovenia,
went through a protracted and diﬃcult phase of recovery from the ﬁnancial crisis
in 2008-2009. Other countries, foremost amongst them Poland, which hardly
experienced a dip at all after the ﬁnancial crisis, had a strong recovery, lifting
4 Labour Markets: Structural Characteristics and the Impact of Two Crises 171
their GDP levels in 2019 to between 40% up to close to 80% above the GDP
levels of 2005 (reference year).
– Secondly, a distinct feature of the EEE in comparison to the EU is the rather large
gap between GDP and employment developments (except Croatia, Hungary, and
Slovenia), which reﬂects the relatively strong (labour) productivity catching-up
– here shown at the aggregate level – of the EEE relative to the EU. The rather
ﬂat development of employment over a long period when the EEE experienced
relatively high GDP growth could also be characterised as ‘job-less growth’. In
other words, growth in the EEE is characterised by low ‘employment elasticity’.
– Thirdly, we see minimal diﬀerences between male and female employment trends
in the EEE (exceptions are the better male-to-female employment developments
in Poland and – more recently – in Croatia), while in the EU as a whole female
employment levels showed a more positive trend than male employment over the
period (a feature that has more recently also characterised Slovenia).
– Fourthly, the contraction in GDP during the recent pandemic is much stronger than
in the initial phase of the ﬁnancial crisis, which reﬂects the additional element of
imposed ‘lock-downs’; this is shared by both the EU as a whole and the EEE who
also share the typical ‘V-shaped’ recovery typical for the pandemic experience.
– Fifthly, in line with developments in the EU (but, as we shall see later, even
more markedly), there is a shallow employment response to the ‘V-shaped’ GDP
pattern in the recent pandemic - this will be further explored in section 4.9 of this
chapter.
4.5 Sectoral Employment Structures and the Impact of
Economic Recessions
In this section, ﬁrstly, we intend to point to important characteristics of sectoral employment
structures in the EEE and, secondly, analyse the dynamic contributions of
sector-level employment to aggregate employment developments during the phases
of crises and recovery.
Figure 4.15 illustrates features of sectoral employment structures in the EEE. We
singled out Germany as the comparator Western European country, because it is the
dominant EU economy that the EEE has strong economic connections with, and it
is also an example of a highly developed EU economy with a strong manufacturing
sector.
A number of interesting features emerge from the comparison of employment
structures in the EEE and Germany, which also reveals some distinct diﬀerences
amongst the members of the EEE:
– Even in comparison with an economy which has a strong manufacturing sector,
such as the German economy, a major sub-group of the EEE (Czechia,
Hungary, Slovakia, and Slovenia) shows a higher share of employment in manufacturing.
In these economies, manufacturing employment features very strongly,
172 Martin Guzi and Michael Landesmann
Fig. 4.14: Employment and GDP (2005=100), 2005-2020
Data: Eurostat (2021e, 2021b)
100
120
140
160
100
120
140
100
105
110
115
120
90
100
110
120
100
110
120
130
100
150
200
100
120
140
160
100
110
120
130
100
120
140
160
2005q1 2020q4 2005q1 2020q4 2005q1 2020q4
BG CZ EU
HR HU PL
RO SI SK
Employment females Employment males GDP
reﬂecting their strong position in European cross-border production networks (see
Landesmann & Schröder, 2020). This is not, or much less, the case in the other
group within the EEE, where the employment share in manufacturing is similar
to that in Germany (Bulgaria, Poland, and Romania) or is below it (Croatia).
– As expected for lower income economies that still have a more signiﬁcant rural
sector, the share of persons employed in agriculture, forestry etc., is higher and, in
some countries, signiﬁcantly higher than in Germany (see particularly Bulgaria,
Hungary, Poland, and Romania), although the share diﬀerence has narrowed over
time in some of them (Croatia, Poland, Romania, and Slovenia).
– Further, given the past history during the Communist period when the services
sector was particularly underdeveloped, in many of the EEE, the share of employment
in a variety of services sectors is below that of Germany, although there are
signiﬁcant diﬀerences across these economies:
– In general, there is a stronger deﬁciency (compared to Germany) of employment
in non-tradable services than in tradable services (in fact, Croatia, Czechia, and
Slovenia are not dissimilar to Germany in the tradable sectors). Given the importance
of tourism in Bulgaria and Croatia, we see large shares of ‘non-tradable’
service sector employment (accommodation, recreation, food services are in our
4 Labour Markets: Structural Characteristics and the Impact of Two Crises 173
Fig. 4.15: Employment shares by sectors in the EEE relative to Germany, in
percentage points
Data: own calculations based on WIIW (2021),
Note: The categories correspond to the following industry standard classiﬁcation
(NACE) Agriculture: A; Manufacturing: C; Construction: F: Tradable Services:
H+J+K+M; Non-tradable Services: G+I+L+N+R+S+T; Non-market Services:
O+P+Q
(a) Czechia
-10.0 -5.0 0.0 5.0 10.0
Agriculture
Manufacturing
Construction
Tradable Services
Non-tradable Services
Non-market Services
2009-2013 2014-2019 2020
(b) Hungary
-6.0 -4.0 -2.0 0.0 2.0 4.0
Agriculture
Manufacturing
Construction
Tradable Services
Non-tradable Services
Non-market Services
2009-2013 2014-2019 2020
(c) Poland
-10.0 -5.0 0.0 5.0 10.0 15.0
Agriculture
Manufacturing
Construction
Tradable Services
Non-tradable Services
Non-market Services
2009-2013 2014-2019 2020
(d) Slovakia
-10.0 -5.0 0.0 5.0 10.0
Agriculture
Manufacturing
Construction
Tradable Services
Non-tradable Services
Non-market Services
2009-2013 2014-2019 2020
classiﬁcation included in non-tradables, although the expenditure by tourists falls
into this category and this contributes positively to the balance of payments;
however, these activities also cater to the domestic population).
– It is interesting that there are large gaps of employment shares in non-market
services (comprising public administration, education, and health) in all the EEE
economies compared to Germany; moreover, in some these gaps have been growing
over time.
