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THE HUNGARIAN LABOUR MARKET, 2019; IN FOCUS: YOUNG
THE HUNGARIAN
LABOUR MARKET
2019

EDITORS
KÁROLY FAZEKAS
MÁRTON CSILLAG
ZOLTÁN HERMANN
ÁGOTA SCHARLE

AND IN THE LABOUR MARKET

PEOPLE IN EDUCATION
INSTITUTE OF ECONOMICS, CENTRE
FOR ECONOMIC AND REGIONAL STUDIES
BUDAPEST, 2020
The Hungarian Labour Market
2019
 The Hungarian Labour Market
Editorial board of the yearbook series
Irén Busch – Head of Department, Ministry of Interior • Károly
Fazekas – senior research fellow, Institute of Economics, Centre
for Economic and Regional Studies • Jenő Koltay – senior re-
search fellow, Institute of Economics, Centre for Economic and
Regional Studies • János Köllő – scientific advisor, Institute of
Economics, Centre for Economic and Regional Studies • Judit
Lakatos – senior advisor, Hungarian Central Statistical Office
• Ágnes Szabó-Morvai – research fellow, Institute of Econom-
ics, Centre for Economic and Regional Studies
Series editor
Károly Fazekas
THE HUNGARIAN
LABOUR MARKET
2019

EDITORS
KÁROLY FAZEKAS
MÁRTON CSILLAG
ZOLTÁN HERMANN
ÁGOTA SCHARLE

Institute of Economics, Centre

for Economic and Regional Studies,
Budapest, 2020
Edition and production: Institute of Economics, Centre
for Economic and Regional Studies.
Copies of the book can be ordered from the Centre for Economic
and Regional Studies.
Mailing address: 4. Tóth Kálmán street, 1097 Budapest
Phone: (+36-1) 309 26 49
Fax: (+36-1) 319 31 36
E-mail: biblio@krtk.mta.hu
Web site: https://www.mtakti.hu/en/publikacio/publikacio-katego-
ria/labour_market_yearbook/

Translated by: Anna Kiss (Foreword, In Focus: 2.3, K2.5, 3.2, 5.3, 5.5,
7.1, 8.1); Krisztina Olasz (The Hungarian Labour Market in 2018,
In Focus: 2.1, 2.2, 2.5, 6.2, 7.2, 7.3, 8.2, Labour Market Policy Tools);
Dániel Tordai (In Focus 2.4) Heléna Hönich (Pocketbook); Éva
Czethoffer (Statistical Data); Szolnoki Nikolett (In Focus : Introduc-
tion, 1., 3.1, 3.3, 4.1, K4.1, 4.2, 5.1, K5.1, 5.2, 5.4, 6.1)
Revised by: Stuart Oldham

The publication of this volume has been financially supported

by the Hungarian Academy of Sciences.
Copyright © Institute of Economics, Centre for Economic and Re-
gional Studies, 2020
Cover photo © Dániel Kovalovszky

ISSN 1785-8062

Publisher: Imre Fertő

Copy editor: Anna Patkós
Design, page layout: font.hu
Typography: Garamond, Franklin Gothic
CONTENTS
Foreword........................................................................................................................ 9
The Hungarian labour market in 2018 (Tamás Bakó & Judit Lakatos).......... 15
Introduction ........................................................................................................... 17
Drivers of change in employment ...................................................................... 18
Labour supply and demand ................................................................................. 24
Wages, income from work and labour costs ..................................................... 27
In Focus: Young people in education and in the labour market ..................... 33
Introduction (Márton Csillag, Zoltán Hermann & Ágota Scharle) ............ 35
1 Young people in the labour market and in education – changes
between 2002 and 2018 (Márton Csillag, Ágota Scharle, Tamás
Molnár & Endre Tóth) ...................................................................................... 37
2 School education ................................................................................................ 45
2.1 The impact of reading and mathematics test results on future
earnings and employment (Zoltán Hermann, Dániel Horn, János
Köllő, Anna Sebők, András Semjén & Júlia Varga) ...................................... 45
2.2 The impact of school tracks on student performance in upper-
secondary education (Zoltán Hermann) ..................................................... 53
2.3 Application to and admission into upper-secondary education
(Zoltán Hermann & Júlia Varga) ................................................................... 59
2.4 The effect of the 2013 vocational education reform on student
achievement (Zoltán Hermann, Dániel Horn & Dániel Tordai) ............ 64
2.5 The impact of decreasing compulsory school-leaving age on
dropping out of school (Zoltán Hermann) ................................................ 70
K2.5 What do 17-year-olds who don’t go to school do? (János Köllő &
Anna Sebők) ......................................................................................................... 77
3 Gaining work experience .................................................................................. 79
3.1 Student work (Bori Greskovics & Ágota Scharle) ..................................... 79
3.2 The short-term labour market effects of apprenticeship training in
vocational schools (Dániel Horn) .................................................................. 82
3.3 Casual and other forms of work (Bori Greskovics & Ágota Scharle) .... 86
4 Early unemployment and later labour market outcomes ........................... 89
4.1 Does the economic recession have permanent effects? (Márton
Csillag) ................................................................................................................. 89
K4.1 What are the consequences of young people entering the labour
market during an economic crisis? International outlook (Endre Tóth) ... 92
4.2 Unemployment among labour market entrants (Márton Csillag) ....... 95

5
 5 The impact of employment policies ............................................................... 99
5.1 Job search behaviour of young people not in education, training or
employment (Tamás Molnár) ......................................................................... 99
K5.1 Job search channels (Tamás Molnár) ................................................... 104
5.2 Active labour market instruments targeting young people and the
Youth Guarantee Programme (Judit Krekó, Tamás Molnár & Ágota
Scharle) ............................................................................................................. 105
5.3 The effect of the job protection action plan (András Svraka) ............ 110
5.4 The role of the minimum wage in the evolution of young people’s
earnings (Márton Csillag) ............................................................................. 112
5.5 Youth in public employment, with particular emphasis on early
secondary school leavers (György Molnár) ............................................... 115
6 The situation of Roma youth ......................................................................... 121
6.1 Schooling and employment of Roma youth: changes between 2011
and 2016 (Ágota Scharle) ............................................................................... 121
6.2 Neighbourhood-related differences in the share of youth not in
education, employment or training before and after lowering the
school-leaving age (János Köllő & Anna Sebők) ....................................... 126
7 Adult education and training and over-qualification ............................... 131
7.1 Workplace and non-formal education and training of youth (Júlia
Varga) ................................................................................................................. 131
7.2 The growing importance of non-cognitive skills in job search and at
work (Károly Fazekas) ................................................................................... 134
7.3 The labour market situation of young graduates, overqualification
and the value of higher education degrees (Júlia Varga) ........................ 137
8 Geographic and occupational mobility ..................................................... 144
8.1 Occupational mobility among youth with different educational
attainment levels (Júlia Varga) ..................................................................... 144
8.2 Outward migration of youth – Young people working abroad
(Ágnes Hárs & Dávid Simon) ...................................................................... 150
Labour market policy tools ( June 2018 – May 2019) (Miklós Hajdu,
Ágnes Makó, Fruzsina Nábelek & Zsanna Nyírő) ........................................ 155
1. Institutional changes ...................................................................................... 157
2. Benefits ............................................................................................................. 158
3. Services ............................................................................................................. 159
4. Active labour market polices and comprehensive programmes ........... 160
5. Policy tools affecting the labour market .................................................... 162
Statistical data........................................................................................................... 167
1 Basic economic indicators............................................................................... 169
2 Population...........................................................................................................171
3 Economic activity............................................................................................. 174

6
 4 Employment....................................................................................................... 182
5 Unemployment.................................................................................................. 192
6 Wages................................................................................................................... 209
7 Education........................................................................................................... 216
8 Labour demand indicators...............................................................................221
9 Regional inequalities........................................................................................ 224
10 Industrial relations.......................................................................................... 232
11 Welfare provisions.......................................................................................... 237
12 The tax burden on work................................................................................ 243
13 International comparison.............................................................................. 247
14 Description of the main data sources......................................................... 250
Index of tables and figures...................................................................................... 256

7
 Authors
Tamás Bakó – IE CERS
Márton Csillag – Budapest Institute
Éva Czethoffer – CERS
Károly Fazekas – IE CERS
Bori Greskovics – Budapest Institute
Miklós Hajdu – Corvinus University of Budapest
Ágnes Hárs – KOPINT–TÁRKI
Zoltán Hermann – IE CERS
Dániel Horn – IE CERS
János Köllő – IE CERS
Judit Krekó – IE CERS
Judit Lakatos – Hungarian Central Statistical Office
Ágnes Makó – Institute for Economic and Enterprise Research
György Molnár – IE CERS
Tamás Molnár – Budapest Institute
Fruzsina Nábelek – Institute for Economic and Enterprise Research
Zsanna Nyírő – Institute for Economic and Enterprise Research
Ágota Scharle – Budapest Institute
Anna Sebők – IE CERS
András Semjén – IE CERS
Dávid Simon – ELTE TÁTK
András Svraka – Ministry of Finance
József Tajti – Ministry of Finance
Dániel Tordai – CEU
Endre Tóth – Budapest Institute
Júlia Varga – IE CERS
FOREWORD
The Hungarian Labour Market Yearbook series was launched in the year 2000
by the Institute of Economics of the Hungarian Academy of Sciences with the
support of the National Employment Foundation. The yearbook presents the
actual characteristics of the Hungarian labour market and employment policy,
and provides an in-depth analysis of a topical issue each year. The intention of
the editorial board was to deliver relevant and useful information on trends
in the Hungarian labour market, the legislative and institutional background
of employment policy, and up-to-date findings from Hungarian and interna-
tional research studies to civil servants, staff of the public employment service,
municipalities, NGOs, public administration offices, education and research
institutions, the press and the electronic media.
It was an important focus for the analyses and data published in the yearbook
series to serve as a good source of knowledge for higher education as well, on
the various topics of labour economics and human resources management. The
yearbook series presents the main characteristics and trends of the Hungarian
labour market in an international comparison based on the available statistical
information, conceptual research and empirical analyses in a clearly structured
and easily accessible format.
Continuing our previous editorial practice, we selected an area that we con-
sidered especially important from the perspective of understanding Hungarian
labour market trends and the effectiveness of evidence-based employment pol-
icy. Based on the decision of the editorial board, this year’s ‘In Focus’ revolves
around the labour market situation of youth. The yearbook is broken down
into five main sections.
1. The Hungarian Labour market in 2018
The labour market in 2018 was essentially characterised by surplus demand; the
mobilizable labour force potential has shrunk significantly compared to that of
the previous year. In this context, public employment quotas have been further
reduced by the government in order to encourage those formerly working in
this manner to enter the primary labour market instead. The expansion of the
number of those in employment continued in 2018, however, the rate of the
expansion was slower than that of the previous two years. Based on the labour
force survey of the HCSO, the annual average number of those in employment
changed to 4,470,000, exceeding that of a year before by 1.1 percent, that is, by
47,000 (in contrast with the 1.6 percent growth in 2017 and the 3.4 growth in

9
the hungarian labour market

2016). The number of students fulfilling the criterion of being employed has
grown significantly, compared to the low base. The growth of the employment
rate of students was driven mostly by an increase in wages caused by an excess
demand for labour (and a trend of rising costs of living for those living in sepa-
rate households – especially housing costs); however, working while studying
is still less widespread in Hungary than in most Northern and Western Euro-
pean countries. The labour force potential (the supply) is comprised of the un-
employed, and the inactive who want to work – in EU terms, the so-called un-
deremployed, and within the particularities of the Hungarian labour market,
public workers. (The latter can be considered labour force potential only from
a primary labour market perspective, as they are included in the group of those
registered as employed.) In 2018, all of the above categories shrank in number
significantly, causing the labour supply to decline further. In 2018, the labour
force potential still significantly exceeded the number of job vacancies despite
the decrease, but the geographical (and possibly the structural) mismatch is still
quite significant. An equalisation could be facilitated by the intensification of
domestic migration, however, this is seriously hampered by the fact that hous-
ing costs are the highest in the places where employment opportunities are the
best. The excess demand for labour and government measures related to wag-
es have resulted in a significant increase of wages, with an increase rate that is
marginally below that of the previous year.
As a result of the COVID-19 epidemic that appeared in Hungary in March
2020, the favourable labour market processes were spectacularly broken. The
average monthly number of persons employed was already 56 thousand less
in March than in the previous month and 22 thousand less than in March of
the previous year. Although at the time of writing these sentences, it is not yet
known for sure how much unemployment officially increased in April 2020,
an estimate can be made using the relationship between the number of Google
searches and the number of the registered unemployed.
2. Youth in focus
The chapters of In Focus review the main aspects of the labour market situa-
tion of youth from secondary education to the first job. Chapter 1 presents
the main trends of educational attainment and employment of the past fifteen
years. Chapter 2 analyses the competencies obtainable in school, the choice of
school, and the development of early school leaving. Chapter 3 examines the
channels through which youth leaving school are able to obtain their first work
experiences. Chapter 4 analyses the negative effects of early difficulties on the
rest of the career path, and the extent to which low local labour force demand
and high commuting costs contribute to the unemployment of youth. Chapter
5 presents the extent to which employment policy tools and services available
to youth are able to mitigate or prevent youth unemployment. Chapter 6 pro-

10
 foreword

vides a short review of the educational and labour market disadvantages expe-
rienced by Roma youth. Chapter 7 examines the skills demanded by employers,
how the labour market values the knowledge of fresh higher education gradu-
ates, and the extent to which workplace training or training conducted parallel
to work may bridge skill gaps. Finally, chapter 8 reviews the occupational and
geographical mobility of youth.
In past years, the employment rate of youth increased, while the rate of those
not in education, employment, or training decreased. This could make the tran-
sition from school to work appear seamless, however, the details presented in
In Focus will nuance the picture. This is because the primary explanation for
improving indicators is not the smooth functioning of the institutions assist-
ing the transition. Rather, it is that the majority of better educated youth can
find jobs within a short time without state assistance, while a substantial part of
the undereducated find short-term job opportunities with wage subsidies or as
public workers. An element of youth who leave school early, after the comple-
tion of the eighth grade of elementary school, are “lost” either in public employ-
ment or without a job, and do not receive substantial assistance either to finish
their studies or to enter the primary labour market. Parallel to the rise of the
employment rate, the rate of those participating in full-time education or train-
ing started to decrease, and the attainment level of the fresh graduate age groups
stopped increasing. Additionally, policy steps with regard to public education
and higher education do not support the development of general skills either.
The further improvement of the labour market situation of youth may be
hindered in the long run by two circumstances: On the one hand, as demand
moves towards non-cognitive skills, even a part of fresh graduates considered
educated will not be able to fulfill employers’ requirements, while this may be
even more marked in the case of those with lower educational attainment. On
the other hand, youth who found jobs easily during the upswing period but
whose basic skills are weak will be, in the future, less able to adapt to employers’
demands changing due to technological development. Considering its existing
capacities, it is difficult for the state employment service to provide them with
adequate support to fill the gaps in their skills and find long-term employment.
These issues may affect the generations who enter the labour market in a poten-
tially unfavourable economic climate even more gravely.
3. Labour market policy tools (June 2018 – May 2019)
This chapter summarizes the main legislative changes in connection with labour
market policies between June 2018 and May 2019.
The amendment to the vocational training act introduced the possibility of
closed-system electronic long-distance training with effect from 1 January 2019.
As of January 2019, a chancellery system has been introduced in vocational train-
ing centres within the competence of the Ministry for Innovation and Technol-

11
the hungarian labour market

ogy – following the model already in place in higher education. The chancel-
lor is a senior manager in charge of the institution, appointed by the minister
in charge of the vocational training field. The Vocational Training and Educa-
tion (VET) Innovation Council was established in September 2018, with the
main objective of providing a regular platform for dialogue between the gov-
ernment and the main agents of the vocational training system. In March 2019
the government approved a document that contains the “Vocational training
4.0” strategy. According to this, from September 2020, the-four-plus-one-year
training structure of vocational grammar schools (szakgimnázium) will be re-
placed by the five-year training model of technical grammar schools (technikum),
while secondary vocational schools (szakközépiskola) will be transformed into
three-year vocational training schools (szakképző iskola). In dual vocational ed-
ucation and training, apprenticeship contracts will be replaced by student em-
ployment contracts.
Due to the new regulations in effect as of 1 January 2019, the pensioners’ co-
operatives system lost its financial purpose, since pensioners in their own right
who are in employment as defined by the Labour Code are not under insur-
ance obligation, and thus are exempt from paying pension contributions and
the health insurance contribution in kind, and are only obliged to pay personal
income tax on their wages – in the same way as if they received remuneration
as members of a pensioners’ cooperative of public interest.
With the raising of the minimum wage, the amounts of the related benefits
have respectively also grown. And with regard to labour market services, new
applications may be submitted within the framework of the Széchenyi 2020
scheme, and this time, businesses may apply with plans related to the develop-
ment of labour market adaptability, the strengthening of social responsibility,
and the expansion of their role as service providers as well.
Throughout the past year, the aims of active employment policy were the re-
duction of public education, the encouragement of lawful employment, sup-
porting entrepreneurship, supporting those raising small children to enter the
labour market, the encouragement of the creation of jobs, supporting the estab-
lishment of workers’ hotels, and the development of labour market adaptability.
The amounts of the minimum wage and the guaranteed minimum salaries
continued to increase in 2019. Additionally, a new law on social contribution
tax entered into force, discontinuing the former health contribution tax and
prescribing a social contribution tax with a universal rate of 19.5 percent. The
rate of the tax has been lowered by 2 percent as of July 2019. The range of social
contribution tax relief options has shrunk by several factors, and a new tax re-
lief option has been introduced, available for the employment of new entrants
into the labour market.
The cafeteria system has also been renewed: as of 2019, the Szép card is the
only type of benefit in kind available. The range of certain defined benefits has

12
 foreword

also been reduced, and the tax exemption of several forms of benefits has been
discontinued.
As of the 1 January 2019, the amendment of the Labour Code approved in
December 2018 entered into force. The amendment raises the duration of the
working time banking from 12 months to 36 months in the case of collective
agreements, and also establishes that, based on a written agreement between
the employee and the employer, a maximum of 150 hours of overtime (that is,
“voluntary overtime”) may be ordered each calendar year, on top of the over-
time specified previously.
4. Statistical data
This chapter, in the same structure as in previous years, provides detailed in-
formation on the major economic trends, the characteristics of the population,
labour market participation, employment, unemployment, inactivity, wages,
education, labour demand, regional imbalances, migration, labour relations
and welfare benefits of the period since the political transition, and presents an
international comparison of certain labour market indicators.
The data presented in the chapter have two main sources: on the one hand,
the regular labour-related institutional and population surveys of the Hungar-
ian Central Statistical Office: the Labour Force Survey (LFS), institution-based
labour statistics (ILS), and the labour force account (LFA). On the other hand,
the register of the National Employment Services and its data collections: the
unemployment register database (NES REG), short-term labour market fore-
cast (PROG), wage tariff surveys (WT) and the Labour Relations Information
System of the Ministry for National Economy (LRIS). More detailed informa-
tion on these data sources is available at the end of the statistical section. In ad-
dition to the two main data providers, data on old age and disability pensions
and benefits was provided by the Central Administration of National Pension
Insurance. Finally, some tables and figures are based on information from the
online databases of the Central Statistical Office, the National Tax and Customs
Administration and the Eurostat.
The tables and figures of the chapter can be downloaded in Excel format fol-
lowing the links provided. All tables with labour market data published in the
Hungarian Labour Market Yearbook since 2000 are available at the following
link: http://adatbank.krtk.mta.hu/tukor_kereso.
5. The Hungarian labour market, pocket edition
Continuing the initiative we started last year, we have compiled a collection
of figures related to the theme of In Focus, based on Hungarian data, which
makes the development of the labour market situation of youth over time eas-
ily understandable, via long time series figures.

13
the hungarian labour market

***
The editorial board would like to thank colleagues at the Institute of Economics
– Research Centre for Economic and Regional Studies, Central Statistical Office,
Hungarian State Treasury, colleagues at the Budapest Institute for Policy Analy-
sis, members of the Economics of Human Resources Committee of the Hungarian
Academy of Sciences, and the organisers and participants of the Sziráki Labour
Economics Research Conference 2019 for their help in collecting and reviewing
the necessary information, editing and preparing parts of this publication as
well as discussing it. We would like to thank the Hungarian Academy of Sci-
ences for the financial support provided to this publication.

14
 THE HUNGARIAN
LABOUR MARKET
IN 2018

Tamás Bakó & Judit Lakatos

INTRODUCTION
Hungary’s key economic indicators showed positive trends in 2018: the gross
domestic product increased by 5.1, exports by 4.3 and fixed capital formation
by 17.1 per cent. There was excess demand in the labour market in 2018 and
labour reserves that may be mobilized significantly shrank from the previous
year. Accordingly, the Government continued to reduce public works head-
counts in order to encourage former public works participants to enter the
primary labour market. Excess demand for labour and wage-related measures
by the Government resulted in a significant wage growth only slightly weaker
than in the previous year.
Despite growing labour supply problems, the Government continued to en-
courage job creation. In the period 2011–2017, 255.6 billion HUF non-re-
fundable grants created 35.3 thousand new jobs and a further 27 billion HUF
created 1,400 more in 2018. Because of the relationship between Hungarian
wages and the quality of labour available, Hungary is an attractive location for
foreign investors to establish business premises, and especially so because the
Government supports their investment directly with non-refundable grants
and indirectly with infrastructure improvements. However, the depletion of
domestic labour reserves and the scarcity of cross-border supply put a limit
to job creation investments.
It remains to be seen what steps current and future investors are forced to
take because of increasing labour costs (even though the government has been
trying to counteract this increase by reducing contributions payable by em-
ployers) and worsening difficulties of obtaining labour. Alternatives include
moving production capacities to another country as well as stepping up au-
tomation and robotization. Since the Hungarian economy is open, it is also
vulnerable. Moreover, the automotive industry, which is sensitive to business
cycles, has recently played a key role in industrial development, therefore exter-
nal factors will continue to have a major influence on labour market changes.
Recognising the economic processes from the end of 2018 to the first quar-
ter of 2020, we can see that in addition to the classic business cycles, difficult-
to-predict external shocks can also have a large impact on the labour market.
As a result of the COVID-19 epidemic that appeared in Hungary in March
2020, the favourable labour market processes were spectacularly broken. The
average monthly number of persons employed was already 56 thousand less

17
Tamás Bakó & Judit Lakatos

in March than in the previous month and 22 thousand less than in March of
the previous year. Although at the time of writing these sentences, it is not yet
known for sure how much unemployment officially increased in April 2020,
an estimate can be made using the relationship between the number of Google
searches and the number of the registered unemployed. This was also done by
Köllő–Kónya (2020), who found that the number of registered unemployed
could have exceeded 400,000 in April 2020, an increase of roughly 120,000
compared to February.

DRIVERS OF CHANGE IN EMPLOYMENT

The number of those in employment continued to expand in 2018 but the rate
of the increase was lower than in the previous two years. Based on the Labour
Force Survey, the average annual number of employees rose to 4 million 470
thousand, by 1.1 percent, which is 47 thousand higher than the year before,
compared with a 1.6 per cent increase in 2017 and a 3.4 per cent one in 2016.
The growth rate also slowed down during the year and, due to seasonal effects,
in the last quarter it slightly lagged behind the peak of the third quarter of
the previous year (4 million 487 thousand). The slowdown in the expansion
in employment was due to the diminishing labour supply, indicated by the
number of vacancies growing at the same rate as over the previous year. Ac-
cording to statistical reports, employers would have needed nearly 84 thou-
sand more employees in 2018 on average.
Figure 1: The number of employees in the 15–74 age group
and the employment rate among those aged 15–74, 2010–2018
The number of employed persons The employment rate
of age group 15–74 of age group 15–74
4,500 70

4,300 65
Thousand persons

4,100 60
Percent

3,900 55

3,700 50

3,500 45
2010 2011 2012 2013 2014 2015 2016 2017 2018
Quarters
Source: LFS, Central Statistical Office (CSO).
One of the reasons for the insufficient labour supply is the fact that the size
of generations entering the labour market is significantly smaller than that of
the generations exiting. While in 2010 the number of working-age persons
(aged 15–64) living in private households was 6 million 736 thousand, it de-

18
 The Hungarian labour market in 2018

clined to 6 million 415 thousand in 2017 and by a further 46 thousand to 6

million 369 thousand in 2018. This negative trend is possible to be offset by 1 Graduates from 4-year pro-
the gradual raising of the retirement age until 2020 but from 2021 onwards grammes potentially had 40
qualifying years in 2018 and
retirement will be uniformly linked to reaching the age of 65. The opportu- graduates from 5-year pro-
nity for women to retire after 40 qualifying years has been announced repeat- grammes in 2019.
2 The source of uncertainty is
edly not to be changed by the Government; however, an increasing propor- the population estimate used
tion of women who were full-time students will be able to claim it, therefore for multiplication, since at the
time of its annual update the
the number of persons retiring prior to the date applicable to them may in- accurate number of persons
crease.1 There is no precise data available as to what extent of the working- staying abroad for work is not
known. There is unambiguous
age population is indeed at the disposal of employers.2 It is known that 105 information only about those
thousand of the 4 million 470 thousand employees in 2018 reported work- who are documented as a mem-
ber of a household in Hungary
ing abroad, indicating a decrease within the margin of sampling error com- by the Labour Force Survey.
According to CLFS (Commu-
pared with one year prior. nity Labour Force Survey), 327.7
Several measures have been adopted recently to increase labour supply. Ac- thousand Hungarian citizens,
aged 20–64, were staying not
cess to employment was improved for Serbian and Ukrainian workers in 2017 only occasionally in 2017 in
and there is even organised recruitment in Serbia and Ukraine. Although EU–EFTA countries, 80.6 per
cent of which were employed.
Hungarian wages are higher than wages in Ukraine and employers also pro- The share of women and men
vide for accommodation and transport for workers, Hungary is less attractive among the registered persons
was similar and presumably
than the Czech Republic or Poland, where both the wages offered are higher a significant proportion of
and where it is easier to overcome language barriers because of belonging to them were living in a household
which had no member living
the same language family. Even though the number of work permits issued in Hungary to provide infor-
rose sharply in 2018,3 the statistical reports of employers only included 40 mation about them. Although
both the absolute number of
thousand foreign employees, which implies a 17 percent increase from the the Hungarians registered and
previous year. This alleviated the problems of a few large employers adapted their proportion relative to the
appropriate population were
to employing large numbers of foreign workers but had little impact on the lower than in most other post-
general lack of labour supply.4 communist countries, the la-
bour migration of Hungarians
Due to lower birth rates, women stayed away less from their jobs than for between 2010 and 2015 grew
example in the 1980s; however, the length of their absence barely changed. above the average.
3 According to data from the
According to a survey conducted four years ago, more than three-quarters of Directorate-General for Aliens
mothers wish to use up the entire maternity leave to stay at home with their Policing (former Immigration
and Asylum Office), 150 per
children (CSO, 2015). Measures to expand nursery care and removing the sus- cent more foreigners arrived in
pension of work from the eligibility criteria of provision both aimed at encour- Hungary up to the end of the
third quarter of 2018 to take
aging women to return to the labour market sooner after giving birth.5 The up employment, compared
measures have not yet produced impressive results. In 2014, when the number with one year prior. While last
year one-third of applicants for
of births was similar to that in 2018, 16.8 thousand women claiming parental residence permits arrived for
employment or gainful activi-
leave benefits were employed (undertaking gainful work during the week of ties, in 2018 more than half of
the survey), while this figure was 21.1 thousand in 2018.6 them arrived for such purposes.

4 The large-scale employment of Ukrainian citi- ance because both members of a couple have to
zens sometimes reduces the chances of commut- have a taxable income in order to be eligible for
ers from regions with few jobs, as the latter are the total amount.
less likely to work overtime and their employ- 6 Due to proxy interviews (response from an-
ment may be more expensive. other member of the family) there is higher than
5 Incentives also include the family tax allow- above uncertainty about data analysed here.

19
Tamás Bakó & Judit Lakatos

Full time students and old-age pensioners are also potential labour force.
In order to boost the interest of the latter, pensioner cooperatives were intro-
duced, modelled on student cooperatives, and since January 2019 old-age
pensioner employees and their employers have been exempt from taxes and
contributions. According to the Labour Force Survey, 12.4 thousand full-
time students and 128.2 thousand pensioners were in employment in 2014
on average, while in 2018 the figures were 22.8 thousand and 169.6 thou-
sand respectively. Although the number of pensioners working is significant,
it must be taken into account that due to the rising retirement age a growing
proportion of them may not be able to work because of their health even if
they wanted. Compared to a low base, the number of students meeting the
eligibility criteria for employment increased sharply. The absolute figure is
probably underestimated7 but the surge is real (Table 1). The increasing em-
ployment of students was primarily the result of increasing wages driven by
excess demand for labour (as well as the increasing cost of living, especially
accommodation costs of those living in separate households); however, work-
ing during studies is still less common in Hungary than in most Northern and
Western European countries.
Table 1: Employee groups according to their secondary status, 2018
Number of employees
Number
of employees as a percentage of
as a percentage as a percentage
(persons) the total headcount
of the 2014 figure of the previous year
Status of this status
On parental leave 21,119 125.8 106.3 8.3
Full-time student 22,760 183.4 113.6 3.5
Pensioner 169,646 132.3 119.1 9.5
Source: LFS, CSO.
Public works participants are also a source of labour supply for the primary
labour market. Increasing the headcounts of public works in 2012 princi-
pally aimed at tackling unemployment but probably also at satisfying pub-
lic opinion, which resists the idea of an income without work (that is to say
unemployment benefits). The annual headcount of public works participants
is determined by the Government and not by the demand for such work-
places. Because of increasing demand in the primary labour market, the
7 Old-age pensioners, and es-
pecially students, often under-
headcounts of public works participants were reduced already in 2017, fol-
take casual employment, there- lowed by a similar step in 2018 (Figure 2). Employers are encouraged by, in
fore the annual headcount may
be substantially greater than addition to doubling the employment allowance in November, the fact that
the average headcount. the public works wage did not change in 2018 after a slight raise in 2017.8
8 When introduced in 2012, the The increased labour demand was mainly exploited by better qualified pub-
public works wage was 77.2 per
cent of the “normal” minimum lic works participants with fewer health and family problems and living in
wage but this proportion had
decreased to 59.1 per cent by
towns with better transport links: they were more likely to be able to enter
2018. the primary labour market.

20
 The Hungarian labour market in 2018

Figure 2: Changes in the headcount of public works participants, 2010–2018

250,000

200,000

150,000

100,000

50,000

0
2010 2011 2012 2013 2014 2015 2016 2017 2018
Months
Source: CSO, Monthly labour report.
An increase in headcount was only seen in the primary labour market in 2018,
with 98.8 thousand new jobs (Table 2). In addition to the unemployed and
inactives, some of the former public works participants also managed to find
employment and the number of respondents reporting a job abroad declined
in the labour force survey as well.9
Table 2: Changes in the number of employees broken down by major characteristics
Number of employees (thousand persons) Change 2018/2017
thousand
2016 2017 2018 percentage
persons
Total 4,351.6 4,421.4 4,469.5 48.1 101.1
Gender
Male 2,362.5 2,417.3 2,446.2 28.9 101.2
Female 1,989.1 2,004.1 2,023.3 19.2 101.0
Type
Domestic primary labour
4,014.3 4,117.8 4216.6 98.8 102.4
market
Public works 220.9 194.0 148.2 –45.8 76.4
Foreign site 116.4 109.6 104.7 –4.9 95.5
Region
Budapest 840.3 845.3 833.8 –11.4 98.6
Pest 565.6 578.0 595.2 17.2 103.0
Central Transdanubia 487.9 498.7 499.1 0.4 100.1
Western Transdanubia 457.0 469.6 481.9 12.3 102.6
Southern Transdanubia 370.7 369.3 374.0 4.8 101.3
Northern Hungary 466.6 474.8 485.3 10.5 102.2
Northern Great Plain 613.9 631.1 639.7 8.6 101.4
Southern Great Plain 549.5 554.8 560.5 5.7 101.0
Status
Employee 3,884.4 3,964.4 4,003.9 39.5 101.0 9 The Labour Force Survey in-
cludes persons working abroad
Member of partnership or who commute abroad daily or
148.0 156.8 149.4 –7.4 95.3
cooperative work abroad for extended pe-
Entrepreneur, self-employed riods but regularly come home
319.3 300.2 316.2 16.0 105.3 and contribute to the subsist-
and family helper
ence of the household provid-
Source: LFS, CSO. ing the data.

21
Tamás Bakó & Judit Lakatos

Since 2010 a total of 609 thousand unsupported new jobs have emerged in the
Hungarian economy, which amounts to an annual average increase of nearly
70 thousand. In 2018 the number of employees rose in all regions (if Budapest
and Pest county are considered jointly, in line with the earlier regional classi-
fication). Proportionately to the labour force, the largest increase was seen in
Western Transdanubia, in spite of the depletion of internal labour reserves.
Thus the main source of the increase was presumably internal migration, still
modest because migration is restricted by the regionally considerably differ-
ent accommodation costs.
The employment rate of the population aged 15–64 was 69.2 per cent in
2018; however, half of the 1.1 percent year-on-year improvement was due to
a decline in the population size used in the denominator of the rate. The em-
ployment rate of men slightly exceeded the EU-28 figure, while that of women
was slightly lower. The Europe 2020 strategy sets 75 per cent as a target for
the population aged 20–64, and the Hungarian rate of 74.4 per cent was only
slightly below that in 2018. This target may even be reached with stagnating
employee numbers owing to the continuous population decline.
While 82.1 per cent of men in the age group was in employment in 2018,
the rate was only 66.8 per cent for women (Figure 3). One of the reasons for
the significant difference in the employment rate of genders is the consider-
ably long parental leave, compared to other European countries, and the re-
cipients of parental leave benefits (unless they undertake gainful work during
the week of the survey) are regarded by Hungarian statistics as economically
inactive irrespective of their employment status. Another important reason is
that although the rising retirement age applies equally to both genders, only
women can retire after 40 qualifying years. Therefore whereas in the fourth
quarter of 2018 56.4 per cent of men aged 60–64 were in employment, only
27.3 per cent of women worked. Furthermore, the gap between the employ-
ment rates of men and women has slightly widened in recent years. A further,
less significant, factor for the difference is that a larger proportion of women
in the younger age groups follow higher education studies and therefore en-
ter the labour market at a later age. Caring for the family, another reason for
inactivity, is also almost exclusively undertaken by women.
The employment chances of the low-qualified used to be below the EU av-
erage in Hungary but as a result of public works schemes, which increasingly
became a type of employment typically for the low-qualified, the difference
has disappeared. In the not very populous qualification group of those with-
out a lower-secondary qualification, aged 20–64, nearly 15 percent were in
10 The denominator also in-
employment in 2018 (Table 3),10 which is an almost 5 percentage point im-
cludes those who are unable to provement on 2014. The employment rate of the same age group with a low-
participate in formal education
because of their health or dis-
er-secondary qualification increased by 11 percentage points, with a substan-
ability. tially more marked increase among men.

22
 The Hungarian labour market in 2018

Figure 3: Employment rates of men and women aged 20–64, 2014–2018

100

80 Female
Percent

Male

60

40
2014 2015 2016 2017 2018
Quarters
Source: LFS, CSO.
Table 3: Employment rate of the population aged 20–64, broken down
by educational attainment and gender, excluding public works participants,
2014, 2017, 2018 (percentage)
2014 2017 2018
male female total male female total male female total
Without a lower-second-
13.7 7.5 10.3 18.1 10.1 13.6 20.3 11.2 14.8
ary qualification
Lower-secondary 45.3 32.1 37.9 56.2 36.7 45.3 60.5 39.2 48.9
Upper-secondary, with-
71.4 57.1 66.2 80.1 60.9 73.2 82.2 64.2 75.7
out a Matura
Upper-secondary, with
72.2 59.3 65.0 78.8 64.8 71.1 79.2 64.8 71.3
a Matura
Higher education 86.8 75.8 80.4 91.2 78.4 83.9 91.8 79.7 84.8
Total 70.3 57.7 63.9 78.1 62.2 70.1 79.8 64.1 71.9
Source: LFS, CSO.

The employment rate of respondents identifying either as Roma or non-Roma

in the Labour Force Survey, aged 20–64, improved but the ethnicity-based
difference barely decreased (Table 4). In 2018, less than 34 per cent of the
Roma aged 20–64 were in employment as opposed to the 73 per cent among
the non-Roma.
The large difference stems from the joint impact of two factors: the employ-
ment rate of the Roma lags behind that of the non-Roma at all qualification
levels, except for the few higher education graduates, and at the same time
the low-qualified, whose share in employment is lower than the average, are
strongly overrepresented in the Roma population. The difference between
the rate of Roma and non-Roma men is substantial but still much lower than
the gap in the case of women. Only one in five Roma women with a lower-
secondary qualification at most was in employment in 2018. Proportionately
this is about half of the participation rate of Roma men and non-Roma wom-
en with identical qualification levels.

23
Tamás Bakó & Judit Lakatos

Table 4: Employment rate of the Roma and non-Roma population, aged 20–64,
broken down by educational attainment, 2017, 2018 (percentage)
2017 2018
male female total male female total
Non-Roma
Without a lower-secondary
17.2 10.8 13.6 18.6 13.9 15.8
qualification
Lower-secondary 59.2 38.9 47.8 63.6 41.6 51.5
Upper-secondary, without
80.5 61.5 73.7 82.5 64.5 76.0
a Matura
Upper-secondary, with
78.8 64.9 71.1 79.2 64.9 71.3
a Matura
Higher education 91.3 78.4 83.9 91.8 79.7 84.8
Total 79.1 63.5 71.2 80.8 65.3 73.0
Roma
Without a lower-secondary
19.9 9.1 13.4 23.3 7.0 13.3
qualification
Lower-secondary 36.9 21.2 28.5 40.0 20.2 29.9
Upper-secondary, without
62.0 37.5 53.4 63.9 47.2 57.6
a Matura
Upper-secondary, with
75.7 42.4 54.6 63.1 49.7 56.4
a Matura
Higher education 12.9 90.0 56.3 … 100.0 85.1
Total 42.4 22.7 32.2 44.2 23.3 33.7
Source: LFS, CSO.

Partly because of being concentrated in regions or types of municipalities

with adverse labour market conditions and partly because of lower than av-
erage educational attainment, a larger share of the Roma participate in public
works schemes, compared with the non-Roma. In 2018 32 per cent of Roma
employees, and more than 40 per cent of Roma women were employed in such
schemes. Nevertheless, the growth of jobs in the primary labour market in
comparison with public works was in line with the average in the case of Roma
employees. The permanent poor labour market outcomes of the Roma is likely
to be reinforced by the fact that 68.4 per cent of the 18–24 age group were
early school leavers, that is they did not acquire an upper-secondary educa-
tion until the age of 24, compared with the 9.3 per cent of the non-Roma, and
40.1 per cent of Roma youth aged 15–24 were not in education, employment
or training (NEET) compared with the 9.1 per cent among the non-Roma.