Figure 4.16 looks at the same breakdown of employment across sectors, but
focuses on employment developments in three phases: the 2009-2013 period immediately
following the impact of the ﬁnancial crisis, the 2014-2019 recovery period,
and in 2020, when the Covid-19 pandemic hit. The ﬁgure shows the contributions
to overall employment growth (or contraction) in the three periods in the diﬀerent
174 Martin Guzi and Michael Landesmann
Fig. 4.15: Cont. Employment shares by sectors in the EEE relative to Germany, in
percentage points
Data: own calculations based on WIIW (2021)
(e) Slovenia
-10.0 -5.0 0.0 5.0 10.0
Agriculture
Manufacturing
Construction
Tradable Services
Non-tradable Services
Non-market Services
2009-2013 2014-2019 2020
(f) Bulgaria
-10.0 -5.0 0.0 5.0 10.0
Agriculture
Manufacturing
Construction
Tradable Services
Non-tradable Services
Non-market Services
2009-2013 2014-2019 2020
(g) Romania
-20.0 -10.0 0.0 10.0 20.0 30.0
Agriculture
Manufacturing
Construction
Tradable Services
Non-tradable Services
Non-market Services
2009-2013 2014-2019 2020
(h) Croatia
-10.0 -5.0 0.0 5.0 10.0 15.0
Agriculture
Manufacturing
Construction
Tradable Services
Non-tradable Services
Non-market Services
2009-2013 2014-2019 2020
sectors (contributions to overall employment growth were calculated as percentage
changes in employment multiplied/weighted by the share of that sector in overall
employment). The following interesting features emerge:
– In a major sub-group of economies, we can see the great importance of the contribution
of the manufacturing sector to overall employment in the recovery period;
this is particularly true for Czechia, Hungary, Poland, Slovakia, and Slovenia, and
much less for Bulgaria and Romania.
– There is an interesting diﬀerentiation in the contribution of the manufacturing
sector to employment between the two crisis phases (2009-2013 and 2019-2020):
in many members of the EEE, employment in manufacturing experienced a strong
contraction during the phase following the ﬁnancial crisis, which had a strong
negative impact on overall employment. This was particularly true for some of the
countries with a strong manufacturing sector (Czechia, Slovakia, and Slovenia),
but also for those with a weaker manufacturing sector (Bulgaria, Croatia, and
Romania).
– The pattern during the pandemic crisis repeats the negative contribution to total
employment of the manufacturing sector in Bulgaria, Czechia, Hungary, Poland,
4 Labour Markets: Structural Characteristics and the Impact of Two Crises 175
Fig. 4.16: Contributions to employment growth by sectors in the EEE relative to
Germany, in percentage points
Data: own calculations based on WIIW (2021),
Note: The categories correspond to the following industry standard classiﬁcation
(NACE) Agriculture: A; Manufacturing: C; Construction: F: Tradable Services:
H+J+K+M; Non-tradable Services: G+I+L+N+R+S+T; Non-market Services:
O+P+Q
(a) Czechia
-1.5 -1.0 -0.5 0.0 0.5 1.0
Manufacturing
Construction
Tradable Services
Non-tradable Services
Non-market Services
2009-13 2014-19 2019-2020
(b) Hungary
-1.0 -0.5 0.0 0.5 1.0
Manufacturing
Construction
Tradable Services
Non-tradable Services
Non-market Services
2009-13 2014-19 2019-2020
(c) Poland
-1.0 -0.5 0.0 0.5 1.0
Manufacturing
Construction
Tradable Services
Non-tradable Services
Non-market Services
2009-13 2014-19 2019-2020
(d) Slovakia
-1.0 -0.5 0.0 0.5 1.0
Manufacturing
Construction
Tradable Services
Non-tradable Services
Non-market Services
2009-13 2014-19 2019-2020
and Romania; but interestingly, much less so in Slovakia, which seems to have
largely maintained employment levels in manufacturing in the ﬁrst year of the
pandemic.
– As regards employment developments in the service sectors during the diﬀerent
phases (crisis and recovery), we see the following:
– The marked diﬀerence between the Covid-19 crisis in 2020 and the previous
episodes is clearly reﬂected in Figure 4.16 in that non-market services (that
comprise amongst others wholesale and retail, recreation and food services)
had a strongly negative impact on employment developments in all the economies
(except for Romania), and this reﬂects the impact of the pandemic
176 Martin Guzi and Michael Landesmann
Fig. 4.16: Cont. Contributions to employment growth by sectors in the EEE relative
to Germany, in percentage points
Data: own calculations based on WIIW (2021)
(e) Slovenia
-2.0 -1.0 0.0 1.0 2.0
Manufacturing
Construction
Tradable Services
Non-tradable Services
Non-market Services
2009-13 2014-19 2019-2020
(f) Bulgaria
-3.0 -2.0 -1.0 0.0 1.0
Manufacturing
Construction
Tradable Services
Non-tradable Services
Non-market Services
2009-13 2014-19 2019-2020
(g) Romania
-1.5 -1.0 -0.5 0.0 0.5 1.0
Manufacturing
Construction
Tradable Services
Non-tradable Services
Non-market Services
2009-13 2014-19 2019-2020
(h) Croatia
-1.0 -0.5 0.0 0.5 1.0
Manufacturing
Construction
Tradable Services
Non-tradable Services
Non-market Services
2009-13 2014-19 2019-2020
through lock-downs, social distancing, a sharp fall in international travel, and
other features.
– As regards the other two service categories (tradable services and non-market
services), the picture is more mixed: tradable services play an important positive
role in the recovery phase after the ﬁnancial crisis in all economies, but
during the recent Covid crisis the situation is more varied in that in some
economies it has contributed positively to employment growth (Hungary, Poland,
and Slovenia), while in others it has contributed negatively (Bulgaria,
Croatia, Czechia, and Slovakia). In the latter case, it is likely that two factors
play a role: whether an economy relies strongly on tourism (Bulgaria and Romania),
and whether tradable services – just like manufacturing – suﬀered a
downturn in international trade and its linkage with other tradables (e.g., with
manufacturing, as in Czechia and Slovakia).
– The contribution of non-market services to employment growth also shows a
diﬀerentiated picture, reﬂecting the heterogeneity within the sector, comprising
4 Labour Markets: Structural Characteristics and the Impact of Two Crises 177
health and education (and public administration more generally). The former
(health) was in very strong demand during the recent epidemic, while the latter
(education) fell victim to lock-downs aﬀecting educational institutions. The
diﬀerentiation across economies, with positive contributions in some countries
(Czechia and Slovenia) and negative ones in others (Croatia, Hungary, and
Slovakia) might also reﬂect local ﬁscal policy stances and policy decisions.
4.6 Wage Growth, Minimum Wage, and the Cost of Living
Using Structure of Earnings Survey data from 2006, 2010, 2014, and 2018, we
construct a relative comparison of hourly wage rates with German wages for diﬀerent
education groups. Figure 4.17 illustrates the convergence process in wages of the
EEE, indicating the faster wage growth in the EEE compared to Germany. Similarly
Austria and Italy saw higher wage growth than Germany (except for the last year in
the comparison). The convergence process was stronger in countries with relatively
low wages in 2006 (Bulgaria, Romania, and Slovakia). In this year, wages in these
countries were at 10-15% levels relative to German wages, and increased to 20-30%
by 2018. In Czechia, Hungary, and Poland, wages grew more slowly but reached
25-35% of the level of German wages by 2018. In contrast, in Croatia and Slovenia
wages have not risen faster than in Germany, reﬂecting severe economic crises during
this period.
Remarkably, wage growth was very similar for workers with diﬀerent educational
attainment. Wages paid to low-educated workers have increased slightly more in
Czechia, Poland, Romania, and Slovakia, while in Hungary highly educated workers
beneﬁted from stronger wage growth. Wages are expressed in gross terms and,
therefore, diﬀerences in mandatory payroll deductions (personal income tax is higher
in Germany compared to the EEE) and diﬀerences in the cost of living are not
accounted for. However, the overall picture illustrates very favourable developments
in the labour markets in some of the EEE.
Besides wage growth the adequacy of wages is even more important. Adequate
minimum income is one of the principles of the European Pillar of Social Rights.
Guzi (2021) presents the latest calculation of the income level necessary to secure
a decent standard of living for a standard family in EU-27 countries. It is based on
approximately 300,000 prices of goods and services and 100,000 housing prices,
collected through web-surveys in combination with the standard survey data. Because
the concept of a living wage is normatively based, it oﬀers an additional metric of
economic adequacy that reﬂects the needs of workers and their families.
The living wage represents the amount of money suﬃcient to enable cultural
and social participation in society. The calculation of the living wage is composed
of components including food, housing, transportation, health, education, phone,
clothing and footwear, personal care, recreation, culture, and restaurant costs (Guzi
& Kahanec, 2019; Goedemé, Storms, Penne & Van den Bosch, 2015). The cost of
living is assumed for a family with two adults and two dependent children. The family
178 Martin Guzi and Michael Landesmann
Fig. 4.17: Comparison of hourly wages to German wages for diﬀerent education
groups, 2006-2018
Data: (Eurostat, 2021j)
Note: Education categories: low (ISCED 0-2), medium (ISCED 3-4), and high
(ISCED 5-6)
0.80
0.90
1.00
0.05
0.10
0.15
0.20
0.40
0.30
0.35
0.40
0.20
0.25
0.30
0.50
1.00
1.50
0.25
0.30
0.35
0.10
0.20
0.30
0.40
0.50
0.60
0.20
0.30
0.40
2006 2010 2014 2018 2006 2010 2014 2018
2006 2010 2014 2018
AT BG CZ
HR HU IT
PL RO SI
SK
Low edu Medium edu High edu
employment rate is assumed at 1.8, which means one spouse is a full-time worker
and the second spouse works four days a week (i.e., 80% part-time employment).
The living wage is calculated in a way that the total disposable income earned by
two adults working for a living wage is suﬃcient for a deﬁned living standard.
Table 4.2 presents the living wage lower bound that assumes a cost-optimizing
household seeking cheaper-than-average housing, food and other indispensable
goods or services. Guzi (2021) also calculates the upper bound using prices taken at
the 50th percentile (median). Living wages are presented as monthly gross amounts
i.e., corrected for mandatory payroll dedication (personal income tax, and social
contributions) to be directly comparable to minimum wage and real wages, which
are gross earnings. The calculated living wages are presented together with national
statutory minimum wages, aiming to increase awareness concerning the gaps
between the two levels.
The minimum wage gap identiﬁes the diﬀerence between the minimum wage and
the living wage. When minimum wages are distant from living wages, low-income
4 Labour Markets: Structural Characteristics and the Impact of Two Crises 179
households depending on minimum wages are likely unable to cover their living
costs. The minimum wage gap is narrowest (below 10%) in Slovakia and Germany,
while it is largest in Croatia approaching 77% (this means the minimum wage would
need to increase by 77% to match the living wage). Minimum wage in Slovenia
exceeds 1,000 EUR and is the highest among the EEE, yet the minimum wage gap is
approaching 50%. The gap is similar in other members of the EEE, ranging between
40 and 60%.
Table 4.2: Living wage estimate in comparison with minimum wage (monthly
gross amounts in EUR)
Data: Guzi (2021)
Country Minimum
Wage
Living
Wage
Minimum
Wage Gap
(1) (2) (2-1/1)
BG 332 473 42%
HU 438 665 52%
RO 456 550 21%
HR 561 998 78%
CZ 581 887 53%
PL 608 962 58%
SK 623 684 10%
SI 1024 1510 47%
DE 1614 1640 2%
AT 1250
IT 1290
Income poverty assessments are favourable in the EEE (Chapters 1 and 9), but
poverty indicators based on relative incomes do not inform whether household
income levels are suﬃcient to make ends meet (Fabo & Guzi, 2019). In addition to
determining the cost of a decent life, policies to boost the income of low-earning
households should be adopted. Income adequacy will be considered in the process
of setting minimum wage and tax policy instruments.
Yearly adjustments in statutory minimum wages are decided by governments after
consultations with social partners. The presentation of the minimum wage in gross
amounts and their comparison to average wages is less suitable for the assessment
of wage adequacy. Minimum wages are subject to mandatory payroll deductions
paid by employees. For illustration, Figure 4.18 shows the gross and net amounts
expressed for a single minimum wage earner with no children. The tax rate (the share
of gross minimum wage taken in tax and social security contributions) is highest in
Hungary (35%), and the lowest in Romania (10%). Figure 4.18 further illustrates
180 Martin Guzi and Michael Landesmann
that the order of countries varies if minimum wages are compared in gross or in net
amounts. For instance, the gross minimum wage is higher in Poland than in Czechia
and Slovakia, but the Polish net minimum wage is below Czech and Slovak net
minimum wages. In some EEE, high income taxes and social security contributions
signiﬁcantly reduce the income of low-paid workers.
Fig. 4.18: Minimum wage in gross and net amounts for a single minimum wage
earner with no children (EUR, 2020)
Data: own calculations based on Eurostat (2021f); iCalculator (2021); K&K
Accounting (2021)
Note: National currency is converted to EUR using the annual average rate from
Eurostat. The tax rate is the share of gross minimum wage taken in tax and social
security contributions. Net minimum wage is calculated using online wage
calculators for a single adult without children.
For illustration, the Slovak economy exhibits positive wage developments. The
average gross monthly wage grew from EUR 824 to EUR 1,133 (or by 37%) between
2013 and 2020. Over the same period, the minimum gross wage grew by 71% from
EUR 338 to EUR 580. In 2020, the minimum wage reached 51% of the average wage
in gross terms, and 55% in net terms. These developments suggest the improvement
of working conditions and income situation of all Slovak households. Using Slovak
administrative records, Páleník (2019) observes that 10–15% of employees are formally
employed on part-time contracts for pay below the mandatory minimum wage,
and presumably receive unoﬃcial income on the side. Furthermore the number of
employees regularly paid below the minimum wage and without other legal income
4 Labour Markets: Structural Characteristics and the Impact of Two Crises 181
doubled between 2012 and 2019 (Páleník, 2020). The practice is more widespread in
accommodation and food, wholesale and retail, and construction sectors. The number
of employees regularly receiving a salary below the minimum wage is higher in
mostly rural areas with fewer job opportunities. These ﬁndings need to be further
conﬁrmed by labour inspections, but the government will observe and strengthen the
protection of low-income workers, and their rights to decent work standards.