LABOUR SUPPLY AND DEMAND

The labour market is increasingly facing limits to the expansion of employ-
ment, the gap between demand and supply widens, the available labour supply
is not of the needed qualification structure and not where it is needed. The
increase in labour demand is revealed by changes in the number of vacancies,

24
 The Hungarian labour market in 2018

although the relevant statistics measure trends more precisely than the actual
number of missing employees at a point in time.11 In the first quarter of 2018,
there were 79.4 thousand vacancies at businesses with at least 5 employees and
state-funded institutions and this figure rose to 83.6 thousand in the second
quarter, 87.7 thousand in the third quarter and then it fell to 83.3 thousand
in the fourth quarter. The annual average of vacancies at present and in the
near future was nearly 23 percent above the level one year prior. 60.3 thou-
sand of the vacancies were in the business sector in the fourth quarter, which
amounted to nearly 2.8 per cent of the total number of vacancies (Figure 4).
Figure 4: The number of vacancies in the business sector,* 2010–2018
80,000
70,000
Number of job vacancies

60,000
50,000
40,000
30,000
20,000
10,000
0
2010 2011 2012 2013 2014 2015 2016 2017 2018
Quarters
*
Businesses with at least five employees.
Source: CSO, vacancy statistics.

Compared to the overall vacancies, the highest figure was reported in the ad-
ministrative and service support sector, where 5.6 per cent of jobs were vacant.
More than half of the 10 thousand vacancies advertised were for jobs not re-
quiring a qualification, typically posted by temporary agencies active in this
sector. Another 21.8 thousand of the vacancies in the business sector were
reported in the manufacturing industry and 6.2 thousand at trading compa-
nies. The proportion of vacancies in the public sector exceeded 3 per cent in
the last three months of the year. There were 4.1 thousand openings in edu- 11 In some segments, profes-
cation including 2.7 thousand for higher education graduates (presumably sional advocacy organisations
report many times higher
teachers). The highest number of vacancies in the public sector were reported shortage than the aggregated
in healthcare and social services. 2.2 thousand of the 8.6 thousand vacancies statistical reports of enterprises
active in the segment.
were for higher education graduates capable of working independently, 4.6 12 As regards the share of va-
thousand for higher education or upper-secondary graduates with a relevant cancies, the situation is mark-
edly different in member states.
diploma but nearly one thousand unskilled workers were also missing. The highest figure was reported
The Hungarian job vacancy rate was only slightly above the European aver- by the Czech Republic in the
third quarter of the year, where
age12 but its growth was outstanding. 5.9 out of 100 jobs were vacant,
whereas in Greece, not yet
Potential labour reserve (supply) includes, in addition to the unemployed, emerging from the economic
the inactive wishing to work, the underemployed according to the EU defini- crisis, the rate was only 0.6.

25
Tamás Bakó & Judit Lakatos

tion, and public works participants, specific to the Hungarian labour market.
(The latter are regarded as reserve only for the primary labour market, since
they are included in the category of employees.) All the above groups shrank
in 2018, thus the labour supply continued to decrease. In 2018, the annual
average number of the unemployed, as defined by the ILO, the specialised
agency of the UN, fell to 172 thousand, and the unemployment rate was 3.7
per cent. This figure was 20 thousand lower year-on-year, while the unem-
ployment rate decreased by 0.4 percentage points. In the last quarter of the
year, the number of the unemployed was 167 thousand, and as a result of the
high volume of labour demand, both the average duration of unemployment
and the proportion of those searching for a job for at least one year decreased.
Although the potential labour reserve only includes the unemployed as de-
fined by the ILO, there are two more unemployment data, based on a differ-
ent definition, which are important for describing the labour market situation.
The monthly average number of jobseekers registered at the National Employ-
ment Service was 255 thousand, nearly 10 per cent lower or 28 thousand fewer
than a year earlier. The number of registered jobseekers declined in the first
half of 2018, it then slightly increased in the third quarter due to fewer sea-
sonal and public works job vacancies and then it fell again to 243 thousand
in the last quarter. The number of the insurance-based jobseekers’ allowance
recipients (for six months at most) was essentially constant in recent years
and thus their share within registered jobseekers grew. However, the propor-
tion of those who receive some kind of unemployment-related cash benefit
increased, although most of them still received employment substitution sup-
port belonging to social benefits, the amount of which has been unchanged
since 2013 (HUF 22,800/month). The number of those who reported being
unemployed in the Labour Force Survey of the Central Statistical Office also
decreased. An average of 287 thousand classified themselves as such in 2018,
which is approximately identical to the joint number of the ILO-definition
unemployed and the inactive belonging to the potential labour reserve.
Figure 5: The average duration of job seeking (months, right-hand axis) and the
share of the long-term unemployed (left-hand axis) , 2010–2018
Long-term unemployed Average length of
(left axis) job seeking (right axis)
60 19

50 16
Per cent

Month

40 13

30 10
2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018
Source: LFS, CSO.

26
 The Hungarian labour market in 2018

Table 5: The number of employees and the potential labour reserve

(thousand persons)
2010 2016 2017 2018 Change
thousand persons 2018/2017
Total employees 3732.4 4351.6 4421.4 4469.5 101.1
Including:
– underemployed 59.2 50.6 40.4 33.1 81.9
– public works partici-
72.5 220.9 194.0 148.2 76.4
pants
Unemployed 469.4 234.6 191.7 172.1 89.8
Inactive:
– seeking a job but
10.3 6.9 6.8 8.0 117.6
unavailable
– wishes to work and is
200.8 128.5 120.6 104.0 86.2
available
Source: LFS, CSO.
In spite of the decrease, the potential labour reserve still significantly exceed-
ed the number of vacancies in 2018; however, the geographical (and probably
also structural) mismatch between the two remains considerable (Figure 6).
This may be remedied by a boost in domestic migration, which is hindered
by the fact that accommodation costs are the highest at locations with the
most employment opportunities.
Figure 6: Labour reserve and labour demand by county, 2018
50 000
40 000
30 000
20 000
10 000
0

Number of job vacancies

relative to potential labor
reserve (%)
6.4 – 11.8
11.9 – 19.8
19.9 – 36.9
37.0 – 67.6

Total number of labour reserves

Number of job vacancies
Source: LFS, CSO, vacancy statistics.

WAGES, INCOME FROM WORK AND LABOUR COSTS

The insufficient labour supply in the economy enhances the bargaining power
of employees, which had a strong impact on changes in wages over the past
two years. After a 12.8 per cent increase in gross earnings in 2017, the wages

27
Tamás Bakó & Judit Lakatos

of people working at enterprises with at least 5 employees, state-funded insti-

tutions and non-profit organizations designated to provide data increased by
another 11.3% in 2018. In addition to economic trends, Government meas-
ures also contributed to this high growth rate. As a result of wage convergence,
the minimum wage is increasing continuously, although in 2018 less than the
average wage increase, which resulted in low-paid workers falling behind. Not
unrelated to the competition for labour, the wages of employees at 200 state-
owned businesses continued to increase in 2018 (by 12–13 per cent on aver-
age, in line with the third phase of the three-year wage agreement concluded
in 2016) and certain staff groups of state-funded institutions enjoyed wage
correction measures also this year. 1.1 percentage point of the 11.3 percent
increase was due to a decrease in the number of public works participants to
two-thirds of the previous year’s figure.
On 1 January 2018, the minimum wage rose by 8 per cent to HUF 138
thousand, which was lower than the 15 percent rise over the previous year.
At the same time, the guaranteed minimum wage applicable to skilled work-
ers increased by 12 per cent (following an outstanding 25 percent rise of the
previous year) to HUF 180.5 thousand. The increase in the minimum wage
has a direct impact on the wages of low-earners and therefore it is mostly felt
in sectors where the share of minimum-wage earners is traditionally signifi-
cant. Earlier studies showed that at enterprises with fewer than 10 employ-
ees the average wage is very near the minimum wage, accordingly its increase
substantially contributes to cleaning up the economy. Recent minimum wage
increases had a peculiar effect on wages in the public sector. Since the base sal-
ary has been the same for a decade in several segments of the sector, salaries
in the lowest wage categories had to be adjusted to the (guaranteed) mini-
mum wage. This partly uses up funds dedicated to wage development in the
segment and also equalizes earnings. The indirect effect entails that the mini-
mum wage increase also raises higher wages so that the wage ratio is preserved,
at least in segments where results enable employers to do so and where they
are forced by the competition for employees. State-funded institutions have
far fewer means to avoid wage compression than enterprises and in this way
wages may tend toward one another more easily.
The average gross earnings at enterprises with at least 5 employees in the
business sector (excluding the few thousand public workers employed here)
were HUF 342.2 thousand, 10.8 per cent up on the previous year (Figure 7).
Among segments dominated by the business sector, earnings grew more than
the average, by 15.7 per cent in real estate activities, followed by a 14 percent
13 This is one of the segments growth in administrative and support services where, among others, temporary
where employing workers at agencies are classified, which play an increasingly important role in supplying
the minimum wage and paying
the rest cash-in-hand is rather the necessary workforce. The minimum wage increase in itself significantly
common. boosted wages in construction.13 In addition, the increasing lack of skilled

28
 The Hungarian labour market in 2018

workers, coupled with excess demand for construction capacities, also influ-
enced the level of earnings. These factors resulted in a 12.9 percent increase in
earnings, but even so, construction was the second worst-paying segment in
the business sector after accommodation and catering (according to earnings
statistics), with an average gross earnings of HUF 254.7 thousand. Earnings
in the transport and storage sector grew only by 11.2 per cent despite the fact
that the employees of large state-owned employers (MÁV – the state railway,
Hungarian Post and regional transport companies) received an average increase
of 12–13 per cent under multi-annual wage agreements. In manufacturing, the
segment with the highest number of employees, the growth rate of earnings
has been similar to that of the entire business sector; within this, the earnings
growth rate of individual industry branches ranged from 4.8 per cent (pharma-
ceutical)14 to 12.5 per cent (other manufacturing and basic metals). A similarly
average (11.8 percent) increase was reported in trade. Wages were boosted by
the increasing minimum wage (primarily the guaranteed minimum wage) in
small rural shops and by successful wage negotiations due to labour shortages
in large retail chains. With a rate of increase (8.3 per cent) below the average
in the business sector, the finance and insurance industry still had the highest
gross earnings at HUF 608.2 thousand in 2018. It was followed by the ICT
industry, with an average of HUF 561.4 thousand and a growth rate of 9.5
per cent. The lowest average wage of HUF 212 thousand, only exceeding the
skilled workers’ minimum wage by its one-fifth, was reported by enterprises
employing at least five people in the accommodation and catering industry.
Figure 7: The rate of increase in gross earnings (2013 = 100 per cent)
180

Guaranteed wage minimum

160
Public workers’ wage
Minimum wage
Percent

140 Public sector

without public workers
Public Sector
120
Business Sector

100
2013 2014 2015 2016 2017 2018
Source: CSO, Monthly labour report.
14 The low rate is explained by
The growth rate of wages in the public sector, excluding public works partici- a year-on-year decrease in the
amount of non-regular earn-
pants, was 9 per cent, therefore its modest advantage over the business sector, ings, which resulted in a lower
than 100 wage index in some
gained in recent years, slightly decreased in 2018. Wage adjustment measures cases in the last month of the
were implemented in various areas of the sector recently but a comprehensive quarter (at the time of bonus
payments). Regular earnings
review of the earnings and promotion scheme did not take place and the ef- increased 1.8 percentage points
fects of wage adjustments were quickly worn off. faster than total earnings.

29
Tamás Bakó & Judit Lakatos

The earnings of workers in public administration, defence and compulsory

social security are the highest within the three state budget areas, despite the
fact that these segments had the highest proportion of workers in 2018 who
were left out of the wage adjustment measures of recent years.15 The average
monthly gross earnings of the nearly 70 thousand (mostly uniformed) man-
ual workers were HUF 352.3 thousand and of the 196 thousand non-manual
workers it was HUF 425.7 thousand.
Average earnings of people working in state-funded education institutions
increased by 7.9 per cent to HUF 321.4 thousand, mainly driven by the mul-
ti-annual salary adjustment of lecturers and researchers working in higher ed-
ucation, which started in the autumn of 2016. As a result, their guaranteed
wages increased by another 5 per cent on average on 1 January 2018. In pri-
mary and secondary education, which received a significant increase in earn-
ings in 2013, it causes tensions that annual growth is not adjusted to changes
in the minimum wage and that the wage increase, which was indeed consid-
ered substantial five years ago but was linked to extra workload, has by now
lost much of its value.16
The most significant wage correction measures of the past one and a half
years were aimed at those working at state-funded institutions in human
healthcare and social care. In November 2017, earnings of specialised doc-
tors and specialised pharmacists in hospitals rose by HUF 100 thousand, those
of their colleagues without specialist qualification by HUF 50 thousand, and
also the earnings of skilled health workers increased by 12 per cent on average.
This was followed by another increase of 8 per cent on average in January 2018.
As a result, gross earnings increased by 16.6 per cent in human healthcare and
9.5 per cent in social care in 2018. Thus gross average wages in the former rose
to HUF 338.4 thousand and in the latter to HUF 237.4 thousand.17
Since 2011, a non-wage compensation has been granted to staff at state-
15 The wages of government funded institutions whose net earnings decreased due to changes in income tax
officials are also expected to and social security contribution rules in 2011 and 2012. Due to staff chang-
increase (by 30 per cent on
average) in 2019, accompanied es and wage increases, the number of recipients of the monthly HUF 8,200
by a 15 percent downsizing in compensation had decreased from 400 thousand to 78 thousand by 2018.18
2018 in order to cover the re-
lated costs and by increased Since the personal income tax rate and the employees’ contributions rates
daily work hours and reduced in 2018 were identical to those in the previous year, net earnings (excluding
duration of paid holiday, that
is by increased workload. the family tax benefit) increased at the same rate as gross earnings. Exclud-
16 Teachers were promised an- ing public works participants, average net earnings were HUF 227.6 thou-
other 30 percent pay rise to take
place in 2020. sand in businesses employing at least 5 persons and HUF 230 thousand in
17 Excluding public works par- state-funded institutions. Consumer prices rose by 2.8 per cent on average
ticipants.
18 A few thousand workers at
in 2018, resulting in an 8.3 percent improvement in real earnings. The only
non-profit organisations with significant tax change affecting a wide range of employees was the further in-
delegated public duties are
also eligible to compensation,
crease in the family tax benefit for families with two children (Table 6). As
in their case HUF 7,300. a result, they were able to deduct HUF 116 670 per child from their person-

30
 The Hungarian labour market in 2018

al income tax base in 2018, compared with HUF 100 thousand per child in
2017 (and HUF 62,500 at the launch of the scheme in 2011). Those with
one child were able to reduce their tax base by HUF 66,670, similarly to the
previous year, while parents with three or more children were able to claim
a deduction of HUF 220 thousand a month. The tax benefit can be shared
by parents and can also be deducted from social security contributions in the
case of low-income workers.
Table 6: Net and real wages taking into account the family tax benefit, 2018
Net wages Real wages Share of employees
Estimated net earn- belonging to the
ings (HUF/person/ Compared with 2017 household type
Number of depend- month)
ent children (percentage)
0 child 214,739 11.2 8.1 54.4
1 child 224,294 11.3 8.2 22.3
2 children 258,288 12.1 9.0 17.1
3 or more children 273,751 10.1 7.1 6.2
National economy,
227,975 11.2 8.2 100.0
total
Source: CSO, Monthly labour report and the microsimulation model relying on data
from the EU statistics on income and living conditions (SILC).

Income from work, including the so-called “other income from work” (with
cafeteria benefits as the largest item) was HUF 346.7 thousand in 2018. The
amount received in addition to wages (“other income from work”) amount-
ed to HUF 16.8 thousand. The growth rate of income from work was 0.1
percentage point higher (11.4 per cent) than that of wage, thus the weight of
other income from work within total income from work slightly increased
but still did not reach 5 per cent thereof. The growth rate and share of other
income from work in 2018 was particularly significant in public administra-
tion, defence and compulsory social security, where in the first half of the
year a few employees received substantial extra remuneration in the form of
Erzsébet vouchers, in addition to a bonus payment.
Increasing wages make labour more expensive, which may damage competi-
tiveness and reduce (or even zero out) the profits of enterprises. In order to
counteract negative trends in 2017, the rate of social contributions payable
by employers was reduced by another 2.5 percentage points after a decrease
of 5 percentage points last year (from 27 per cent to 22 per cent). Neverthe-
less, according to Eurostat data, the unit cost of labour, which is a generally
accepted indicator of competitiveness of production, increased in Hungary
the most among the Visegrad countries between 2010 and 2018. At the same
time, Hungarian labour is still cheap compared with eurozone countries and
was even cheaper in 2018 than the Czech or Slovakian labour in 2017, while
it was approximately equal to the Polish one.

31
Tamás Bakó & Judit Lakatos

References
CSO (2015): A kisgyermeket nevelő nők és a munkaerőpiac [Women raising young chil-
dren and the labor market]. Statisztikai Tükör [Statistical Reflections], Central Sta-
tistical Office, No. 55.
Köllő, J.–Kónya, I. (2020): How has unemployment risen – Estimate based on inter-
net searches.

32
 IN FOCUS
YOUNG PEOPLE IN EDUCATION
AND IN THE LABOUR MARKET

Edited by
Márton Csillag
Zoltán Hermann
Ágota Scharle
INTRODUCTION
Márton Csillag, Zoltán Hermann & Ágota Scharle
The chapters of In Focus review the main stages of young people’s entry into
the labour market, from acquiring their education to getting their first job.
The level of detail in each chapter is inevitably varied: those that rely on pre-
vious research can obviously offer a more thorough analysis, while others are
more descriptive. Some chapters present completely new results based on re-
search funded by the Hungarian Ministry of Finance.
The first chapter presents the main trends of the past fifteen years in edu-
cation and youth employment. The second chapter analyses school choice
and dropping out as well as the development of competences that can be at-
tained at school. The third chapter examines the channels through which
school-leavers can gain their first experience at work. Chapter four examines
the scarring effects of troubled labour market entry on future careers and ex-
amines whether low levels of local labour demand and high commuting costs
may contribute to youth unemployment. Chapter five explores the impact of
employment policies and services on youth unemployment. Chapter six pro-
vides a short review of the disadvantages Roma youth face in education and
the labour market. Chapter seven explores employers’ skills requirements, the
returns on tertiary education and the role of on-the-job training in supple-
menting the missing skills. Finally, chapter eight focuses on the occupational
and geographical mobility of youth.
Considering the recent increase in employment and the decrease in the num-
ber of NEET (Not in Education, Employment, or Training) young people,
the school to work transition seems smooth. However, the details depicted by
the chapters of In Focus suggest that, these favourable developments cannot
be attributed to the well-oiled operation of the relevant labour market insti-
tutions (see chapters 3 and 5). Instead, the underlying reason is more likely
that, due to the high demand for skilled labour, the majority of skilled youth
can find employment without support from public services. At the same time
a significant share of unskilled youth only find short-term employment with
wage subsidies or in public works. Many of the young people who drop out
after finishing primary school end up in public works or unemployment, and 1 This is indicated by the grow-
ing rate of employment of those
receive little support for continuing their studies or entering the labour market. with a secondary education
Whilst the demand for employees with at least secondary education has (ISCED 3A or 3B) and higher
education (see Tables 4.15 and
further increased,1 the rise in the average level of education has stalled among 4.16).

35
Csillag, Hermann & Scharle

new labour market entrants (see sub-chapters 2.3 and 8.2). Furthermore the
recent policy measures related to public education and higher education (for
instance the lowering of the school-leaving age, see sub-chapters 2.5 and 6.2;
the reform of vocational education, see sub-chapters 2.4 and 2.2; or the cut
in the number of state subsidised places in higher education) do not support
the accumulation of general skills.
Further improvement of the situation of young people in the labour market
over the long term may be curbed by two obstacles. On the one hand, as de-
mand is shifting towards non-cognitive skills, an increasing share of entrants
with secondary or tertiary education, and most of those with primary educa-
tion will lack the skills required by employers (Nedeloska–Quintini, 2018, see
sub-chapter 7.2). On the other hand, the youth with weak basic skills who
could easily find employment during the economic expansion, will face the
2 Those with a vocational edu-
cation have weaker basic skills risk of losing their jobs during the next crisis or due to the advancement of
than those who completed technology, and lack the ability to adapt to such changes.2 With its current,
secondary education (ISCED
3A or 3B). As they get older, limited capacities, the public employment service will not be able to offer ap-
they are more likely to work propriate support in gaining skills or finding stable jobs (chapter 5). These
in unskilled jobs (Varga, 2018),
even though they had no such problems could arise even more severely for those generations which may en-
disadvantage just after leaving
school with freshly gained vo-
ter the labour market in an unfavourable economic situation (sub-chapters
cational skills (sub-chapter 8.1). 4.1 and 4.2).
References
Nedelkoska, L.–Quintini, G. (2018): Automation, skills use and training. OECD So-
cial, Employment and Migration Working Papers, No. 202, OECD Publishing, Paris.
Varga, J. (2018): Labour mobility in Hungary. In: Fazekas, K.–Köllő, J. (eds.): The Hun-
garian Labour Market, 2017. Institute of Economics, Centre for Economic and Re-
gional Studies, Hungarian Academy of Sciences, Budapest, pp. 158–166.

36
 1 Young people in the labour market and in education...

1 YOUNG PEOPLE IN THE LABOUR MARKET AND IN

EDUCATION – CHANGES BETWEEN 2002 AND 2018
Márton Csillag, Ágota Scharle, Tamás Molnár
& Endre Tóth

The educational attainment and labour opportunities of young people deserve

significant attention for several reasons. First, the characteristics of the young
generations predict future changes in the size, composition and quality of la-
bour supply, which in turn affects economic growth. Their educational and
career choices could support the adaptation of labour supply to the changing
demands of employers. Second, school-leavers have less information and ex-
perience than older employees and require more support in finding a job. The
EU’s Youth Guarantee Programme requires (and finances) Member States to
ensure this support (EU Council, 2013). The appropriate design of these ser-
vices also requires thorough analysis. Finally, it is a well-established fact in
the international literature that long-term unemployment experienced shortly
after leaving school can lead to permanent losses in terms of employment op-
portunities and wages (e.g. Bell–Blanchflower, 2011, Burgess et al, 2003). In
Hungary, in addition to the 2008 financial crisis and the subsequent increase
in migration, public education and vocational training underwent a number
of reforms after 2010: this makes the analysis of young people’s labour mar-
ket opportunities particularly important.
Labour market trends in international comparison
Recent trends in the employment and unemployment of young people have
been more or less favourable, whilst indicators of educational attainment have
somewhat worsened, particularly in comparison to the European Union.1 The
employment rate of 15–29 year-olds significantly decreased during the crisis 1 We compared the Hungarian
of 2008–2011: way below the EU average, it was the lowest among the Viseg- data to the Visegrád Group
countries and the EU aver-
rád Group countries (Figure 1.1). Due to the fast rate of growth observed in age: the international data is
assumed to indicate improve-
recent years, this gap essentially disappeared by 2016. These tendencies ap- ment that can be attained in
ply to both genders, but in the case of men, both the decrease during the cri- theory, and we can evaluate
the developments in Hungary
sis and the subsequent increase were more prominent. Some of this may be compared to these. The calcula-
attributed to supply-side developments, as suggested by the fact that the in- tions described in the chapter
are presented in more detail in
crease after 2012 was faster for 15–29 year olds than among 30–34 year-olds.2 Csillag et al (2019).
Unemployment followed the development of the economic cycle, i.e. it slow- 2 The employment rate of men
aged 30–34 rose from 85 per-
ly increased before the crisis, soared during the crisis, and declined steadily dur- cent to 92 percent between
ing the subsequent recovery. There is little variation in this amongst the Viseg- 2012 and 2018, while the em-
ployment rate of those aged
rád Group (and the EU average), except for the Czech performance, which was 15–29 rose from 38 percent to
of a somewhat more favourable nature than in other countries in the region. 53 percent.

37
Csillag, Scharle, Molnár & Tóth

Figure 1.1: Employment rate of men and women aged 15–29, 2002–2018
Men Women
60 50

45
50

40

40
35

30 30
2002 2004 2006 2008 2010 2012 2014 2016 2018 2002 2004 2006 2008 2010 2012 2014 2016 2018
EU-28 Czechia Hungary Poland Slovakia
Note: The employed include public works participants.
Source: Eurostat.

The share of youth not in education, employment, or training (NEET) among

men aged 15–29 years developed similarly to unemployment: during the cri-
sis, it was above the averages of the European Union and the Visegrád Four,
amounting to around 14 percent, while in more recent years it declined rap-
idly, even compared to other countries, reaching 7.5 percent by 2018. The
effect of the economic cycle was visible in the case of women as well, it was
albeit weaker, and the improvement in recent years was also smaller in their
case, therefore the NEET-rate stayed high in regional comparison (Mascherini
et al, 2017). In 2018, the female NEET rate was 18.6 percent in Hungary,
whilst the EU28 average, as well as the Czech or the Polish indicators did
not exceed 16.5 percent.
As for participation in full time education, recent trends vary both across
the Visegrad countries and in the European Union. In Hungary and Slovakia,
the share of youth in full time education followed the development of the eco-
nomic cycle: during the recession it was high (around 67–68 percent), then
throughout the economic boom it began to decrease. In Hungary it dropped
to around 60–61 percent, and in Slovakia it began to increase again only in
2017. In Poland (where employment did not decline during the recession)
the share of students was decreasing until 2018, whilst in Czechia and the
European Union average, it increased continuously. The Hungarian indicator
trailed behind the EU28 average (67 percent) by 7 percent, and also behind
the Czech indicator (70 percent) by 10 percent. To summarise, whilst employ-
ment and unemployment developed favourably, the decrease in the share of
students diverges from the EU trends in an unfavourable way.

38
 1 Young people in the labour market and in education...

Factors shaping young people’s labour market outcomes

The employment rate can increase for various reasons. It is a positive develop-
ment if the increase is due to new entrants and young unemployed people find-
ing work faster, or if mothers with small children return to the labour market
sooner, as this implies that the unemployed or inactive period, which erodes
ability to work, has shortened. The role of services assisting in job search is
discussed in Chapter 5 of this volume.
The role of demographic trends influencing labour supply cannot be ne-
glected either. The decrease of birth rates can increase employment, or if the
cohort of youth in their 20ies is more numerous than the cohort of youth in
their teens, who are typically less likely to participate in the labour market.
It is less favourable if employment grows because young people drop out
of school, and fewer of them study further in secondary and higher educa-
tion (see sub-chapter 2.5), as this reduces the human capital of the affected
cohorts (and consequently their average productivity), which is expensive to
correct in adult education.
Lastly, one should also consider the forms of work that are favoured by the
expansion. If employment is growing in stable jobs that support skills develop-
ment, that is favourable both for young people’s careers and economic growth.
If the expansion is mainly in casual, temporary jobs, fixed-term contracts or
public works, that may be less favourable. This is because if young people spend
a long time in a job where there is no opportunity for either career advance-
ment or learning, this can result in lower productivity and wages through-
out their entire future career – this is discussed in sub-chapters 3.3 and 5.5.
The drastic decrease of employment in the years of the recession deserves
special attention, as that may have long lasting consequences for the genera-
tions that entered the labour market during the recession. Chapter 4 explores
this scarring effect in more detail.
Labour market status by age group
To get one step closer to understanding the observed trends, we examine the
development of employment and education by gender and age group (Fig-
ures 1.2, 1.3 and 1.4). In order to more accurately assess the improvement of
the employment indicator, we calculated the employment rate for the past 15
years using the labour force survey of the Hungarian Central Statistical Of-
fice (HCSO), excluding public works participants (as they do not work on
the primary labour market),3 as well as those who were in full-time education, 3 The proportion of youth in
and those receiving childcare who did not work. Youth not in employment, public employment is not sig-
nificant. 1–1.5 percent of those
education, or training (NEET) were defined as those who were not employed aged 17–19 work in this form, it
is the greatest among men aged
(except if in public works), and did not participate in any form of education 20–24, where the rate is around
or training (full-time or part-time, within or out of the school system). 3 percent.

39
Csillag, Scharle, Molnár & Tóth

Figure 1.2: Employment rate by age and gender, 2002–2018 (percent)

Men Women
100 80
70
80
60

60 50
40
40 30
20
20
10
0 0
2002 2004 2006 2008 2010 2012 2014 2016 2018 2002 2004 2006 2008 2010 2012 2014 2016 2018
Age 17–19 Age 20–24 Age 25–29
Source: Own calculations using the Hungarian Labour Force Survey (second quarter).
Figure 1.3: Share of full-time students, by age group, 2002–2018 (percent)
Men Women
100 100

80 80

60 60

40 40

20 20

0 0
2002 2004 2006 2008 2010 2012 2014 2016 2018 2002 2004 2006 2008 2010 2012 2014 2016 2018
Age 15–16 Age 17–19 Age 20–24 Age 25–29
Source: Own calculations using the Hungarian Labour Force Survey (second quar-
ter).
The time series shown by age group in Figures 1.2, 1.3, and 1.4, clearly de-
pict the effect of the 2008 recession on the labour market, especially on the
employment outcomes of men. During the recession, the employment rate
of men aged 20–29 decreased, the share of full-time students stayed intact,
while the NEET rate increased. In the following period of growth, the em-
ployment rate and the NEET-rate showed a rapid recovery. The indicators for
women aged 20–29 developed similarly to those for men, except that dur-
ing the recession their employment did not decrease as much. In the case of
women aged 25–29 the effects of the recession are less significant on all indi-
cators, which may be explained by the improvement of their educational at-
tainment or delayed childbearing.
There is also a clear decline, and then a reversal of the prior, fast improvement
in the level of schooling, which can be explained in part due to the booming

40
 1 Young people in the labour market and in education...

demand for labour, and in part due to policy measures (the centralization of
education, the lowering of the compulsory school-leaving age, the reform of
secondary education, the cut in publicly funded places in higher education).
In recent years, the rate of full-time students decreased in the 17–19 and the
20–24 age groups, most significantly in the case of men aged 20–24, where
the indicator dropped 10 percentage points between 2012 and 2017.
Figure 1.4: Share of youth not in employment, education or training (NEET)
by age group and gender, 2002–2018 (percent)
40

35

30

25

20

15

10

5
2002 2004 2006 2008 2010 2012 2014 2016 2018
Age 17–19 (all) Age 20–29 men Age 20–24 women Age 25–29 women
Source: Own calculations using the Hungarian Labour Force Survey (second quar-
ter).
The development in the composition of NEET youth also points to the ef-
fects of the recession: the share of those young people who have been search-
ing for a job for some time as well as those young people who are discouraged4
and inactive decreased by 25 percentage points between 2012 and 2017. In
economically disadvantageous regions, however, the rate of long-term unem-
ployed and (or) discouraged young people remained high. The reasons for this
are discussed in sub-chapters 5.1 and 6.2.
The wage returns of educational attainment and experience
There were some changes in the wage returns of education between 2002 and
2016, but the observed slight decline in returns are not large enough to ex-
plain the observed drop in enrolment in tertiary education after 2012. The
wage premium of higher education decreased – especially in the case of new
entrants – but it remained significant (Table 1.1). The relative wages of grad-
uates were influenced by numerous factors in this period, which all pointed 4 The categorization follows the
towards the narrowing of the wage premium. First, the previous expansion method of Eurofound (see also,
in greater detail in sub-chapter
in higher education and the Bologna Process increased the supply of gradu- 5.1) (Mascherini–Ledermaier,
ates, and the share of those entering the labour market after achieving a Bach- 2016).

41
Csillag, Scharle, Molnár & Tóth

elor’s degree. Second, the minimum wage and the guaranteed wage minimum
which essentially affects for those with primary and secondary education con-
tinued to increase (cf. sub-chapter 5.4). Finally, the removal of the top income
tax bracket could also slow down the increase in the average gross wages of
graduates.5 The wage returns to experience, however, increased in the case of
graduates (especially for men). Chapter 2 and sub-chapter 7.3 examine these
developments in detail.
Table 1.1: New entrants’ monthly gross real wages in the business sector,
by education, gender and work experience, 2012–2016
Men Women
0–1 years 5 years 0–1 years 5 years
of work experience
2012
Primary 102,896 108,438 (105%) 98,238 103,161 (105%)
Vocational school 106,785 115,290 (108%) 100,451 105,162 (105%)
Secondary 120,963 135,520 (112%) 117,393 127,494 (109%)
Higher 246,253 283,602 (115%) 193,447 230,372 (119%)
2016
Primary 151,854 157,430 (104%) 155,887 157,099 (101%)
Lower secondary 164,994 173,246 (105%) 155,771 159,826 (103%)
Upper secondary 179,216 194,712 (109%) 159,898 172,331 (108%)
Higher 244,920 323,837 (132%) 214,871 264,046 (123%)
Note: Percentages show the wage premia compared to new entrants (2016 = 1).
Source: PES Wage survey, own calculations.

Decomposing changes in employment and the NEET rate

The above discussed aggregated changes in the labour market and education
indicators are shaped by long-term demographical trends, policy actions and
the economic environment as well. The growth of the employment rate and
the recently seen significant decrease of the NEET-rate may be explained
not only by the economic boom for instance, but also by the (slowing) rise
in school participation.
We show the relative importance of these main processes via factor decom-
position. We compare the later years to the labour market outcomes of youth
recorded in 2002. We examine three pivotal years: 1) 2007, the last year be-
5 Before 2011, personal income
fore the recession; 2) 2012, the lowest point of the recession in terms of youth
tax was a progressive tax (in the employment; 3) 2017, the last year for which we have data available.
top bracket, 32 percent above
an annual salary of 5 million
To decompose the main factors, we used the quarterly waves of the Hun-
HUF), this was replaced by the garian Labour Force Survey and we considered five levels of educational at-
flat rate tax at 19 percent.
6 The five categories are: pri-
tainment,6 three age groups, and finally the seven (NUTS2) regions, and we
mary education, vocational performed the analysis by gender. When analysing employment, we consid-
education, general secondary, ered those in public works as unemployed (as they do not work in the la-
secondary with vocational edu-
cation, higher education. bour market), but did not count them among the NEET youth. We used the

42
 1 Young people in the labour market and in education...

Oaxaca-Blinder decomposition method, which decomposes the changes (in

percentage points) between the given pair of years into two parts. The com-
position-effect shows to what extent the difference (across two years) in the
population’s composition in terms of age, educational attainment and resi-
dence explains the change observed in the NEET- and the employment rate.
The parameter effect captures the effect of all other changes.
Findings are summarised in Tables 1.2 and 1.3, where we present the changes
in the NEET rate and the employment rate calculated in percentage points.
We portray, again in percentage points, the contribution of changes in the
composition of youth, particularly the changes in the composition of young
people’s educational attainment, as well as what can be attributed to other
economic factors (this is shown by the parameter effect).
Tables 1.2: Decomposing the employment rate of the population aged 16–29,
2002–2017 (percentage points)
Men Women
2007 2012 2017 2007 2012 2017
Total difference –3.91 –14.06 –0.42 –4.36 –7.52 –0.09
Composition effect –2.93 –6.25 –4.55 –0.89 –1.69 –1.26
of which: education –2.61 –4.54 –4.38 –0.69 –0.80 –1.20
Parameter effect –0.98 –7.82 4.13 –3.47 –5.84 1.17
Source: Own calculations using the Hungarian Labour Force Survey (second quar-
ter).
Table 1.3: Decomposing the NEET-rate of the population aged 15–29,
2002–2017 (percentage points)
Men Women
2007 2012 2017 2007 2012 2017
Total difference –3.32 0.95 –5.78 –5.53 –4.09 –7.47
Composition effect –0.61 –1.03 –1.06 –1.34 –2.88 –2.29
of which: education –0.34 –0.61 –1.04 –1.13 –1.81 –2.22
Parameter effect 2.71 –1.99 –4.72 –4.19 –1.20 –5.18
Source: Own calculations using the Hungarian Labour Force Survey (second quar-
ter).
Changes in employment between 2002 and 2017 were governed primarily
by economic processes, while – especially for women – changes in the edu-
cational composition of youth also played a minor role.7 7 Throughout the observed
In the decrease of the NEET-rate, however, the increase in young women’s period, the composition based
average education-levels (which in itself would have decreased the NEET-rate on education continued to im-
prove, albeit slowly, which de-
by 2 percentage points) had an important role, which grew over time. At the creased the employment rate
amongst the youth through
same time, out of the 7 percentage point decrease in the NEET-rate by 2017, increasing the rate of those in
economic and social processes account for more than 5 percentage points, further education (whilst it
increases it in the age group
which contributed to improving the NEET rate within particular education- following the completion of
al and age groups as well. In the case of young men, the improvement of the higher education).

43
Csillag, Scharle, Molnár & Tóth

educational composition only reduced the NEET-rate by around 1 percent-

age point. The labour market prospects of the NEET youth are examined in
more detail in sub-chapters 5.1. and 5.2.
References
Bell, D.–Blanchflower, D. G. (2011): Young people and the Great Recession. Oxford
Review of Economic Policy, Vol. 27, No. 2, pp. 241–267.
Burgess, S.–Propper, C.–Rees, H.–Shearer, A. (2003): The class of 1981: The effect
of early career unemployment on subsequent unemployment experiences. Labour
Economics, Vol. 10, No. 3, pp. 291–309.
Csillag, M.–Molnár, T.–Scharle, Á.–Tóth, E. (2019): Fiatalok a munkapiacon és
az iskolában a 2002 és 2018 közötti időszakban. Kutatási jelentés, Budapest Intézet.
EC Council (2013): Council Recommendation of 22 April 2013 on establishing a Youth
Guarantee. OJ, C 120/01.
Hanushek, E. A.–Schwerdt, G.–Ludger Woessmann, L.–Zhang, L. (2017) General
Education, Vocational Education, and Labor-Market Outcomes over the Lifecycle.
The Journal of Human Resources, Vol. 52, No. 1, pp. 48–87.
Mascherini, M.–Ledermaier, S. (2016): Exploring the diversity of NEETs. Euro-
found, Publications Office of the European Union, Luxembourg.
Mascherini, M.–Ledermaier, S.–Vacas-Soriano, C.–Jacobs, L. (2017) Long-term
unemployed youth: Characteristics and policy responses. Eurofound, Publications
Office of the European Union, Luxembourg.
Nedelkoska, L.–Quintini, G. (2018) Automation, skills use and training. OECD So-
cial, Employment and Migration Working Papers, No. 202, OECD Publishing, Paris.

44
 2.1 The impact of reading and mathematics test results...