4.7 Unit Labour Costs and Their Decomposition: Dynamics Over
Crisis and Recovery Phases
Unit labour costs (ULC) are deﬁned as the ratio of labour compensation (cost of
labour) to output produced. It is often used as a measure of ‘cost competitiveness’
or of the ‘real exchange rate’, although it does not include all cost components or
any measures of the mark-up, which also relevantly impact ‘price competitiveness’.
For this chapter on labour market developments, however, ULC reﬂect two decisive
components, i.e., wages (or, better, labour compensation) and labour productivity,
and it also takes account of the interplay between these two variables and the
exchange rate. In the following, we show through a decomposition how the diﬀerent
components of ULC behaved in the pre-crisis, crisis and post-crisis phases in the
EEE.
ULC calculated at international exchange rates can thus be decomposed into
compensation per employee (in short ‘wages’ but also including other labour costs
such as social security contributions), labour productivity (the inverse of labour input
per unit of output), and the nominal exchange rate.
ULC = Exch∗W ∗
1
Prod
(4.1)
where Exch refers to the nominal exchange rate (EUR per 1 unit of domestic currency);
W to hourly compensation per employee; Prod to labour productivity level,
i.e., output per employee. The decomposition into these three components is shown
in Figure 4.19 in the ﬁrst (left-hand) group of column diagrams.
Figure 4.19 plots also a further ULC decomposition by distinguishing further the
two components of labour productivity, i.e., output and employment. This is shown
in the second (right-hand) group of column diagrams:
ULC = Exch∗W ∗
Emp
Out
(4.2)
In fact, what is really shown in Figure 4.19 is the changes of ULC and their
decompositions over the diﬀerent periods, calculated as follows:
ΔULC = ΔExch + ΔW −ΔProd = ΔExch + ΔW + ΔEmp−ΔOut (4.3)
182 Martin Guzi and Michael Landesmann
Variables W, Prod, and Out are ﬁrst expressed in national currencies, and through
multiplication with Exch are converted into an international currency (i.e., EUR).
Fig. 4.19: Changes in the components of unit labour costs over periods: 2004-2008,
2009-2013, 2014-2018, and 2019-2020
Data: own calculations based on WIIW (2021)
(a) Czechia
8
12
04-08
4
0
-4
-8
09-1314-1819-20 04-0809-1314-1819-20
(b) Hungary
-8
-6
-4
-2
0
2
4
6
8
10
04-0809-1314-1819-20 04-0809-1314-1819-20
(c) Poland
12
8
4
0
-4
-8
04-0809-1314-1819-20 04-0809-1314-1819-20
(d) Slovakia
04-0809-1314-1819-20 04-0809-1314-1819-20
20
15
10
5
0
-5
-10
For interpretation of the ﬁgures (and the above formula): an appreciation of
the currency ceteris paribus increases ULC, as does an increase in the wage rate,
while an increase in productivity reduces ULC; further, with productivity changes
decomposed into output growth and employment growth, the ﬁrst reduces ULC,
while an increase in the latter increases ULC. We keep this in mind when we
interpret the sets of diagrams in Figure 4.19:
– In the pre-ﬁnancial crisis period (2004-2008), we would expect - and also see the
typical picture of ‘normal times’: an increase in wage compensation increases
ULC, and productivity growth reduces ULC; on top of this, many of the EEE
experienced an appreciation of their currency (in relation to the EUR). We can
also see the diﬀerentiation with respect to exchange rate regimes, such as Bulgaria
- which had (and still has) a currency board (i.e., a very extreme form of a ‘hard
peg’ to the EUR) - showing no movement in the exchange rate throughout the
period; while Slovenia and Slovakia, which joined the euro-system just before the
4 Labour Markets: Structural Characteristics and the Impact of Two Crises 183
ﬁnancial crisis, show no movements in the exchange rate after the ﬁrst period.
Growth in wage compensation was particularly high in Romania and outstripped
by far the productivity growth in the pre-ﬁnancial crisis period, which was also
the case in some other economies (Bulgaria, Hungary, Slovenia, and Croatia) and
much less in others (Czechia, Poland, and Slovakia).
– Comparing the pre-ﬁnancial crisis period (2004-2008) with the recovery period
(2014-2018), we see in general a more moderate growth in productivity in the
latter period (with the exception of Poland, which continued with relatively high
output growth and more moderate employment growth than in the pre-crisis
period).
– In the two crisis periods (i.e., the immediate period after the impact of the ﬁnancial
crisis, 2009-2013, and the pandemic, 2019-2020), in countries which kept ﬂexibility
in their exchange rates, currency depreciation played an important role in the
fall in ULC or led to more moderate increases in ULC than these economies would
have had without exchange rate ﬂexibility (the role of exchange rate ﬂexibility in
crisis periods is/was particularly marked in Hungary, Poland and Romania; while
Czechia showed much more moderate exchange rate adjustments). As mentioned
earlier, Slovenia and Slovakia had joined the euro-system by then and the option
of exchange rate adjustment was no longer open to them. One question which
could be further explored is whether this loss of exchange rate ﬂexibility changed
the behaviour with respect to wage bargaining, and hence to what extent it might
have brought wage growth more in line with (labour) productivity growth. We
shall, however, not pursue this issue further (for an analysis of this issue, see
Astrov et al., 2021).
– We also examine the behaviour of the two variables determining (labour) productivity
growth, i.e., output and employment growth (or contraction) over the
two crisis periods. In a number of countries, the two variables switch during
crisis periods and become a mirror image of how they contribute to ULC growth
in periods of expansion: thus while in periods of expansion, output growth contributes
to ULC growth and employment growth negatively, in crisis periods
with output contracting and employment contracting as well, these two variables
switch roles regarding their impact on ULC, with output contraction increasing
ULC and employment contraction reducing ULC. This is the picture we observe
in many economies during the most recent epidemic (Bulgaria, Czechia, Hungary,
and Slovakia), while in other countries this pattern might be true for one of
the two variables but not necessarily for both, thus output contraction aﬀecting
ULC negatively in Croatia and Slovenia, and employment contraction aﬀecting
ULC positively in Romania, and marginally in Poland. The relative movements
of output and employment determine in turn whether (labour) productivity impacts
ULC positively or negatively. This is a theme which will be interesting to
observe also in the course of the post-Covid recovery, as it pertains to the extent
employment growth will expand in line with output growth.
184 Martin Guzi and Michael Landesmann
Fig. 4.19: Cont. Changes in the components of unit labour costs over periods:
2004-2008, 2009-2013, 2014-2018, and 2019-2020
Data: own calculations based on WIIW (2021)
(e) Slovenia
04-0809-1314-1819-20 04-0809-1314-1819-20
10
8
6
4
2
0
-2
-4
-6
-8
(f) Bulgaria
04-0809-1314-1819-20 04-0809-1314-1819-20
15
10
5
0
-5
-10
(g) Romania
-10
-5
0
5
10
15
20
25
04-0809-1314-1819-20 04-0809-1314-1819-20
(h) Croatia
-6
-4
-2
0
2
4
6
8
10
04-0809-1314-1819-20 04-0809-1314-1819-20
4.8 Regional Disparities in Labour Markets
High and persistent regional disparities in the labour market are of particular concern
to policy makers both for their impact on social cohesion and the productivity
of the economy. Large diﬀerences across regional labour markets suggest a low
internal migration of workers and problems in the supply and demand for work. For
example, the high rate of regional long-term unemployment points to areas where
individuals have inadequate skills to ﬁnd a job. The following ﬁgures present the
regional variation in educational attainment levels of the working age population,
in participation, unemployment, NEET, and long-term unemployment. Plotted are
ratios of maximum and minimum regional characteristics over time.
In most members of the EEE, the regional variation of the working age population
with low (lower secondary) and high (tertiary) education is signiﬁcant, while the
population with secondary education is typically spread evenly between regions (see
Figure 4.20). The share of population with tertiary education in Czechia and Romania
is more than three times higher than in the regions with the lowest number of the
tertiary educated. Over the last decade, regional variation in education has increased
in Hungary, Poland, and Romania, while it has decreased in Czechia. Smaller states,
4 Labour Markets: Structural Characteristics and the Impact of Two Crises 185
such as Croatia, Slovakia and Slovenia, have four and fewer NUTS 2 regions, thus
the regional variability is diﬃcult to evaluate2.
Fig. 4.20: Regional variation in education of working age population (15-64
year-olds)
Data: Eurostat (2021i)
Note: Plotted are ratios of maximum and minimum regional values. Number of
NUTS 2 regions varies between countries: AT (8), BG (6), CZ (8), DE (37), HR
(2), HU (7), IT (21), PL (15), RO (8), SI (2), SK (4). Education categories: low
(ISCED 0-2), medium (ISCED 3-4), and high (ISCED 5-6)
1
2
1
2
3
2
4
6
0
2
4
1
1.5
1
2
3
1
2
1
2
0
2
4
1
1.5
1
2
3
2008 2020
2008 2020 2008 2020
AT BG CZ
DE HR HU
IT PL RO
SI SK
Low edu Medium edu High edu
The regional variation in unemployment follows diﬀerent patterns in the two
crises (Figure 4.21). In Germany and Italy, regional dispersion increased both after
the ﬁnancial crisis and during the outbreak of Covid-19, which indicates that crises
impacted more regions with higher unemployment. In Bulgaria, Czechia, and Poland,
the ﬁnancial crisis impacted regions with higher unemployment, while the pandemic
impacted more aﬄuent regions with lower unemployment, therefore in 2020 regional
variation in unemployment declined. Hungary shows the opposite pattern.
2 Eurostat publishes regional disparities only for countries with more than four regions.
186 Martin Guzi and Michael Landesmann
In all countries except for Poland, the regional disparity in participation rates
declined in both crises, meaning that the crises impact more intensively regions with
higher labour market activity.
Fig. 4.21: Regional variation in participation and unemployment
Data: Eurostat (2021i)
Note: Plotted are ratios of maximum and minimum regional values. Number of
NUTS 2 regions varies between countries: AT (8), BG (6), CZ (8), DE (37), HR
(2), HU (7), IT (21), PL (15), RO (8), SI (2), SK (4). Education categories: low
(ISCED 0-2), medium (ISCED 3-4), and high (ISCED 5-6)
2.0
4.0
0.0
5.0
2.0
3.0
4.0
2.0
4.0
1.0
1.1
1.2
2.0
3.0
4.0
4.0
6.0
8.0
1.0
2.0
3.0
2.0
4.0
1.0
2.0
2.0
4.0
1.07
1.08
1.09
1.14
1.16
1.18
1.06
1.08
1.10
1.10
1.12
1.14
1.00
1.10
1.10
1.15
1.20
1.40
1.60
1.05
1.10
1.15
1.10
1.15
1.20
1.00
1.02
1.04
1.10
1.20
2008 2020 2008 2020 2008 2020
2008 2020 2008 2020 2008 2020
2008 2020 2008 2020 2008 2020
2008 2020 2008 2020
AT BG CZ
DE HR HU
IT PL RO
SI SK
Participation rate Unemployment rate (right axis)
Regional variation in the share of long-term unemployment (as a percentage of
total unemployment) is large and has increased since the ﬁnancial crisis in Bulgaria,
Hungary, and Poland (although less dramatically than in Italy) even though they
encountered a positive growth in employment over the same period. This may be
related to the lower employability of low-educated people (see Figure 4.22), who are
concentrated in a few regions in these countries. It seems that growing employment
has also reduced the regional variation in long-term unemployment in Czechia and
Romania. The regional disparity in NEET rates is the highest in Czechia and exhibits
an increasing trend in the other members of the EEE.
4 Labour Markets: Structural Characteristics and the Impact of Two Crises 187
Fig. 4.22: Regional variation in NEET and long-term unemployment (as a
percentage of total unemployment)
Data: Eurostat (2021i)
Note: Plotted are ratios of maximum and minimum regional values. Number of
NUTS 2 regions varies between countries: AT (8), BG (6), CZ (8), DE (37), HR
(2), HU (7), IT (21), PL (15), RO (8), SI (2), SK (4). Education categories: low
(ISCED 0-2), medium (ISCED 3-4), and high (ISCED 5-6)
1.5
2
2.5
2
4
6
8
2
4
6
8
2
4
6
8
1
1.5
2
3
4
5
0
20
2
4
6
8
2
4
6
8
1
1.5
2
2
4
6
8
2008 2020 2008 2020 2008 2020
2008 2020 2008 2020 2008 2020
2008 2020 2008 2020 2008 2020
2008 2020 2008 2020
AT BG CZ
DE HR HU
IT PL RO
SI SK
NEET LTU
4.9 The Eﬀects of the Covid Shock
As shown in section 4.4, the EEE underwent a similar ‘V-shaped’ pattern of GDP development
but with milder aggregate employment responses than were experienced
in other EU economies. Section 4.5 showed sectoral employment patterns during
the ﬁrst year in which the pandemic hit the economies with signiﬁcant diﬀerences in
employment impacts across sectors (non-tradable market services being hit strongly
and, in a number of economies, falling trade and vulnerability of cross-border production
networks hitting manufacturing).