2 SCHOOL EDUCATION
2.1 THE IMPACT OF READING AND MATHEMATICS TEST
RESULTS ON FUTURE EARNINGS AND EMPLOYMENT
Zoltán Hermann, Dániel Horn, János Köllő, Anna
Sebők, András Semjén & Júlia Varga
Introduction
Until the recent decades, educational attainment was measured by qualifica-
tions or completed years of schooling, when researchers tried to explore its
impact on labour market prospects, because there were simply no other data
comparable across time and space. This substantially distorted findings, since
it was not possible to take into account differences in the quality of education,
its efficacy and the knowledge gained outside school. Owing to the spreading
of the standardised assessment of competences, enabling comparison across
regions and over time, in recent decades it has become possible to measure
the competence level of school leavers. As a result, later research has increas-
ingly focused on the level of cognitive skills and their impact on labour mar-
ket outcomes and earnings (cf. Hanushek, 2009).1
Studies typically find that higher test scores, implying better cognitive skills,
are associated with an easily quantifiable wage advantage throughout working
life. Research has also demonstrated that test results are strongly associated
with future earnings, even after controlling for educational background, work 1 Another major group of stud-
ies (inspired by Bowles–Gintis,
experience and other typical explanatory variables. For example, the literature 1976 and Jencks, 1979) explored
review by Hanushek compared several studies based on American data, and the impacts of non-cognitive
skills and abilities (also called
concluded that one standard deviation increase in test results at the end of personality traits) on labour
upper-secondary school translates into 12 percent higher annual earnings in market outcomes and wages,
in addition to (or sometimes
adulthood on average.2 instead of) the impacts of cog-
One could say that although the association between test results and future nitive skills. For more details
see Subchapter 7.2.
earnings is undeniable, there is no causal relationship between them and the 2 Several other studies from de-
assumed positive impact of test results on earnings is in fact due to differenc- veloped and developing coun-
tries have come to similar con-
es in innate abilities and intelligence. However, the findings of some studies clusions: there is a statistically
(including Lazear, 2003) strongly suggest that general intelligence, that is and economically significant
association between skills and
the level of innate abilities, does not entirely determine subsequent labour wages (and other labour mar-
market outcomes. Indeed, learning improves specific cognitive skills and in ket outcomes, such as employ-
ment). However, the size of the
this way the results of intelligence tests, and even if it does not enhance gen- wage premium, attributed to
a standard deviation increase
eral intelligence (cf. Ritchie et al, 2015), the improved specific cognitive skills in skills, varies a lot (from 5 per
are reflected in increasing earnings (Finnie and Meng, 2001; Fazekas, 2018). cent to 48 per cent), since the
utilized model form and con-
Therefore it is crucial to investigate to what extent cognitive skills correlate trol variables affect the results
with labour market success. This subchapter is the first in Hungary to ana- considerably.

45
Hermann, Horn, Köllő, Sebők, Semjén & Varga

lyse how standardised test results of upper-secondary school students relate

to subsequent wages in young adulthood and unemployment probabilities.
Data and methods
We used a panel of linked administrative data (Admin3) compiled in 2019
by the Databank of the Centre for Economic and Regional Studies (Sebők,
2019), which contains individual-level, anonymized data of 50 percent of the
Hungarian population between 2003 and 2017, stored in administrative da-
tabases. The database contains data indispensable for this study such as the
reading and mathematics skills of individuals assessed during their school
years, as well as their qualifications, age, wages and labour market status in
early adulthood.
The sample includes those who were tested either for mathematics or read-
ing or both in grade 10 during the National Assessment of Basic Competences
(NABC) in 2008, provided that there is information available concerning their
labour market status in 2017. This is one cohort of upper-secondary school
students, aged 25–26 in 2017.
Inactive persons and students (including the ones working and studying
simultaneously) were excluded from the sample, in addition to those whose
labour market status was unknown at the time of the survey.3 Therefore la-
bour market chances are analysed from a narrow perspective (employed versus
registered unemployed or unemployed receiving benefits).
Our earnings estimations refer to those who were employed on 15 Octo-
ber 2017 and whose actual wage data are available from the database. Our
unemployment estimations are based on the sample including registered un-
employed, recipients of unemployment benefits, participants of labour mar-
3 The latter group may include ket programmes or public works and the number of employees, respectively,
the unemployed or inactives
not receiving social benefits as of October 2017.
and may also include those Mincer earnings functions (Mincer, 1974) were estimated first, with earn-
working or studying abroad.
Thus it is not possible to iden- ings regressed on mathematics and reading test scores in grade 10, and edu-
tify the inactive population not
in education accurately.
cational attainment, gender, estimated labour market experience4 as well as
4 The estimated labour market the latter squared in 2017. Certain subsequent regressions were controlled
experience is defined as the for sectors, occupations and place of residence (district level). The depend-
number of years between the
time of obtaining the highest ent variable for the earnings regressions was the logarithm of monthly wages,
qualification (school attain- thus the results can be interpreted as percentages.5
ment) and October 2017.
5 For those who did not work In addition, estimations on unemployment probabilities are also provided,
in their job throughout Oc- where the dependent variable is a dummy variable taking the value 1 for reg-
tober 2017, a monthly wage
was calculated from the wage istered unemployed and public works participants and 0 for employees.
observed, taking into account The mathematics and reading scores of grade 10 pupils at the NABC in 2008
the number of days actually
worked. Unfortunately, the da- were standardised (i.e. converted into variables with means of 0 and variances
tabase cannot at present differ-
entiate between full-time and
of 1), therefore the coefficients in the estimations below can be interpreted as
part-time workers. changes in standard deviation.

46
 2.1 The impact of reading and mathematics test results...

Results – total sample

Figures 2.1.1 and 2.1.2 show the raw association between standardised test
scores and the logarithm of earnings, or unemployment probability, respec-
tively. The Figures reveal, on the one hand, that both mathematics and read-
ing are strongly associated with labour market outcomes, and, on the other
hand, that the connection is almost linear, just slightly diverging from the
straight line at the ends of the distribution, thus a linear form is adequate to
use in the employment and wage equations.
Figure 2.1.1: Association between test scores in grade 10 and the logarithm of earnings
Mathematics Reading
12.6 12.6

12.4 12.4
Log earnings

Log earnings
12.2 12.2

12.0 12.0

11.8 11.8
−2 −1 0 1 2 −2 −1 0 1 2
Standardized test score Standardized test score
Note: Averages calculated for 20 groups based on test scores.
Source: Authors’ compilation.
Figure 2.1.2: Association between test scores in grade 10 and unemployment
Mathematics Reading
0.25 0.25

0.20 0.20
Unemployment rate

Unemployment rate

0.15 0.15

0.10 0.10

0.05 0.05
0.00 0.00

−2 −1 0 1 2 −2 −1 0 1 2
Standardized test score Standardized test score
Note: Averages calculated for 20 groups based on test scores.
Source: Authors’ compilation.

Tables 2.1.1 and 2.1.2 show the Mincer regressions estimated for the entire
sample described above. The impact of test scores in grade 10 on earnings is
shown in Table 2.1.1 and the impact of test scores on unemployment prob-
ability is presented in Table 2.1.2. The estimated coefficients are expected to
be positive in the first case, and negative in the second.
Column (2) of Table 2.1.1 relies on mathematics scores as the sole indicator
of cognitive skills. It reveals that students achieving one standard deviation

47
Hermann, Horn, Köllő, Sebők, Semjén & Varga

higher scores in grade 10 are likely to have 8.5 per cent higher wages in the la-
bour market. Estimates in Column (3) include both mathematics and reading
test scores. The results indicate that mathematics scores are more strongly asso-
ciated with wages than reading scores. For understanding the underlying rea-
sons, a more thorough research is needed than the present descriptive study.6
Table 2.1.1: Impact of test scores in grade 10 on the logarithm
of earnings in young adulthood
(1) (2) (3) (4)
0.122** 0.137*** 0.138*** 0.0966**
Vocational school
(0.0532) (0.0529) (0.0529) (0.0471)
Secondary school 0.203*** 0.144*** 0.137*** 0.0987**
(Matura) (0.0512) (0.0510) (0.0511) (0.0454)
Higher education 0.591*** 0.454*** 0.440*** 0.280***
degree (0.0585) (0.0588) (0.0590) (0.0528)
0.0850*** 0.0752*** 0.0448***
Mathematics scores
(0.00499) (0.00622) (0.00561)
0.0166*** 0.00685
Reading scores
(0.00636) (0.00571)
11.71*** 11.81*** 11.82*** 11.70***
Constant
(0.0628) (0.0627) (0.0628) (0.0940)
Fixed effects
Sector Yes
Occupation Yes
District Yes
N 28,188 28,188 28,188 28,136
R2 0.077 0.087 0.087 0.299
Note: OLS-estimations.
Dependent variable: logarithm of monthly wages. Control variables not shown in the
table: gender, potential work experience and its square, dummy variables signifying
missing values for experience and test scores.
The reference category for qualifications is lower secondary school (8-year general
school).
Standard errors are indicated in brackets.
***
Significant at a 1 per cent, **5 per cent, *10 per cent level.
Source: Authors’ compilation.
The estimation in column (4) of Table 2.1.1 contains further control varia-
bles that may have an impact on wages regardless of test scores, for example
the sector and type of occupation and the location of work. The estimation
in Column (4), including all control variables, shows a lower value for the
coefficient of the mathematics test score achieved in grade 10, as compared
to the figure in Column (3), but the connection is still significant and strong.
6 Considering the rather strong The results of equation (4) indicate that better cognitive skills not only en-
association between the two able people to get jobs in better paid occupations, but persons with higher
test scores, it is difficult to sep-
arate the effect sizes. The cor- mathematics scores also tend to have higher earnings within a given occupa-
relation between the two test tion. It may be concluded that in Hungary cognitive skills in upper second-
scores ranges between 0.7–0.8
in Grade 10 in the various years. ary school are strongly associated with subsequent wages in early adulthood.

48
 2.1 The impact of reading and mathematics test results...

Table 2.1.2 shows the association between cognitive skills and the proba-
bility of unemployment in the total sample. Similarly to wages, mathematics
test scores achieved in grade 10 are strongly associated with the probability
of becoming unemployed. Column (2) relies on mathematics test scores as
the indicator of cognitive skills. Our results indicate that one standard de-
viation increase in results in grade 10 reduces the probability of unemploy-
ment by 2.7 percentage points in young adulthood. Column (3) includes both
mathematics and reading scores. Similarly to wages, the explanatory power of
mathematics test scores is stronger, but unemployment probabilities of indi-
viduals are also significantly explained by their reading skills. After taking lo-
cal labour market control variables into account, coefficients in Column (4)
are lower but still significant.
Table 2.1.2: Impact of test scores in grade 10 on the probability
of unemployment in young adulthood, marginal effects
(1) (2) (3) (4)
–0.0395*** –0.0397*** –0.0395*** –0.0300***
Vocational school
(0.00842) (0.00765) (0.00761) (0.00682)
Secondary school –0.0928*** –0.0664*** –0.0604*** –0.0507***
(Matura) (0.0159) (0.0147) (0.0145) (0.0129)
Higher education –0.0977*** –0.0655*** –0.0585*** –0.0531***
degree (0.00992) (0.0106) (0.0110) (0.00918)
–0.0270*** –0.0202*** –0.0146***
Mathematics scores
(0.00155) (0.00195) (0.00172)
–0.0112*** –0.00481***
Reading scores
(0.00196) (0.00173)
District fixed effect Yes
N 31,855 31,855 31,843 31,326
Estimated average
0.0674 0.0674 0.0674 0.0683
probability
Note: Probit estimates.
Dependent variable: Registered unemployed or public works participant (1) or em-
ployee (0). Control variables not shown in the table: gender, potential work experi-
ence and its square, dummy variables signifying missing values for experience and
test scores.
The reference category for qualifications is lower secondary school.
Standard errors are indicated in brackets.
***
Significant at a 1 per cent, **5 per cent, *10 per cent level.
Source: Authors’ compilation.

Results by school attainment

Tables 2.1.3 and 2.1.4 show the last specifications of the tables above, con-
taining all control variables, estimated for subgroups by school attainment.
Table 2.1.3 indicates that the association between cognitive skills and wages
is stronger among higher education graduates, compared to those with lower
qualifications, but the situation completely different for labour market chanc-

49
Hermann, Horn, Köllő, Sebők, Semjén & Varga

es: Table 2.1.4 shows that cognitive skills at upper-secondary school are far
more strongly associated with subsequent labour market outcomes (employ-
ment status, or the chance of becoming unemployed) among the low-qualified
than among higher education graduates.
Table 2.1.3: Impact of test scores in grade 10 on the logarithm of earnings
in young adulthood by school attainment
Low-education level
Secondary level Higher education level
(lower-secondary
(Matura) (BA or above)
or vocational school)
0.0192 0.0370*** 0.0758***
Mathematics scores
(0.0128) (0.00808) (0.00996)
0.00673 0.0102 0.00650
Reading scores
(0.0123) (0.00801) (0.0112)
N 4,948 14,644 8,544
R2 0.300 0.250 0.319
Note: OLS-estimation.
Dependent variable: logarithm of monthly wages. Control variables not shown in the
table: gender, potential work experience and its square, dummy variables signify-
ing missing values for experience and test scores as well as sector, occupation and
district fixed effects.
Standard errors are indicated in brackets.
***
Significant at a 1 per cent, **5 per cent, *10 per cent level.
Source: Authors’ compilation.
Table 2.1.4: Impact of test scores in grade 10 on the probability of unemployment
in young adulthood by school attainment, marginal effects
Low-education level
Secondary level Higher education level
(lower-secondary
(Matura) (BA or above)
or vocational school)
–0.0319*** –0.0136*** –0.0160***
Mathematics scores
(0.00618) (0.00274) (0.00305)
–0.0194*** –0.00201 –0.00301
Reading scores
(0.00609) (0.00274) (0.00337)
N 5,624 15,642 6,615
Estimated average
0.141 0.0699 0.0385
probability
Note: Probit estimates.
Dependent variable: Registered unemployed or public works participant (1) or em-
ployee (0). Control variables not shown in the table: gender, potential work experi-
ence and its square, dummy variables signifying missing values for experience and
test scores.
Standard errors are indicated in brackets.
***
Significant at a 1 per cent, **5 per cent, *10 per cent level.
Source: Authors’ compilation.

The above findings reveal that in the case of the low-qualified better cogni-
tive skills strongly contribute to avoiding unemployment; however, they do
not substantially contribute to higher (observed) wages. By contrast, among

50
 2.1 The impact of reading and mathematics test results...

higher education graduates, better skills result in a smaller reduction in the

probability of unemployment, but contribute more substantially to higher
wages. Considering that the standard deviation of wages is smaller at the
lower end of the qualification distribution than at the top, while the stand-
ard deviation of the probability of unemployment is much smaller at the top,
the findings of this study are by no means surprising. Nevertheless, in order
to better understand the mechanism behind the associations between cogni-
tive skills assessed at upper-secondary school and labour market outcomes in
early adulthood in Hungary, further research is warranted.
Conclusion
The impact of the quality of education on the level of cognitive skills has been
well documented. Other factors also influencing their level include – among
others – family environment, peer groups and health. The latter are not easily
influenced by social policy instruments, while the quality of education, which
may significantly improve the cognitive skills, and thus the labour market
chances of the population, is much easier to raise.
This study is the first to show in Hungary that the cognitive skills of pu-
pils assessed in grade 10 are strongly associated with labour market success in
young adulthood. Our findings indicate that one standard deviation incre-
ment in mathematics test results may increase future earnings even by 8–9
per cent on the whole, but within a given occupation or sector, the increase
is also about 5 per cent. Furthermore, better cognitive skills are likely to sub-
stantially reduce the risk of unemployment: overall, one standard deviation
increase in mathematics test results decreases the probability of unemploy-
ment by approximately 2.7 percentage points. Accordingly, the likelihood of
unemployment for adults with skills considerably (i.e. by about 2 standard
deviation units) better than the average are negligible – between 1 and 2 per
cent –, while those with substantially worse than average skills face a more
than 10 per cent probability of becoming unemployed.
When analysing cognitive skills and labour market success by qualification
level, it is seen that cognitive skills are more likely to have an impact on wages
among the highly qualified, while they are more strongly associated with un-
employment risks among the low-qualified. One of the reasons for this pat-
tern is that better cognitive skills contribute to avoiding unemployment on
the one hand (if this is an immediate threat, for example in the case of the
low-qualified), and, on the other hand, they result in higher earnings through
better occupations, higher positions and higher wages, which is mainly ob-
served among the highly qualified.

51
Hermann, Horn, Köllő, Sebők, Semjén & Varga

References
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stitute of Economics, Centre for Economic and Regional Studies, Hungarian Acad-
emy of Sciences, Budapest, pp. 149–157.
Finnie, R.–Meng, R. (2001): Cognitive Skills and the Youth Labour Market. Applied
Economics Letters, Vol. 8. No. 10. pp. 675–679.
Hanushek, E. A. (2009): The Economic Value of Education and Cognitive Skills. In:
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Jencks, C. (1979): Who Gets Ahead? The Determinants of Success in America. Basic
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Lazear, E. P. (2003): Teacher incentives. Swedish Economic Policy Review, Vol. 10.
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Mincer, J. (1974): Schooling, Experience and Earnings. New York: National Bureau of
Economic Research.
Ritchie, A. J.–Bates, T. C.–Deary, I. J. (2015): Is education associated with improve-
ments in general cognitive ability, or in specific skills? Developmental Psychology,
Vol. 51. No. 5. pp. 573–582.
Sebők, A. (2019): The Panel of Linked Administrative Data of CERS Databank. Buda-
pest Working Papers on the Labour Market, BWP-2019/2.

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 2.2 The impact of school tracks on student...

2.2 THE IMPACT OF SCHOOL TRACKS ON STUDENT

PERFORMANCE IN UPPER-SECONDARY EDUCATION
Zoltán Hermann
The effects of school tracks on student performance and disparities between stu-
dents have long been debated both in international and Hungarian literature
alike. Some studies found the academic track to increase student performance
(for example Guyon et al, 2010, Pop-Eleches–Urquiola, 2013, Horn, 2013),
while others did not report a significant effect of tracks (for example Mala-
mud–Pop-Eleches, 2010, Dustmann et al, 2012) or mainly attributed disparities
between tracks to student selection on admission (Manning–Pisch­ke, 2006).
This chapter explores the effects of the three upper-secondary educational
tracks in Hungary: general secondary school which is a pure academic track,
vocational secondary school which is a track with a mixed academic and vo-
cational orientation and vocational school.
The analysis relies on data from the National Assessment of Basic Compe-
tences (NABC). NABC measures the mathematics and reading skills of all
students in Grades 6, 8 and 10, except for students with special educational
needs and those absent from school on the day of the assessment, using a scale
that enables comparison across years and grades. Our analysis includes a sin-
gle cohort (except for Figure 2.2.1): students in Grade 8 in 2014. The sample
contains students progressing without grade repetition and those who repeat-
ed a grade only once between two assessments (Grades 8 and 10 or Grades 6
and 8), while those completing the two grades in more than three years were
excluded from the analysis.
Figure 2.2.1 presents the differences in test results in Grade 10 across school
tracks in 2017 (for skill levels see Balázsi et al, 2014). The differences are huge.
A quarter of vocational school students possess exceptionally poor skills and
have difficulties solving the easiest exercises (skill levels 1 and below 1 on
a 7-point scale), half of them perform very poorly (skill level 2 at most) and
four-fifths of them poorly (skill level 3 at most). By contrast, 60 per cent of
general secondary school students demonstrate fairly good (skill level 5 at
least), one-third of them very good (levels 6 and 7) skills at the assessment,
while poor assessment results are very rare. The majority of vocational second-
ary school students achieve average results.
Figure 2.2.2 shows the test scores in Grade 10 of students achieving similar
scores in Grade 8, broken down by track. It is obvious that students performing
better in Grade 8 also achieved proportionately higher scores in Grade 10 in
each of the three tracks. However, students achieving equal scores in Grade 8
performed better in secondary schools ending in a secondary school leaving ex-
amination (Matura) than in vocational schools. For example, students achiev-

53
Zoltán Hermann

ing 1400 points in mathematics in Grade 8 also achieve about 1400 points
in Grade 10 in vocational school on average, while those of them studying in
general or vocational secondary schools obtain nearly 1500 points. This ap-
proximately 100-point difference amounts to half a standard deviation unit
(the standard deviation of test scores in Grade 8 is 200 points). In another re-
spect, the 100-point difference is somewhat smaller than the difference between
the average students of two consecutive skill levels (skill levels cover ranges of
about 140 points). The Figure reveals that there is also a slight difference in
the performance of general and vocational secondary schools (to the advan-
tage of the former) but this is much smaller than the lag of vocational schools.
Figure 2.2.1: Skill levels of Grade 10 students by school track,a 2017 (percentage)
Mathematics Reading
100 100

80 80

60 Skill level 7 60
Skill level 6
Skill level 5
40 Skill level 4
Skill level 3 40
Skill level 2
Skill level 1
20 Below skill 20
level 1

0 0
General Vocational Vocational General Vocational Vocational
secondary secondary school secondary secondary school
school school school school
Unweighted proportions.
a

Source: Authors’ calculations based on data from NABC 2017.

Figure 2.2.2: Test scores in Grade 10 as a function of Grade 8 scores,
by school track; students in Grade 8 in 2014
Mathematics Reading
2200 2000

2000 1800
Test score, Grade 10

Test score, Grade 10

1800 1600

1600 1400

1400 1200

1200 1000
1200 1400 1600 1800 2000 2200 1000 1200 1400 1600 1800 2000
Test score, Grade 8 Test score, Grade 8
General secondary school Vocational secondary school Vocational school
Note: The dots in the Figure represent unweighted averages of groups of students of
about 1000.
Source: Authors’ calculations based on data from NABC 2014–2017.

54
 2.2 The impact of school tracks on student...

These disparities are of course not entirely attributable to upper-secondary

education: they primarily emerge in primary and lower-secondary education
and reflect the impact of selection on admission to upper-secondary school.
The question is whether there is a significant difference in the performance
of school tracks in addition to the effects of selection.
The differences revealed by Figure 2.2.2 suggest that upper-secondary school
tracks contribute to developing basic skills to varying degrees. However, these
differences may be due to differences between students, since students study-
ing in the various tracks may not only differ in earlier academic achievements.
It is possible that vocational school students have more learning difficulties,
develop more slowly or they are less motivated or diligent.
Based on Figure 2.2.3, indirect conclusions may be drawn about the ef-
fects of these factors. The Figure presents the average test scores of students
attending one of the three tracks in Grade 10 and their scores in Grade 6 and
8. In the first two grades of upper-secondary school, between Grade 8 and
Grade 10, the average test scores of students in general and vocational second-
ary schools increase, while vocational school students achieve equal reading
score and lower mathematics score in Grade 10 than in Grade 8 on average.
What is particularly interesting, is that the increase between Grades 6 and
8 is similar in the three groups. Although future vocational school students
obtained lower scores in Grade 6 and 8 than students subsequently attend-
ing tracks concluding with a Matura, their average test score increased to the
same extent over the two years. This suggests that it is not impossible to im-
prove basic skills in this group.
Figure 2.2.3: Changes in average test scores between Grades 6 and 10,
by school track; students in Grade 8 in 2014
Mathematics Reading
1800 1800

1700 1700

1600 1600

1500 1500

1400 1400

1300 1300
6. 8. 10. 6. 8. 10.
Grade Grade
General secondary school Vocational secondary school Vocational school
Note: Unweighted averages.
Source: Authors’ calculations based on data from NABC 2014–2017.

Estimates in Table 2.2.1 quantify the effect of school tracks more accurately.
These regression models include test scores in Grade 10 as dependent variables.

55
Zoltán Hermann

Estimates in Columns (1) and (3) include the following control variables: in-
dicators of prior student performance (test scores in both competence areas
in Grades 6 and 8 as well as grades at the end of Grades 8 and 6, the latter as
dummy variables) and individual characteristics of students (gender, special
education needs and severe disadvantage, educational attainment of mother
and father and the number of books possessed, all as dummy variables). The
missing values of control variables were substituted by average or typical val-
ues and missing values are denoted by independent dummy variables. Con-
trolling for these factors, coefficients show how students are likely to perform
in Grade 10 depending on the school track they attend.
Table 2.2.1: Regression estimates of the effect of school tracks on student
performance in Grade 10; students in Grade 8 in 2014
Mathematics Reading
(1) (2) (3) (4)
18.59*** 14.20*** 26.65*** 15.00***
General secondary school
(1.434) (1.437) (1.265) (1.472)
–51.28*** –71.09*** –58.87*** –63.94***
Vocational school
(2.138) (2.581) (1.803) (2.730)
General secondary school × test 0.0924*** 0.0696***
score in Grade 8 (0.00671) (0.00730)
Vocational school × score score in –0.108*** –0.0524***
Grade 8 (0.0107) (0.0116)
Number of observations 67,115 67,115 67,171 67,135
Number of schools 2,518 2,518 2,518 2,518
R2 0.702 0.706 0.729 0.703
Note: Unweighted OLS estimates.
Reference category for school track: vocational secondary school.
Control variables: test scores in both competence areas in Grades 6 and 8, the cat-
egories of grades at the end of Grades 8 and 6, gender, special education needs and
severe disadvantage, the categories of educational attainment of mother and father,
the categories of the number of books possessed as well as dummy variables denot-
ing the missing values of control variables.
Standard errors clustered for upper-secondary schools are shown in parentheses.
***
Significant at a 1 per cent, **5 per cent, *10 per cent level.
Source: Authors’ calculations based on data from NABC 2014–2017.
The findings show that a vocational school student achieves 50 points less in
mathematics and 60 points less in reading on average than a vocational sec-
ondary school student with equal performance in lower secondary school.
The advantage of a general secondary school student is 20–25 points. The
standard deviation of test scores is about 200 points, thus the lag of voca-
tional school students increases by a quarter, while the advantage of general
secondary school students increases by one-tenth of a standard deviation unit.
Estimates in Columns (2) and (4) of Table 2.2.1 also include the interaction
terms of school tracks and the test score in Grade 8 in the given competence
area. The coefficients of the interaction terms indicate that the effect of track

56
 2.2 The impact of school tracks on student...

is associated with performance in lower-secondary school: the better the test

result of a student in Grade 8, the more advantage studying in a general sec-
ondary school is likely to entail compared to a vocational secondary school,
and the more disadvantage results from studying in a vocational school. These
findings are consistent with the trend seen in Figure 2.2.2, which reveals the
difference between school tracks increases with test scores in Grade 8.
Table 2.2.2 presents these correlations from another aspect. The models
show how school track is associated with the probability of high or low per-
formance in Grade 10, after controlling for individual factors. Since low per-
formance levels are less frequent both in the total sample and among average
students than high performance, the three lowest performance level together
with below level 1 were defined as low performance, while levels 6 and 7 were
regarded as high performance. Control variables are identical to those used
in earlier estimates.
Table 2.2.2: Regression estimates of the effects of school tracks on the probability
of high and low student performance; students in Grade 8 in 2014, marginal effects
Mathematics Reading
performance level
medium-low high medium-low high
(level 0–3) (level 6–7) (level 0–3) (level 6–7)
(1) (2) (3) (4)
–0.0451*** 0.0130*** –0.0432*** 0.0409***
General secondary school
(0.00572) (0.00143) (0.00365) (0.00278)
0.142*** –0.0159*** 0.116*** –0.0512***
Vocational school
(0.00901) (0.00258) (0.00620) (0.00474)
Number of observations 67,115 67,115 67,171 67,171
Number of schools 2,518 2,518 2,518 2,518
Pseudo R2 0.4497 0.5259 0.4851 0.5114
P average 0.3518 0.1797 0.2629 0.2447
Note: Unweighted OLS estimation.
Reference category for school track: vocational secondary school.
Control variables: test scores in both competence areas in Grades 6 and 8, the cat-
egories of grades at the end of Grades 8 and 6, gender, special education needs and
severe disadvantage, the categories of educational attainment of mother and father,
the categories of the number of books possessed as well as dummy variables denot-
ing the missing values of control variables.
Standard errors clustered for upper-secondary schools are included in parentheses.
***
Significant at a 1 per cent, **5 per cent, *10 per cent level.
Source: Authors’ calculations based on data from NABC 2014–2017.

35 per cent of students achieved a low performance level in Grade 10 in math-

ematics, while 26 per cent of them did so in reading. Estimates in Column
(1) and (3) indicate that if an average student goes on to study at vocational
school instead of vocational secondary school, the probability of low perfor-
mance increases significantly: by 14.2 percentage points in mathematics and

57
Zoltán Hermann

11.6 percentage points in reading. By contrast, if continuing their studies at

general secondary school, this probability decreases by 4.5 and 4.3 percent-
age points respectively. The trend is just the opposite for high performance
[Columns (2) and (4)]. The share of high performers is 18 per cent in mathe-
matics and 24 per cent in reading. General secondary school, compared with
vocational secondary school, increases the probability of high performance
by 1.3 and 4.1 percentage points in Grade 10 respectively among average stu-
dents, while vocational school decreases the probability by 1.6 and 5.1 per-
centage points respectively.
It is important to note that the above results are not exclusively attribut-
able to the impact of school track, as it cannot be ruled out that for example
students in vocational schools take the tests less seriously, which slightly in-
creases the estimated effect of this track compared with its actual effect. Nev-
ertheless, earlier estimates that compared the data of students “just admitted”
and “just rejected” in order to control for unobserved characteristics such as
students’ motivation and aspirations found slightly weaker but very similar
trends (Hermann, 2013).
In conclusion, the results indicate that the huge differences emerging by the
end of lower-secondary education between students continuing their studies
in one of the three school tracks increase further over the first two years of
upper-secondary school. This increase is significant, especially between vo-
cational school students and students studying in the other two tracks con-
cluding with a Matura.
References
Balázsi, I.–Balkányi, P.–Ostorics, L.–Palincsár, I–Rábainé Szabó, A.–Szepesi,
I.–Szipőcsné Krolopp, J.–Vadász, Cs. (2014) Az Országos kompetenciamérés tar-
talmi keretei. Szövegértés, matematika, háttérkérdőívek. Oktatási Hivatal, Budapest.
Dustmann, C.–Puhani, P. A.–Schönberg, U. (2012): The Long-term Effects of School
Quality on Labor Market Outcomes and Educational Attainment. CReAM Discus-
sion Paper, No. 08/12.
Guyon, N.–Maurin, E.–McNally, S. (2010): The effect of tracking students by ability
into different schools. A natural experiment. Fondazione Eni Enrico Mattei, Nota
di Lavoro, No. 152.
Hermann, Z. (2013): Are you on the right track? The effect of educational tracks on stu-
dent achievement in upper-secondary education in Hungary. BWP, No. 16.
Horn, D. (2013): Diverging Performances: the detrimental effects of early selection on
equality of opportunity in Hungary. Research in Social Stratification and Mobil-
ity, Vol 32. pp. 25–43.
Malamud, O.–Pop-Eleches, C. (2010): General Education versus Vocational Train-
ing: Evidence from an Economy in Transition. Review of Economics and Statistics,
Vol. 92. No. 1. pp. 43–60.
Manning, A.–Pischke, J.-S. (2006): Comprehensive versus Selective Schooling in Eng-
land and Wales: What Do We Know? IZA Discussion Paper, No. 2072.
Pop-Eleches, C.–Urquiola, M. (2013): Going to a Better School: Effects and Behavio-
ral Responses, American Economic Review, Vol. 103. No. 4. pp. 1289–1324.

58
 2.3 Application to and admission into upper-secondary...

2.3 APPLICATION TO AND ADMISSION INTO UPPER-

SECONDARY EDUCATION
Zoltán Hermann & Júlia Varga
The progression into secondary education has a fundamentally important effect
on the future educational path of students, and consequently, their future career
path in the labour market and their success (see for example Kézdi et al, 2008;
Horn, 2014, Makó–Bárdits, 2014, Hajdú et al, 2015). In past years, significant
changes have been made in the schedules and contents of vocational training
schools,1 which – combined with the changes in the labour market environ-
ment – may influence students’ plans and opportunities regarding further edu-
cation. In this subchapter, we present how the proportion of those applying to,
and those admitted into, particular types of secondary schools changed between
2005 and 2017, and how the proportion of those applying to, and those admit-
ted into, schools offering vocational training changed, by vocational groups.
The analysis is based on the data of the Information System – Secondary
School Admission Database (KIFIR) on the period between 2005 and 2017.
This database contains the application and admission data of students applying
for secondary education having completed the eighth grade of elementary school
(or the fourth or sixth grade, in the case of eight- and six-year secondary schools).
Secondary schools rank applicants based on their elementary school and en-
trance examination results. Taking these, and application priorities into consid-
eration, a central admission algorithm determines which student gets admitted 1 One change worth highlight-
into which school. A small number of students do not get admitted into any ing is that the length of train-
ing in vocational secondary
of the schools via this application and admission process, because they applied schools (named vocational
only to schools that either rejected them outright or filled their numbers with schools until 2016) has been re-
duced to three years in 2013, re-
applicants that ranked higher. These students will look for a school where they placing the former 4- or 5-year
can continue their studies personally, but this database does not contain the training period. Since 2016, the
proportion of vocational con-
results of that process. In the analysis, we presumed that these students would tent in the training material
study in the school type with the lowest prestige in their application list, but and of professional practice/
traineeship has increased sig-
at the same time, this group is not included in the analysis regarding the voca- nificantly in vocational gram-
tional groups. Their rate dropped from 8 percent in 2005 to 3 percent in 2017. mar schools (named vocational
secondary schools until 2016),
In the analysis, we present rates relative to the number of students studying while the hours of general edu-
in secondary education in the given school year. This is the sum of all appli- cation subjects have seen a de-
crease. First, state-managed
cants who had completed the eighth grade of secondary school (presuming vocational training institutions
were transferred to the Minis-
that students who did not get admitted anywhere in the first round of the ad- try of National Economy in
mission process would also continue to secondary education) and the num- 2015, which organised the vari-
ous institutions into vocational
ber of students who gained admission to eight- or six-year secondary schools. training centres. Then in 2018,
We did not take into consideration students applying unsuccessfully to eight- the Ministry of Innovation and
Technology was appointed to
or six-year secondary schools, as they will go into secondary education after be in charge of secondary vo-
completing the eighth grade of elementary school. cational training institutions.

59
Zoltán Hermann & Júlia Varga

Figure 2.3.1 shows the changes in the shares of school types in secondary
education (figure on the left); the proportion of students who were not ad-
mitted into the school type designated as their first choice in their priority
list; and the type of school they were admitted into instead of the school type
designated as their first choice in their priority list (figure on the right). After
2012, the rate of applications to vocational schools started decreasing follow-
ing the stagnation observed between 2005 and 2011, and by 2017, less than
a quarter of students wanted to get admitted into this school type. The rate
of applications to general secondary schools grew between 2009 and 2011,
and then continued to grow after 2012; this is the school type in which the
highest number of students wish to continue their studies. Vocational sec-
ondary schools are considered decreasingly popular among students. The
rate of students applying to this type of school decreased both in 2016 and
2017. The majority of students can continue their studies in the school type
of their choice (right side of Figure 2.3.1). The rate of those who only man-
aged to gain admission into a vocational school instead of a general or voca-
tional secondary school that provides a secondary school diploma decreased
from 5 percent in 2005 to 2 percent in 2017, and the rate of those who had
no choice but to study in a vocational secondary school instead of a general
secondary school decreased from 4 percent to 2 percent.
Figure 2.3.1: The proportion of those continuing their education at the secondary level and those who were not
admitted into the school type designated as their first choice in their priority list,
by school type, 2005–2017 (percentage)
Those who were not admitted into the school type
Those continuing their education at the secondary level designated as their first choice in their priority list
45 6
5
40
4
35 3
30 2
1
25
0
2005 2007 2009 2011 2013 2015 2017 2005 2007 2009 2011 2013 2015 2017
General secondary Vocational Vocational Instead of General secondary school: Vocational secondary school
school secondary school school Instead of Secondary school: Vocational school

Source: Authors’ compilation.

A significantly higher proportion of females study in general secondary schools

than males, while a higher proportion of males continue their studies in vo-
cational secondary schools and vocational schools. After 2012, the growth of
the proportion of males continuing their studies in general secondary schools
began to speed up, and the composition of the school types of males continu-
ing their education after elementary school has been significantly rearranged.
In 2017, the highest number of males continued their education in general

60
 2.3 Application to and admission into upper-secondary...

and vocational secondary schools, and the rate of those doing so in vocational
schools dropped below 30 percent (Figure 2.3.2).
Figure 2.3.2: The proportion of those continuing their studies in secondary education,
by school type and gender, 2005–2017 (percentage)
Females Males
50 50

40 40

30 30

20
20
2005 2007 2009 2011 2013 2015 2017 2005 2007 2009 2011 2013 2015 2017
General secondary school Vocational secondary school Vocational school
Source: Authors’ compilation.

Secondary vocational applications (vocational secondary school, vocational

school) were aggregated into 12 vocational groups. The changes in the pro-
portions of these can be followed in Table 2.3.1 both separately by school
type and combined. In a few vocational groups, education or training is con-
ducted only or predominantly in one of the school types (such as health care,
IT, economics, administration).
Table 2.3.1: The percentage of those continuing their studies
in vocational secondary schools or vocational schools, among those continuing
their education at a secondary level, by vocational groups, 2007, 2017
Vocational Vocational
Combined
secondary school school
Vocational group 2007 2017 2007 2017 2007 2017
Not classified 1.0 0.0 0.3 0.0 1.3 0.0
Health care 1.7 2.0 0.2 0.0 1.9 2.0
Social, education 2.2 3.8 0.3 0.7 2.7 4.5
Mechanical engineering 5.3 3.4 4.4 4.8 9.6 8.1
IT 6.1 5.7 0.6 0.3 6.7 6.0
Construction 1.0 0.7 2.9 1.5 3.9 2.2
Chemical and light industries 2.3 1.2 3.0 1.8 5.4 3.0
Economics, administration 4.9 4.2 0.2 0.2 5.1 4.4
Trade and commerce 4.5 2.4 2.5 2.0 7.0 4.4
Agriculture, food industry 2.0 1.6 3.2 3.8 5.2 5.4
Other services, public services 1.1 4.2 1.2 0.3 2.3 4.5
Transport 1.8 1.6 0.5 0.8 2.3 2.3
Vocations not listed in the Hungarian
0.0 0.0 0.01 0.0 0.01 0.0
National Qualifications Register (OKJ)
Hospitality and tourism 3.1 3.0 3.8 6.3 6.9 9.3
Source: Authors’ compilation.

61
Zoltán Hermann & Júlia Varga

Participation rates decreased in the mechanical engineering-electrical engi-

neering-electronics vocational group and the construction, chemical and light
industries and trade and commerce vocational groups. Participation increased
in the social and service industries, education, arts, hospitality, tourism and
other services, and in public services vocational groups.
These changes occurred in certain vocational groups parallel to a rearrange-
ment among school types. Thus, in the mechanical engineering, electrical en-
gineering, electronics vocational group, the rate of training programmes of-
fering a secondary school diploma shrank, just as in the trade and commerce
vocational group. In other vocational groups, such as in the social services,
education and arts vocational group, the rate of training programmes offer-
ing a secondary school diploma increased.
Overall, the rate of those continuing their studies in vocational secondary
schools decreased within the technical vocational groups, while the rate of
the trade and commerce and economic services vocational groups increased.
The share of technical vocational groups decreased in vocational schools as
well, while the rate of those continuing their studies in a human services field
increased.
Significant differences can be found between vocational groups by gender
as well, which is shown in Figure 2.3.3. While the majority of females con-
tinue their studies in the fields of trade and commerce and services, the ma-
jority of males choose a vocation within the fields of industry or IT (see more
in Csillag et al, 2019).
Figure 2.3.3: The proportion of those continuing their studies after elementary school, by gender, 2017
Females Males
Health care Health care
Social, education Social, education
Mechanical engineering Mechanical engineering
IT IT
Construction Construction
Chemical, light industries Chemical, light industries
Economics, administration Economics, administration
Trade and commerce Trade and commerce
Agriculture, food industry Agriculture, food industry
Other services Other services
Transport Transport
Hospitality, tourism Hospitality, tourism

0 5 10 15 0 5 10 15

Source: Authors’ compilation.