Here we intend to explore further the diﬀerence between the impact of the 2008-
2009 ﬁnancial crisis and the Covid crisis by showing the diﬀerences in pre- and
post-crisis patterns on a number of labour market variables.
188 Martin Guzi and Michael Landesmann
Figure 4.23 allows a comparison of both GDP and employment developments in
the course of the two crises: each time the variables are indexed to the beginning of
the respective crisis (ﬁnancial crisis 2008q3=100; Covid crisis 2019q4=100), and
we also show a few comparator countries (Austria, Germany, Italy, and the EU-27
as a whole). In order to develop some conjectures regarding possible features after
the immediate crisis impact, the lines are drawn for the ﬁnancial crisis period for 12
quarters, while for the Covid crisis only ﬁve quarter ﬁgures are available.
The following features emerge from Figure 4.23. The much sharper fall in GDP
than in employment is common to all the economies in the initial phase of the Covid
crisis. This is also true, but in a more protracted way, during the early phase of
the ﬁnancial crisis. As we have also remarked, in a number of EEE we see a very
limited response of employment levels during the Covid crisis, while GDP follows a
sharp ‘V-shaped’ pattern (Czechia, Croatia, and Poland; plus very mild employment
responses in Hungary and Slovakia). Employment developments after the ﬁnancial
crisis are diﬀerent in the sense that they follow quite a smooth downward path (with
the exception of Poland), in some countries continuing to decline even after 12
quarters (Bulgaria, Croatia, and Romania), while in the others employment decline
levels oﬀ, but remains lower at the end of the ﬁrst three years after the crisis (between
3 and 7 percentage points).
If on this basis we want to make a conjecture about future employment trajectories
following the Covid crisis, we could say that employment follows output with a lag.
Consequently, over time the fall in GDP will lead to a signiﬁcant fall in employment,
and this fall will in the medium-term be more pronounced after the Covid crisis than
after the ﬁnancial crisis, because the furlough and subsidised short-term working
schemes were operating at much higher levels during the Covid crisis. Moving from
the medium-term to the longer-term impact, we expect a markedly diﬀerentiated
pattern to evolve across the EEE, the way we saw it happen in other EU economies
after the ﬁnancial crisis. We conjecture that this diﬀerentiation could be highly
pronounced. It will depend on a number of diverse structural characteristics within
the EEE to what extent each economy will beneﬁt from a general recovery in
the European and global economy through its trade and production linkages, to
what extent it is able to cope with the speeding up of technological (digitisation,
robotisation) and fundamental structural changes, and to what extent policy measures
(overall ﬁscal stance, but also direct employment-supporting policies) are used to
support a labour market recovery.
Next, we look at developments in unemployment rates, comparing again the
patterns of the two crises (the ﬁnancial crisis and the Covid crisis) and also looking
at diﬀerentiated gender patterns (see Figure 4.24).
What seems to emerge as a signiﬁcant diﬀerentiating feature is that after the
ﬁnancial crisis, there was a much stronger increase in the unemployment rate of
males than of females: this was the case in the EU-27 and the comparator countries
in Western Europe (Austria, Germany, and Italy), as well as in the overwhelming
majority of the EEE (Bulgaria, Croatia, Czechia, Hungary, Poland, Slovakia, and
less so in Slovenia), with the only exception being Romania.
4 Labour Markets: Structural Characteristics and the Impact of Two Crises 189
Fig. 4.23: GDP and employment developments following the start of the crisis
(2008q3=100, 2019q4=100)
Data: Eurostat (2021e, 2021f)
Note: Quarterly data. The quarter zero indicates 2008q3 for the ﬁnancial crisis and
2019q4 for the Covid-19 pandemic.
85
90
95
100
90
95
100
90
95
100
90
95
100
105
85
90
95
100
85
90
95
100
85
90
95
100
80
85
90
95
100
90
100
110
120
90
95
100
85
90
95
100
90
95
100
105
0 12 0 12 0 12
AT BG CZ
DE EU HR
HU IT PL
RO SI SK
GDP 2008 GDP 2020
Employment 2008 Employment 2020
During the ﬁrst phase of the Covid crisis, on the other hand, unemployment rates
grew faster for women than for men, both in Germany and in many members of the
EEE (Bulgaria, Czechia, Romania, and Slovakia); the only exception being Poland;
while in the other economies (Austria and Italy, and amongst the EEE, Croatia,
Hungary, and Slovenia) the increase in male and female unemployment rates was
fairly similar.
The overall increase in unemployment rates between the two crises (as can be
compared for the ﬁrst phases of the ﬁnancial and the Covid crises) was not markedly
diﬀerent, although there are some exceptions (Romania showing a much sharper
rise in female unemployment during the current Covid crisis than in the previous
ﬁnancial crisis; this is also the case in Hungary, as well as in the two comparator
countries Austria and Germany, but not in Italy).
While Figure 4.25 shows unemployment rates in index form (e.g., an unemployment
rate increase from an initial level of 100 to 200 means a 100% rise in
unemployment rates), we should keep in mind that unemployment varied by coun-
190 Martin Guzi and Michael Landesmann
Fig. 4.24: Changes in unemployment following the start of crisis by gender
(2008q3=100, 2019q4=100)
Data: Eurostat (2021e)
Note: Quarterly data. The quarter zero indicates 2008q3 for the ﬁnancial crisis and
2019q4 for Covid-19 pandemic.
100
150
200
100
150
200
250
100
150
200
80
100
120
140
100
120
140
160
100
150
200
250
100
120
140
160
80
100
120
140
100
120
140
160
100
120
140
160
180
100
150
200
100
150
200
0 12 0 12 0 12
AT BG CZ
DE EU HR
HU IT PL
RO SI SK
Females 2008 Females 2020
Males 2008 Males 2020
try, which we can see in Figure 4.25 for the Covid experience alone. Here, we see
that diﬀerent countries within the EEE moved into the Covid crisis with diﬀerent
levels of unemployment rates (Czechia, Poland, and Hungary well below 4%; Bulgaria,
Slovakia, and Slovenia around 4%; Romania and Croatia around 6%), and the
immediate impact was also quite diﬀerent, with a strong surge in Hungary, Bulgaria,
Slovakia, and Croatia, more moderate and more stretched increases in Czechia, and
Romania, and a delayed and rather mild increase in Poland. Again, we point to structural
characteristics and diﬀerences in policy actions that account for these, without
being able to quantitatively evaluate the impact of diﬀerent factors in detail.