62
 2.3 Application to and admission into upper-secondary...

References
Csillag, M.–Greskovics, B.–Molnár, T. (2019): Girls in Hungarian vocational ed-
ucation. In: Fazekas, K.–Szabó-Morvai, Á. (ed.): The Hungarian Labour Market,
2018. Institute of Economics, Centre For Economic and Regional Studies, Hungar-
ian Academy of Sciences, Budapest, pp. 110–113.
Hajdu, T.–Hermann, Z.–Horn, D.–Kertesi, G.–Kézdi, G.–Köllő, J.–Varga, J. (2015):
Az érettségi védelmében [In defence of the Matura]. Budapesti Munkagazdaságtani
Füzetek, BWP, 2015/1.
Horn, D. (2014): The Effectiveness of Apprenticeship Training a within track compari-
son of workplace-based and school-based vocational training in Hungary. Budapest
Working Papers on the Labour Market, BWP, 2014/5.
Kézdi, G.–Köllő, J.–Varga, J. (2009): The Failures of “Uncertified” Vocational Train-
ing. In: Fazekas, K.–Köllő, J. (eds.): The Hungarian Labour Market, Review and
Analysis, 2009. Institute of Economics IE HAS–National Employment Founda-
tion, Budapest, pp. 92–144.
Makó, Á.–Bárdits, A. (2014): A pályakezdő szakmunkások munkaerő-piaci helyzete
– 2014 [Labour market success of vocational training school graduates]. MKIK Gaz-
daság- és Vállalkozáskutató Intézet, Budapest.

63
Hermann, Horn & Tordai

2.4 THE EFFECT OF THE 2013 VOCATIONAL EDUCATION

REFORM ON STUDENT ACHIEVEMENT
Zoltán Hermann, Dániel Horn & Dániel Tordai
Introduction
In the autumn of 2013 a reform of the Hungarian vocational education sys-
tem took place, the main purpose of which was to make vocational educa-
tion more practically oriented. The reform affected vocational schools, i.e. the
upper-secondary track without the final maturity exam (érettségi vizsga in
Hungarian). These changes came with the reduction in the number of theo-
retical classes, especially in the first two years. The higher tracks, general sec-
ondary education and vocational secondary education were only slightly or
no affected by the reform.
The bill that established the reforms was passed in 2011 and started to have
an effect from September 2013.1 The earlier four or five years long vocation-
al programmes which did not have the final maturity exam at the end of the
programme were replaced by three year-long dual educational programmes, in
which students had practical classes from the onset, and the opportunity of an
apprenticeship contract with a firm from 9th grade was created.2 Therefore, time
spent in practical vocational training was increased. However, both vocation-
al and general theoretical education has decreased, and especially the number
of general education classes has lessened significantly (Bükki et al, 2014). Fol-
lowing the reform, the name of the vocationally oriented tracks was changed.
Before the 2013 reform, in the four year-long programme, general educa-
tion subjects took place only in the first two years, but in those two years
both ‘Mathematics’ and ‘Hungarian Language and Literature’ were taught in
3 classes of each per week, and foreign language and science were included in
the curriculum as well. Following the 2013 reform in 9th grade both ‘Math-
ematics’ and ‘Hungarian Language and Literature’ had only 2 classes of each
per week, 1 class of each per week in 10th grade, and in the final, 11th grade
they were not included at all in the curriculum.3 So, following the reform stu-
1 The CLXXXVII. Law of 2011
on vocational education. dents had 1 less Maths and 1 less Literature class per week in 9th grade, and 2
2 The reform of 2013 was not less of each in 10th grade compared to the pre-2013 levels.
unprecedented, in a share of
vocational schools in 2010 the
As communicated to the public, the primary goal of the reform was for the
maintainer could have intro- vocational students to gain more experience in real-life workplaces, so they
duced so called “early vocation-
al” programmes, which lasted can enter the labour market more easily after their education. During the plan-
also for 3 years and had a simi- ning of the reform the German vocational education system was taken as the
lar structure to the one that was
introduced in the reform. example, where the number of general, academic classes is also minimal, and
3 CXC law of 2011 on public the emphasis is on practical training, which is mostly done by firms (Dogossy,
education, 8 th supplement of
the 51/2012. (XII. 21.) EMMI
2016). It is important to note however, that while German students start their
regulation. vocational education after attending 7155, but in certain regions even 7950

64
 2.4 The effect of the 2013 vocational education reform...

general education classes, this number in Hungary is only 5742 (Hajdu et al,
2015). So, a Hungarian student participating in vocational education spends
about two or three years less with general education subjects, than his German
counterparts. Another goal of the reform was to create an educational struc-
ture more in line with the demands of the economy, to have a more transparent
and cost-effective operation, and to keep the unprivileged students in schools
and to help them catch up. However, keeping the youth in schools was made
harder by another reform, in which the government lowered the compulsory
schooling age from 18 to 16 after 2012 for those who had completed 9th grade.4
According to the literature important differences might arise if they improve
occupationally specific skills of students graduating from public education at
the expense of their general skills. Although they may find a job that fits to
their qualifications more easily when they enter the labour market (Level et al,
2014, Ryan, 2001, van der Velden–Wolbers, 2003), this advantage in the long
run is overturned and those with a more general education find themselves
in a better position (Hanushek et al, 2017). Due to the lack of general skills,
graduates won’t be able to adapt to the changing labour market environment,
so they become unemployed more easily, or can obtain a job only for lower
wages. However, the lengthening of the duration of secondary educational
vocational programmes doesn’t necessarily provide benefits even in a younger
age group (Oosterbeek–Webbink, 2007, Hall 2016).
In this subsection we examine the effect of the 2013 reform on mathemat-
ics and reading by comparing the changes of test scores between the 8th and
10th grade of cohorts before and after the reform. Reading and mathematics
competencies are important elements of general skills, so their decrease might
mean – according to the literature – that in the long run the labour market
position of the given students might worsen.
Analysis
For our analysis we use 8th and 10th grade test scores from the National Assess-
ment of Basic Competencies programme from 2010 until 2017. From 2010
the results were evaluated on the same scale, which makes the comparison of
different years’ results possible. The effect of the reform is examined on the
cohorts which commenced their 8th grade between 2010 and 2014. The av-
erage score in the whole sample every year is around 1600, and the standard
deviation is around 200.
Our independent variable is the change in mathematics and reading scores
between 8th and 10th grade. Our sample also contains students who had to re-
peat a year on 9th or 10th grade and had to do it only once. In the case of stu-
dents repeating a year in the 10th grade we considered their earlier test result.
We excluded vocational education programmes for special education needs 4 CXC law of 2011 on public
students from the sample. education.

65
Hermann, Horn & Tordai

Figure 2.4.1 shows the average score changes from 8th to 10th grade for both
mathematics and reading for the whole sample, and then divided for men and
women. After the introduction of the reform the difference between those,
whose programme concludes with a maturity exam and those whose doesn’t,
grows already in the first year. The effect of the reform is more apparent with
mathematics test scores, where the score change of those in a vocational school
is not only smaller compared to students in the other two educational forms
(vocational secondary and general secondary school), but it is becoming small-
er even to previous values of this school type. It can be easily observed from
the figure that reading and mathematics points of cohorts affected by the re-
form worsen compared to the students of the other two schools.
Figure 2.4.1: Average test score change between the 8th and 10th grade
for different school types. 2010–2014
Whole sample
Mathematics Reading
60 80
40 60
20
40
0
20
−20
0
−40
2010 2011 2012 2013 2014 2010 2011 2012 2013 2014
Men
Mathematics Reading
80
60
40 60

20 40

0 20

−20 0

2010 2011 2012 2013 2014 2010 2011 2012 2013 2014
Women
Mathematics Reading
40 80
20 60
0 40
−20 20
−40 0

−60 −20
2010 2011 2012 2013 2014 2010 2011 2012 2013 2014
General secondary school Vocational secondary school Vocational school
Note: The year notes the year of the 8th grade test.
Source: Authors’ compilation.

66
 2.4 The effect of the 2013 vocational education reform...

We are going to estimate the effect of the reform by difference-in-differences

method. In our analysis we are going to compare the vocational school stu-
dents’ (treated group) performance change between the pre and post-reform
years with the performance change of the vocational secondary school stu-
dents (control group) in the two periods. (We basically get the same results if
we include general secondary school students in the sample.) Supposing that
all other factors affecting the test scores – including all the other education
policy changes – had a similar effect on the students in the two tracks, the
estimates show the causal effect of the reform.
The dependent variable is the test score change, i.e. the difference of 10th and
8 grade points. The main explanatory variables are the dummy variables not-
th

ing the vocational school, the after reform period, and the interaction of these
two. As a control variable we use in our estimation the first, second, and third
power of the 8th grade mathematics and reading test scores, the gender of the
students, whether the students have special educational needs or have a dis-
advantageous status, schooling of mother and father, the number of books at
home, and fixed effects concerning the cohorts and schools. Apart from the
test scores every variable is a dummy variable in the model. Missing values
were replaced by typical values and the missing values are noted by a separate
dummy variable. The effect of the reform is shown by the interaction variable
‘vocational school x reform’ (Table 2.4.1).
Table 2.4.1: The effect of the vocational school reform on test score change
between the 8th and 10th grade
Whole sample Men Women
mathematics reading mathematics reading mathematics reading
(1) (2) (3) (4) (5) (6)
Vocational school × –19.48*** –9.823*** –15.40*** –4.094** –23.81*** –13.28***
Reform (1.733) (1.494) (2.068) (1.852) (2.581) (2.105)
14.17*** 11.57*** 8.638*** –4.333*** 22.19*** 31.78***
Reform
(1.203) (1.041) (1.496) (1.354) (1.679) (1.397)
–64.97*** –73.41*** –74.53*** –81.11*** –49.82*** –62.53***
Vocational school
(1.160) (1.012) (1.372) (1.258) (1.643) (1.343)
R2 0.362 0.308 0.337 0.321 0.415 0.320
Number of observa-
199,975 200,097 112,754 112,780 87,221 87,317
tions
Number of schools 25,477 25,482 19,479 19,482 17,143 17,150
Note: Unweighted OLS estimates.
Control variables: first, second, and third power of the 8th grade test scores in both
fields, gender, special educational need and disadvantageous status, categories of
schooling of mother and father, categories of the number of books at home, catego-
ries of the 8th grade test’s year, and the dummy variables noting the missing values
of the control variables.
Standard errors clustered at the school level in parenthesis.
Significant at a ***1 per cent, **5 per cent, *10 per cent level.
Source: Authors’ compilation based on NABC data for 2010–2017.

67
Hermann, Horn & Tordai

The results confirm our conclusion, drawn based on Figure 2.4.1. After the
reform test scores decreased in both fields, but the reform had a bigger effect
on maths score changes.
Test score change of vocational schoolers between 8th and 10th grade was
19.5 points smaller due to the 2013 reform. For men this change was slight-
ly smaller (–15.4 points), for women it was bigger (–23.8). The reform had
a smaller effect on the change of reading scores, –9.8 points on the entire sam-
ple, –4.1 for men, and –13.3 for women. The cause of this difference can be
that students use their reading skills more outside the classroom than their
mathematics skills, so supposedly school has a stronger effect on the latter.
According to the estimated effects we can say that before the reform the av-
erage difference between vocational schools and vocational secondary schools
in mathematics was approximately 180, in reading 200 points. Our results
suggest that due to the effect of the reform this difference grew by more than
10% in mathematics, and by 5% in reading.
The estimation of the reform’s effect can be biased, since from 2012 the com-
pulsory schooling age was decreased from 18 years to 16 years, and therefore
the composition of students in 10th grade could have changed.5 We can expect
that mostly vocational school students older than 16 years old will fall out of
school due to this change. Since students with worse skills have a higher chance
of dropping out, we can expect the average scores of vocational schoolers to
be better after 2012 than before. So, this change can distort the estimations.
However, data shows that this is not behind the results. The reduced com-
pulsory schooling age was introduced first for those entering secondary edu-
cation in 2012. So, the cohort that was in 8th grade in 2012 was affected by
the lowered compulsory schooling age, but not by the vocational education
reform. Recalculating the estimations for the sample including only 2012 and
2013 cohorts yields basically unchanged results, so the reform decreased stu-
dents’ performance even with the same age limit.
Conclusions
To sum up, we can say that the 2013 reform worsened the mathematics and
reading skills of those studying in vocational education concluding without
the maturing exam, especially in mathematics and to a greater extent in the
5 The estimation might be bi-
case of women. Although our analysis cannot answer the question whether
ased due to a change in chang- these students can find a job more easily after school, we see that after two
ing school choice decisions, years of vocational education the general skills of students are worse after
and therefore the composition
of students in vocational and the reform than before, and that this effect is significant. Our interpretation
mixed schools changed signifi-
cantly However, this is not very
is that this deterioration happened due to the decrease in general education
likely since none of the average classes. Furthermore, it is likely that even if the more practice-oriented educa-
values of individual charac-
teristics changed to a notable tion helps students to find a job quickly following graduation, the decreasing
extent. general skills will worsen their position on the labour market in the long run.

68
 2.4 The effect of the 2013 vocational education reform...

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Tárki-Tudok (2012): Előrehozott szakképzés. Záró Tanulmány. Tudásmenedzsment
és Oktatáskutató Központ Zrt., Budapest.
Van der Velden, R. K.–Wolbers, M. H. J. (2003): The Integration of Young People
into the Labour Market: The Role of Training Systems and Labour Market Regu-
lation. In: Müller, W.–Gangl, M. (eds.): Transitions from education to work in Eu-
rope. The integration of youth into EU labour markets. Chapter 7, Oxford Univer-
sity Press, Oxford, pp. 186–211.

69
Zoltán Hermann

2.5 THE IMPACT OF DECREASING COMPULSORY

SCHOOL-LEAVING AGE ON DROPPING OUT OF SCHOOL
Zoltán Hermann
Early school leaving has profound significance for both employment and eq-
uity since the lack of upper-secondary qualification (Matura and vocational
training certificate in Hungary) results in immensely poor labour market
prospects and often leads to persistent poverty. The proportion of early school
leavers in Hungary is somewhat higher than the European average: in 2018
it was 12.5 per cent of the population aged 18–24, while the European aver-
age was 10.6 per cent (EC, 2019, p. 54). The 3.4 percentage point lag of girls
is indeed significant, whereas for boys the difference is only 0.4 percentage
points. Compared to the other Visegrad countries, Hungary has by far the
highest proportion of early school leavers (Figure 2.5.1). Moreover, while
the proportion of early school leavers decreased in most European countries
between 2009 and 2018, in Hungary it started to grow after 2015, making
it even more challenging to achieve the target of 10 per cent set by the Euro-
pean Union by 2020 and also adopted by the Hungarian government. This
Subchapter explores what role the reduction of the school-leaving age from
18 to 16 in 2012 had in this increase.1
Figure 2.5.1: The share of early school leavers in the population aged 18–24
in Hungary, in the Visegrad countries and in the EU on average, 2009–2018
14

12

10

4
2008 2010 2012 2014 2016 2018
Hungary Czech Republic Slovakia Poland EU 28
Note: Early school leaver: with a lower-secondary (ISCED 2) qualification at most
and not in education.
Source: Eurostat.
This study analyses the process of dropping out but does not directly evalu-
ate the dropout rate or early school leaving. This can only be reliably assessed
when the majority of pupils have completed upper-secondary education and
1 Act CXC of 2011 on School
anyone who has not obtained an upper-secondary qualification is unlikely to
Education. obtain one. As in Hungary a significant proportion of pupils only complete

70
 2.5 The impact of decreasing compulsory...

upper-secondary education at the age of 21–22 (Varga, ed., 2018), the im-
pact of the 2012 reform cannot yet be assessed in this respect. This Subchap-
ter examines changes in the proportion of those not in education and lack-
ing an upper-secondary qualification at a specific point in time. Since some
of the school leavers later return to continue their studies and some are not
enrolled because of switching schools, this indicator cannot be regarded as
a direct measure of the dropout rate. Thus the value of the indicator we use
is not sufficiently informative in itself but changes to it reveal the impact of
the school-leaving age reforms.
Earlier research suggests that it is advisable to examine the obtaining of
qualifications and the process of dropping out together, because they may
yield a different picture. After raising the compulsory school-leaving age to
18 in Hungary in the early 2000s, participation in education increased in the
age group 17–18 (Varga, ed., 2018); however, the share of those acquiring
an upper-secondary qualification did not increase as a result of the reform
(Adamecz-Völgyi, 2018). There is no consensus in international literature re-
garding the impact of raising the school-leaving age. Several studies found that
raising the school-leaving age increased participation rates but did not have
an impact on obtaining an upper-secondary qualification (for example Rai-
mondi–Vergolini, 2019, in Italy, Mackey–Duncan, 2013, in the United States),
while others found a positive effect in both areas (for example Wenger, 2002,
in the United States, Cabus–De Witte, 2011, in the Netherlands). Whereas
these studies investigate the impact of raising the school-leaving age, the anal-
ysis below looks at the impact of lowering the school-leaving age.
Data and methods
The analysis is based on the Admin3 dataset of the Centre for Economic and
Regional Studies containing linked administrative data, which contains in-
dividual-level data of 50 per cent of the Hungarian population in 2003 up
to 2017 (Sebők, 2019). In the dataset school enrolment status is recorded on
a monthly basis, the highest qualification of young people and the results of
pupils in the National Assessment of Basic Competences (NABC). The sam-
ple includes participants of the assessment of Grade 8 pupils between 2010 and
2013. The descriptive analysis compares these four cohorts of pupils, while the
econometric estimation only includes the 2011 and 2012 cohorts. The school-
leaving age of 16 applied to those who did not attend upper-secondary school
in the academic year of 2011/2012, that is they started upper-secondary stud-
ies in September 2012 or later. Consequently, the school-leaving age of 18
applied to 8th graders taking the test in 2010 or 2011 and the school-leaving
age of 16 applied to those in Grade 8 in 2012 or 2013. At the same time, pu-
pils in Grade 8 in 2013 were also affected by in the vocational education and
training reform (see Subchapter 2.4). Moreover, they cannot be observed for

71
Zoltán Hermann

sufficiently long time in the database. Therefore the econometric estimation

includes only two cohorts.
This study assesses how the proportion of young people not attending school
(not enrolled at a school) and lacking an upper-secondary qualification changes
in these cohorts of pupils one, two, … and five years after the competence assess-
ment in Grade 8. This essentially covers the educational trajectory of pupils af-
ter lower-secondary education, since the large majority of them complete their
lower-secondary studies in the school year of the assessment in Grade 8: grade
repetition rate at this point is below 1 per cent (Varga, ed., 2018). The sam-
ple comprises about 43–49 thousand pupils from each grade; the total num-
ber of cases at the time of the competence assessment in Grade 8 was 184,542.
In the following, a descriptive analysis of changes in the share of those not
in education and without an upper-secondary qualification and the share of
school leavers with an upper-secondary qualification is provided first. Then
probit models are used to evaluate the probability of being not in education
and without an upper-secondary qualification and that of acquiring an upper-
secondary qualification before and after the changes to the school-leaving age,
controlling for the observed characteristics of pupils. We performed an analysis
on the total sample and on a subsample of pupils with a disadvantaged family
background2 because the latter are more likely to attend vocational education
not ending in an upper-secondary school leaving examination (Matura) and
to drop out of school (see for example Fehérvári, 2015).
The proportion of young people not in education and without
a qualification and the proportion of school leavers with
a qualification
Figure 2.5.2 presents a monthly breakdown of the proportion of young people
not in education and lacking a qualification, over the years after completing
lower-secondary education, for two cohorts of lower-secondary pupils affect-
ed by the reforms of the school-leaving age and two cohorts not affected by
the reforms. The starting date is the date of the competence test taken by 8th
graders in May and the first months of subsequent school years are indicated
on the horizontal axis. The left-hand Figure presents proportions in the total
sample. It shows that approximately 4 per cent of pupils do not attend school
in September following the competence assessment, and then the difference
2 This sub-sample includes pu-
pils whose mother or father has between the cohorts affected and not affected by the reform starts to grow.
a lower-secondary qualification While before the reform the proportion of pupils not in education increases
at most or they have fewer than
50 books in the household or very slowly until the end of the third school year, a faster increase is observed
they are officially classified as from the beginning of the second school year after the school-leaving age is
severely disadvantaged. Pu-
pils defined as disadvantaged lowered to 16. At the end of the third school year, when the majority of pupils
according to the above defini-
tion account for 30 per cent of
are aged 17–18, the share of those not enrolled in education is nearly twice as
the total sample. high after the reform than before it, the difference is 5–7 percentage points.

72
 2.5 The impact of decreasing compulsory...

However, the difference starts to decrease in the fourth and fifth school years
and by 5–6 years after finishing lower-secondary education the proportion of
young people not in education and lacking a qualification is equally about 15
per cent both in the cohorts before and after the reform.
Figure 2.5.2: The proportion of those not in education
and lacking a qualification after finishing lower-secondary education
Total sample Disadvantaged pupils
0.20 0.20

0.15 0.15

0.10 0.10

0.05 0.05

0.00 0.00
1. 2. 3. 4. 5. 6. 1. 2. 3. 4. 5. 6.
School year after competence test School year after competence test
8th graders in 2010 8th graders in 2011 8th graders in 2012 8th graders in 2013
Note: The ordinal number of the school year signifies the month September.
The right-hand Figure shows the same pattern in the subsample of disadvan-
taged pupils. The trends observed are essentially identical to those in the to-
tal sample. The share of young people not attending school is much higher in
this group: by the fifth school year it is over 30 per cent.
Figure 2.5.3: The proportion of those with an upper-secondary qualification
4 and 5 years after completing lower-secondary education
Total sample Disadvantaged pupils

4th school year 4th school year

after Grade 8 after Grade 8

5th school year 5th school year

after Grade 8 after Grade 8

0.0 0.2 0.4 0.6 0.8 0.0 0.1 0.2 0.3 0.4 0.5 0.6
8th graders in 2010 8th graders in 2011 8th graders in 2012 8th graders in 2013

Figure 2.5.3 presents the share of pupils at the end of the fourth and fifth
school year who have already obtained an upper-secondary qualification. In
the first cohort affected by the school-leaving age of 16, the proportion of
those who obtained a qualification is similar to that of the preceding years.
In the cohort of 2013, there is a slight decrease at the end of the fourth year
in the total sample; however, that cohort was also affected by the vocational
education and training reform (see Subchapter 2.4) – in the case of this co-
hort data about the end of the fifth year is not observed in the dataset.

73
Zoltán Hermann

On the whole, this suggests that lowering the school-leaving age resulted in
considerably lower participation rates but it possibly did not have a major im-
pact on obtaining an upper-secondary qualification and on the “ultimate” early
school leaving rate, which is only observed after the completion of school educa-
tion. In other words, apparently, dropping out following the reform primarily
increased because it occurred earlier than in the cohorts preceding the reforms.
Table 2.5.1 uses probit regression models to assess the differences seen in
Figures 2.5.2 and 2.5.3. Each column of the table contains estimates, at a given
date, for the difference between the last cohort before the reform and the first
cohort after the reform in the probability of not attending school and lack-
ing an upper-secondary qualification, controlling for the effect of observed
individual characteristics such as gender, age at the end of Grade 8, family
background (educational attainment of parents, severe disadvantage) and
pupil performance in Grade 8 (NABC test results, mathematics grade). The
“Reform” variable denotes the cohort in Grade 8 in 2012, while the reference
category is the 2011 cohort. In the case of “not attending school”, the third
month of the school year is considered, because in the sixth school year this is
the latest date the 2012 cohort can still be observed. As for obtaining a quali-
fication, the last month of the school year is taken into account.
The estimates confirm the above description. The proportion of those not
attending school increased in the third and fourth school year the most fol-
lowing the reform both in the total sample and among disadvantaged pu-
pils, then the difference had disappeared by the sixth school year. The share
of those acquiring a qualification did not decrease: the estimates even show
an increase of 1–2 percentage points after controlling for individual factors.
The above analysis compared data from cohorts preceding and following the
reform. The results may be interpreted as to show the impact of the reform but
they are not conclusive. To what extent is this the impact of the school-leaving
age? We can get closer to answering this question if taking into account that
even in classes completing lower-secondary education in 2012 or later not
all students were affected by the reform at each date. Pupils younger than 16
were not affected but those aged 16–17 were. Cohorts aged over 18 were in-
directly affected, since some of them had already dropped out as a result of
the reform. If the differences between the cohorts indicate the impact of the
changes in the school-leaving age, it must be apparent in the 16–18 age group.
Figure 2.5.4 shows the proportions of young people not attending school
and lacking a qualification broken down by age. It is evident that in the cohorts
not affected by the reform the proportion of those not attending school starts
to grow after age 18, while after the reform it increases in the 16-year-old age
group and continues to grow until age 19. By that time, most of the difference
between the cohorts disappear. As all cohorts of 8th graders consist of pupils
of different age, the database containing data up to the end of 2017 does not

74
 2.5 The impact of decreasing compulsory...

allow the cohorts affected by the reform to be followed beyond age 19. Nev-
ertheless, Figure 2.5.4 reveals a similar picture to Figure 2.5.2 and Table 2.5.1.
Table 2.5.1: The effect of the school-leaving age reform on the status “not attending
school and lacking a qualification” and on acquiring an upper-secondary
qualification, marginal effects
Not attending school and Having an upper-second-
lacking a qualification ary qualification
2th school 3th school 4th school 5th school 6th school 4th school 5th school
year year year year year year year
at the end of November
(1) (2) (3) (4) (5) (6) (7)
Total sample
0.012q*** 0.0313*** 0.0385*** 0.00466*** 0.00105 0.0196*** 0.00512**
Reform
(0.00123) (0.00138) (0.00174) (0.00181) (0.00182) (0.00333) (0.00242)
N 91 310 91 295 91 280 91 262 91 240 91 268 91 251
p-average 0.0503 0.0728 0.121 0.147 0.150 0.641 0.790
Disadvantaged pupils
0.0257*** 0.0889*** 0.0930*** 0.0215*** 0.0102 0.0236*** 0.00320
Reform
(0.00304) (0.00391) (0.00559) (0.00635) (0.00643) (0.00699) (0.00693)
N 22,114 22,109 22,110 22,103 22,096 22,106 22,101
p-average 0.0717 0.127 0.235 0.304 0.313 0.464 0.608
Note: Probit estimates.
Dependent variable: Not attending school and lacking a qualification at a given date
(1–5) and having an upper-secondary qualification (6–7).
Control variables: Gender, age when taking the test in Grade 8, severe disadvantage,
categories of parents’ educational attainment, categories of numeracy and literacy
performance levels in the competence test in Grade 8, categories of mathematics
grades at the end of Grade 7 as well as the dummy variables for missing test results,
parents’ educational attainment and grades.
Standard errors are shown in parantheses.
Significant at a ***1 per cent, **5 per cent, *10 per cent level.
Figure 2.5.4: The proportion of those not attending school and lacking a qualification, by age
Total sample Disadvantaged pupils
0.20 0.4

0.15 0.3

0.10 0.2

0.05 0.1

0.00 0.0
16 17 18 19 16 17 18 19
Age Age
8th graders in 2010 8th graders in 2011 8th graders in 2012 8th graders in 2013

The tendency of a gradual increase after age 18 before the reform is easily ex-
plained by the fact that education was compulsory until the end of the school

75
Zoltán Hermann

year, that is somewhere between the age of 18 and 19, depending on the month
of birth. After the reform, when the school-leaving age applies to the actual
age of pupils, although there is a break at age 16, the proportion of those not
attending school also increases gradually, suggesting that they do not drop
out immediately after reaching the school-leaving age.
Table 5.2.2 uses regression models similar to those in Table 5.2.1 to assess
the differences in the probability of not attending school between cohorts
preceding and following the reform in age groups below 16 and 16–18. Es-
timates cover the period until the middle of the second school year because
this is when both age groups may be observed in significant numbers. The
proportion of those not attending school did not, in fact, increase as a result
of the reform among pupils below 16, while in the directly affected 16–18 age
group the proportion of those not in education increased. This is consistent
with the interpretation that the difference between the two cohorts is due to
the impact of the raised school-leaving age.
Table 2.5.2: The impact of the school-leaving age reform on not attending school
and lacking a qualification in two age groups, marginal effects
Total sample Disadvantaged pupils
1th school year 2th school year 2th school year 1th school year 2th school year 2th school year
April November February April November February
Reform × be- 0.00610*** 0.00189 0.00371 –0.00542 –0.00889 0.000234
low age 16 (0.0150) (0.00253) (0.00408) (0.00363) (0.00586) (0.00920)
Reform × 16– 0.0356*** 0.0186*** 0.0276*** 0.0516*** 0.0409*** 0.0628***
18 age group (0.00299) (0.00160) (0.00155) (0.00635) (0.00385) (0.00389)
Note: Probit estimates.
Dependent variables: Not attending school and lacking a qualification at a given date.
Control variables: Gender, age group, severe disadvantage, categories of parents’ edu-
cational attainment, categories of numeracy and literacy performance levels in the
competence test in Grade 8, categories of mathematics grades at the end of Grade 7
as well as the dummy variables for missing test results, parents’ educational attain-
ment and grades.
Standard errors are shown in parantheses.
Significant at a ***1 per cent, **5 per cent, *10 per cent level.

Conclusion
The results suggest that lowering the school-leaving age increased the propor-
tion of young people not attending school, particularly in the 16–18 age group.
This, however, did not seem to coincide with a substantial decrease in the
proportion of pupils acquiring an upper-secondary qualification, since most
of the pupils who dropped out below the age of 18 as a result of the reform
would have dropped out when reaching the age of 18 if compulsory school-
ing age had remained unchanged. This implies that a higher school-leaving
age in itself is not sufficient to reduce early school leaving: this requires multi-
ple education policy measures, with school-leaving age as one of the elements.

76
 2.5 The impact of decreasing compulsory...

References
Adamecz-Völgyi, A. (2018): Increased Compulsory ington, DC.
School Leaving Age Affects Secondary School Track Raimondi, E.–Vergolini, L. (2019): ‘Everyone in
Choice and Increases Dropout Rates in Vocational School’: The Effects of Compulsory Schooling Age
Training Schools. CERS-IE, BWP, 2018/1. on Drop-out and Completion Rates, European Jour-
Cabus, S. J.–De Witte, K. (2011): Does school time mat- nal of Education, Vol. 54, No. 3, pp. 471–490.
ter? On the impact of compulsory education age on Sebők, A. (2019): The Panel of Linked Administrative
school dropout. Economics of Education Review, Vol. Data of CERS Databank. Budapest Working Papers
30, No. 6, pp. 1384–1398. on the Labour Market, BWP-2019/2.
EC (2019): Education and Training Monitor 2019. Euro- Varga, J. (ed.) (2018): A közoktatás indikátorrendszere,
pean Commission. 2017. Authors: Hajdu, T.–Hermann, Z.–Horn, D.–Var-
Fehérvári, A. (2015): Lemorzsolódás és a korai iskolael- ga, J., MTA KRTK KTI, Budapest, 1 February.
hagyás trendjei. Neveléstudomány, 2015/3, pp. 31–47. Wenger, J. W. (2002) Does the Dropout Age Matter? How
Mackey, P. E.–Duncan, T. G. (2013): Does raising the Mandatory Schooling Laws Impact High School Com-
state compulsory school attendance age achieve the pletion and School Choice. Public Finance & Manage-
intended outcomes? Department of Education, Wash- ment, Vol. 2. No. 4. pp. 507–534.

K2.5 What do 17-year-olds who don’t go to school do?

János Köllő & Anna Sebők
As we have seen in subchapter 2.5, the rising trend do not proceed with education at later ages either.2
of the share of those in formal education was bro- The average NEET rate of five percent cannot be
ken in 2012, and participation dropped to the level deemed negligible, especially since it hides signifi-
of ten years before by 2016. cant regional differences (see subchapter 6.2). The
What do 17-year-olds who don’t go to school do? problem is not only that 17-year-olds who do not go
It is shown in the two panels of Figure K2.5.1. The to school do not acquire vocational or secondary
proportion of those in employment within the age school qualifications (significant numbers did not
group can be seen in the left panel, distinguishing acquire them even when the school leaving age was
(starting with May 2000) market-based employ- 18 years), but the so-called “incapacitation effect”
ment from total employment that includes public as well, that is, the fact that youth spend their time
works. It can be seen that employment rates do start at school. See the works of Machin et al (2011) and
to increase in parallel to the decrease of participa- Adamecz–Scharle (2018) on the preventive effects
tion in education; it rose from a rate of just above of this with regard to criminal activity and teen-
zero to a rate of 1.5–2%, or 2–2.5%, including pub- age pregnancy.
lic works. However, this could not offset the de-
crease in educational participation: as it is shown in
1 There is hardly any difference between the shares of
the right panel, the share of seventeen-year-olds not the genders.
in education, employment, or training rose to a rate 2 Also according to the data of the labour force survey,
of 5–6 percent, from a rate of 3 percent observed an average of less than six percent of seventeen-year-
before the lowering of the school leaving age.1 olds not in education, employment, or training par-
The rise in the share of passive 17-year-olds ticipated in non-formal training between 2011–2018.
(The rate was calculated for a longer period because
(NEETs) is a worrying development since the un- of the low number of cases.) This is approximately 0.3
employment risk of this group is very high and stays percent of the entire cohort, which does not influence
so into adulthood, as early school leavers typically the proportions shown in the figure significantly.

77
János Köllő & Anna Sebők

Figure K2.5.1: The share of 17-year-olds in employment, and not in education, employment, or training
(NEET), 1992–2018
In employment (percentage) Not in education, employment, or training (percentage)
8 15

6
10
Percentage

5
2

0 0
1992 1995 1998 2001 2004 2007 2010 2013 2016 2019 1992 1995 1998 2001 2004 2007 2010 2013 2016 2019
With public works Without public works Public works=working Public works=not working
Note: The annual rate is calculated as the arithmetic vey (but have not reached the age of 18), and have not
mean of the rates of the first, second and fourth quar- attained a level of education higher than elementary.
ters. Because of the changes in the administration of In employment: employed as defined in the ILO-
the summer holidays, the data of the third quarter OECD convention.
cannot be examined in a long time series model, and Source: Authors’ calculations, based on the version of
are a priori uncertain. The data are representative of the labour force survey of KSH handled in the Data-
those who reached the age of 17 by the date of the sur- bank of MTA KRTK.

References
Adamecz-Völgyi, A.–Scharle, Á. (2018): The Ef- the Labour Market, BWP 2018/2.
fects of Increased Compulsory School Leaving Age on Machin, S. J.–Marie, O.–Vujic, S. (2011): The Crime
the Teenage Fertility of Roma Women, a Disadvan- Reducing Effect of Education. The Economic Journal,
taged Ethnic Minority. Budapest Working Papers on Vol. (121): No. No. 552. pp. 463–484.

78
 3.1 Student work

3 GAINING WORK EXPERIENCE

3.1 STUDENT WORK
Bori Greskovics & Ágota Scharle
It usually takes some time for young people starting their careers to find
their first job and sign their first employment contract (Pastore-Zimmermann,
2019). This can be explained by several factors. On the one hand, entrants tend
to be less experienced in job search and have fewer acquaintances who can
help them find the right job, than those who have been working for several
years. On the other hand, they have little work experience, so their employ-
ment poses a greater risk to employers, especially if their expected productiv-
ity is around or below the (guaranteed) minimum wage. At the same time,
not finding a job for a long time can also permanently worsen their future
job opportunities. It is therefore particularly important to assess the forms of
work where they can gain experience while studying or after leaving school.
Full-time students can work while studying outside the framework provided
by the school: in this subchapter, we examine its prevalence based on the data
of the Hungarian Labour Force Survey.
Student work, as it can take time away from studying, does not necessar-
ily improve future employment opportunities. However, according to inter-
national literature, working outside school hours, during breaks, or for a few
hours, as well as working in a field related to their studies reduces students’
school performance less, and according to certain estimates, it improves fu-
ture employment opportunities (Nevt et al, 2018).
The share of those who work while studying is traditionally low in Hun-
gary by European standards (Bajnai et al, 2009, p. 73). Between 2003 and
2010, 1 percent of full-time students aged 15–29 worked, in the following
years 1.5 percent worked, and in recent years the proportion of those work-
ing while studying remained below 3 percent. Student work is more common
only among those who have already obtained their first degree, but even in
this special group (accounting for 2 percent of all full-time students), the
proportion of employees is only 10–15 percent (Figure 3.1.1). Those direct-
ly entering into higher education after secondary school rarely start working
before graduating: the share of employees in this group is barely 2–3 percent.
Working while studying shows a slow increase after 2011, especially among
students staying in education after vocational secondary education (Figure
3.1.1). We do not find significant differences between the sexes in the preva-
lence of student work (Figure 3.1.2). Young women continuing their studies
after their first degree worked at a higher rate than men before 2010, but be-
tween 2010 and 2016, the employment of female students declined, while that
of men increased, so the difference between the sexes decreased to a minimum.

79
Bori Greskovics & Ágota Scharle

Figure 3.1.1: Share of those working while studying full-time by completed

education, 2002–2017 (15–29 years old, per cent)
25

20 Tertiary

Vocational secondary
15
General secondary
10
Vocational school*

5 Primary or less

0
2002 2004 2006 2008 2010 2012 2014 2016
*
ISCED3C.
Source: Own calculation based on CSO Labour Force Survey.
Figure 3.1.2: Share of those working while studying full-time by completed
education and sex, 2002–2017 (15–29 years old, per cent)
30

25
Tertiary: women
20
Vocational secondary:
15 women
Tertiary: men
10
Vocational secondary:
men
5

0
2002 2004 2006 2008 2010 2012 2014 2016
Source: own calculation based on CSO Labour Force Survey (average of four quar-
ters).
Among young people leaving school, while only a few have work experience,
this experience is largely (81 per cent on average in the past 10 years) related
to their intended profession which can make their transition to work easier.1
In theory, the Labour Force Survey of the HCSO would allow a more detailed
examination of this issue, if we compared the labour market outcomes after
leaving school among the formerly employed and the non-employed. How-
ever, the low proportion of those working while studying also means that the
sample of the Labour Force Survey includes very few student workers, only
1 Geel–Backes-Gellner (2012), 150–200, per quarter. If we further narrow the group of working students to
for example, found in a Swiss those who have just finished school (in 2017, this would be 18 percent of full-
survey on graduates’ careers
that only part-time work re- time students), the number of observations drops to a few dozen. Therefore,
lated to their field of studies due to the low number of observations we are unable to examine how work-
has a positive effect on later
employment and wages. ing while studying affects post-graduate employment.

80
 3.1 Student work

It is possible that student work is inaccurately measured by population sur-

veys, especially in the case of those studying far from their homes, as in this
case the student is usually absent when the survey is conducted, and the fam-
ily member responding to the questionnaire may not be aware of the student
working, especially if it is casual. This source of error can be checked by com-
paring the share of those in employment in cases where it was the student
in full-time education herself who answered the questionnaire with those
where another family member responded. Among those who answered the
questionnaire about themselves, we found that one and a half to two percent
were employed, but even these proportions are low (on average 3 percent of
the total student population in the years examined), and the difference may
be partially due to the fact that in this group the share of young people living
separately from their parents is greater, and who therefore are presumably in
greater need of labour income.
According to large-sample population surveys conducted between 2000 and
2016, specifically limited to 15–29 year-olds, the share of those working while
studying is low as well, although the pre-2012 measurements among students
in higher education showed a continuous increase (Szőcs, 2014).2 According
to the 2016 survey, 35 percent of students who stayed in education after their
first degree worked, while 13.5 percent of students with a secondary educa- 2 According to the summary of
Szőcs (2014), in the Youth 2000
tion worked (Szanyi-F.–Susánszky, 2018). The former figure is much higher survey, only 3–5 percent, in the
2008 survey 11 percent of uni-
while the latter is similar to what we calculated based on the Labour Force versity students worked regu-
Survey, but neither reaches the levels observed in other European countries. larly in addition to studying.