Finally, let us refer to the importance of the changes in work organisation that have
taken place during the current pandemic. The pandemic forced many employers to
adopt remote work arrangements as a common form of work. Before the pandemic, in
some members of the EEE the right to teleworking was based exclusively on a mutual
agreement between the employee and the employer. The expansion of teleworking
largely depends on employment structure and sectoral specialisation. Whereas many
4 Labour Markets: Structural Characteristics and the Impact of Two Crises 191
Fig. 4.25: Diﬀerentiated country experiences of unemployment rates during the
Covid crisis
Data: Eurostat (2021e)
Note: Monthly data. The vertical line indicates the start of Covid-19 pandemic
(2020m3).
2
4
6
8
10
2019m1 2020m3 2021m6
EU BG CZ HR HU
PL RO SI SK
high-skilled jobs in knowledge and ICT-intensive services could be done at home,
very few jobs in agriculture, manufacturing, and the service sector are of the same
nature. Dingel and Neiman (2020) estimate that with existing technologies, 36% of
jobs could be done from home at the EU-27 level, while the estimates are lower
for economies with a large manufacturing sector. Before the pandemic, the share
of employees usually working from home was below 1% in Bulgaria and Romania,
and only Slovenia reached a rate of above 5% (the EU-27 average). Teleworking has
shown an increasing trend over the last decade, but as expected, it increased massively
during the pandemic (Figure 4.26). Interestingly, the prevalence of teleworking is
higher among women in the entire EEE.
192 Martin Guzi and Michael Landesmann
Fig. 4.26: Employees usually working from home as a percentage of total
employment by gender, 2008-2020
Data: Eurostat (2021e)
5
10
15
20
.5
1
1.5
2
2
4
6
8
0
5
10
15
5
10
15
1
2
3
4
1
2
3
4
0
5
10
15
4
6
8
10
0
1
2
3
4
4
6
8
10
3
4
5
6
7
2008 2020 2008 2020 2008 2020
AT BG CZ
DE EU HR
HU IT PL
RO SI SK
Females Males
4.10 Impact of the Covid-crisis on Occupations
In this section, we examine the impact of the Covid crisis on people employed in
diﬀerent occupations and in diﬀerent sectors of the economy. This analysis is based
on detailed Labour Force Survey data looking at two dimensions: employment in a
particular sector, and employment in a speciﬁc occupation. We looked at the most
important occupation-sector combinations for the overall employment situation in
the entire EU-27.
In Table 4.3 we present the number of workers in 2019, the shares in total
employment in 2019, and employment growth rates over the 2011-2019 and 2019-
20 periods comparing the EU and the EEE. To facilitate the comparison, only the
major occupations employing the most workers in the EU economy are presented
(although these are not necessarily the same in each country).
The three main occupational-sector groupings in the European Union are service
and sales workers in wholesale and retail trade, workers in craft and related trades
in manufacturing, and professionals working in the educational sector. In Central
and South-Eastern European countries, the ﬁrst two groups also feature among those
4 Labour Markets: Structural Characteristics and the Impact of Two Crises 193
with the highest employment shares. Beyond that, there are some diﬀerences: in a
number of economies, there is another group of manufacturing workers, i.e., plant
and machine operators and assemblers in manufacturing, featuring among the top
three, revealing again the strong position that manufacturing employment has in many
members of the EEE; in some, there is also the group of agricultural workers, which
features prominently particularly in the populous states of Poland and Romania. In
many members of the EEE, professionals in the educational sector also feature high
on the list, and in Slovenia and Croatia in the top three.
We also intend to point to gender diﬀerences in employment shares across
occupational-sector groupings (not shown in tables): one feature is the much stronger
representation of females among wholesale and retail sector employees (on average
a diﬀerence of about 4-5 ppoints between females and males). On the other hand,
males are much more strongly represented in manufacturing, both among craft workers
and machine operators and assemblers (a diﬀerence of about 6-8 ppoints between
males and females) in the same way as also among construction workers. On the
other hand, female representation is much more prominent among professionals in
the education sector (a diﬀerence of about 5 ppoints) and in health and social work
activities (a diﬀerence of about 1.5-2.0 ppoints between females and males).
Table 4.3 shows the annual growth rates in employment over the pre-pandemic
period (2011-2019), which can be compared to the employment developments in the
ﬁrst year of the Covid crisis (2019-2020). It shows both dimensions: occupations
and the sectors in which they are employed. Identifying the two dimensions shows
quite clearly that employment developments are determined both by changes in
the occupational mix (workers, technicians, professionals, etc.) and by the sectoral
employment impacts of trends and crises.
As we can see in Table 4.3, the occupational grouping that was most (negatively)
aﬀected by the Covid crisis was service and sales workers in the accommodation
and food services industry. In wholesale and retail trade, we see that the crisis had
a strongly (negative) impact on services and sales workers, as well as on workers
in craft and related trades in the European Union as a whole, while the picture is
mixed in the EEE, with some countries experiencing strong job losses in some of the
occupational groupings in this sector, while others experiencing job growth. We also
see some strong negative employment impacts in manufacturing on workers (craft
and related trades, and machine operators and assemblers) in some economies of
the EEE (Bulgaria, Czechia, Hungary, Romania, and Slovakia), which reﬂects the
strong impact of the crisis on cross-border production networks. On the positive side,
we see that professionals, especially in information and communications and also
in scientiﬁc and technical activities, recorded strongly positive employment growth.
Somewhat surprisingly, the employment picture is mixed during the ﬁrst year of the
Covid crisis in the health and social work activities sector, both for technicians and
professionals.
194 Martin Guzi and Michael Landesmann
Table 4.3: Employment share and growth in occupations with most workers by
economic sectors, EU
Data: Eurostat (2021e)
Occupation Economic sector EU
Empl.
share
2019
Growth
2011 to
2019
Growth
2019 to
2020
Service and sales workers Wholesale and retail trade 6.1 1.7 -6.2
Craft workers Manufacturing 5.0 -0.2 -5.1
Professionals Education 4.8 7.8 9.2
Craft workers Construction 3.8 -3.8 -6.0
Plant and machine operators Manufacturing 3.3 4.3 -5.3
Associate professionals Human health 3.3 18.1 -8.8
Professionals Human health 3.2 20.5 5.4
Skilled agricultural workers Agriculture 3.1 -21.1 -2.3
Professionals Scientiﬁc and technical activities 2.8 32.9 3.0
Service and sales workers Accommodation and food service 2.8 16.6 -13.3
Associate professionals Manufacturing 2.6 8.5 3.3
Plant and machine operators Transportation and storage 2.3 12.0 -6.5
Service and sales workers Human health 2.3 0.4 -3.4
Associate professionals Public administration 1.8 4.0 -9.0
Professionals Information and communication 1.7 43.3 9.6
Associate professionals Wholesale and retail trade 1.6 1.5 -0.9
Professionals Manufacturing 1.6 24.3 12.9
Craft workers Wholesale and retail trade 1.4 9.4 -9.7
Associate professionals Scientiﬁc and technical activities 1.3 13.3 -4.8
Professionals Public administration 1.3 12.7 10.5
As for gender diﬀerences (not shown here), one might want to point out that in
general, females in the health and social work sector lost their jobs (at all levels:
workers, technicians, and professionals) less in the EEE than in the EU as a whole,
and some groups even experienced substantial employment gains in this sector. On
the other hand, females in the accommodation and food services sector were severely
aﬀected in some members of the EEE (Bulgaria, Czechia, Slovakia, and Slovenia). As
regards male employment, it is interesting that workers in the construction industry
were less (negatively) aﬀected in the EEE than in the EU as a whole, possibly
reﬂecting the overhang of EU-ﬁnanced construction projects.