References
Bajnai, B.–Hámori, Sz.–Köllő, J. (2009): The Hungarian Labour Market – A Euro-
pean Perspective. In: Fazekas, K.–Köllő, J. (eds.): The Hungarian Labour Market
Review and Analysis, 2009. IE-HAS–National Employment Foundation. Buda-
pest, pp. 44–94.
Geel, R.–Backes-Gellner, U. (2012): Earning while learning. When and how student
employment is beneficial. Labour, Vol. 26, No. 3, pp. 313–340.
Neyt, B.–Omey, E.–Verhaest, D.–Baert, S. (2018), Does student work really affect
educational outcomes? A review of the literature. Journal of Economic Surveys, Vol.
33. No. 3. pp. 896–921.
Pastore, F.–Zimmermann, K. F. (2019): Understanding school-to-work transitions.
International Journal of Manpower, Vol. 40, No. 3, 374–378. o.
Szanyi-F., E.–Susánszky, P. (2016): Iskolapadból a munkaerőpiacra – magyar fiatalok
karrierpályaszakaszainak elemzése. In: Székely, L.(ed.): Magyar fiatalok a Kárpát-
medencében. Magyar Ifjúság Kutatás, Kutatópont Kft.–Enigma 2001 Kiadó–Mé-
diaszolgáltató Kft., Budapest, pp. 207–230.
Szőcs, A. (2014): Aktív fiatalok a munkaerőpiacon. In: Szabó, A. (ed.): Racionálisan
lázadó hallgatók II. Apátia – radikalizmus – posztmaterializmus a magyar egye-
temisták és főiskolások körében. Belvedere Meridionale–MTA TK PTI, Budapest–
Szeged, pp. 252–260.

81
Dániel Horn

3.2 THE SHORT-TERM LABOUR MARKET EFFECTS OF

APPRENTICESHIP TRAINING IN VOCATIONAL SCHOOLS
Dániel Horn
The main goal of apprenticeship training is the acquisition of vocation-spe-
cific knowledge, but it can also facilitate the employment of fresh graduates.
According to the majority of studies examining primarily Western European
education systems with a dual structure, apprenticeship training makes it
easier for youth – especially youth with less favourable family backgrounds
who do not apply for higher education – to enter the labour market (Breen,
2005, Müller–Shavit, 1998, Wolter–Ryan, 2011). These studies emphasise
mostly the fact that in countries with dual education systems, that is, where
school-based theoretical education is combined with practical education
conducted at companies, the initial unemployment rate of students in voca-
tional training is lower, and young employees do higher quality work than
those in countries with non-dual vocational training. This is attributed to
the fact that apprentices, essentially, step into the labour market sooner, and
1 During the period examined, to the fact that it is easier to teach academically less successful youth the
the period of general education
in typical Hungarian general
skills that are important for the labour market in real workplace conditions.
grammar schools and vocation- From a public policy perspective, it would be important to know whether
al secondary schools was four
years, while vocational schools it is the early entry or the development of students’ skills that leads to these
only provided two years of initial differences.
general education (foundation
training) to students, with the This subchapter summarises the results of Horn (2014), which looked at the
next (typically) two years be- effects of Hungarian apprenticeship training using the Tárki–Educatio Life-
ing dedicated to preparation
for the chosen vocation. This course Survey on the 2006–2012 period (before the substantial rearrange-
structure was modified signifi- ments of the vocational training system that began in 2011).1 After the years
cantly via Act CLXXXVII of
2011, under which the length of of initial training, students in vocational schools had to participate in com-
training in vocational schools pulsory, practical vocational training, which they could choose to undertake
has been decreased to three
years, and students receive at the school, at training workshops outside of school, or at a company (or-
vocational training already ganised individually or by the school). The primary focus of this study is to
from the 9th grade. In Sep-
tember 2016, former vocational seek answers to the following question: do students of vocational schools who
schools were renamed to voca- spent their practical vocational training at private companies (i.e. apprentices)
tional secondary schools, and
former vocational secondary have better labour market chances in the short term than their companions
schools were renamed to voca- with similar characteristics who, instead of a company, spent their internship
tional grammar schools. This
paper uses the former names at school (i.e. those who did not take apprenticeships)?
of the schools, effective at the
time of data recording. Data and methodology
2 In the sample, low performing
students are over-represented. The analysis uses the database of the Life-course Survey of the Tárki Social
Both this and panel sampling
losses are corrected for by Research Institute, which followed a sample of 10,022 taken from the pop-
weighing so that the survey can ulation of eighth-grade students in 2006 for six years.2 These students were
be representative for the entire
population. surveyed in every year of their school career, plus for an additional two years

82
 3.2 The short-term labour market effects...

– in the years of their labour market entry or their further education follow-
ing the secondary level. The responses contain the monthly data of any regu-
lar work carried out during the last school year and in the two years following
graduation, thus providing us with a more or less continuous picture of the
labour market integration of students. The 2006 scores of the eighth-grade
mathematics and reading comprehension tests of the national survey on com-
petences are available for all students participating in the panel survey, as well
as data on their school careers and family backgrounds. Making use of the
variance between the distribution of company and school based training place-
ments, this subchapter attempts to estimate the effect of an internship spent
at a company on initial labour market outcomes. As there was a significant
number of students at vocational schools who conducted their internship at
the school or at the school’s training workshops, comparing similar students
provides an opportunity for analysing the labour market effect of an appren-
ticeship spent at a company instead.
The distribution of training placements at companies among the applicant
students was probably not arbitrary: companies could select from among stu-
dents, in hopes of better labour force (cf. Bertschy et al, 2009). The analysis of
the Life-course Survey suggests that application to the apprenticeship train-
ing was indeed not arbitrary, but was much more related to the characteristics
of the local labour market than to the individual characteristics of students.3
Consequently, the estimates presented below probably provide a good esti-
mate of the labour market effect of the apprenticeship training.
The relation of the apprenticeship to employment after graduation
Even though in our analysis we applied various probability models in order to
expose an association between the apprenticeship training and employment,
there was no statistically significant difference in any of the cases between the
employment probabilities of those who did and those who did not spend their
internship at a company, one year after graduation. Although there was a mi-
nor difference between the students of the two groups, the estimated effect
size was only 6 percentage points, and statistically was not significant. And
as for students entering the labour market solely (in employment or unem-
ployed), we not only got a statistically not significant result, but the result was 3 The selection between train-
less significant from a public policy perspective as well (~3 percentage points). ing placements acquired indi-
vidually versus those organised
As can be seen in Figure 3.2.1, the probability of employment one year after by the school can be examined
the completion of school in June is approximately 6 percentage points higher in a similar way among ap-
prentices. Results show that
in the case of apprentices compared to those who were not apprentices, but although a few individual char-
acteristics do have an effect of
this is not significantly different from zero. Additionally, it has become clear minor significance, they disap-
that immediately after the end of the school year, the employment probability pear when the effect of the lo-
cal labour market in considered
of both apprentices and those who did not apprentice increases significantly. (county × vocational group
Although directly after graduation the employment probability of appren- fixed effects).

83
Dániel Horn

tices is slightly higher, this significant difference disappears very quickly, one
month after graduation, and the remaining difference continues to decrease.
Figure 3.2.1: The expected employment probability of students of vocational
schools, 2011–2012
0.5

Estimated probability of employment

End of school year
0.4

0.3

0.2

0.1

0.0

Sept Nov Jan Mar May July Máj

Okt Dec Feb Apr June Aug
___ Apprentice _ _ _ Not apprentice

The form of apprenticeship training placement and the size of the

company
Examining the differences between apprentices and those who did not take
apprenticeship on the basis of how the training placement was arranged and
the size of the company, we gain insight into the mechanisms behind the
correlations as well. Apprentices trained on site at medium and large com-
panies (over 50 employees) who arranged their placements individually were
much more likely to find a regular job directly after graduation – in July and
August – than their peers with similar individual characteristics, within the
same county and vocational group. This strong significant difference, in the
case of large companies, can be explained by several factors. From one per-
spective, it is possible that large companies are much more committed to
the training of apprentices than small companies, since they typically take
a longer term view and are aware of the fact that their productivity depends
substantially on the productivity potential of the local labour force. What
contradicts this hypothesis of differing training efficiency by company size
4 This is also confirmed by is the fact that these differences are not visible in the case of training place-
the observation that students ments organised by schools. What is much more likely is that within a given
completing their traineeship at
training placements acquired industry, the difference is not between training structures, but in selection
individually are more likely to mechanisms. A plausible explanation is that it was the more motivated voca-
find a job directly after gradua-
tion than apprentices complet- tional school students who applied to large companies on an individual ini-
ing their traineeship at place-
ments organised by the school,
tiative,4 and the effect of their motivation is also visible in their labour mar-
regardless of company size. ket outcomes later on.

84
 3.2 The short-term labour market effects...

Overall, what is more likely suggested by the data is that in Hungary, there
was – even in the very short term – no difference between the labour market
success of vocational school students who did and those who did not spend
their internship at a company.
References
Bertschy, K.–Cattaneo, M. A.–Wolter, S. C. (2009): PISA and the Transition into
the Labour Market. Labour, Vol. 23, pp. 111–137.
Breen, R. (2005): Explaining Cross-National Variation in Youth Unemployment: Market
and Institutional Factors. European Sociological Review, Vol. 21, No. 2, pp. 125–134.
Horn, D. (2014): A szakiskolai tanoncképzés rövidtávú munkaerőpiaci hatásai [The
short-term labour market effects of apprenticeship training in vocational schools].
Közgazdasági Szemle, Vol. 61, No. 9, pp. 975–999.
Müller, W.–Shavit, Y. (1998): The Institutional Embeddedness of the Stratification
Process: A Comparative Study of Qualifications and Occupations in Thirteen Coun-
tries. In: Shavit, Y.–Müller, W. (eds.): From School to Work: A Comparative Study
of Educational Qualifications and Occupational Destinations. Clarendon Press,
Oxford. pp. 1–48.
Wolter, S. C.–Ryan, P. (2011): Apprenticeship. In: Hanushek, E. A.–Machin, S.–Woess-
mann, L. (eds.): Handbook of the Economics of Education. Elsevier. Vol. 3, pp.
521–576.

85
Bori Greskovics & Ágota Scharle

3.3 CASUAL AND OTHER FORMS OF WORK

Bori Greskovics & Ágota Scharle
In addition to the student work and apprenticeships examined in the previ-
ous two subchapters, forms of contracts that pose less of a risk to employers
(casual work, temporary work, fixed-term contracts) and family businesses
can also provide an opportunity to gain first experience on the labour market.
Casual or fixed-term employment makes it easier for employers to obtain in-
formation on the performance of new entrants, but it can also be detrimental
for employees if it makes it more difficult for them to move on to a more stable
job. According to international literature, it depends on the institutional envi-
ronment on the labour market whether flexible contracts act as a springboard
or a trap (Eichhorst, 2014). In highly segmented, dual labour markets (where
it is difficult to move from the secondary labour market which offers worse,
less secure work, to the primary market which offers better paid, more secure
jobs) the proliferation of fixed-term jobs is less favourable and may even lead to
a decline in wages and employment opportunities (cf. García-Pérez et al, 2019).
The share of fixed-term contracts is low in Hungary in international com-
parison: according to the Labour Force Survey, 7–9 percent of employers
had fixed-term contracts during the years of the crisis, their share decreased
to 6.5 percent between 2014–2018 (HCSO, 2019).1 Among young people,
the share of those working with such contracts was much higher than aver-
age (17 percent) in 2018, while 83 percent worked with a non fixed-term
contract (Figure 3.3.1).
Figure 3.3.1: The share of non fixed-term contracts
among 15–29 years old employees, by education, 2002–2017 (percent)
100
Tertiary
80
Vocational secondary
1 Eichhorst (2014) mentions
four European countries (Po- 60 General secondary
land, Spain, Portugal, the
Netherlands), where in 2012
the share of fixed-term con- Vocational school*
40
tracts was around 20 percent
or more among all employees; Primary: non fixed-term
the Hungarian indicator of 20 Primary: Public
around 10 percent was in the works (fixed term)
lower third of the countries.
2 Public works is a non-negligi- 0
2002 2004 2006 2008 2010 2012 2014 2016
ble part of fixed-term contracts.
In the waves of the Labour ISCED3C (with no access to tertiary education).
*

Force Survey before 2011, pub- Source: Own calculation based on CSO Labour Force Survey (average of four quarters).
lic workers can be identified
less accurately, therefore we Before and during the Great Recession, the share of non fixed-term contracts
show the share of non fixed-
term contracts in the long time
in all education groups declined somewhat, but this trend has stopped or re-
series. versed in the past few years.2 Among uneducated young people, the growing

86
 3.3 Casual and other forms of work

share of fixed-term contracts is clearly related to the expansion of public works:

in this group, an increasing amount of fixed-term contracts were signed in
the framework of public works (Figure 3.3.1).
However, the role of fixed-term contracts and other forms of contract with
less risk for employers is not negligible: in the year of leaving school, a higher
share of young workers enter into such a contract (Table 3.3.1). The share of
young people in their first job entering a non fixed-term contract was 20–30
percentage points lower than average.3 The difference is also related to the
level of education: it seems that during the crisis (before 2013), employers
concluded more fixed-term contracts with less educated entrants (at most
with vocational training or with a general secondary education), while dur-
ing the growth period they had more fixed-term contracts than those with
vocational secondary education. Among women entrants, the share of those
with fixed-term contracts is higher in both periods (and significant in almost
all education groups).
Table 3.3.1: Share of fixed-term contracts in the year of graduation among entrants
(without public works, percentage)
2008–2012 2013–2017
men women men women
Vocational school or less 33 35 37 43
General secondary 44 42 24 44
Vocational secondary 29 37 44 42
Tertiary 20 32 18 29
Note: Public works participants were excluded from both fixed term contracts and
total employment (this may induce a small upward bias in the share of fixed term
contracts between 2008 and 2012).
Source: Own calculations based on the CSO Labour Force Survey.

Casual work occurred in 1–2 percent of first jobs during the period exam-
ined; the share of new entrants to work as entrepreneurs or in the family
business was only around 2–4 percent as well (slightly higher for men and
lower for women).
Based on the above, descriptive data, it seems that among flexible forms of
work, primarily fixed-term contracts can play a significant role in facilitating
the school-work transition. Even if there is segmentation, the share of sec-
ondary jobs that do not offer progression does not yet reach the critical level 3 The lower rate may also apply
experienced in Spain or Portugal.4 Although it is true that the share of fixed- to newcomers to a given firm
(but not as entrants), this was
term contracts increased among the less educated after the crisis, this is not not examined.
necessarily the sign of increasing segmentation, even as the share of fixed-term 4 Huszár–Sik (2019) find that
contracts in the total working population has been declining since the reces- the there is indeed a second-
ary labour market in Hungary,
sion. It is also possible that as labour shortages worsen, and possibly with an however, based on their calcu-
lations, it cannot be ascertained
increase in the range of wage subsidies offered to encourage the employment if it equals or expands beyond
of young people, employers become more open to giving a chance to jobseek- public works.

87
Bori Greskovics & Ágota Scharle

ers thought of as more risky (such as long-term unemployed or Roma) with

whom they typically enter into fixed-term contracts.
References
Eichhorst, W. (2014): Fixed-term contracts. Are fixed-term contracts a stepping stone
to a permanent job or a dead end? IZA World of Labor, No. 45.
CSO (2019): Munkaerő-piaci jellemzők (2003–2018). Központi Statisztikai Hivatal, Bu-
dapest.
García-Pérez, J. I.–Marinescu, I.–Vall Castello, J. (2019): Can Fixed-term Con-
tracts Put Low Skilled Youth on a Better Career Path? Evidence from Spain. The
Economic Journal, Vol. 129, No. 620, pp. 1693–1730.
Huszár, Á.–Sik, E. (2019): Szegmentált munkaerőpiac Magyarországon az 1970-es
években és napjainkban [Segmented labour markets in Hungary in the 1970s and
today]. Statisztikai Szemle, Vol. 97, No. 3, pp. 288–309.

88
 4.1 Does the economic recession have permanent effects?

4 EARLY UNEMPLOYMENT AND LATER LABOUR MARKET

OUTCOMES
4.1 DOES THE ECONOMIC RECESSION HAVE PERMANENT
EFFECTS?
Márton Csillag
What happens if a young person enters a particularly bad labour market after
completing their studies? Can we trust that, once an economic recession has
passed, these young people will benefit as much of the fruits of the economic
recovery as their older counterparts? Or will they be in a permanently weaker
position due to the erosion of their human capital – as they are out of work
or in “bad jobs,” or in positions that do not match their knowledge? This is
the issue we are exploring here based on Hungarian data. 1 Although follow-up data ex-
Throughout our analysis, we examined the trajectory of school graduation ist at an individual level in the
Admin3 database of the CERS
cohorts.1 We included young men who entered the labour market between Databank, however education
2001 and 2015, and since we used data from 2002–2017, we followed their level in that dataset can only
be determined after 2009, and
lives for up to 15 years. Our basic question is the following: if someone gradu- thus the time series available
ated in a year and/or county which has a high unemployment rate,2 then how would have been too short.
2 Here, we used the registered
much more likely is it that they will be non-employed or have lower wages, unemployment rate of the Na-
than a similar young person, who graduated in a better year (or in a county tional Employment Service in
annual and county breakdown.
with more favourable labour market conditions), even years after a negative 3 We did not include those in
labour market shock? We used the data of the 2002–2017 CSO Labour Force the analysis who did not finish
primary school.
Survey for our analysis. Our sample included those men born between 1970 4 This has the consequence that
and 1999 who completed their studies between 2001 and 2015.3 Although if young people move to places
with better labour market con-
we know in which year the responder graduated, but we do not know where ditions after graduation, the
they lived at the time, therefore we assume that this corresponds with their estimate of the effect of un-
employment on entry will be
current residence.4 biased towards 0.
On Figure 4.1.1 we present the probability of the members of the cohorts5 5 The average unemployment
rate varied in three groups: 9.5
who finished their studies in different years – to be employed6 – depending percent in 2002–2003, 10.6
on labour market experience. Based on the figure it is clear that even though percent in 2005–2006, 14.6
percent in 2009–2010.
in the years of the recession the employment rate of young entrants was quite 6 In the following, we do not
low, the current conditions of the labour market more strongly influence the consider those in public works
jobs to be employed, as we fo-
labour market status than the unemployment rate characterizing market entry. cus on primary labour market
Therefore, for instance the employment of the cohort finishing in 2003–2004 employment. Naturally, full-
time students are not included
dropped significantly around 2008–2010 (after 4–7 years of work experience). in the sample either.
We present the results of our first, basic regression analysis on Figure 4.1.2. 7 In addition, potential labour
We used simple linear regression, where the key independent variable was the market experience, educational
attainment, and micro-region
county unemployment rate in the year of graduating, as well as its interaction of residence, as well as the
calendar year and the month
with (potential) labour market experience.7 The figure illustrates, that if the of the survey conducted were
young person left school in a year (or country) in which the unemployment included as control variables.

89
Márton Csillag

rate was 1 percentage point higher,8 then how much do the labour outcomes
of the youth change throughout the years spent on the labour market. Our
results show that the unfavourable labour market situation quite significantly
decreases the employment probability of new entrants, and although this unfa-
vourable effect later subsides, it does not disappear. This means that if a young
person entered the labour market during the recent recession, when unemploy-
ment was around 5 percentage points higher (than in previous years), then
the probability of them being employment 6–7 years later was 4 percentage
points lower. Similarly, the labour market shock increased the risk of unem-
ployment as well9 by roughly 3 percentage points even 8–10 years afterwards.
Figure 4.1.1: Youth employment in terms of labour market experience,
in three graduation cohorts (between 2003–2017)
95
90
Employment rate (%)

85
80
75
70
65
60
55
0−1 year 2−3 years 4−5 years 6−7 years
Years of experience
2003−2004 2006−2007 2009−2010
Source: Own calculation based on the CSO Labour Force Survey data 2003–2017.
Figure 4.1.2: The effects of the unemployment rate in the year of graduation on
youth labour market status in terms of labour market experience, 2002–2017
(percentage points)
market at entry effect (percentage point)

1.5
Unemployment rate of the labour

1.0

8 It should be noted, that 0.5

there is significant variance in 0.0
our key variable (the county
unemployment rate) both in −0.5
terms of time and territory.
That is, during the years of the −1.0
crisis (between 2009–2012),
the unemployment rate was on −1.5
average 5.5 percent higher than
0−1 year 2−3 years 4−5 years 6−7 years 8−10 years
in 2002. At the same time, the
rate in Borsod-Abaúj-Zemplén Years of experience
or Jász-Na g yku n-Szol nok Employed NEET Unenmployed
countries was on average 16
percentage points higher than Source: Own calculation based on the CSO Labour Force Survey data 2003–2017.
in Budapest.
9 We only included those active
As the labour market is segmented by education level (amongst other things),
in the labour market. we also examined the extent to which the lasting negative effects of the labour

90
 4.1 Does the economic recession have permanent effects?

market shock varies amongst young people with different levels of education.
According to Figure 4.2.3, the so-called scarring effect which impacts sub-
sequent employment, or even the whole career, appears mainly in the case of
those with secondary or higher education, whilst the negative effects are less
significant in the case of those with lower educational attainment.10
Figure 4.1.3: The effects of the unemployment rate in the year graduation on youth
employment, by educational attainment groups, 2002–2017 (percentage points)
0.2
market at entry effect (percentage point)
Unemployment rate of the labour

0.0
−0.2
10 In the case of unemployment
−0.4
probability, there is no such dif-
−0.6 ference for those with different
educational qualifications, only
−0.8 the negative effect on those
−1.0 with vocational education is
exceptionally high.
−1.2 11 We did this with the help
0−1 year 2−3 years 4−5 years 6−7 years 8−10 years of the 2001–2016 Wage Tariff
Years of experience Survey of NES. Sample selec-
Elementary Vocational tion was done similar to the
Secondary Tertiary above analysis. Our depend-
education school
ent variable was the logarithm
Source: Own calculation based on the CSO Labour Force Survey data 2003–2017. of gross monthly real earnings
(including 1/12 of non-regular
Next, we examined another measure of labour market success, earnings, and income). The basic equation in-
we tried to draw conclusions regarding position and the quality of the com- cluded educational attainment
(7 categories), categories of ex-
pany.11 Our results demonstrate that in general, the lasting effects of a poor perience, the country, the cal-
labour market start are negligible, as seen in the first column of Table 4.1.1. endar year. In addition to the
basic equation, we first includ-
At the same time, those with higher education were paid around 5 percentage ed the occupation (three-digit
points less even 4–6 years after the recession, than those entering a favour- FEOR code), and then indicator
variables for the identity of the
able labour market (assuming that they entered the market in a year with a 5 company.
percentage point higher unemployment rate).12 Surprisingly, this is not due 12 At the same time, it is pos-
sible that because employment
to lower position or that the young person “got stuck” at a low-wage compa- declined, selection intensified
ny.13 We also tried to find out whether those entering the labour market dur- and therefore we can only give
a lower estimate of the wage
ing the recession are “overqualified”, i.e. if they are in an occupation which effect.
is characterized by lower education-levels than theirs. Similar to the above 13 That is, in the models in
which we included occupation
results, we did not find any indication that those youth leaving education in and corporate fixed effects, the
the years of the recession would get stuck at a low quality jobs. results did not change.
Our results suggest that the scarring effects of entering the labour market 14 That is, it is possible that
the short-term effects of the
in a recession in Hungary appeared mainly in permanently lower employ- negative labour market shock
will not become permanent for
ment. Wage disadvantages emerged only for those with tertiary education.14 those with lower education, as
It should also be noted that it is possible that the negatives effects on those in their case there is no serious
depreciation of human capital.
with higher education could have been greater if the cohorts entering the la- 15 These preliminary results
bour market during the recession would not have been significantly smaller further justify a closer exami-
than the generations in the early 2000s.15 Our results echo other European nation of the careers of young
people with higher education
analyses which found the effects on wages to be smaller, but they showed that (see Chapter 8 of In Focus).

91
Márton Csillag

employment permanently decreases as a result of a poor labour market entry

situation. (See Box 4.1 for more information.)
Table 4.1.1: The effects of early-stage unemployment on wages
Total Sample Vocational school Secondary Tertiary educated
Unemployment rate (year –0.007576** –0.01099* –0.006326 –0.01395***
or entry) (0.003562) (0.006086) (0.004918) (0.004507)
Unemployment rate× 2–3 0.004923* 0.007763 0.007343* 0.004012
experience (0.002521) (0.005978) (0.004131) (0.003403)
Unemployment rate × 4–5 0.002816 0.006745 0.006205 0.003514
experience (0.002554) (0.005959) (0.004015) (0.003353)
Unemployment rate × 6–7 0.001162 0.009388 0.002933 0.001706
experience (0.002616) (0.005953) (0.004070) (0.003462)
Unemployment rate × 8–10 6.973e–04 0.009268 0.005014 0.001634
experience (0.002711) (0.005974) (0.004160) (0.003595)
R2 0.469 0.295 0.228 0.252
N 204,057 46,132 65,462 76,668
Note: The basic equation included educational attainment (7 categories), categories
of experience, the country, the calendar year. Clustered (at the level of the firm)
standard errors are displayed in brackets.
Significant at the ***1 percent level, **5 percent level, *10 percent level.
Source: Own calculation based on the NES Wage Survey data 2002–2016.

K4.1 What are the consequences of young people entering the labour market
during an economic crisis? International outlook
Endre Tóth
The scarring effect refers to those negative conse- ic and financial recession in 2008, and in order to
quences which affect young people who begin their eliminate these negative consequences, the Euro-
careers with a potential period of unemployment. pean Union introduced their Youth Guarantee Pro-
In labour economics, two different issues are exam- gramme in 2013.1
ined under this term. First: whether young people The majority of research on the scarring effect
who leave school during a recession and start their examines this phenomenon via regression model
career therefore face higher risks of early-stage un- building, analysing young people belonging to dif-
employment are permanently “scarred” by these ferent cohorts, where the key independent varia-
circumstances. Second: for those young people ble is the labour market situation of the youth’s
who experience lasting unemployment when start- place of residence at the time of leaving school. In
ing their career, does this episode have long-term
negative effects on their later career? This topic re- 1 For the implementation of this Hungary, see
emerged in the literature due to the severe econom- Subchapter 5.2.

92
 K4.1 What are the consequences of young people...

their analysis¸ the researchers do not only have to tions presents a trap for young people, because it
properly filter out differences of other origins be- has a long-term negative impact on most the ca-
tween the individuals, but they also have to deal reers of most young people (similar to unemploy-
with potentially distorting effects such as the en- ment).4 Young people from vulnerable backgrounds
dogenous relationship between unemployment and experience lower upward mobility and slower wage
the place and year of graduation, and migration. growth that those who began their careers in jobs
In order to remedy the potentially distorting ef- which match their qualifications.5 There can be two
fects, researchers are including new control vari- main explanations for the lasting negative effects
ables (for example: place of birth, unemployment of early-stage unemployment or overqualification.
measured at the start of the training). Every re- The first is the negative signalling function of ear-
search paper2 we examined drew the conclusion ly unemployment, i.e., employers view it as a sig-
that young people entering a labour market in a re- nal of lower productivity, which seriously affects
cession with high unemployment must face lasting the perception of job-seekers (Cockx–Pichio, 2011).
negative consequences. In their case, lower wages, Another possible explanation is the decline of pro-
fewer hours worked, lower quality job and higher fessional knowledge and skills due to cognitive de-
risk of unemployment can be detected even 7–15 cline, or that the acquisition of new skills is rare in
years after starting their career, compared to their low-skilled jobs.
counterparts who started working at a more for-
tunate time (Kahn, 2010). When the initial unem- 2 On the topic of the scarring effect, most of the re-
ployment rate that is one percentage point higher, search is based on North American data (Schwandt–
von Wachter, 2018, Kahn, 2010, Altonji et al, 2014,
the rate of loss of income is estimated at 6–10 per- Speer, 2016, Oreopoulos et al, 2012). But several
cent in the year of graduation by studies examining excellent studies used data from European countries
higher education degree-holders, which then slowly (Cutler et al, 2014, Liu et al, 2016, Cockx–Ghirelli,
decreases, but stays around 2–3 percent even ten 2016), and there are also studies examining multiple
years later. (Kahn, 2010, Altonji et al, 2014). Several continents and larger groups of countries (Cutler et
al, 2014, Liu et al, 2016, Cockx–Ghirelli, 2016). Most
studies have pointed out that the negative effects research based on North American data analyses ex-
may be more significant in the case of less educated clusively newly graduated young people (Kahn, 2010,
young people, who experience a more significant Altonji et al, 2014, Oreopoulos et al, 2012), but there
decrease of employment (Schwandt–von Wachter, are also studies which exclusively include those with
2018, Cockx, 2016), and amongst graduates, those lower education (Speer, 2015), or those that examine
all young people, regardless of their education.
with lower abilities (Oreopoulos et al, 2012). It seems 3 This can have very significant negative consequenc-
that stricter labour market regulation increases the es, for example, Gregg–Tominey (2005) found that
persistence of the scarring effect, with young peo- young people who experienced long-term unemploy-
ple getting “stuck” in low-paying jobs that do not ment early in their career, had earnings around 12
match their qualifications in a more rigid labour percent lower than their luckier counterparts, even
twenty years later.
market structure (Kawaguchi–Murao, 2014). Re- 4 See for example, Büchel–Mertens, 2004, Mendes de
search based on individual-level data not only ana- Oliveira et al, 2000, Baert et al, 2012, Liu et al, 2012.
lysed the effect of early-stage unemployment,3 but 5 This is in contrast to previous North American re-
also the consequences of a young person accept- sults, where accepting positions that did not match
ing a job for which they are overqualified. Stud- qualifications might have been a good choice in terms
of subsequent higher than average mobility opportu-
ies examining data from European countries with nities (i.e., it provided a kind of “springboard func-
a relatively inflexible labour market show that ac- tion”). See for example: Sicherman (1991) and Rubb
cepting a job not compatible with their qualifica- (2003).

93
Endre Tóth

References
Altonji, J. G.–Kahn, L. B.–Speer. J. D. (2016): Cash- ment. Journal of Money, Credit and Banking, Vol. 46,
ier or consultant? Entry labor market conditions, field No. 2, pp. 95–116.
of study, and career success. Journal of Labor Eco- Liu, K.–Salvanes, K. G.–Sörensen, E. (2016): Good
nomics, Vol. 34, No. 1, pp. 361–401. skills in bad times: Cyclical skill mismatch and the
Baert, S.–Cockx, B.–Verhaest, D. (2013): Overedu- long-term effects of graduating in a recession. European
cation at the start of the career: Stepping stone or trap? Economic Review, Vol. 84, pp. 3–17.
Labour Economics, Vol. 25, pp. 123–140. Mendes De Oliveira, M.–Santos, M. C.–Kiker,
Büchel, F.–Mertens, A. (2004): Overeducation, un- B. F. (2000): The role of human capital and technologi-
dereducation, and the theory of career mobility. Ap- cal change in overeducation. Economics of Education
plied Economics, Vol. 36, No. 8, pp. 803–816. Review, Vol. 19, No. 2, pp. 199–206.
Cockx, B. (2016): Do youths graduating in a recession Oreopoulos, P.–Von Wachter, T.–Heisz, A. (2012):
incur permanent losses. IZA World of Labor, No. 281, The short- and long-term career effects of graduating in
pp. 1–11. a recession. American Economic Journal: Applied Eco-
Cockx, B.–Ghirelli, C. (2016): Scars of recessions in nomics, Vol. 4, No. 1, pp. 1–29.
a rigid labor market. Labour Economics, Vol. 41, pp. Rubb, S. (2003): Overeducation: a short or long run phe-
162–176. nomenon for individuals? Economics of Education Re-
Cockx, B.–Picchio, M. (2011): Scarring Effects of view, Vol. 22, No. 4, pp. 389–394.
Remaining Unemployed for Long-Term Unemployed Schwandt, H.–Von Wachter, T. (2018): Unlucky Co-
School-Leavers., IZA Discussion Papers, No. 5937. horts: Estimating the Long-Term Effects of Entering the
Cutler, D.–Wei Huang, M.–Lleras-Muney, A. Labor Market in a Recession in Large Cross-Sectional
(2015): When does education matter? The protec- Data Sets. NBER Working Paper No. 25141.
tive effect of education for cohorts graduating in bad Sicherman, N. (1991): Overeducation in the Labor
times. Social Science & Medicine, Vol. 127, pp. 63–73. Market. Journal of Labor Economics, Vol. 9, No. 2, pp.
Gregg P.–Tominey E. (2005): TheWage Scar from 101–122.
Male Youth Unemployment. Labour Economics, Vol. Sicherman, N.–Galor, O. (1990): A Theory of Career
12, No. 4, pp. 487–509. Mobility. Journal of Political Economy, Vol. 98, No. 1,
Kahn, L. B. (2010): The long-term labor market conse- pp. 169–192.
quences of graduating from college in a bad economy. Speer, J. D. (2016): Wages, hours, and the school-to-
Labour Economics, Vol. 17, No. 2, pp. 303–316. work transition. The consequences of leaving school in
Kawaguchi, D.–Murao, T. (2014): Labor market in- a recession for less-educated men. The B.E. Journal of
stitutions and long-term effects of youth unemploy- Economic Analysis & Policy, Vol. 16, No. 1, pp. 97–124.

94
 4.2 Unemployment among labour market entrants

4.2 UNEMPLOYMENT AMONG LABOUR MARKET

ENTRANTS
Márton Csillag
If a young person does not find a (suitable) job for a lengthy period of time
at the beginning of their labour market career, it can significantly impact
their further progress. This is due to the fact that a) their human capital is
not developing – precisely at the time when their peers are learning the ba-
sics of the profession in practice, or that b) in the eyes of the employers their
unemployment (or the fact that they began working in a job for which they
were overqualified) is a negative sign. This issue has not been studied by many
researchers in Hungary – mainly because individual-level longitudinal data
was not available –, except in the case of young graduates (see, for example
Galasi–Varga, 2001, Varga, 2013, and sub-chapter 7.3 in this In Focus). In this
sub-chapter, we briefly present evidence on what is the labour market position
five years after graduation of a young person spending a significant amount of
time as a registered jobseeker (or public worker) in the two years after gradua-
tion, compared to their peers who had begun their careers “smoothly”.
During the course of our analysis, we build on the linked public adminis-
tration panel database of the CERS Databank (for more details, see Sebők,
2019). We are examining a specific group: those young men1 who finished
1 The labour market situa-
secondary education (ISCED 3A or 3B) in 2011–2012 and did not go on to tion of young women is not
higher education.2 The database not only has the advantage that we are able addressed in this short piece
because it would require the
to observe the labour market trajectory of the youth relatively accurately, but modelling of childbearing.
we also have data measuring their cognitive skills,3 therefore we hope that 2 More specifically, the sam-
the bias stemming from unobservable characteristics is relatively small. The ple includes those who were
born between 1990 and 1994
key information upon which we build our analysis is how many months the and who had their tenth grade
competency test results; and
young persons were registered jobseekers (or public workers) in the two cal- those who attended full-time
endar years after finishing secondary school. education for less than one year
in the two calendar years after
We present some background characteristics on Table 4.2.1: reading com- completing secondary school.
prehension and mathematics test scores as well as the district unemployment 3 Tenth grade reading and
rate. Based on the length of registered job-seeking or public work, we placed mathematics test scores were
used.
the young people into six groups, distinguishing those who were (also) in 4 This is particularly evident
public works. at the bottom of the skills dis-
tribution, among those young
Table 4.2.1 shows first and foremost that roughly 85 percent of the young people who were long-term un-
people in the cohort examined were unemployed for a short period of time, employed and in public works
the rate of those with weak or
while 5 percent of them were in public works in the two years after entering very weak skills is nearly three
the labour market. Additionally, it is evident that those who were long-term times more than among those
who were not unemployed.
unemployed had significantly lower cognitive skills.4 It is also clear that the (Reading comprehension: 14.3
percent compared to 5.5 per-
length of unemployment is strongly influenced by the local labour market: cent; Maths: 21 percent com-
young people who experienced long-term unemployment lived in a district pared to 7.6 percent.)

95
Márton Csillag

where unemployment was more than one and a half times higher than among
those who did not experience unemployment. Another important lesson from
the table is that those who were in public works (on top of unemployment),
came from a particularly disadvantaged background in every respect.5
Table 4.2.1: Characteristics of young men by categories based on time spent as
registered jobseekers or in public works in the two calendar years after graduation
Reading Unemployment
Length of time spent in registered Proportion Mathematics
comprehension rate
unemployment or public works (percent)
average score (percent)
None 59.4 1608 1664 6.69
1–6 months 25.2 1582 1639 8.24
7–12 months, no PW 5.9 1578 1632 9.03
7–12 months and PW 3.2 1561 1605 10.34
13–24 months, no PW 3.7 1556 1603 10.35
13–24 months and PW 2.6 1529 1568 11.95
Sample: those young men who finished secondary school (ISCED 3A or 3B) in 2011–
2012 and did not go on to higher education.
Note: Data from the two calendar years after completing upper-secondary education,
the length of registered unemployment or public works participation is summed up
5 We note that those who con- (and PW participation is noted separately).
tinue their studies two years Source: Own calculation based on linked public administration panel database of the
af ter f inishing secondar y CERS Databank.
school (although they did not
complete higher education un-
til 2019) have higher cognitive In Table 4.2.2, we summarised the results of multiple regression analyses
skills. In this short paper we
do not deal with the fact that
in which we measured the labour market situation of the young person in
the current state of the labour the fifth calendar year after finishing secondary school, depending on the
market may also affect the con-
tinuation of studies. number of months spent as a registered jobseeker (or in public works).6 First,
6 In the analysis we use the we were curious about how many more months those who experienced diffi-
entire calendar year, therefore culty entering the labour market spent as registered unemployed or in pub-
we consider the average daily
wages as well as the proportion lic works (or less time employed in the primary labour market). Second, we
of time spent as overqualified
within employment. It is im-
examined whether, if a young person was employed in the primary labour
portant to emphasise that when market, they received lower (daily) wages, and whether it was more likely
we talk about employment, we
are looking at employment that they were overqualified7 for their job if they had been previously un-
(and the earnings or occupa- employed/in public works. The key variables were divided into the six cat-
tional status) on the primary
labour market. egories in Table 4.2.1.8
7 Here we use the same ap- Estimation results show that shorter unemployment (not exceeding six
proach as Jú l ia Va rga i n
subchapter 7.3. Those who
months) does not significantly worsen the labour market outcomes of youth.
worked in occupations belong- Those who had been unemployed for a longer period of time and were (also)
ing to HSCO major group 8 or 9
were classified as overqualified. in public works were particularly unfortunate, while the labour market out-
8 Regressions included tenth come of those who spent the 7–12 months as registered jobseekers (but not
grade test scores (and their
squares), year of birth, region
in public works) deteriorated only slightly.
of residence, and how many First, an individual’s participation in public works clearly predicts getting
months the young person stud-
ied as a full-time student in the “stuck” in subsequent unemployment (or further public works): the long-term
two years after graduation. unemployed who were also in public works, spent nearly three months more

96
 4.2 Unemployment among labour market entrants

in a similar status even in the fifth year after graduation. Similarly, members
of this group spent about 1.3 months less on the primary labour market than
those who had not been unemployed. All this suggests that in terms of em-
ployment status, those who are long-term unemployed and who were in pub-
lic works are the worst off. If a young person was long-term unemployed but
was not in public works or was a registered jobseeker and in public works but
was able to get out of this situation within a year also had negative, but not
so unfavourable, consequences.
Table 4.2.2: The relationship between unemployment in the first two years of the
career and the labour market outcomes in the fifth year
after finishing secondary school, finished secondary school in 2011–2012
Length of time spent in regis- Registered job- Employed on the Occupation over-
Daily earnings
tered unemployment or public seeker or public primary labour educated
(logarithm)
works works (months) market (month) (percent)
0.3534*** 0.4214*** 0.003006 7.0275***
1–6 months
(0.05196) (0.1125) (0.01434) (1.1160)
0.5386*** 0.2794 –0.02838 12.992***
7–12 months, no PW
(0.1081) (0.2016) (0.02500) (2.0245)
2.0151*** –0.5261* –0.09400** 8.1912***
7–12 months and PW
(0.2311) (0.2802) (0.03794) (2.6704)
1.3562*** –0.6197** –0.1067*** 9.2666***
13–24 months, no PW
(0.2010) (0.2686) (0.03308) (2.7348)
2.9125*** –1.3137*** –0.1134*** 16.300***
13–24 months and PW
(0.2893) (0.3183) (0.03754) (3.2424)
R2 0.145 0.051 0.064 0.063
N 11,147 11,147 8,904 8,904
Average of the outcome
0.818 7.962 8.526 30.391
variable
Key independent variable: number of months spent as registered jobseeker or public
works participant in the two calendar years following secondary school gradua-
tion. Regressions included tenth grade test scores (and their squares), year of birth,
region of residence, and how many months the young person studied as a full-time
student in the two years after graduation.
Source: Own calculation based on linked public administration panel database of the
CERS Databank.