4 Labour Markets: Structural Characteristics and the Impact of Two Crises 195
Table 4.3: Cont.Employment share and growth in occupations with most workers
by economic sectors, EEE
Data: Eurostat (2021e)
Occupation Economic sector EEE
Empl.
share
2019
Growth
2011 to
2019
Growth
2019 to
2020
Service and sales workers Wholesale and retail trade 6.9 -2.8 -1.7
Craft workers Manufacturing 7.5 0.6 -5.4
Professionals Education 4.6 7.2 -0.1
Craft workers Construction 4.5 5.5 1.8
Plant and machine operators Manufacturing 5.7 2.7 -6.1
Associate professionals Human health 1.6 4.5 5.2
Professionals Human health 2.5 5.8 -0.5
Skilled agricultural workers Agriculture 7.5 -7.7 -0.3
Professionals Scientiﬁc and technical activities 2.0 11.2 7.2
Service and sales workers Accommodation and food service 2.1 3.5 -14.1
Associate professionals Manufacturing 2.4 11.3 1.9
Plant and machine operators Transportation and storage 3.3 8.3 -0.8
Service and sales workers Human health 1.1 14.6 -1.2
Associate professionals Public administration 1.5 -5.5 -1.5
Professionals Information and communication 1.6 29.3 9.4
Associate professionals Wholesale and retail trade 1.5 2.0 -2.2
Professionals Manufacturing 1.6 15.9 0.8
Craft workers Wholesale and retail trade 1.3 12.3 -1.9
Associate professionals Scientiﬁc and technical activities 0.9 4.0 0.3
Professionals Public administration 1.5 5.3 0.8
4.11 Long-term Prospects and Policy Recommendations
In comparison with some of the other EU member countries which encountered
diﬃcult labour market situations and were heavily impacted by both the ﬁnancial
and the Covid crises (in particular Greece, Italy, Portugal, and Spain in Southern
Europe) the EEE showed relatively good recovery from the ﬁnancial crisis although
it was lengthier in Croatia, Hungary, and Slovenia. From a structural point of view,
people with low educational attainment levels had worse labour market prospects,
and regional diﬀerences are large in several EEE regarding a number of labour
196 Martin Guzi and Michael Landesmann
market indicators (particularly Bulgaria, Hungary, Poland, Romania, and Slovakia).
The unemployment of the low-educated is related to geographical agglomeration
eﬀects, linked particularly to clusters of manufacturing activities generated by (FDIdriven)
cross-border production linkages, as well as the strong agglomeration of
business and administrative services in capital cities and other major towns. As
regards the strong variation of the labour market across skills groups, this points to
insuﬃcient matching between educational/training structures and the requirements
of the labour market. Lack of suﬃcient cross-regional mobility may contribute to
sustaining regional disparities over time in labour market indicators; the selection
eﬀect of cross-regional mobility may, on the other hand, also contribute to increasing
cross-regional disparities as younger, more active and better skilled workers leave
peripheral regions for more dynamic regions.
Accelerated trends towards digitisation, changes in the structures of employment
demand and in work organisation will require enhanced attention in the post-Covid
period: the trends towards digitisation and robotisation will have strong employment
(and skills) implications in the economies that were successful in attracting FDI
and building strong manufacturing capacities; however, these are also increasingly
aﬀecting other sectors and occupations that were traditionally less aﬀected by these
technologies (Baldwin, 2019). Furthermore, changes in work organisation that got
a strong push during the Covid-shock phase are likely to lead to an acceleration of
the digitisation trends, making diﬀerent impacts on occupations (see section 4.10)
and people with higher and lower skills levels. Thus, diﬀerentiation of labour market
prospects by skills and in diﬀerent occupations will proceed and likely accelerate.
Furthermore, the period since the ﬁnancial crisis has dampened foreign direct
investment, and the discussion on the future of EEE development (see Gattini,
Gereben, Kollár, Revoltella and Wruuck (2021)) has increasingly emphasised the
need to move towards a ‘new growth model’. This would rely less on foreign direct
investment and more on an upgrading of the country’s own capacities to move towards
the ‘technological frontier’, by building R&D capacities, better educational
and training institutions and making an eﬀort to ‘move upstream’ in the context of
the ‘functional specialisation’ of EEE businesses in international production networks
(IPNs) and cross-border value chains (for the latter, see Stöllinger (2021)). All
these developments will push towards further diﬀerentiation regarding the employment
prospects of diﬀerent skills groups and the speciﬁc skills required in diﬀerent
occupations.
Another issue emphasised in this chapter (see section 4.2) is the likelihood of
labour shortages as the legacy of the ‘post-transition demographic shock’ (reduced
birth rates) and the fairly high net migration rates of the young have led to a switch
in the labour market from an ‘excess’ towards a ‘labour shortage’ characterised by
shortages of speciﬁc skills. The Covid crisis aggravated shortages in speciﬁc areas,
especially of doctors, nurses, and other health personnel. An important new area in
which considerable expansion can be envisaged and which has already taken hold in
quite a few members of the EEE is the increase in ‘tele-working’ and ‘tele-migrants’
(i.e., persons working for international corporations but resident in the country they
are already located in). Particularly in the IT sector, tele-working has become very
References 197
important, and major hubs have developed in many members of the EEE. This again
points towards the need to further strengthen training/education in this speciﬁc area,
which is vital for new labour market entrants but also for the skills renewal of the
older work force. Life-long learning is still underdeveloped in the EEE educational
system (compared to more advanced Western and Northern European EU member
states).
The restrictive stance towards migration in quite a few members of the EEE
(Hungary being the most extreme example) is not particularly helpful to alleviate
the labour shortage, which became evident during the Covid recovery phase (see
also Astrov et al. (2021)). Adjusting the migration policy framework towards an
eﬃcient monitoring of labour market requirements and attempts to keep in contact
with people studying, working, and training abroad, coupled with incentives to
return or develop joint business ventures would deﬁnitely be helpful in the current
EEE context. In the same way, coordination of educational and training programs
with some of the main neighbouring host countries (Austria, Germany, Italy, and
Scandinavian countries) would also encourage circular and return migration and
potential joint business development.
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