Second, in terms of wages and the quality of work five years after labour
market entry, the ranking based on the status immediately following enter-
ing the labour market is not so clear. Members of those three groups whose
employment was negatively affected by the experiences of the first two years
also received around 10 percent lower daily wages. In terms of jobs, all young
people who had been unemployed for a significant period of time were about
10 percentage points more likely to be forced to accept a job for which they
were overqualified. Those who were both long-term unemployed and in public
works were particularly disadvantaged as they were about one and a half times
more likely to be overqualified than young people who were not unemployed.

97
Márton Csillag

Based on our research, it cannot be ruled out that long-term unemployment

after entering the labour market permanently worsens the labour market op-
portunities of young people, especially if it is associated with public works.
Further research is required in two directions. On the one hand, it is appro-
priate to examine which of the differences identified here may be related to
weakness in terms of unobservable skills. On the other hand, it should be ex-
amined as to what factors cause the lasting negative consequences: employer
preferences, erosion of knowledge, or being stuck in a particularly disadvan-
taged place of residence.
References
Galasi, P.–Tímár, J.–Varga, J. (2001): Pályakezdő diplomások a munkaerőpiacon In:
Semjén, A. (ed.): Oktatás és munkaerőpiaci érvényesülés. MTA Közgazdaságtudo-
mányi Kutatóközpont, Budapest, pp. 73–89.
Sebők, A. (2019): The Panel of Linked Administrative Data of CERS Databank. Buda-
pest Working Papers on the Labour Market, BWP-2019/2.
Varga, J. (2013): A pályakezdő diplomások munkaerő-piaci sikeressége 2011-ben. In:
Garai, O.–Veroszta, Zs. (eds.): Frissdiplomások, 2011. Educatio Társadalmi Szolgál-
tató Nonprofit Kft., Budapest, pp. 143–171.

98
 5.1 Job search behaviour of young people...

5 THE IMPACT OF EMPLOYMENT POLICIES

5.1 JOB SEARCH BEHAVIOUR OF YOUNG PEOPLE NOT IN
EDUCATION, TRAINING OR EMPLOYMENT
Tamás Molnár
Young people’s unemployment at the start of their careers can cause long-term
problems (see Chapter 4), thus, it is particularly important to see how quickly
they find a job after leaving school and whether the public employment ser-
vices can offer them effective help. In this subchapter, we look at how youth
unemployment has changed over recent years and what factors affect the
speed of finding employment, with particular emphasis on the role of public
employment services.
In recent years, the number of young people not in education, employment
or training (NEET) has decreased, and their composition has also changed
significantly (Figure 5.1.1).1 It can be clearly seen that the recovery from the
crisis and the increasing demand for labor have absorbed those unemployed
youth who were relatively close to the labor market: while in the second half
of 2013 nearly 30% of NEET young people aged 16–29 were short-term un-
employed, by the second quarter of 2018, only 17 percent of NEET youth
were in this group. Similarly, the share of long-term unemployed and discour-
aged job-seekers (those who no longer actively look for a job) in the NEET
youth group has decreased, indicating a particularly strong demand effect, as
even those who had been looking for a job for more than a year previously 1 Young people not in educa-
can now find employment. tion, training or employment
were divided into seven groups
Figure 5.1.1: Changes in the number of NEET groups in the 16–29 age group, following the Eurofound meth-
od (Mascherini–Ledermaier,
2013–2018 (thousands) 2016). Re-entrants who will
soon start to study or work at
300 a particular job, short-term
unemployed looking for a job
250 for less than 1 year and long-
term unemployed looking for
a job for over a year. Discour-
200 aged workers who want to work
Thousands

but are not actively looking for

150 work because they think they
Other will not find a relevant job.
100 Care obligations Those unavailable due to illness
Long term illness or disability who are not able
Discouraged to go to work because of their
50 Long term unemployed illness, while those unavailable
Re-entrans due to family responsibilities
0 Short term unemployed who cannot work because they
2013 2014 2015 2016 2017 2018 are typically caring for children
Source: Own calculation based on LFS second quarter waves. or other family members. The
other category includes every-
one who could not be classified
Parallel to this, the number of those who are unavailable due to family respon- in the above groups due to lack
sibilities has increased slightly and the number of people who are unavailable of data or for other reasons.

99
Tamás Molnár

due to illness or disability has stagnated, resulting in a significant increase of

their combined share in the overall NEET youth population, reaching over 60
percent by 2018. In other words, in 2018 more than half of the unemployed
young people were unavailable for family or health reasons.
Job search duration
Although the favorable economic situation in recent years has made it easier
for young people to find employment, many people may still need help. And
if growth rates decrease, it can become a critical issue – influencing the entire
career path – for even more people, how effectively job centers can help them
find their first jobs. We perform an analysis similar to the work of Mickle-
wright–Nagy (1999), we used Labor Force Survey individual data from 2015
to 2018 to investigate the factors that influence the employment prospects
of 15–29-year-old NEETs.2
The data on those who have recently become NEET also shows that almost
everyone who wanted and was able to work could get a job relatively quickly
in recent years. With the exception of unavailable NEETs (either due to fam-
ily or health reasons), the proportion of those still in NEET status has fallen
below 40 percent in each group within four quarters. Furthermore, most re-
entrants (those waiting for a call-back) and job seekers (ILO unemployed)
have found employment within six months (Figure 5.1.2).
Figure 5.1.2: Number of year quarters until exit from NEET status to employment
by type of unemployment, 2015–2018
1.0

0.8
Share of NEETs

0.6

0.4

0.2

0.0
0 1 2 3 4
2 We used a survival analysis, Quarter
merging the waves of the Labor Short term unemployed Re-entrant Long term illness
Force Survey 2015–2018. The Care obligations Discouraged Other
sample included those who
did not have NEET status in Source: Own calculation based on LFS data.
the first wave and then became
NEET in one of the six quarters. In addition to labor demand and individual motivation, help from the pub-
As output variable we used the
time until exiting the NEET lic employment services can also shorten the duration of job search through
status, and we controlled for providing jobseekers with specific job offers, training or advice to improve
– among others – level of edu-
cation, age, gender, region, and the effectiveness of individual job search (see also section 5.2). Identifying the
quarter. Leaving NEET status
to study has not been taken into
causal effect is difficult because there is a two-way relationship: registration
account here. can improve the efficiency of job search, but registration itself can be a step

100
 5.1 Job search behaviour of young people...

in job search.3 Therefore, our analysis is descriptive: in survival models, we

estimated how motivation and job search affect the length of time to return
to work or return to school.4 The results are shown in Table 5.1.1.
Table 5.1.1: Correlation of various factors with the time until exit from NEET status
to employment and to education, 2015–2018
Exit to Exit to further Male, exit to Female, exit to
employment education employment employment
Wants to work, not seeking 0.6480 1.0878 0.8562 0.5512
a job actively, not available (0.2348) (0.2157) (0.4406) (0.2831)
Wants to work, not seeking 2.4735*** 0.6922 2.3153*** 2.7977***
a job actively, available (0.3800) (0.1800) (0.4520) (0.7244)
Wants to work and actively 2.8805*** 0.9875 2.6407*** 3.5212***
seeks a job (0.3943) (0.2084) (0.4864) (0.7440)
3.6825*** 1.0715 5.1245*** 2.6285**
Re-entrant
(0.8888) (0.5595) (1.5977) (1.0694)
1.0687*** 0.8356*** 1.0678*** 1.0805***
Age
(0.01386) (0.01597) (0.01871) (0.02219)
Registered jobseeker in the 1.0970 0.4887*** 1.1075 1.1237
previous period (0.1432) (0.1236) (0.1890) (0.2403)
1.4640*** 0.5450*** 1.5463*** 1.2131
Vocational school
(0.1763) (0.1169) (0.2235) (0.2777)
Secondary or tertiary educa- 1.1800 1.4030*** 0.9851 1.4680** 3 In addition, registrants may
tion (0.1289) (0.1742) (0.1468) (0.2535) be filtered by other features
(that are not documented in
0.8537* 1.1870* population surveys): those
Female
(0.08042) (0.1227) who expect little from the
0.002385*** 0.4294 0.002882*** 0.001040*** job center because they have
Constant good connections, or are bet-
(0.001253) (0.2977) (0.001873) (9.778e-04)
ter informed, may have a lower
Number of observations 2,578 2,452 1,429 1,149 registration rate; while those
Note: Coefficients express the effect on the logarithm of the odds ratio. Coefficients who already know which em-
greater than 1 mean that this factor speeds up the placement process, while factors ployer will provide them with
an internship opportunity or
with a coefficient less than 1 impede it. (for different reasons) those
***
Significant at a 1 per cent, **5 per cent, *10 per cent level. who did not succeed in finding
Source: Own calculation from LFS data. a job on their own might have
a higher registration rate.
When looking at those who entered employment, not only the life situation, 4 Time spent until leaving the
NEET status to further edu-
but also the self-reported willingness to work has a significant explanatory cation or training increases
effect. Those who are available to work within two weeks will find a job sig- significantly with age, it is also
slowed down by vocational ed-
nificantly faster, even if they did not actively seek job opportunities in the ucation, but it is accelerated by
previous year quarter.5 However, contacting the employment office does not secondary education or higher
compared to having only pri-
significantly reduce the duration of job search. mary education.
The role of the public employment services may be different for certain 5 At the same time, re-entrants
are the ones who start employ-
groups of young people not in education or training. Examining separately ment in the shortest time, in
the groups created based on motivation, we find that registration with PES line with the results of Mick-
lewright–Nag y (1999). Our
significantly reduces the duration of the NEET status for those who want to results differ from this earlier
work but are not actively searching for a job themselves (Table 5.1.2). This research in that active jobseek-
ers do not find a job faster than
implies that the support of the employment services is not significantly help- those who just want a job.

101
Tamás Molnár

ful for those who are able to look for a job on their own (see Box K5.1), while
those who themselves are not seeking employment for some reason may be
activated by the help of PES.
Table 5.1.2: Relationship between motivational factors and registration with time
until exit from NEET status to employment, 2015–2018
Wants to Does not want Wants to work and Wants to work but does
work to work actively seeks a job not actively seek a job
Registered jobseeker in the 1.3582** 1.2549 0.9139 1.9937***
previous period (0.1812) (0.6490) (0.1654) (0.4319)
Number of observations 934 1,644 529 405
Note: Coefficients express the effect on the logarithm of the odds ratio. Coefficients
greater than 1 mean that this factor speeds up the placement process, while factors
with a coefficient less than 1 impede it.
***
Significant at 1 percent, **5 percent, *10 percent level.
Source: Own calculation from LFS data.

Within the group of NEET young people, those who are the closest to the
primary labor market (short-term unemployed and re-entrants) register with
the employment services at the highest rate. The somewhat more problemat-
ic groups (long-term unemployed, discouraged jobseekers and other NEETs
who want to work), for whom the services of the PES would likely be more
helpful, register at a lower and, in recent years, declining rate. Not surpris-
ingly, people who, for whatever reason, are unable to work are registered at
a very low rate at PES offices.
Figure 5.1.3: Registration rate by distance from the primary labor market,
2013–2018
100

80

60
Percent

40

20

0
2013 2014 2015 2016 2017 2018
Close to the primary Further away from the Cannot work
labour market primary labour market
Note: Close to the primary labour market: re-entrant or short term unem-
ployed. Further away from the primary labour market: long term unem-
ployed or discouraged worker.
Source: Own calculation based on LFS second quarter waves.

Overall, we have found that young people not in education, training or em-
ployment face different opportunities in the labor market: a smaller and de-

102
 5.1 Job search behaviour of young people...

clining portion finds work quickly, others remain unemployed for a more
protracted period, while an increasing portion remain stuck in NEET sta-
tus due to their care responsibilities or health issues. The employment service
reaches no more than half of the second group and less than one in twenty
young persons from the third group. From our descriptive analysis, it seems
that registration at the job center can accelerate the employment of young peo-
ple who are further away from the labor market and want to work. Therefore,
in order to further reduce the number and proportion of young people not
in education, employment or work, the access of vulnerable young people to
employment services needs to be increased, and the efficiency of services for
job-seekers (see section 5.2) and social services that can reduce (or compen-
sate for) barriers related to illness or care should be improved.
References
Mascherini, M.–Ledermaier, S. (2016): Exploring the diversity of NEETs. Euro-
found, Publications Office of the European Union, Luxembourg.
Micklewright, J.–Nagy, Gy. (1999): The informational value of job search data and
the dynamics of search behaviour: Evidence from Hungary. Budapest Working Pa-
pers on the Labour Market, 1999/1. Institute of Economics, Hungarian Academy
of Sciences, Budapest.

103
Tamás Molnár

K5.1 Job search channels

Tamás Molnár
We examined the job search channels used by eco- ary education (ISCED 3 A or B), while most peo-
nomically active, job-seeking young people aged ple look for work with the help of acquaintances or
18–29 besides registering at the public employment read advertisements.
services, and we also looked at the differences be- Roughly half of those who completed secondary
tween job search channels used by young people education also use the help of public employment ser-
with and without secondary education. vices, but this proportion has shown a declining trend
According to Labor Force Survey data, those who in recent years, with most people reading advertise-
finished vocational school use the help of public ments and looking for jobs through acquaintances.
employment services to find a job in a slightly high- In addition to this, graduates are more likely to ac-
er proportion than those who completed second- tively post or respond to an ad than non-graduates.

Figure K5.1.1: Search tools used by unemployed Figure K5.1.2: Search tools used by ILO unemployed
young people who completed vocational school, young people who completed secondary education,
2014–2018 2014–2018
100 100
Proportion of NEETs (percent)
Proportion of NEETs (percent)

80 80

60 60

40 40

20 20

0 0
2014 2015 2016 2017 2018 2014 2015 2016 2017 2018
Public employment services Private employment services Public employment services Private employment services
Answers/posts job adverts Reads job adverts Answers/posts job adverts Reads job adverts
Contacts employers Family and acquaintances Contacts employers Family and acquaintances
Note: here we look at the unemployed as defined by Note: here we consider the unemployed as defined by
the ILO (not just registered jobseekers). the ILO (not just registered jobseekers).
Source: LFS second quarter data. Source: LFS second quarter data.

104
 5.2 Active labour market instruments targeting...

5.2 ACTIVE LABOUR MARKET INSTRUMENTS TARGETING

YOUNG PEOPLE AND THE YOUTH GUARANTEE
PROGRAMME
Judit Krekó, Tamás Molnár & Ágota Scharle

Recognising the long-term risks of youth unemployment, European Union

countries established the Youth Guarantee (YG) scheme in 2014. Under the
YG scheme, Member States have committed themselves to providing young
people under the age of 25 with a specific, good quality offer from the or-
ganisation implementing the scheme (this is usually the public employment
service) shortly after completing their studies or losing their job, starting in
2015.1 The meaningful opportunity offered could be, for instance, a job sup-
ported by wage subsidies, training, participation in programmes promoting
traineeship or entrepreneurship (Tóth–Temesszentandrási, 2019).
Most of the above mentioned labour market measures were on offer by the
Hungarian labour offices before the YG scheme. The novelty of the YG pri-
marily lies in the fact that the state guarantees that it will provide early, mean-
ingful and personalised assistance to all young people not in employment, ed-
ucation or training (NEET), for the implementation of which EU funds will
provide significant financial instruments. Most of the programme elements of
the YG system are operated by employment services for registered jobseekers.
In this subchapter, we evaluate these programme elements, we do not analyse 1 At the start of the programme
in 2015, the commitment
traineeship and entrepreneurship programmes offered by other organisations.2 in Hungary was for an offer
The results of the YG scheme can be evaluated on three levels. The first ques- within six months after regis-
tration, currently an offer must
tion is whether the employment services succeed in reaching and bringing the be made to young jobseekers
NEET youth into contact with employment services. The next question is in within four months.
2 The employment service oper-
what proportion and within what timeframe young people already registered ates the GINOP 5.2.1. and the
as jobseekers at the employment services3 are given an offer, and whether their VEKOP 8.2.1. programmes.
The internship programme
inclusion in the most appropriate active labour market programme will be (GINOP 5.2.4.) is implemented
successful. The final question of the effectiveness of the YG scheme is the ex- by vocational training centres,
and the programmes support-
tent to which the programme’s tools improve the longer-term labour market ing entrepreneurship (GINOP
prospects of young people. 5.2.3., 5.1.9., 5.2.7.) are imple-
mented by consortia of profes-
For the time being, we do not have sufficient data available to examine the sional organisations.
employment effects of the YG programme elements, therefore we do not deal 3 The employment service has
been part of government offices
with the impact assessment of the individual measures here either.4 since 2015, and its branches
In terms of the first level, reaching the NEET youth, the programme did operate as the employment de-
partment of the district offices.
not show significant results. The Council recommendations preparing the 4 The Youth Guarantee Pro-
introduction of the YG prioritise addressing vulnerable, inactive young peo- gramme is being evaluated
at an early stage in a study by
ple facing multiple barriers by developing effective information strategies and Hétfa Research Institute (Ágnes
strengthening cooperation with relevant partners (EU, 2013). Szabó-Morvai et al, 2015).

105
Judit Krekó, Tamás Molnár & Ágota Scharle

Prior to the introduction of the Youth Guarantee Programme, the employ-

ment service reached nearly 60 percent of those NEET youth who would like
to work, but certain barriers (such as weak motivation or lack of qualification)
make it difficult for them to get a job, so they would be particularly in need
of the assistance the programme could provide.5 The rate of registrants in this
group increased from 48 percent observed before the introduction of the pro-
gramme to 58 percent in the first year, however in the following two years, it
fell below the previous levels (see Figure 5.1.5 in the previous subchapter). In
the group of young people who are not hindered in their job hunt, the rate of
registrants is 10–15 percent higher and has decreased less since the introduc-
tion of the programme. This indicates that the programme did not, or it only
temporarily strengthened the partnerships or the inclusion tools which made
it possible to reach inactive young people. A similar conclusion was reached by
Szabó-Morvai et al (2015) based on data from the first months following the in-
troduction of the programme, and this is supported by the interviews conduct-
ed in the labour organisation in the spring of 2019 (Budapest Institute, 2019).
On the second level, we analyse the timing and the types of programmes
that the registered unemployed youth entered and evaluate the targeting and
the relevance of the programmes. Based on the data of young people under
the age of 25 entering active labour market programmes, the distribution of
labour market programmes has significantly changed in recent years.
Based on individual level data of registered jobseekers, we examined where
young people entering the labour register between January 2015 and June
2017 end up in the first six months after entry (Figure 5.2.1). According to
this, since 2015, the chances of a young person entering the register to get
into an active measure within half a year increased, and at the same time the
probability of a young person entering public works or not to participate in
any programme while remaining in the register decreased. More than half of
all entrants are removed from the register within six months without entering
either public works or an active programme. They either found work without
help or became inactive.
The distribution of the active programmes by type is shown on Figure 5.2.2.
Based on this, in addition to the dynamic increase of wage subsidies6 of al-
most 70 percent, the number of entrants to training programmes stagnated
5 Based on LFS data, see
between 2015 and 2018, so the weight of training within active programmes
subchapter 5.1 for more details. decreased overall.
We classified in separate groups
those who could not work due The fact that the labour market environment in Hungary has changed sig-
to illness or family ties: the rate nificantly in recent years also plays an important role in the transformation
of registrants in this group is
barely 5 percent. of the distribution of labour market instruments: besides the dynamic expan-
6 Wage subsidies do not include sion of employment, unemployment, including youth unemployment, has
subsidies provided by the Job
Protection Act, the latter is dis-
also decreased. In any case, the reduction of the public works programme
cussed in subchapter 5.4. and the increase in the rate of wage subsidies are positive developments, as

106
 5.2 Active labour market instruments targeting...

the recipients of wage subsidies work in the open labour market at a higher
wage level than in public works. International analyses show that wage subsidy
schemes providing employment in the private sector could improve post-pro-
gramme employment opportunities to a greater extent than public employ-
ment schemes7 (Card et al, 2018). The results of early analyses of the Hun-
garian public works scheme also demonstrate that public employment does
not aid long-term employment (Cseres-Gergely–Molnár, 2015, Köllő–Scharle,
2012). At the same time, based on the significant increase in employment and
the increase in labour shortages, it is likely that the wage subsidy instruments
supported, in part, the employment of young people who could have found
employment without support.
Figure 5.2.1: What happens to young people under the age of 25 registered
as jobseekers in the first six months after registration?
100
90
80
70
Percentage

60
50
40
30
20
10
0
2015 2016 2017*
Enters active measure Enters public works
Enters ALMP and public works Remains jobseeker,
Leaves the register without programme no programme
Until 30 June 2017. The horizontal axis shows the year of registration.
*

Source: Own calculation based on the Admin3 database of Institute of

Economics Data bank. We would like to thank István Boza for his help
in processing the database.
Figure 5.2.2: The number of entrants into the Youth Guarantee Programme
by active instruments
20
18
Number of entrants, thousand

16
14
12
10
8
6
4
2
0
2015 2016 2017 2018
Wage subsidy Combination
Training Other*
7 In fact, the employment im-
*
Housing subsidy, entrepreneurship subsidy. pacts of the latter are typically
Source: Ministry of Finance. negligible or negative.

107
Judit Krekó, Tamás Molnár & Ágota Scharle

Regarding the targeting of the programmes, we examine the extent to which

vulnerable and low-educated groups in the labour market benefited from the
expansion of active labour market programmes. In order to answer this ques-
tion, we used a linear probability model to examine what factors influence
whether the young people who registered as jobseekers between 30 June 2015
and 30 June 2017 are enrolled in the Youth Guarantee Programme within half
a year. The results are shown in Table 5.2.1.
Table 5.2.1: Regression estimate of the factors determining the entry of registered
jobseekers under the age of 25 into the Youth Guarantee Programme
Year of entry to unemployment registry
2015–2017a 2015 2016 2017a
–0.003 –0.010*** 0.004 0.000
Male
(0.002) (0.003) (0.004) (0.007)
–0.102*** –0.092*** –0.109*** –0.122***
Max. primary education
(0.003) (0.004) (0.004) (0.008)
–0.003 0.004 –0.007 –0.018**
Below the age of 20
(0.003) (0.004) (0.005) (0.009)
Number of unemployment –0.018*** –0.012*** –0.013*** –0.038***
spells (0.002) (0.003) (0.004) (0.009)
0.095*** 0.092*** 0.086*** 0.126***
With no work experience
(0.003) (0.004) (0.005) (0.009)
0.252*** 0.248*** 0.211*** 0.348***
Constant
(0.006) (0.008) (0.011) (0.020)
Number of observations 104,582 51,866 38,126 14,590
R2 0.039 0.049 0.032 0.052
Average probability of entry 0.21 0.20 0.21 0.28
a
Until 30 June 2017.
Standard errors in parenthesis.
Dependent variable: binary variable with a value of 1 if the registered jobseeker be-
low the age of 25 enters an active measure of the programme within half a year after
registration.
Significant on ***1 percent, **5 percent, *10 percent level.
Source: Own calculation based on the Admin3 dataset of KTI KRTK.
The regression results show that those with no more than primary education
and those re-entering the register are less likely to be included in an instrument
of the Youth Guarantee Programme, and this disproportion did not decrease
between 2015 and 2017. As a consequence, the labour market instruments are
less likely to reach those young people who inherently have worse employment
opportunities. This is probably mainly due to the fact that the low-skilled are
more difficult to place in the labour market, even with wage subsidies.
In light of the results, the currently low and stagnant rate of training can be
considered unfavourable, and more intensive involvement of young people
with low educational attainment in training could presumably improve the
situation. However, under the Young Guarantee Programme, young jobseek-
ers can participate primarily in vocational training courses that directly pro-

108
 5.2 Active labour market instruments targeting...

mote employment, and training focusing on the development of basic com-

petences is not included in the elements of the Programme in a significant
portion of the districts. In the case of unskilled young people, in addition to
the lack of vocational training, in many cases the lack of basic skills hinders
employment. Furthermore, based on international experience (e.g. Kluve et
al, 2019), the involvement of low-skilled, disadvantaged young people can be
improved by more intensive use of personal counsellors and mentors, who can
help young people choose an appropriate programme after registration based
on personal abilities and needs.8
Therefore, overall the data show that active instruments, including wage
subsidies, reach an increasingly high proportion of young people registered 8 A lthough signif icant f i-
as jobseekers within an increasingly short period, in which, however, in ad- nancial resources are avail-
able in the Youth Guarantee
dition to the Youth Guarantee Programme, the growth of demand for labour Programme, the regulation
and the decrease in unemployment played a role as well. NEET youth fur- of the programme does not
allow employment offices to
ther away from the labour market are less likely to be included in the register, account for internal mentors
while low-skilled people are less likely to be included in the scheme’s active (PES-employees) within the
programmes, so it can only be
instruments than their better-off peers. Thus, in order to improve the Youth done through public procure-
Guarantee Programme, greater efforts should be made to reach young peo- ment with the help of external
suppliers, which is lengthy and
ple in need, and training programmes that improve general competencies and usually involves a significant
mentors should be used in greater proportion. administrative burden.

References
Budapest Institute (2019): Mind the Gap! Roma fiatalok elérése és részvétele a ma-
gyarországi Ifjúsági Garancia keretrendszerben – jó gyakorlatok, tanulságok és
ajánlások [Mind the Gap! Access and participation of Roma youth in the Youth
Guarantee – good practices, lessons and recommendations]. Manuscript, Budapest
Institute for Policy Analysis.
Card, D.–Kluve, J.–Weber, A. (2018): What Works? A Meta Analysis of Recent Ac-
tive Labor Market Program Evaluations. Journal of the European Economic Asso-
ciation, Vol. 16. No. 3. pp. 894–931.
Cseres-Gergely, Zs.–Molnár, Gy. (2015): Labour market situation following exit
from public works. In: Fazekas, K.–Varga, J. (eds.): The Hungarian Labour Market
in 2015. IE HAS, Budapest, pp. 148–159.
EU (2013): Council recommendation of 22 April 2013 on establishing a Youth Guaran-
tee. The Council of the European Union. 2013/C 120/01.
Kluve, J.–Puerto, S.–Robalino, D.–Romero, J. M.–Rother, F.–Stöterau, J.–Wei-
denkaff, F.–Witte, M. (2019): Do youth employment programs improve labor
market outcomes? A quantitative review. World Development, Vol. 114. pp. 237–253.
Köllő, J.–Scharle, Á. (2012): The impact of the expansion of public works programs
on long-term unemployment. In: Fazekas, K.–Kézdi, G. (eds.): The Hungarian La-
bour Market, 2012 Research Centre for Economic and Regional Studies and Hun-
garian Academy of Sciences. National Employment Non-profit Public Company Ltd,
Budapest, Budapest, pp. 123–137.
Szabó-Morvai, Á.–Balás, G.–Remete, Zs.–Grócz, M.–Hollósy, B. (2015): Az Ifjú-
sági Foglalkoztatási Kezdeményezés elemzése. Hétfa Kutatóintézet, Budapest.
Tóth, R.–Temesszentandrási, J. (2019): Az Ifjúsági Garancia az Európai Unióban
és Magyarországon. Munkaügyi Szemle, Vol. 62. No. 6. pp. 55–61.

109
András Svraka

5.3 THE EFFECT OF THE JOB PROTECTION ACTION PLAN

András Svraka
From 2013, the employment of several labour market groups with a low em-
ployment rate was supported by reliefs on the social contribution tax, offered
by the job protection action plan. The main relief types were given for the
employment of individuals under the age of 25 and above the age of 55, and
unskilled labourers (ISCO-08 category 9). The amount of the reliefs was half
of the social contribution tax of 27 percent, but capped at HUF 100,000 per
month (which was more or less equal to the amount of the minimum wage at
the time of the introduction). A new feature of the reliefs was that they could
be applied not only to new employees, but without a time limit and also to
staff already in employment. Additionally, no authentications were needed
for the reliefs, they could be deducted from tax liabilities via the monthly em-
ployers’ contribution declaration.1
A generally available tax relief that is independent of income level and that
targets groups in disadvantaged labour market positions has never before been
available in Hungary. The reliefs were applied to approximately 600,000 indi-
viduals in 2013, which grew to 900,000 in 2018. Youth-specific reliefs were
applied to 110,000 individuals in 2013, and 170,000 in 2018. The range of
reliefs was slightly expanded during this time frame, but the increase could
be connected primarily to a rise in employment. Thus, the extent to which
the expansion can be attributed to the employment incentive effect of the
reliefs, and how cost effective such a targeted relief system is, are important
questions of economic policy.
The employment-related effect of the tax reliefs was examined by Svraka
(2019a). The study estimated the employment-related effects for the main
target groups using econometric tools, on the basis of individual level, an-
onymised tax authority micro data from the 2009–2015 period. It can be seen
from the nature of the reliefs that entitlement was established along a criteri-
on that draws a sharp cut-off: everyone under the age of 25 was entitled, but
1 Additionally, the action plan
also included reliefs related to no one was entitled above the age of 25.2 Thus, from among individuals who
new employment. Up to HUF were similar based on other features and their labour market chances, some
100,000 per month, social con-
tribution tax was not payable in could be employed with lower costs, while others could not. Taking advantage
the first two years of employ- of this quasi-experimental setup, we can compare the labour market output
ment for those returning after
long-term unemployment or of those on the two sides of the cut-off – those that the reliefs applied to and
childcare leave, and for youth
with a work experience of up
the control group. Also controlling for the effects of the differing economic
to 180 days. environments before and after the introduction of the reliefs, via a difference
2 There were no data available in differences type econometric method, the employment-related effect of the
for an in-depth analysis of the
relief for youth with work ex- reliefs can be established and separated from any other factors.
perience of up to 180 days, thus
the effect of this is also visible
The results show that the effect of the tax reliefs has proven to be significant:
in the general relief for youth. the rate of employment increased in the three main target groups already in

110
 5.3 The effect of the job protection action plan

the year of introduction, and these effects rose continuously until 2015. By
2015, the employment probability of those under the age of 25 rose by 2.6
percent compared to a control group of similar individuals to whom the re-
lief did not apply.3 Making an estimate for those above the age of 55 using
a similar method, this change was 0.8 percent, and among unskilled labour-
ers – using occupations requiring low educational attainment and offering
similar wages, to which the relief did not apply, as the control group – it was
2.7 percent. Among youth and the unskilled, there was no significant differ-
ence between the changes in the employment chances of males and females,
but among older age groups, the whole effect can be attributed to the higher
employment rate of females.
As a result of the reliefs, due to the change in relative labour costs, employ-
ers might have employed individuals that the relief applies to, instead of in-
dividuals who do not belong to any of the target groups. Among youth and
older age groups, Svraka (2019a) did not find any signs indicating such po-
tential substitution, but the employment rate of individuals with low educa-
tional attainment to whom the relief was not applicable did decrease slightly. 3 The Youth Guarantee Pro-
gramme, which also targets
Considering this, by 2015, the reliefs generated an expansion in employment youth aged under 25, was in-
by 53,000 individuals, 16,000 of which were under the age of 25. troduced in 2015: the effects
estimated for the subsequent
The effect of the excess budgetary income generated by a higher employ- years may partly capture the
ment rate manifesting through taxes and contributions, calculated based on impact of that Programme.
4 This indicator does not ac-
the abovementioned partial equilibrium results, without broader macroeco- count for potential deadweight
nomic consequences, was HUF 55 billion in 2015 – which is 40 percent of loss and measures short term
returns. In the long run, as
the cost of the reliefs that year. This cost efficiency indicator was, however, costs increase (as the subsidy is
different for different target groups: 42 percent for youth, 70 percent for also available to those already
in employment), cost efficiency
the undereducated, and only 14 percent for the older generations, in 2015.4 is likely to deteriorate.

References
Svraka, A. (2018): The Effect of Labour Cost Reduction on Employment of Vulnera-
ble Groups – Evaluation of the Hungarian Job Protection Act. MPRA Paper, 88234.
University Library of Munich, Germany.
Svraka, A. (2019a): A Munkaerőköltség csökkentésének hatása a sérülékeny csoportba
tartozók foglalkoztatására. Pénzügyi Szemle, Vol. 58, No. 1, pp. 70–93.
Svraka, A. (2019b): The Effect of Labour Cost Reduction on Employment of Vulnerable
Groups – Evaluation of the Hungarian Job Protection Act. Public Finance Quar-
terly, Vol. 58, No. 1, pp. 72–92.

111
Márton Csillag

5.4 THE ROLE OF THE MINIMUM WAGE IN THE EVOLUTION

OF YOUNG PEOPLE’S EARNINGS
Márton Csillag
Since the large-scale increase of the minimum wage in 2001–2002, the min-
1 The ‘guaranteed minimum imum wage has remained consistently high compared to the average wage,
wage’ was introduced from the
1st of July 2006. This minimum while additional minima for skilled workers and graduates1 have also been
applies to all jobs which require introduced. According to the international literature, uneducated young peo-
(at least) a vocational degree. It
means that in principle it applies ple work at the minimum wage at the highest rate, and their employment is
to all workers with a level of edu- most sensitive to changes in the minimum wage (Dolado et al, 1996, Neu-
cation ISCED 3 (and above).
2 After a significant increase mark–Wascher, 2004).2 We did not have the opportunity to assess the impact
in the minimum wage in of the development of the minimum wage. In this subchapter, we use data of
2001–2002, the employment
opportunities of certain groups the Wage and Earnings Survey to examine the proportion of young people
– uneducated workers in small working full-time in the private sector who worked near the minimum wage
companies – have deteriorated
(Kertesi–Köllő, 2004). At the (or the guaranteed minimum wage), that is, 95–105 percent of the minimum
same time, the authors point wage.3 We do this to show how effective the different wage minimums were
out that – in contrast to the
situation in the United States and whether they became the cornerstones of wage formation. If a minimum
or Western Europe – the mini- wage is so low that it directly affects only a negligible proportion of workers,
mum wage has had a significant
effect on the wages of not only there is little chance that it could have an effect on employment.
young people. According to our data, those under 30 employed in the private sector indeed
3 We did not examine whether
the employee works in a job to work at a higher proportion for the minimum wage (or guaranteed minimum
which the guaranteed mini- wage) than workers over 30, the difference being 4–5 percentage points. Fig-
mum wage applies or in a job
where the minimum wage ap- ure 5.4.1 also shows that after the increase in the minimum wage in 2002, the
plies. (The guaranteed mini- share of young people employed for the minimum wage decreased. Although
mum wage applies to a worker
employed in a job that requires the guaranteed wage minimum has significantly changed the wage setting
at least a secondary education practices for young people, it has hardly changed the rate at which they are
or a secondary vocational edu-
cation.) affected by one or another mandatory minimum wage.
Figure 5.4.1: The distribution of wages of young people aged 16–25, by education, 2002, 2006 (thousand HUF)
a) Primary school b) Secondary

0.020 0.010

0.015 0.008
0.006
0.010
0.004
0.005 0.002
0.000 0.000
0 100 200 300 400 0 200 400 600
Total monthly gross earnings, thousand HUF, real (2016 = 1) Total monthly gross earnings, thousand HUF, real (2016 = 1)
2002 2016
Notes: The wage dsitribution for those with vocational school (ISCED3C) is very
similar to that of those with secondary education (see: Csillag et al, 2019).
Source: Wage Survey (NES), private sector; own calculations.

112
 5.4 The role of the minimum wage in the evolution...

The minimum wage (or the guaranteed wage minimum) can affect not only
the low-educated, but also those who completed vocational school or second-
ary school (Table 5.4.1). It is also noteworthy that the guaranteed minimum
wage has become the norm among young people who completed vocational
school or secondary school (in the spirit of the law).
Table 5.4.1: The percentage of 16–29 and 30–64 year-olds employed in the private
sector who earn around the minimum wage or the guaranteed minimum wage
2002 2009 2016
minimum minimum guaranteed minimum guaranteed
Level of Education wage wage minimum wage wage minimum wage
Below age 30
Primary school 25 17 13 17 10
Vocational school 30 10 20 8 23
Secondary 23 5 14 4 18
Tertiary 9 1 4 1 9
Total 24 7 13 6 16
Above age 30
Primary school 21 15 12 15 13
Vocational school 22 6 16 6 19
Secondary 14 3 10 3 14
Tertiary 8 1 3 1 4
Total 17 5 11 5 14
Source: Wage Survey (NES), private sector; own calculations.

Examining the wage distribution of young people, several developments be-

come apparent. On the one hand, the role of the minimum wage has some-
what decreased for unskilled workers and vocational school graduates, and
wage differentiation has increased. On the other hand, the wage distribution
of vocational school graduates and secondary school graduates is “truncated”
from the bottom to a significant extent by compliance with the guaranteed
wage minimum. That is, in the case of vocational school and secondary school
graduates, the guaranteed wage minimum has become effective (i.e., a sub-
stantial portion of employees would have lower wages if paying the minimum
wage were not mandatory). This may have contributed to the increase in real
wages in the lower half of the wage distribution, but may have held back em-
ployment growth for some groups. For this reason, it may be justified to ex-
amine active instruments that provide tax incentives and wage subsidies for
companies employing young people (see subchapter 5.3 for more details); and
to conduct more detailed studies on the employment impact of the introduc-
tion of the guaranteed minimum wage.

113
Márton Csillag

References
Csillag Márton–Molnár Tamás–Scharle Ágota–Tóth Endre (2019): Fiatalok
a munkapiacon és az iskolában a 2002 és 2018 közötti időszakban [Young persons
ont he labour market and in school between 2002 and 2018]. Research Report, Bu-
dapest Institute for Policy Analysis, Budapest.
Dolado, J.–Kramarz, F.–Machin, S.–Manning, A.–Margolis, D.–Teulings, C.–
Saint-Paul, G.–Keen, M. (1996): The economic impact of minimum wages in Eu-
rope. Economic Policy, Vol. 11, No. 23, pp. 317–372.
Kertesi Gábor–Köllő János (2004): A 2001. évi minimálbér-emelés foglalkoztatási
következményei [The employment effect of the 2001 minimum wage hike]. Közgaz-
dasági Szemle, Vol. 51, No. 4, pp. 293–324.
Neumark, D.–Wascher, W. (2004): Minimum wages, labor market institutions, and
youth employment: a cross-national analysis. Industrial & Labor Relations Review,
Vol. 57, No. 2, pp. 223–248.

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 5.5 Youth in public employment, with particular...

5.5 YOUTH IN PUBLIC EMPLOYMENT, WITH PARTICULAR

EMPHASIS ON EARLY SECONDARY SCHOOL LEAVERS*
György Molnár
Since the introduction of the Youth Guarantee scheme in 2015, youth register-
ing as unemployed must be given, in principle, a substantial offer, which can
be a training opportunity or an open labour market job (see subchapter 5.2).
In previous years, especially since the school leaving age was lowered, there
were no significant barriers to school-leaving youth gaining their first work
experience in public employment. In this subchapter we examine the influ-
ence of the lowering of the school-leaving age1 on the entry of youth between
the ages of 16–19 into public employment, and how this changed upon the
introduction of the Youth Guarantee scheme.
The data
The analysis is based on the so-called Admin3 administrative database of
the Centre for Economic and Regional Studies, which contains the data of
50 percent of the Hungarian population of 2003 at an individual level up to
2017. Our data on public employment starts with 2011. Our analysis focuses
on those who were registered as unemployed and taken into public employ-
ment before the age of 20, between 2011 and 2017. At the beginning of each
public employment episode, the educational attainment of the individual
at the date of the registration is recorded. Wherever there were gaps in the
educational attainment data received from the Hungarian Educational Au-
thority, we remedied those with data from the public employment database
to the extent possible.
In the presentation of the results, for the sake of better clarity, we provided
absolute figures multiplied by two, and we did not wish to burden the read-
er with elaborating on the minor statistical errors resulting from a sample of
50 percent.
Youth registered as unemployed or in public employment
While in 2011 and 2012 hardly any 16-year-olds and only a small number
* I would like to thank Zsuzsan-
of 17-year-olds registered as unemployed, 2013 and 2014 saw a significant na Sinka-Grósz for her invalu-
rise in these figures. The total number of registrants under the age of 20 rose able help in the processing of
the data.
dynamically both in 2012 and 2013, but it stagnated in 2014, while people 1 As of 1 September 2012, the
registered at an increasingly young age – which is presumably in connection school-leaving age has been
with the lowering of the school leaving age. The number of the youngest new lowered from 18 to 16 years of
age. The first group to whom
entrants peaked in 2016 (Table 5.5.1). this was relevant was those
who had not yet commenced
The increase can be explained neither by the developments in unemploy- the 9th grade in the 2011/2012
ment (see Table 5.7 of the chapter on Statistical data), nor demographic data. school year.

115
György Molnár

These are relatively low figures: exactly 2 percent of 16-year-olds and 4 per-
cent of 17-year-olds in 2016.
Table 5.5.1: The number of youth between the ages of 16–19 registering
as unemployed for the first time, 2011–2017
Age at the time of first
2011 2012 2013 2014 2015 2016 2017
registration
16 44 106 738 1,980 1,716 1,948 1,670
17 284 472 1,412 2,660 3,676 3,924 3,718
18 6,796 9,360 10,196 9,714 8,562 7,742 6,754
19 10,660 12,902 12,728 10,482 9,344 8,240 7,306
Total 17,784 22,840 25,074 24,836 23,298 21,854 19,448
The total number of the
477,855 479,224 465,768 447,224 427,252 407,023 395,715
age group of 16–19
Note: The results obtained from the sample of 50 percent were multiplied by 2 in the
table.
Source: Author’s calculations, based on the Admin3 database; source of the demo-
graphic data: the demographic database of the Hungarian Central Statistical Office.
The increase can be explained neither by the developments in unemployment
(see Table 5.7 of the chapter on Statistical data), nor demographic data. These
are relatively low figures: exactly 2 percent of 16-year-olds and 4 percent of
17-year-olds in 2016.
In the case of 18- and 19-year-olds, a more marked rise can only be seen
between 2011 and 2012, which continued slightly in 2013 as well, in the
case of 18-year-olds. In this year, the share within the age group of 18-year-
olds who registered as unemployed for the first time during that year was 8.6
percent, while that of 19-year-olds was 10.5 percent; and it continuously de-
creases from then on.
Table 5.5.2 shows the number of those entering public employment for the
first time, broken down by age. In 2011 and 2012, there were essentially no
16- and 17-year-olds in public employment, and even the number of 18- and
19-year-olds was relatively low. The number of youth entering public employ-
ment suddenly increased in 2013, and peaked the next year at a figure of 8400.
Even though the Youth Guarantee scheme was launched in 2015, eligible
youth could still enter public employment if they initiated it themselves. Thus
in the case of the two younger groups, the number of those entering public
employment continued to grow after 2014, peaking in 2016. Their share also
grew continuously within the group of those entering public employment for
the first time, and reached 13.6 percent in 2016.
The entry of youth into public employment received substantial media cov-
erage; numerous news articles reported on cases where children of poor fami-
lies left school due to the lure of public workers’ wages (see Fülöp, 2016).
In response to the phenomenon, the regulation was amended: Government
Decree 1139/2017 (20th March) provided that those under the age of 25 “may

116
 5.5 Youth in public employment, with particular...

enter public employment schemes only if the Youth Guarantee labour market
scheme does not offer them any other realistic opportunities”. This resulted in
a significant drop in the numbers of all age groups in 2017, and the share of
those under the age of 20 within the number of new entrants also decreased
somewhat (to 10.3 percent). In the course of the seven years reviewed, a total
of nearly 40 thousand youth entered the public employment system.
Table 5.5.2: The number of youth between the ages of 16–19 entering public
employment for the first time, and their share within the group of all first entrants,
2011–2017
Age at the time of first entry
2011 2012 2013 2014 2015 2016 2017 Total
into public employment
16 2 6 190 630 500 744 352 2,424
17 8 60 398 1016 1132 1434 692 4,740
18 372 1,180 2,390 3,148 2,366 2,430 1,052 12,938
19 1,850 2,266 3,928 3,610 2,400 2,372 1,044 17,470
Total 2,232 3,512 6,906 8,404 6,398 6,980 3,140 37,572
Share (percentage) 1.0 3.6 6.1 10.0 10.8 13.6 10.3 5.7
Note: The public employment of a small section of those entering for the first time in
2011 already commenced in 2010. The results obtained from the sample of 50 per-
cent were multiplied by 2 in the table.
Source: Author’s calculations, based on the Admin3 database.
Nearly 30 percent of newly registered 16-year-olds became a public employ-
ee within 90 days (Table 5.5.3). The highest figure can be seen in 2016. The
younger someone was, the more likely it was that they would become a pub-
lic worker within 90 days. In 2013, the proportion of youth becoming pub-
lic workers within three months rose significantly, and this trend kept grow-
ing in 2014, when it peaked at 21 percent. It may be an effect of the Youth
Guarantee scheme that in 2015 the rate of those starting public work early
decreased somewhat among the age group of 17–19-year-olds, but it stagnat-
ed among 16-year-olds, and then kept growing steadily in 2016. A marked
decrease only occurred in 2017.
Table 5.5.3: The share of those among the 16–19-year-olds registering ias
unemployed for the first time who entered public employment within 90 days,
2011–2017 (percent)
Age at the time of
2011 2012 2013 2014 2015 2016 2017 Average
first registration
16 5 6 28 28 28 36 19 28
17 1 11 24 27 20 24 11 20
18 3 9 18 23 17 17 8 14
19 2 6 11 15 12 13 6 9
Total 2 8 15 21 16 19 9 13
Source: Author’s calculations, based on the Admin3 database.

117
György Molnár

A similar trend emerges when we examine entering public works within 30

or 180 days. In 2016, 26 percent of those registering at the age of 16 became
public workers within only 30 days, while 42 percent within 180 days.
It is worth reviewing this inversely as well – how much time youth enter-
ing public works spent in the registry before they become public workers. Ac-
cording to the figures, on average, nearly 60 percent of those becoming public
workers at the age of 16 spent 30 days or less in the registry before entering
the scheme: thus, presumably, they registered with the explicit aim of becom-
ing public workers (Table 5.5.4). A similar situation is found in the case of
44 percent of 17-year-olds. With the increase of age, this value keeps decreas-
ing somewhat. As we progress in time, a significant increase in the number of
those becoming public workers within a very short period of time occurs in
2014, and their share essentially stagnates after that.
Table 5.5.4: The share of those among youth entering public employment for the first
time who, before doing so, spent not more than 30 days in the unemployment
registry, 2012–2017 (percentage)
Age at the time of first entry
2012 2013 2014 2015 2016 2017 Average
into public employment
16 67 63 53 56 63 59 58
17 40 46 41 45 45 43 44
18 41 35 44 41 37 37 39
19 20 18 28 33 34 31 26
Total 27 27 38 40 40 39 35
Note: In the case of those already in public employment on 1 January 2011, start date
is known only for those who had previously done public works managed by the
Public Employment System, thus we omitted that year.
Source: Author’s calculations, based on the Admin3 database.

Early school leaving

Approximately one tenth of the under 20 age group entering public employ-
ment have not even completed elementary education. Their share among
16-year-olds is more than 20 percent. The share of those entering public em-
ployment having completed vocational school or secondary school is also ap-
proximately 10–10 percent (Table 5.5.5).
Nearly 60 percent of those who had not completed elementary school at
the time of entry into public employment did not complete the eighth grade
of elementary school at a later time either, and whether they have completed
it or not is unknown for a further 20 percent.
At least 80 percent of those under the age of 20 who entered public em-
ployment having completed the eighth grade of elementary school had also
attended some type of secondary school (it is unclear for some of them). Pro-
gressing in time, the proportion of those who attended secondary school con-
tinuously increases (Table 5.5.6). Thus the proportion of those who become

118
 5.5 Youth in public employment, with particular...

public employees after leaving secondary school increases. A sharp increase

occurred between 2013 and 2014, among 16-year-olds. While in 2013 “only”
52 percent of those entering public employment with the eighth grade of el-
ementary school completed left secondary school, in 2014 this figure was
78 percent. Therefore the increase in numbers showed in Table 5.5.2 was to
a great extent due to those leaving secondary school.
Table 5.5.5: The total distribution of educational attainment at the time of entry into
public employment in the period between 2011 and 2016 (percentage)
Educational attainment
lower than the
the eighth grade
Age at the time of eighth grade of vocational secondary
of elementary total
first entry into elementary school school
school
public employment school
16 23 77 0 0 100
17 18 78 4 0 100
18 11 70 11 8 100
19 6 60 15 18 100
Total 11 67 11 11 100
Source: Author’s calculations, based on the Admin3 database.
Table 5.5.6: The share of those among youth entering public employment with an
educational attainment of the eighth grade of elementary school who also attended
secondary school, broken down by the year of entry, 2011–2017
2011 2012 2013 2014 2015 2016 2017 Total
Total no. of
766 1,834 3,626 4,482 3,638 4,128 1,780 20,254
group
Share (percent-
49 71 75 84 89 90 91 81
age)
Note: The number of the total group obtained from the sample of 50 percent was
multiplied by 2.
Source: Author’s calculations, based on the Admin3 database.
In 2011, 11 percent of those entering public employment with an educational
attainment of the eighth grade of elementary school and having attempted
attending secondary school became public employees within three months of
leaving school. This rate rose to approximately 30 percent from 2012. These
are those who became public employees essentially immediately after leaving
school, or following a short “technical break”.
Among those who entered public employment before 2016 having an edu-
cational attainment of the eighth grade of elementary school and having at-
tended secondary school, the share of those who obtained secondary level
qualification two years later2 is only 3 percent (Table 5.5.7). Only 10 percent
of this 3 percent obtained a secondary school diploma, while the rest attended 2 The two years were calculated
as calendar years, since the ex-
vocational training. In 2011, the proportion of those completing secondary act time of the completion of
school within two years was somewhat higher than in the other years, and school is not known.

119
György Molnár

this proportion does rise to a minimal extent broken down by age at entry
into public employment, but these differences are statistically not significant.
Table 5.5.7: The share of those among youth entering public employment with an
educational attainment of the eighth grade of elementary school and having started
secondary school who obtained secondary-level qualification within two years,
2011–2015 (percentage)
Age at the time of
2011 2012 2013 2014 2015 Average
first entry
16 0 0 0 0 2 1
17 0 0 2 1 3 2
18 4 2 2 3 3 2
19 7 4 3 4 5 4
Total 6 3 3 3 4 3
Note: The data regarding educational attainment levels two years after entry into
public employment are only known from the data of the Hungarian Educational
Authority, thus the number of cases in this table is only 14,346.
Source: Author’s calculations, based on the Admin3 database.

Main conclusions
Overall, it can be concluded that the number of 16- and 17-year-olds entering
public employment rose significantly after the lowering of the school leaving
age: in the period under review, a total of approximately 7 thousand people
under the age of 18, and 38 thousand people under the age of 20 became public
employees. The rate of new labour market entrants entering public works did
not decrease upon the launch of the Youth Guarantee scheme, only after the
relevant government decree was issued in 2017. The educational attainment
of nearly 80 percent of youth entering public works was not higher than the
eighth grade of elementary school, and having entered public employment,
their chance of completing secondary school within a few years is insignifi-
cant, even if they had started it before their entry into public employment.
References
Fülöp, Zs. (2016): Újratermelni a nyomort. Fiatalkorúak a közfoglalkoztatásban. Ma-
gyar Narancs, Vol. 17., 28 April.
Sebők, A. (2019): The Panel of Linked Administrative Data of CERS Databank, Buda-
pest Working Papers on the Labour Market, BWP-2019/2.

120
 6.1 Schooling and employment of Roma youth...

6 THE SITUATION OF ROMA YOUTH

6.1 SCHOOLING AND EMPLOYMENT OF ROMA YOUTH:
CHANGES BETWEEN 2011 AND 2016
Ágota Scharle
Roma youth complete fewer grades in school – and, closely connected to that,
are less likely to get a job – than non-Roma youth (Kemény–Janky, 2005, Kertesi,
2005). The disadvantages that accumulate over generations (and are difficult to
overcome), the discriminatory, segregating attitudes of mainstream society and
policy measures (or the lack of intervention) all contribute to the disadvantages
of the Roma. While the role of institutional factors in explaining the schooling
gap has been examined in several recent studies (e.g. Hajdu et al, 2014, Kertesi–
Kézdi, 2014, 2016, Kiss¸ 2018), there is very limited information and analysis
available on the Roma non-Roma employment gap. This sub-chapter provides
a descriptive overview of recent trends, based on the HCSO’s population sur-
veys, which measure Roma ethnicity more accurately than before.
Education
For the sake of brevity, trends in schooling are portrayed by using two indica-
tors, the share of those completing matriculation1 and the share of full-time
students. The share of matriculated youth by age (Figure 6.1.1)2 is shaped
mainly by developments in the period before 2011: those who were 29 years
old in 2016 completed secondary school around 2005–2006, and those who
were 19 years old at that time also acquired their basic skills in public educa-
tion before the reform of 2011. In the non-Roma population, there is a sig-
nificant change only in the case of men: in all cohorts over the age of 18 the
share of matriculated boys is significantly higher (5–6 percentage points) in
1 This is an exam (comparable
2016 as compared to 2011 (Figure 6.1.1). In the Roma population, there is to A levels in the United King-
a significant improvement both among boys and girls. For Roma girls, the dom) that closes the academic
track of secondary education
share of matriculated students starts to increase only in the younger cohorts, (ISCED 3A or 3B) and passing
but the improvement is large – almost twice as large as for boys. it is one of the conditions of en-
tering tertiary education.
The importance of the improvement observed in the case of Roma youth is 2 The employment opportu-
underlined by the fact that their schooling is hindered by several factors, ac- nities and expected wages of
matriculated students are sig-
cording to previous research. Hajdu et al. (2014) estimate3 that more than half nificantly better than those
of the Roma – non-Roma differences observed in the chances of dropping out of non-graduates (Hajdu et al,
2015).
of secondary education is explained by the level of knowledge acquired by the 3 The study examined the
end of primary school, the quality of the secondary school, and the material school performance and entry
to tertiary education of a full
and human resources available during secondary studies. A significant part of higher secondary school co-
the remaining difference can be traced back to social isolation: the fact that hort of Roma and non-Roma
students based on data from
Roma youth are much less likely to have close links with those who do well the Career Tracking Survey
in school than non-Roma youth. between 2006–2012.

121
Ágota Scharle

Figure 6.1.1: Share of matriculated youth by age, 2011, 2016 (percentage)

Women Men
1.0 1.0

0.8 0.8

0.6 0.6

0.4 0.4

0.2 0.2

0.0 0.0
18 19 20 21 22 23 24 25 26 27 28 29 18 19 20 21 22 23 24 25 26 27 28 29
Age Age
Non-Roma, 2011 Non-Roma, 2016 Roma, 2011 Roma, 2016
Source: Calculations of Tamás Molnár using the 2011 Census and 2016 Microcensus
4 Act CXC of 2011 on National of the Hungarian Statistical Office.
Public Education lowered the
compulsory schooling age from Most recent developments are captured by the other indicator: the share of
18 to 16. The age limit of 16 first full-time students by cohort shows the share of young people who continue
applied to those who started
eighth grade in the 2011/2012 their education in secondary and higher education after primary school. The
school year. reduction of the compulsory school age introduced in 20114 increased the
5 The measurement of Roma
identity was very similar in share of early school leavers among both Roma and non-Roma youth, but
two surveys, but the census may this effect was significantly higher for Roma, particularly Roma men (Figure
include a higher rate of those
who claim to be Roma for two 6.2). As discussed in subchapter 6.2, this effect was above the average in dis-
reasons: on the one hand, the advantaged small regions. Comparing the data of the 2011 Census conduct-
sample is comprehensive, while
in the sample of the micro- ed before the reform and the 2016 Microcensus five years later,5 the share of
census, Roma settlements are full-time students decreased by 4–7 percentage points for non-Roma, and by
underrepresented, and on the
other hand a special campaign 14 (women) and 27 (men) percentage points for Roma youth in the 17 year-
encouraged the assumption of
Roma identity at the time of the
old cohort. The decrease is already significant among 16 year-olds in the case
2011 census. of Roma youth.
Figure 6.1.2: The share of full-time students among Roma and non-Roma youth, 2011, 2016
(percentage)
Women Men
1.0 1.0

0.8 0.8

0.6 0.6

0.4 0.4

0.2 0.2

0.0 0.0
15 16 17 18 19 20 21 22 23 24 25 15 16 17 18 19 20 21 22 23 24 25
Age Age
Non-Roma, 2011 Non-Roma, 2016 Roma, 2011 Roma, 2016
Source: Calculations of Tamás Molnár using the 2011 Census and 2016 Microcensus
of the Hungarian Statistical Office.

122
 6.1 Schooling and employment of Roma youth...

The comparison of the data for 2011 and 2016 also shows that Roma youth
are less involved in the expansion of higher education: in the 21–23 cohort,
the share of full-time students among non-Roma increased by an average 5
percentage points, while among Roma it increased by only 3 percentage points
in five years. This also means that the disadvantage of Roma people has fur-
ther increased in participation in higher education.
Employment
According to the CSO Labour Force Survey, in 2017, 55 percent of men and
36 percent of women were employed in the Roma population aged 15–64,
while within the non-Roma population, the proportion of the employed was
76 and 62 percent, respectively. The employment of Roma people increased
more than that of non-Roma between 2014 (the first year when ethnicity was
included in the Labour Force Survey) and 2017, so that the Roma employment
gap has somewhat narrowed. At the same time, the already huge disadvantage
of the Roma further increased regarding the rate of early school leavers and
youth not in employment, education or training (NEET). These two indica-
tors slightly improved in the non-Roma population between 2014 and 2017,
while they worsened or remained unchanged in the case of Roma youth.6 In
the five years between the 2011 Census and the 2016 Microcensus (roughly
corresponding to a period of steady economic growth), the employment rate
increased from 16 to 25 percent for Roma men, and from 7 to 12 percent for
Roma women aged 16–25 (excluding public works).
Table 6.1.1: The 16–25 year-old population by ethnicity and labour market status,
2011 and 2016 (thousand people)
Roma Non-Roma
2011 2016 2011 2016
Men
Full time education 9.8 7.9 311.1 261.6
Employment 5.0 9.2 184.2 213.8
Public works 1.7 4.3 5.1 9.1
Parental leave 0.3 0.1 0.3 0.3
Other 15.4 15.7 99.9 69.5
Total 32.2 37.2 600.6 554.3
6 The rate of early school leav-
Women ers increased from 57 to 65
Full time education 8.3 6.9 303.8 258.8 percent (decreased from 10 to
Employment 2.1 3.6 149.0 171.6 9 percent among non-Roma),
Public works 0.7 2.0 3.7 7.4 the NEET-rate was 38 percent
in both years (decreased from
Parental leave 9.2 7.6 29.7 30.6 13 to 9 percent among non-
Other 9.8 10.1 84.6 59.5 Roma). The former indicator
Total 30.1 30.2 570.8 527.9 is published by the CSO on the
18–24 year-old age group, and
Source: Own calculations using the 2011 Census and 2016 Microcensus of the Hun- the latter on the 15–24 year-old
garian Statistical Office. cohort (2018).

123
Ágota Scharle

The large sample of the 2011 Census and the 2016 Microcensus also provides
an opportunity to examine employment trends independently of education.
Comparing the data of these two surveys, the disadvantage of the Roma is still
large in employment, though it is significantly smaller for those completing
at least secondary school (with matriculation, i.e. ISCED 3A or 3B) than for
the unskilled (Figure 6.1.3). The disadvantage of young Roma women did not
decrease between 2011 and 2016, despite an increase in the employment rate
of both education categories. In the case of young Roma men, there is a sig-
nificant decrease in the disadvantage of the unskilled, but this is largely due to
public works. In 2016, 34 percent of low educated Roma men in employment
participated in public works, while among the non-Roma, the corresponding
ratio was only 8 percent. For working Roma women, the share of public work-
ers is even higher: 40 percent of the unskilled and 21 percent even of those
with at least secondary education were employed in public works in 2016.
Figure 6.1.3: Employment rate of the 16–25 year-old, not in education population, 2011 and 2016
(percent)
100 100
80 80
60 60
40 40
20 20
0 0
2011 2016 2011 2016 2011 2016 2011 2016 2011 2016 2011 2016 2011 2016 2011 2016
Non-Roma Roma Non-Roma Roma Non-Roma Roma Non-Roma Roma
Primary or vocational Secondary education Primary or vocational Secondary education
school or higher school or higher
Public works Employed (excluding public works)
Source: Own calculations using the 2011 Census and 2016 Microcensus of the Hun-
garian Statistical Office.

References
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(2015): Az érettségi védelmében [In defence of the school leaving exam]. Budapesti
Munkagazdaságtani Füzetek, BWP, 2015/1.
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dents in Hungarian Secondary Schools]. Budapesti Munkagazdaságtani Füzetek,
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ban [On the margins of society. Roma in school and in the labour market]. Osiris,
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 6.1 Schooling and employment of Roma youth...

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nist Transition. Budapesti Munkagazdaságtani Füzetek, BWP, 2010/9. .
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lensége [Educational inequalities and the life chances of Roma youth]. Budapesti
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gével való összefüggései a leghátrányosabb helyzetű térségekben [Factors and moti-
vations determining selective school choice and their correlation with school qual-
ity in the most disadvantaged small regions of Hungary]. In: Fejes, J. B.–Szűcs, N.
(eds.): Én vétkem. Helyzetkép az oktatási szegregációról. Motiváció Oktatási Egye-
sület, Szeged, pp. 147–172.
KSH (2018): Munkaerőpiaci helyzetkép, 2014–2018 [Snapshot of the Labour market].
Központi Statisztikai Hivatal, Budapest.

125
János Köllő & Anna Sebők

6.2 NEIGHBOURHOOD-RELATED DIFFERENCES IN THE

SHARE OF YOUTH NOT IN EDUCATION, EMPLOYMENT OR
TRAINING BEFORE AND AFTER LOWERING THE SCHOOL-
LEAVING AGE
János Köllő & Anna Sebők
Unemployment among working-age minors (aged 15–18) continuously de-
creased after the first years of the political changeover and then fell to less than
three percent as a result of raising the school-leaving age to 18.1 This situation
changed when Act CXC of 2011 took effect, which set the school-leaving age
at 16 years again, enabling pupils older than that to exit to the labour mar-
ket and (according to the plans of the decision makers) take up employment.
Using elementary methods, this short Subchapter aims at describing how
the activity composition of the affected age group changed in disadvantaged
and better off neighbourhoods of the country as a result of the measure. We
focus on youth who have passed the age of 17 but have not yet reached 18,
who, with a few exceptions, were subject to compulsory education before
2011 but not thereafter. We point out that participation in education de-
creased the most in the most disadvantaged neighbourhoods, while expan-
sion in employment was unable to prevent, even in the most effective labour
markets, an increase in the number of youth not in education, employment
or training (NEET).
Our analysis is based on the total census population of ten million of the
2011 census and the random sample of one-million of the 2016 micro-census.2
Data on the 17-year-olds are not possible to examine in a detailed geographi-
cal breakdown in this way: in order to reach an adequate sample size and grasp
the characteristics of the micro-environment, the 45,500 Hungarian census
tracts with an average population of 250 were divided into quartiles accord-
ing to various dimensions, based on their data as observed in 2011. The di-
mensions considered are the employment and unemployment rate of the local
population with a lower-secondary qualification; an indicator describing the
size and quality of the labour market accessible for those with a lower-second-
ary qualification, and the proportion of the Roma within the population.3
1 For the impact of the School The changing role of regional differences over time is measured using proba-
Education Act of 1996, see bilistic regression. The outcome variable indicates whether the ith 17-year old
Subchapter 2.5.
2 The calculations were carried living in the jth census district was in education or was NEET in 2011 and
out in the research lab operated 2016. The estimated coefficients in the first three columns of figures in Ta-
jointly by the Central Statisti-
cal Office and CERS HAS. ble 6.2.1 show how likely the individual belonging to a given group (Roma
3 For the detailed calculation boy, Roma girl, non-Roma girl) was employed in the given year compared
method of indicators see the
Appendix at the end of the
with non-Roma boys. In the 4–6th columns of figures, the coefficients indicate
Subchapter. how the probability of the outcome is influenced by the immediate neigh-

126
 6.2 Neighbourhood-related differences in the share...

bourhood belonging to the second, third or fourth worst census tract quartile
(Q2 –Q4) instead of the best quartile (Q1) determined by the 2011 indicator
(employment rate, unemployment rate, etc.).
Table 6.2.1: The impact of gender, ethnic group and neighbourhood characteristics
on participation in education, 2011, 2016 (probabilistic regression)
Non- Census tract quartiles
Roma Roma
Census tract Roma Constant R2 N
boy girl 2. 3. 4.
indicator girl
Employment rate
–10.8 –0.4 –29.2 –0.0 –0.4 –4.0 99.2 0.11 9358
2011
(4.5) (1.5) (9.3) (0.1) (1.5) (5.8)
–25.4 0.0 –26.0 0.1 –3.0 –12.7 97.6 0.14 7464
2016
(9.6) (1.2) (8.9) (0.2) (4.5) (12.5)
Unemployment rate
–12.1 –0.4 –30.3 –0.5 –1.0 –3.0 99.5 0.10 9358
2011
(5.1) (1.5) (9.6) (1.5) (2.6) (4.5)
–29.3 0.1 –30.3 –2.3 –4.3 –10.1 98.3 0.12 7464
2016
(11.3) (0.3) (10.5) (3.7) (6.1) (10.4)
Quality of the labour marketa
–12.8 –0.4 –30.8 –0.5 –0.9 –1.9 99.6 0.10 9358
2011
(5.4) (1.8) (9.8) (1.4) (2.5) (4.4)
–30.7 0.1 –31.4 –1.7 –3.9 –7.1 98.2 0.11 7464
2016
(11.9) (0.2) (10.9) (2.2) (5.1) (8.8)
Proportion of the Roma
–10.3 –0.4 –28.5 –3.6 –3.6 –4.1 95.2 0.11 9358
2011
(4.3) (1.5) (9.0) (4.9) (5.1) (6.5)
–25.6 0.2 –26.3 –7.2 –10.1 –10.8 86.1 0.13 7464
2016
(9.6) (0.4) (8.9) (6.0) (9.2) (10.8)
a
See the Appendix at the end of the Subchapter.
Sample: 17-year-old residents of the census tracts observed in both the 2011 cen-
sus and the 2016 micro-census. A Roma is defined as someone who identifies
themselves as Roma first or secondly when asked about ethnicity or speaks Roma,
Boyash or Romani as a first or second language.
N = the number of individuals observed. Coefficients were multiplied by one-hun-
dred, t-values are provided in brackets.
Roma and non-Roma youngsters are differentiated between because the for-
mer attend much worse basic and secondary schools on average compared
with the non-Roma, their immediate neighbourhood is less likely to encour-
age them to complete their education, or their families are less likely to afford
the additional costs of learning and thus they are more inclined or forced to
drop out of education. In the strongly segregated and typically bad quality
“Roma schools” these effects are further magnified.
Please note that, for 2011 the equations are estimated only for the subset of
census tracts also observed in the micro-census. Q1 – Q4 groups contain the
same census tracts in 2011 as in 2016. (Results concerning the total sample
of the census are similar.)

127
János Köllő & Anna Sebők

Apparently, there is no significant difference in the participation of non-

Roma boys and non-Roma girls either in 2011 or in 2016. A much (about
thirty percentage points) smaller proportion of Roma girls attended school
in 2011 but their situation did not change until 2016 and even slightly im-
proved compared with non-Roma girls. However, there is an enormous de-
cline among Roma boys, who were 10–13 percentage points less likely to
attend school in 2011 and their disadvantage had increased to 25–31 per-
centage points by 2016.4
Considering neighbourhood characteristics, a similar pattern is seen in the
first three blocks of the Table: participation in education was already (2–4 per-
centage points) lower in 2011 in the quartile the most disadvantaged, based
on the given indicator, than in the best quartile. However, this lag had become
much more dramatic (7–13 percentage points, depending on the neighbour-
hood indicator considered) by 2016, when comparing individuals of the same
gender and ethnicity.
The constants of the equations measure the participation in education of
non-Roma boys living in the best neighbourhood: even this indicator dete-
riorated by one to one and a half percentage points.
In the lowest block of the Table, census tracts were divided into quartiles ac-
cording to the proportion of the Roma in the population in 2011. This does
not have an impact on the coefficients obtained with individual variables.
Participation in education was increasingly low towards the fourth quartile
in 2011 and also – to a far greater extent – in 2016. Controlled for ethnicity,
these results suggest participation of non-Roma youth also decreased signifi-
cantly in census tracts with a high proportion of the Roma.
The values of constants in the equations are also different from those in the
first three blocks. The low share (practically zero in the first quartile) of the
Roma does not, in itself, guarantee high participation in education and the
share of boys attending school also declined in these (primarily rural) quartiles.
The dependent variable of the similarly structured Table 6.2.2 is NEET (not
in education, employment or training) status. The estimations using the four
indicators, yielding similar results are not described, only the calculation re-
lying on quartiles based on the 2011 employment rate is presented, this time
4 The estimated value depends focusing more on NEET levels in 2016.
on which census tract indica-
tors were controlled for when
The probability of a 17-year-old Roma boy living in the worst census tract
assessing individual effects. quartile not being in education, employment or training is estimated at 14.8
5 Please note that estimations per cent (11.3 + 3.0 + 0.5) in 2011. Calculated similarly, the probability is at
using weighted and unweight-
ed population figures hardly 38.7 per cent in 2016, which is essentially the same as the actually observed
differ, which is explained by
the fact that the census tracts
figure in the given population (38.5 per cent).5 Although our estimations are
were defined by taking into ac- not pinpoint accurate (as revealed by the relatively low explanatory power of
count the workload of census
takers and thus their size is the equations), they are sufficiently reliable to show that the proportion of
fairly similar. the 17-year-olds attending school decreased significantly between 2011 and

128
 6.2 Neighbourhood-related differences in the share...

2016, which was hardly offset by the increase in employment. As regards the
social consequences, it is especially worrying that by 2016 the proportion of
Roma boys living in a disadvantaged neighbourhood, not in education, em-
ployment or training had increased to an alarmingly high level (at least dou-
ble the 2011 level). In 2016, four out of ten such youth were not in education,
employment or training.
Table 6.2.2: The effect of gender, ethnicity and certain neighbourhood
characteristics on NEET status (not in education, employment or training), 2011,
2016 (probabilistic regression)
Non- Census tract quartiles
Roma Roma
Census tract Roma Constant R2 N
boy girl 2. 3. 4.
indicator girl
Employment rate
11.3 0.5 30.4 0.4 0.9 3.0 0.5 0.10 9358
2011
(4.9) (1.8) (9.7) (1.5) (2.3) (4.4)
28.5 0.5 31.3 2.1 3.7 9.3 0.9 0.13 7464
2016
(11.2) (0.9) (10.9) (3.8) (5.7) (10.2)
Sample: 17-year-old residents of the census tracts observed in both the 2011 cen-
sus and the 2016 micro-census. A Roma is defined as someone who identifies
themselves as Roma first or secondly when asked about ethnicity or speaks Roma,
Boyash or Romani as a first or second language.
N = the number of individuals observed. Coefficients were multiplied by one-hun-
dred, t-values are provided in brackets.

Appendix
Definition of the census tract indicators

Employment rate. The proportion of those engaged in a gainful activity during

the week preceding the interview within the working age population. Those
who were not working that week but were temporarily away from work are
also regarded as employed.
Unemployment rate. Unemployed is defined as someone who does not work,
were actively seeking employment during the month preceding the interview
and would be able to take up a job if found. Their number is compared with
the active age population.
The proportion of the Roma. A Roma is defined as someone who identifies
themselves as Roma first or secondly when asked about ethnicity or speaks
Roma, Boyash or Romani as a first or second language. Their number is com-
pared with the active age population.
Indicator for the quality of the labour market. The labour market for a cen-
sus tract population with certain educational attainment is described with
an indicator (Q = V/A), where V is the number of jobs profitably accessible
for an individual from their census tract and A is the number of competi-
tors for whom these jobs are also accessible. A job is considered accessible if

129
János Köllő & Anna Sebők

the net wages less travel-related monetary and time costs are higher than the
expected amount of available benefits and public works wages. The related
estimation was undertaken by Melinda Tir and János Köllő, using the GEO-
database of the Hungarian Academy of Sciences (http://adatbank.krtk.mta.
hu/adatbazisok___geo).

130
 7.1 Workplace and non-formal education...

7 ADULT EDUCATION AND TRAINING AND OVER-

QUALIFICATION
7.1 WORKPLACE AND NON-FORMAL EDUCATION AND
1 There have been three surveys
so far (in 2007, 2011/2012 and
TRAINING OF YOUTH
2016/2017). The first pilot sur-
vey was conducted in Hungary
in 2007 as a complementary
Júlia Varga survey to the labour force sur-
vey of the HCSO, and then the
Participation in non-formal education and training may play an important subsequent surveys were inde-
pendent ones. The sample size
role in the adaptation of individuals to changing labour market demands. In was 5800–6500 people.
this subchapter, we will examine a subject that is under-researched in Hun- 2 The survey, named Continu-
ing Vocational Training of
gary: changes in the rates of participation of youth in non-formal education Enterprises (CVTS), collects
and training, and the differences in the probability of participation observ- data on vocational trainings
supported in some form by en-
able based on various characteristics. terprises/companies in organi-
Information on the participation in non-formal education and training sations that employ at least 10
people. The CVTS survey has
is available from three statistical data collections. The first one is the Adult also been conducted three
Education Survey of Eurostat (AES), which collects data in the countries times so far, in 2005, 2010 and
2015.
of the European Union on the 12 months before surveying, about the par- 3 The surveys collect data on
ticipation of adults in formal and non-formal education and training and participation in the following
non-formal forms of educa-
the characteristics of these.1 The second is also a Eurostat survey.2 The third tion and training: vocational
data source is the labour force surveys of the HCSO, the regular quarterly courses that do not provide
qua l if ication, non-forma l
surveys of which include the question whether the respondent had partici- trainings within the National
pated in non-formal education and training during the four weeks preced- Qualification Register (OKJ)
system, participation in vari-
ing the survey. ous seminars and conferences,
In the various waves of the labour force surveys, the extent of the detailed- work-related and team-build-
ing trainings at the workplace,
ness of the questions regarding participation in non-formal education and language courses, computer
training changed several times; until 2014, questions about participation were courses, IT trainings, courses
organised within distance
asked in more aggregated groups, while since then, 12–13 different groups of learning. All forms of e-learn-
non-formal education and training have been distinguished for data collec- ing, webinars, private lessons,
health-related courses, train-
tion about the participation in these. The data collection process increasingly ings held by authorities, driver
training, lectures and courses
intends to map all non-formal forms of education and training.3 related to sports, music, and
The three types of data sources show substantially different participation other hobbies.
rates. We have not been able to establish a reason for this based on the infor- 4 The Eurostat explicitly notes
that due to changes in meth-
mation available to us. According to the AES surveys, the participation rate odologies, the AES-results of
of youth between the ages of 25–34 in non-formal education and training in 2007, 2011, and 2016 are not
comparable directly, and thus
Hungary has grown from 9.7 per cent in 2007 to 44.3 per cent in 2011, and “the results cannot be used for
interpreting the changes in
then to 56.6 per cent in 2016.4 The value recorded in 2016 was higher than lifelong learning participation
the average of the EU-28 or the eurozone (Figure 7.1.1). rates between 2007 and 2016”.
[Eurostat Eurostat Adult Edu-
The labour force survey has documented significantly lower participation cation Survey. Reference Meta-
rates. In 2018, 10.1 per cent of 25–34-year-olds participated in training, while data in Euro SDMX Metadata
Structure (ESMS) 15.2. Com-
this is 17.8 per cent on average in EU-28 countries, and 19.5 percent5 in eu- parability – over time].
rozone countries. 5 Eurostat.

131
Júlia Varga

Figure 7.1.1: The participation rates of 25–34-year-olds in non-formal education

and training, according to the data of the AES surveys
70
60
50

Percent
40
30
20
10
0
2007 2011 2016
EU-28 EU-19 (Eurozone) Hungary
Source: Author’s compilation based on the Eurostat AES surveys.
Figure 7.1.2 shows the changes in the participation of 25–34-year-olds by
educational attainment groups between 2005 and 2018.6 Between 2005 and
2012, the already very low participation rates of 25–34-year-olds decreased
continuously in all educational attainment groups, and then between 2013
and 2015, a higher rate of youth reported participation in training. A part of
the increase may be due to the rearrangement of the classification system (see
footnote 3). Participation rates started declining again after 2015. Through-
out the entire period, participation rates were the highest in the “secondary
school diploma with vocational qualification” and “secondary school diplo-
ma without vocational qualification” groups. After 2014, the lowest rates of
participation in education and training were found in “the eighth grade of
elementary school or less as educational attainment” category.
Figure 7.1.2: The participation rates of 25–34-year-olds in non-formal education
and training, according to the data of the labour force survey,
broken down by educational attainment
8

6
Percent

0
2006 2008 2010 2012 2014 2016 2018
At most lower Vocational qualification Vocational qualification
secondary without matriculation exam and matriculation exam
Matriculation exam without Tertiary education
vocational qualification
Source: Calculated from the data of waves 53–108 of the labour force survey.

6 The annual data are the aver- Aggregating the data of the four waves of the labour force survey of 2018,
age of the quarterly data. we examined the probability of the participation of 16–34-year-olds in non-
7 Binary outcome probit model,
whether they participated in formal education and training with a simple probability model as well.7 The
education or training (yes/no). results – the significant marginal effects – are summarised in Table 7.1.1.

132
 7.1 Workplace and non-formal education...

Table 7.1.1: The determinants of the probability of non-formal training among

16–34-year-olds, 2018
Variable Marginal effect dy/dx
Educational attainment level
Vocational school (vocational qualification without 0.031**
a secondary school diploma) (0.01529)
Secondary school diploma without vocational qualifi- 0.042***
cation (0.01304)
0.057***
Higher education
(0.01548)
Labour market status
0.049***
Employed
(0.00948)
Sector
–0.042***
Agriculture
(0.00733)
–0.032***
Industry
(0.00867)
–0.036***
Machinery
(0.01006)
–0.048***
Construction
(0.00534)
–0.037***
Other
(0.01022)
The other control variables used in the model were: Gender, Educational attainment:
vocational qualification with a secondary school diploma, Labour market status:
unemployed, Region binary variables, Place of residence: village, Budapest.
Reference category: Female, the eighth grade of elementary school or less as educa-
tional attainment; inactive, city or town, Southern Transdanubia, vehicle industry.
Standard errors in brackets.
Significant at the ***1 per cent, **5 per cent, *10 per cent levels.
Source: Author’s compilation.

Youth with a vocational qualification were 3.1 per cent more likely, youth
who obtained a secondary school diploma in a grammar school (second-
ary school diploma without vocational qualification) was 4.2 per cent more
likely, and youth with a higher education diploma was 5.7 per cent more
likely to participate in training in 2018 than the reference category of youth
with the eighth grade of elementary school or less as educational attainment.
Youth in employment were 5 per cent more likely to participate in non-for-
mal education and training than inactive youth. Those working in certain
sectors (industry, construction, agriculture) were less likely to participate
in non-formal education and training than the reference category of those
working in the vehicle industry. We did not find significant variabilities in
the probability of participation in education and training based on the rest
of the characteristics recorded (gender, the region of residence, type of mu-
nicipality, other sectors).

133
Károly Fazekas

7.2 THE GROWING IMPORTANCE OF NON-COGNITIVE

SKILLS IN JOB SEARCH AND AT WORK
Károly Fazekas
It is not only the sectoral and occupational structure of the economy that
changes during technological development and transformation of the interna-
tional, regional and social division of labour. There is a substantial shift in the
task content within an occupation, in terms of what skills are required to ac-
complish them. Over the past decades the share of jobs requiring mathematical
and social skills has seen the fastest increase, while the share of jobs requiring
neither mathematical nor social skills has declined the most (Deming, 2017).
Social (non-cognitive) skills are primarily needed for effective cooperation
with others at work. They include the elements of the skill group termed Big
Five in personality psychology: extraversion, agreeableness, conscientiousness,
emotional stability, openness. They also include the theory of mind, which
is the ability to place oneself in another’s position when observing others,
to understand the reasons for other people’s actions and judge their state of
mind from the viewpoint of our goals and actions. The theory of mind ca-
pacity is highly important for the success of cooperation with another person
and within a group in both education and the labour market (DeAngelo–Mc-
Cannon, 2015).1
The increasing importance of non-cognitive skills observed in the past dec-
ades are due to closely related technological, social and demographic reasons.
As a result of technological development (robotization, the spread of produc-
tion and service systems consisting of continuously communicating elements
and the expanding use of artificial intelligence) an increasing proportion of
tasks requiring high-level cognitive skills can be performed by intelligent, com-
puter-controlled equipment. By contrast, the expansion of robotisation has
so far not taken place in occupations requiring non-cognitive skills (Deming–
Kahn, 2018). At the same time, the proportion of these occupations in the
labour market has been steadily increasing partly due to an increase in the
share of employees in the service sector and partly due to the increasing share
of nursing and healthcare jobs, and also because tasks requiring group work,
trust, intuition and social skills play an increasingly important role in mod-
ern business management (Schanzenbach et al, 2016).
Some traditional occupations and jobs will likely disappear, even within
a few years, but new jobs and occupations may emerge in the meantime and
1 Previous volumes of The
Hungarian Labour Market
demand for labour in certain occupations, primarily those requiring non-cog-
have covered the definition nitive skills, is continuously growing. For a long time it seemed that artificial
and measuring of cognitive and
non-cognitive skills in more intelligence is not capable of acquiring or learning non-cognitive skills. How-
detail (Fazekas, 2018a, 2018b). ever, there has also been significant progress in this field recently. According

134
 7.2 The growing importance of non-cognitive...

to forecasts based on results of the most recent developments, robots with

non-cognitive skills will increasingly be able to undertake the necessary tasks
in a wide range of personal services, nursing, elderly care, healthcare, trade
and the creative industries (Morgan et al, 2019).
Considering the expansion of robotization, it is essential that young peo-
ple possess the motivation and abilities necessary for learning the latest skills.
Furthermore, it will be necessary to undertake continuous analysis to reveal
changes in the content of occupations in a labour market and support teach-
ers and educational policy makers in adapting to changes by developing cur-
ricula and methodology (Alabdulkareem et al, 2018).
Although the majority of non-cognitive skills are linked to hereditary traits,
several empirical studies report that parents, the environment and school
are able to develop or modify them to a large extent (Zhou, 2016). Methods
aimed at developing non-cognitive skills (such as project-based groupwork)
are increasingly utilised in educational systems all over the world.2 Several
non-cognitive skills may also be developed in later life, in adult education or
on-the-job training (Hoeschler et al, 2018, Hoeschler–Backes-Gellner, 2018).3
2 The PISA assessment by the
Analysis of job advertisements and recruitment practices shows that the OECD and STEP by the World
level of non-cognitive skills is a significant predictor of successful job search Bank have contained items as-
sessing the non-cognitive skills
(Hoeschler–Backes-Gellner, 2018). This is supported by impact assessments re- of pupils since 2012 (Kautz et al,
porting that programmes for the integration of inactive youth are more suc- 2017, Gaelle et al, 2014).
3 Guerra et al (2014), based
cessful if they also include the development of non-cognitive skills (Guerra on the PR ACTICE model
et al, 2014).4 Numerous examples show that at companies which included developed by the World Bank
specifically for improving non-
the development of non-cognitive skills in their in-company training, in- cognitive skills needed by the
vestment into training yielded significant productivity gains (Adhvaryu et al, labour market, describe what
methods are best suited for
2017, Groh et al, 2012). developing these skills in dif-
In addition to skills development, it is important that employees and em- ferent age groups.
4 For example: Job Corps, Youth
ployers possess relevant information about their skill levels and the yield of Build and Big Brothers Big Sis-
these skills. This information both strengthens the motivation of employees ters in the United States or
EPIDE (Etablissments pour
to improve their skills and increases the willingness of employers to reward l’Insertion dans l’Emploi) in
high-level non-cognitive skills (Bassi–Nansamba, 2019). France (Quintini, 2015).

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Measuring Skills: Improving Cognitive and Non-Cognitive Skills to Promote Life-
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(2019): Toward understanding the impact of artificial intelligence on labor. Proceed-
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7.3 THE LABOUR MARKET SITUATION OF YOUNG

GRADUATES, OVERQUALIFICATION AND THE VALUE
OF HIGHER EDUCATION DEGREES
Júlia Varga
This Chapter overviews how the labour market situation of young graduates
(aged 35 at most) has been changing recently. The expansion of higher educa-
tion, which was typical of the 1990s and the early 2000s, came to a halt after
2012. From 2012 onwards, fewer students entered higher education than ear-
lier1 and from 2015 the share of graduates has been decreasing in the young
population.2 Nevertheless, their share in the total adult population aged 25–
64 continued to grow until 20183 because the more populous working-age
generations had a lower share of graduates. Despite the increase in the number
and proportion of higher education graduates, young graduates continued to
be very successful in the labour market overall.
Figure 7.3.1 shows how the proportion of young graduates from bachelor’s
(BA) and master’s (MA) programmes have changed in the three labour mar-
ket status groups (employees, unemployed and inactive). More than 80 per
cent of MA degree holders were employed over the entire period. BA degree
holders were employed at the start of the period in the same proportion as
MA degree holders, then (probably as a result of the economic crisis) their em-
ployment rate decreased between 2006 and 2010 by 7 percentage points but
this indicator started to increase again after 2010 and by 2016 it had reached
80 per cent. The share of the unemployed grew temporarily around the eco-
nomic crisis but since then has steadily diminished to a very low level of about
1–2 per cent. Changes in the distribution of young graduates by labour mar-
ket status indicate that their chances of employment did not deteriorate but
even improved after the effects of the crisis had worn off.
Figure 7.3.1: The distribution of young (younger than 36) graduates by labour market
status and the level of qualification (MA/BA), 2004–2018 (percentage)
80

60
Percent

40

20

0
1 The indicators of school edu-
2004 2006 2008 2010 2012 2014 2016 2018 cation, Indicator D1.10. (Varga,
Employed BA Employed MA Unemployed BA ed., 2018).
Unemployed MA Inactive BA Inactive MA
2 See: Eurostat.
Source: Calculated from the Labour Force Survey of the Central Statistical Office. 3 See: Eurostat.

137
Júlia Varga

Another key indicator of labour market success is wage return. Young grad-
uates realized very high wage returns on average. The average wage return of
BA/college degree holders younger than 36, compared with those with a lower-
secondary qualification was 130–140 per cent at the beginning of the period.4
It decreased between 2010 and 2012 then started to increase and returned
to the earlier high level, suggesting that the temporary decline was probably
due to the economic crisis. The average wage return of MA degree holders
4 The indicators of school edu- only slightly changed between 2003 and 2016: it ranged between 200–220
cation, Indicator D2.2., D2.8. per cent.5
(Varga, ed., 2018).
5 The indicators of school edu- The average change in the wage return is driven by various trends: it may
cation, Indicator D2.8., Table be a result of returns at all points of the wage distribution changing similarly
D2.8.2. (Varga, ed., 2018).
6 Quantile regression estimates
or also because primarily the well-paid and badly paid graduates experience
were based on a subsample of changes in their wage returns.
the Wage Survey containing
those with at least an upper-
Figure 7.3.2 presents changes, over time, in the wage returns (the estimated
secondary qualification (Mat- parameters of the quantile regressions)6 of young BA and MA degree holders
ura). The dependent variable
was the logarithm of earnings, compared with those with an upper-secondary qualification (Matura). Quantile
while the explanatory variables regressions were run for various points of the wage distribution for each year
were qualification categories as
well as potential labour market between 2006 and 2016. Wage returns were estimated for each point of the
experience and its square and wage distribution by quantile regression method.7 The Figure shows changes
a binary variable for gender.
7 See for example Chamberlain
in the wage returns estimated at the 10th, 25th, 50th, 75th and the 90th percen-
(1994), Martins–Pereira (2004). tiles between 2006 and 2016.
Figure 7.3.2: Wage return to BA/college and MA/university degrees
compared with a Matura, by quantiles, 2006–2016
BA/college MA/university
1.0 1.0
0.8 0.8
0.6 0.6
0.4 0.4
0.2 0.2
0.0 0.0
2006 2008 2010 2012 2014 2016 2006 2008 2010 2012 2014 2016
P10 P25 P50 P75 P90
Source: Calculated from data from the Wage Survey.

Differences within the educational attainment groups increased between

2008 and 2012 and returns in the various percentiles diverged during this
period. The return realized by the best-paid graduates belonging to the 90th
and 75th percentile did not change; however, it declined at other points of the
distribution: the decrease was increasingly conspicuous towards the bottom.
The previously lagging percentiles started to catch up with returns measured
at the top of the distribution after 2012, except for the lowest, the 10th per-
centile. The wage return of the bottom ten percentage diminished steadily

138
 7.3 The labour market situation of young graduates...

and to a large extent after 2012: from about 50 per cent in 2006 to below 10
per cent in 2016. The wage return of MA degree holders slightly dropped as
a result of the crisis at the bottom of the distribution, at the 10th and 25th per-
centile, although it started to improve again at the 25th percentile after 2012
and at the 10th percentile after 2014.
The wage return of youth with either a BA or MA qualification at the top of
the distribution, belonging to the 75th and 90th percentile, was high through-
out the period, whereas the return of those belonging to the 50th and 25th
percentile temporarily declined for a few years, probably as a result of the cri-
sis, then started to grow again and returned to earlier levels. The bottom ten
per cent of BA degree holders, however, permanently fell behind the other
groups. This may be due to skills, differences in the quality of higher educa-
tion institutions and departments or mismatch problems. It is possible that
some young graduates can only find a job which does not require a higher
education qualification.
However, it is not easy to determine which jobs are for higher education
graduates. There are three methods in use. The first create categories based on
the subjective judgement of graduates, relying on interviews. Since there is no
long time series of this available, we did not apply this method.
The second method classifies occupations into categories of graduate and
other occupations according to their task content. Occupational classification
systems, such as the international ISCO system8 or the Hungarian Standard
Classification of Occupations (HSCO), are also based on this; they consider
8 See: ILO.
the content of the actual activity undertaken in an occupation and the key 9 Occupations were classified
criterion for grouping is the level of expertise, knowledge and skills necessary into six groups on the basis of
the main categories of the Hun-
to follow an occupation. garian Standard Classification
Figure 7.3.3 shows how the proportion of young graduates changed in of Occupations (HSCO): 1) oc-
cupations in HSCO category 1
the groups formed using HSCO categories.9 Those working in the groups containing managers, senior
of trade, agriculture, industry, semi-skilled and unskilled work of the Figure officials of public administra-
tion and interest organisations,
are very likely to work in occupations not requiring a degree. Those belong- legislators; 2) HSCO category
ing to the first three groups (managers; professionals; other professionals) 2: professionals – occupations
requiring the autonomous use
obviously work in occupations requiring a higher education degree. It is dif- of higher education qualifica-
ficult to judge what qualification the group “office and management (cus- tions; 3) HSCO category 3:
occupations with other higher
tomer services)” requires since it contains heterogeneous occupations in education or upper-secondary
terms of qualification requirements and therefore they are possible to hold qualifications, 4) HSCO cat-
egory 4: office and manage-
with various qualifications. ment (customer service) occu-
Changes in the proportions of workers within the occupational groups do pations; 5) HSCO categories
5, 6 and 7: occupations mostly
not indicate that MA degree holders cannot find a job in occupations requir- with upper-secondary qualifi-
cations – commercial, services,
ing a higher education qualification. Among BA degree holders, however, the agricultural, forestry, industry
share of those working in semi-skilled or unskilled jobs increased to 10 per and construction industry;
6) HSCO categories 7 and 8:
cent after 2011, which suggests that 10–15 per cent of these young graduates semi-skilled and unskilled oc-
do not find a job suitable to their qualifications. cupations.

139
Júlia Varga

Figure 7.3.3: The proportions of those employed in occupational groups

among young graduates, by qualification level, 2004–2018 (percentage)
BA MA
80 80

60 60
Percent

40 40

20 20

0 0
2004 2006 2008 2010 2012 2014 2016 2018 2004 2006 2008 2010 2012 2014 2016 2018
Managers Professionals Other professionals
BA MA
20 20

15 15
Percent

10 10

5 5

0 0
2004 2006 2008 2010 2012 2014 2016 2018 2004 2006 2008 2010 2012 2014 2016 2018
Clerical support workers Sales workers, agricultural, and industrial workers Plant and machine operators, and
assemblers, elementary occupations
Source: Calculated from the Labour Force Survey of the Central Statistical Office.

It is often misleading to examine changes in the proportions of employees in

graduate and “non-graduate” jobs based on occupational classification systems,
on the one hand, because there may be diverse requirements even within oc-
cupations, and on the other hand because they are rarely updated, as that is
10 The estimates regarded mod- an extremely time-consuming process. At the same time, the task content
al educational attainment as re-
quired qualification. Required of occupations changes constantly, for example as a result of technological
qualification was defined by changes or changes in the labour supply, therefore the “required” qualifica-
differentiating between BA/
college and MA/university tion level also changes regularly.
degrees for four-digit occupa- The third method used for defining the qualification level required for an
tional groups for each year. In
the case of multimodal distri- occupation addresses the above problem by assessing the qualification distri-
butions the higher qualification bution of actual jobholders in occupation and its certain value or its mean
was regarded as required quali-
fication. We assessed whether (Verdugo–Verdugo, 1988) or mode (Duncan–Hoffman, 1981, Mendes de
young people with a higher
education degree have the re-
Oliveira et al, 2000, Galasi, 2004, 2008) is regarded as the qualification nec-
quired, higher or lower quali- essary for the occupation. Nevertheless, the fit measured by this method may
fication and based on this we
determined if someone has also be distorted because the actual occupation–qualification matches ob-
adequate qualification (works served are partly due to supply and demand and do not only reflect qualifica-
in a well-matched occupation),
is overqualified or underquali- tion requirements and changes thereof.
fied. (MA graduates cannot be Based on calculations relying on actual occupation–qualification matches,10
regarded as underqualified,
since this is the highest quali- Figure 7.3.4 presents changes in the proportions of well-matched, underquali-
fication category.) fied and, in the case of BA/college degree holders, overqualified graduates of

140
 7.3 The labour market situation of young graduates...

the two qualification levels. The required qualification level is re-defined for
each year, which makes it possible to take into account potential changes in
qualification requirements.
Figure 7.3.4: The proportion of well-matched, overqualified and underqualified graduates,
by qualification level, 2004–2016 (percentage)
BA MA
80 80

60 60
Percent

40 40

20 20

0 0
2004 2006 2008 2010 2012 2014 2016 2004 2006 2008 2010 2012 2014 2016
Matching Undereducated Overeducated Matching Overeducated
Source: Calculated from data from the Wage Survey.

The proportion of those in a well-matched job dropped slightly first be- 11 The results are not to be compared
directly with the results of Galasi
tween 2007 and 2010 among BA degree holders, then declined more (2004, 2008), on the one hand be-
rapidly between 2013 and 2016, while the proportion of the overquali- cause our sample only includes young
people below 36 with at least an upper-
fied increased. In 2016, about 40 per cent of BA degree holders aged secondary qualification (Matura) and
younger than 36 were working in a well-matched job, 42 per cent of Galasi carried out the estimation for
the entire adult population and all
them were overqualified in their job and 18 per cent of them were un- qualification categories, and on the
derqualified. At the beginning of the period, 57 per cent of young MA hand because those studies also con-
tained variables for the interaction be-
graduates were working in a well-matched job, while 43 per cent of tween labour market experience and
them were overqualified. By the end of the period, these proportions overqualification, underqualification
and well-matched qualification. As our
were roughly the opposite: in 2016, 54 per cent of the graduates were sample only includes those aged below
overqualified and 46 per cent of them were working in a well-matched 36 and we have no information on how
many years of labour market experi-
job, which may indicate that during the transition from education to ence these individuals have spent as
work, an increasing share of young graduates starts their career in a job overqualified, underqualified or well-
matched employees, our study does not
requiring a lower level qualification. use such interaction variables.
Overqualification, underqualification and working in a well-matched 12 Based on Duncan–Hoffman (1981),
we investigated the effects of overqual-
job may have an impact on the wage return of young graduates. Rely- ification and underqualification using
ing on Hungarian data, Galasi (2004, 2008) assessed the influence of Mincer earnings functions by breaking
down the number of years in educa-
over- and underqualification on wage return. His analysis included all tion (S) into three elements: number
qualification levels and covered the entire working-age population. In of required years (R), overqualification
years (O) and underqualification years
the following we present changes in the impact of overqualification, un- (U) (S = R + O – U). We estimated the
derqualification and adequate qualification level on wage returns,11 using following extended Mincer earnings
function: log(W i) = β 0 + β1 R i + β 2Oi +
the model of Duncan–Hoffman (1981).12 Similarly to findings of other β3Ui + β 4 EXPi + β 5 EXPi2 + β 6GENi +
μi, where Wi is earnings, EXP is potential
studies using this method [see the summary of Leuven et al (2011)], the labour market experience and GEN is
results show that the return on “required” and “surplus” years in high- binary variable for gender. The Figures
show coefficients β1, β1 and β2 obtained
er education is positive, while the return on “missing” years is negative. from the cross-sectional regressions es-
The return on required years is higher than that of surplus years. The timated for each year.

141
Júlia Varga

wage return on required higher education years was excessively high, 15–17
per cent, over the entire period. Each surplus year in addition to the required
qualification yielded 10–12 per cent, that is less than the required years but
still a substantial positive return. The return on the surplus years started to
decrease after 2012 and had declined to 7 per cent by 2016. Those who were
underqualified for their jobs (in this case BA graduates who undertook tasks
typically performed by MA graduates), realized 10–12 per cent lower wage
returns with each missing higher education year.
However, this model does not take into account the varying skills and com-
petences of young graduates. Firstly, it is not accidental as to which young
graduate has a BA or MA level qualification: it may be related to their unob-
served skills, which may also change over time due to changes in the propor-
tions of applicants and entrants to higher education. Secondly, they do not
randomly take well-matched or mismatched jobs: this may also be related to
unobserved skills. Thus the estimated return on overqualification and under-
qualification may be associated with other unobserved characteristics of the
human capital stock of young graduates.
Figure 7.3.5: Wage return on required, surplus and missing years, 2004–2016
0.2

0.1

0.0

–0.1

–0.2
2004 2006 2008 2010 2012 2014 2016
Required education Overeducation Undereducation
Source: Calculated data from the Wage Survey.
As presented in Subchapter 8.1 on youth employment mobility, the over- or
underqualification of young graduates may be temporary. It may be reasonable
for the talented and highly qualified to start working in jobs inferior to their
skills if it is compensated by faster promotion prospects (Sicherman–Galor,
1990). Subchapter 8.1 presents how the frequency of occupational change
among young graduates increased after 2010 and that in the case of occupa-
tional change, they are less likely to move downwards or switch to another
occupation of the same level than members of other qualification groups. It

142
 7.3 The labour market situation of young graduates...

warrants further analysis of why young graduates are more likely to choose
this way of entering the labour market.
References
Chamberlain, G. (1994): Quantile regression, censoring and the structure of wages. In:
Sims, C. A. (ed.): Advances in Econometrics. Sixth World Congress. Vol. I. Econo-
metric Society Monographs, Cambridge University Press, Cambridge MA.
Duncan, G.–Hoffman, S. (1981): The incidence and wage effects of overeducation.
Economics of Education Review, Vol. 1. No. 1. pp. 75–86.
Galasi, P. (2004): Túlképzés, alulképzés és bérhozam a magyar munkaerőpiacon, 1994–
2002 [Over-education, under-education and wage premiums on the Hungarian la-
bour market, 1994–2002]. Közgazdasági Szemle, Vol. 51. No. 5. pp. 449–471.
Galasi, P. (2008): The effect of educational mismatch on wages for 25 countries. Buda-
pest Working Papers on the Labour Market, BWP, No. 8.
Leuven, E.–Oosterbeek, H. (2011): Overeducation and mismatch in the labor mar-
ket. In: Hanushek, E. A.–Machin, S.–Woessmann, L. (eds.): Handbook of The Eco-
nomics of Education, Vol. 4. Elsevier, pp. 283–326.
Martins, P. S.–Pereira, T. P. (2004): Does education reduce wage inequality? Quantile
regression evidence from 16 countries. Labour Economics, Vol. 11. No. 3. pp. 355–371.
Mendes de Oliveira, M.–Santosa, M. C.–Kikerb, B. F. (2000): The role of human
capital and technological change in overeducation. Economics of Education Review,
Vol. 19. No. 2. pp. 199–206.
Sicherman, N.–Galor, O. (1990): A theory of career mobility. Journal of Political
Economy, Vol. 98. No. 1. pp. 169–192.
Varga, J. (ed.) (2018): A közoktatás indikátorrendszere, 2017 [Indicator system of the
Hungarian public education]. Authors: Hajdu, T.–Hermann, Z.–Horn, D.–Varga, J.,
MTA KRTK KTI, Budapest, 1 February.
Verdugo, R.–Verdugo, N. (1988): The impact of surplus schooling on earnings. Jour-
nal of Human Resources, Vol. 24. No. pp. 629–643.

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8 GEOGRAPHIC AND OCCUPATIONAL MOBILITY

8.1 OCCUPATIONAL MOBILITY AMONG YOUTH WITH
DIFFERENT EDUCATIONAL ATTAINMENT LEVELS
Júlia Varga
During the period that follows the entry into the labour market, at the be-
ginning of the career path, job changes and occupational changes are usually
more common than in the later stages of the career path. This is a natural fea-
ture of the transition from study to work, since – as the so-called job shopping
models describing occupational change pinpoint, at the time of the entry into
the labour market – at the beginning of the career path, individuals are not
yet aware either of their own competencies or their preferences regarding a job
or occupation, which one can only discover through a certain amount of ex-
perience in the labour market. During this job shopping period, young work-
ers experiment with different occupations and jobs. They learn about their
1 Throughout this paper ‘youth’ own competencies and the requirements and characteristics of occupations
is defined as individuals be-
tween the ages of 16 and 35. and jobs during this period of experimentation and “shopping around”, and
It is appreciated that this may this is how eventually they find the occupation that suits or ‘fits’ them ( John-
not correspond with the gen-
eral English understanding of son, 1978, Topel–Ward, 1992, Longhi–Taylor, 2013). The higher frequency
‘youth’. of occupational changes at the beginning of the career path may also be in
2 An occupational change may
mean that an individual switch- connection with the fact that many young people1 who enter the labour mar-
es to a different workplace and ket accept positions and occupations whose requirements do not match their
a different occupation, but can
also mean that they stay at the qualifications (their educational attainment or specialisation), in the hope of
same workplace, while their getting promoted into a different position or occupation at a later point in
position or occupation changes.
This analysis encompasses both time (Sicherman–Galor, 1990). Some of the studies examining the occupa-
cases, it does not differentiate tional changes that occur at the beginning of the career path indeed found
between occupational changes
that do and do not involve that the more frequent changes in occupation characteristic of this period do
a change in workplace as well.
serve one’s progression on the career ladder (Sichermann, 1990), while other
3 The labour survey of the
HCSO is a representative studies highlighted the fact that some of the less favourable occupations ac-
quarterly survey, with the indi- cepted at the beginning of one’s career path, intended as only temporary, do
vidual observations of approxi-
mately 70,000 people in each not necessarily serve as a stepping stone for the further progression, but can
quarter. The sample is replaced entrap young workers (Scherer, 2004, Buchs–Helbing, 2016).
through a rotation procedure.
The individuals that belong to In this subchapter, the changes in the frequency of the occupational chang-
the households included in the
sample are observed through
es2 of youth and the determinants of the probability of occupational mobil-
six successive quarters, thus the ity are analysed3 using an individual-level panel data set extracted from the
data of individuals observed
through successive quarters 1998–2018 waves of the Labour Force Survey conducted by the HCSO. Oc-
can be connected into a panel, cupational mobility is measured through the changes in HSCO classifica-
and the occupational changes
of individuals can be observed. tions4 occurring in two consecutive quarters. Mobility is examined based on
4 Hungarian Standard Classi- movements between the detailed, four-digit HSCO categories; the aggregated
fication of Occupations – the
occupational classif ication
occupational groups – the two-digit HSCO categories; as well as the main
system used by the HCSO. occupational groups – the one-digit HSCO groups. Quarterly occupation-

144
 8.1 Occupational mobility among youth...

al changes have been aggregated on an annual level, and the development of

mobility is presented on an annual basis.
International comparisons indicate that the frequency of occupational
changes is low in Hungary (Boeri–Flinn, 1997, Berde–Scharle, 2004, Varga,
2018a, 2018b). The mobility of youth (not older than 35 years) is higher in
the job shopping period and much lower in the later stages of the career path,
but even the mobility occurring at the beginning of the career path is low by
international standards. Figure 8.1.1 shows that between 2004 and 2010, the
frequency of occupational changes decreased continuously, and the mobility
of youth hardly surpassed that of older groups. For a part of the period, the
continuous decrease was probably related to the economic recession, as oc-
cupational mobility is pro-cyclical – as demonstrated by numerous studies
(Murphy–Topel, 1987, Carrillo-Tudela–Visschers (2016). After 2010, occu-
pational changes became more frequent both among youth and among older
groups, but the mobility of youth increased at a higher rate, increasing the
difference between age categories. In 2018, somewhat more than 3 per cent
of 16–35-olds changed occupations, which is still extremely low in interna-
tional comparison (Varga, 2018b).
Figure 8.1.1: The share of those changing occupations among youth in employment
(between the ages of 16–35) and among older groups in employment (36–64),
2000–2004, (four-digit HSCO groups, percentage)
3.5
3.0
2.5
2.0
Percent

1.5
1.0
0.5
0.0
2000 2002 2004 2006 2008 2010 2012 2014 2016 2018
16–35 years old 36–64 years old
Source: Author’s compilation.
The low level of occupational mobility is partly explained by the labour market
institutions, the high proportion – in international comparison – of occupa-
tions that require specific qualifications (Varga, 2018b), and the particulari-
ties of the education system as well. The intensity of occupational mobility is
also related to the extent to which the education system provides specialised
knowledge or general knowledge (Lindberg, 2009). This is because one of the
preconditions of occupational mobility is the transferability of (at least a part
of) workers’ competencies and knowledge from one occupation to the other.
In countries where the education policy emphasises the acquisition of general

145
Júlia Varga

knowledge and encourages participation in lifelong learning, occupational

mobility is typically higher, and adaptation to the changing demands of the
labour market is easier; while in countries where the intended primary func-
tion of the education system is the transference of vocation-specific knowl-
edge, mobility is typically lower.
Comparing the changes in youth’s occupational mobility by educational at-
tainment categories (Figure 8.1.2), what emerges is that after 2010, with the
strengthening of mobility, the differences established based on educational
attainment categories increased. Both on the level of occupational groups
(two-digit HSCO group) and detailed occupational categories (four-digit
HSCO group), those with the eighth grade of elementary school or less as
their attainment level changed occupations the most frequently, while those
with a higher education diploma changed occupations the least frequently.
There are no significant differences between those with a secondary school
diploma and those with a vocational qualification but no secondary school
diploma (skilled worker or vocational school graduate).
Figure 8.1.2: The share of youth (between the ages of 16–35) changing occupations among youth in
employment, by educational attainment categories, 2000–2018
Two-digit HSCO Four-digit HSCO
6 6

4 4
Percent

2 2

0 0
2000 2002 2004 2006 2008 2010 2012 2014 2016 2018 2000 2002 2004 2006 2008 2010 2012 2014 2016 2018
At most lower Vocational Secondary Tertiary
secondary education school school education
Source: Author’s compilation.

We examined the probability of the occupational changes of youth via sim-

5 The direction of the occu-
pational change was defined ple probability models as well. On the one hand, via binary outcome mod-
based on switches between els, which examine the probability of an occupational change on the levels of
o ne- d i g it H S C O - g ro u p s ,
omitting the “Occupations of two-digit and four-digit HSCOs (yes/no), whose results are shown in Table
the armed authorities” occupa- 8.1.1. On the other hand, via multiple outcome models, which distinguished
tional group from the analysis,
and creating a separate group between the individual moving upwards or downwards within the occupa-
for those working as public
workers, regardless of the oc-
tional hierarchy,5 or remaining at the same level, as a result of the occupational
cupation they work in. I consid- change. The results of this are presented in Table 8.1.2.
ered the position of the public
workers’ group to be at the bottom of the hierarchy. The HSCO ward mobility if the classification of the new occupation by one-
classification system is established on the basis of a hierarchy: digit HSCO-group had a lower value; I considered it downward
proceeding through the levels by the main groups, the level of mobility if the value become higher; and if its value remained
formal qualifications and other skills needed for the occupations unchanged, I considered its position unchanged within the oc-
keeps increasing. I considered an occupational change as up- cupational hierarchy.

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 8.1 Occupational mobility among youth...

Table 8.1.1: Factors influencing the probability of occupational change, binary

outcome probit estimates (changes occupation: yes/ no)
Two-digit HSCO Four digit HSCO
Variable marginal effect dy/dx
0.0009** 0.0013***
Male
(0.0003) (0.0034)
–0.0000 0.0015
Eighth grade of elementary school
(0.00049) (0.0006)
Vocational qualification but no secondary school –0.00003 –0.0002
diploma (skilled worker or vocational school
graduate) (0.00036) (0.00042)
–0.0019 ***
–0.0015**
Higher education diploma
(0.0004) (0.00048)
0.0004** 0.0006***
Number of years of experience
(0.00013) (0.00015)
–9.81E-06 –0.0000
Number of years of experience squared
(0.00001) (0.00001)
–0.0026*** –0.0031***
Number of years spent at a particular employer
(0.00007) 0.00008
0.0044*** 0.0098***
Public worker
(0.00092) (0.00119)
0.0051*** 0.0068***
Working abroad
(0.00122) (0.00143)
Year Yes Yes
Reference category: Females, with a secondary school diploma, year: 1998.
Standard errors in brackets.
Significant at the ***1 per cent, **5 per cent, *10 per cent levels.
Source: Author’s compilation.

Males are more likely to change occupations both by two-digit and four digit
categories than females, and are also more likely to move downwards within
the occupational hierarchy. Those with a higher education diploma are sig-
nificantly less likely to change occupations, but if they do, they are less likely
to move downwards or stay within the same occupational level than the ref-
erence category of those with a secondary school diploma. Between the other
educational attainment categories, there are no differences in the probability
of an occupational change.
Those with the eighth grade of elementary school as their educational at-
tainment are more likely to move upwards, which could be explained by the
fact that a great proportion of these has public worker status, which is con-
sidered the lowest in the hierarchy. Youth with a vocational qualification but
no secondary school diploma are more likely to stay within the same main oc-
cupational category. The longer time someone has spent at a certain employer,
the less likely they are to switch to a different occupation. Those working as
public workers or working abroad are more likely to switch.

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Júlia Varga

Table 8.1.2: Factors influencing the direction of occupational change, multinomial

logit estimatesa
Switches within the
same main occupa- Moves upwards Moves downwards
tional category
Variable marginal effect dy/dx
–0.0003 0.0010*** 0.0003
Male
(0.00022) (0.00014) (0.00011)
–0.0000 0.0009*** 0.0001
Eighth grade of elementary school
(0.00035) (0.00027) (0.00019)
Vocational qualification but no secondary –0.0007** 0.0007 –0.0001
school diploma (skilled worker or vocational
school graduate) (0.00025) (0.00019) (0.00013)
–0.0010*** 0.0005 –0.0005***
Higher education diploma
(0.00029) (0.00026) (0.00015)
0.000** 0.0003*** 0.0005
Number of years of experience
(0.0001) (0.00006) (0.00005)
–9.81e-06 –0.0000*** –9.36e-07
Number of years of experience squared
(0.00000) (0.00000) (0.00000)
Number of years spent at a particular em- –0.0017*** –0.0010*** –0.0004***
ployer (0.00005) (0.00003) (0.00003)
–0.0067*** 0.0099*** 0.0008
Public worker
(0.0002) 0.00096 (0.00031)
0.0015 0.0020*** 0.0014*
Working abroad
(0.00075) (0.0006) (0.00053)
Year Yes Yes Yes
a
Switches to occupation within the same level = 1, moves upwards = 2, moves down-
wards = 3, reference outcome (does not change occupations) = 0.
Reference category: Females, with a secondary school diploma, year: 1998.
Standard errors in brackets.
Significant at the ***1 per cent, **5 per cent, *10 per cent levels.
Source: Author’s compilation.

References
Berde, É.–Scharle, Á. (2004): A kisvállalkozók foglalkozási mobilitása 1992 és 2001
között [Occupational mobility of small businesses]. Közgazdasági Szemle, Vol. 51,
No. 4, pp. 346–361.
Boeri, T.–Flinn, C. J. (1999): Returns to Mobility in the Transition to a Market Econ-
omy. Journal of comparative economics, Vol. 27, No. 1, pp. 4–32.
Buchs, H.–Helbling, L. A. (2016): Job opportunities and school-to-work transitions
in occupational labour markets. Are occupational change and unskilled employment
after vocational education interrelated? Empirical Research in Vocational Educa-
tion and Training, Vol. 8, No. 17.
Carrillo-Tudela, C.–Hobijn, B.–She, P.–Visschers, L. (2016): The extent and cy-
clicality of career changes: Evidence for the U.K. European Economic Review, Vol.
84, No. C, pp. 18–41.
Johnson, W. R. (1978): A theory of job shopping. The Quarterly Journal of Economics,
Vol. 92, No. 2, pp. 261–277.
Lindberg, M. E. (2009): Student and early career mobility patterns among highly edu-
cated people in Germany, Finland, Italy, and the United Kingdom. Higher Educa-
tion, Vol. 58, No. 3, pp. 339–358.

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Longh, S.–Taylor, M. (2013): Occupational Change and Mobility Among Employed

and Unemployed Job Seekers. Scottish Journal of Political Economy, Vol. 60, No. 1,
pp. 71–100.
Murphy, K. M.–Topel, R. H. (1987): The Evolution of Unemployment in the United
States: 1968–1985. NBER Chapters. Published in: NBER Macroeconomics Annual
1987. National Bureau of Economic Research, Inc. Vol. 2, pp. 11–68.
Scherer, S. (2004): Stepping-Stones or Traps? The Consequences of Labour Market
Entry Positions on Future Careers in West Germany, Great Britain and Italy. Work
Employment and Society. Vol. 18, No. 2, pp. 369–394.
Sicherman, N. (1990): Education and Occupational Mobility. Economics of Education
Review. Vol. 9, No. 2, pp. 163–179.
Sicherman, N.–Galor, O. (1990): A theory of career mobility. Journal of Political
Economy, Vol. 98, No. 1, pp. 169–192.
Topel, R. H.–Ward, M. P. (1992): Job mobility and the careers of young men. The Quar-
terly Journal of Economics, Vol. 107, No. 2, pp. 439–479.
Varga, J. (2018a): Labour mobility in Hungary. In: Fazekas, K.–Köllő, J. (eds.): The
Hungarian labour market, 2017. Institute of Economics, Centre for Economic and
Regional Studies, Hungarian Academy of Sciences, Budapest, pp. 158–166.
Varga, J. (2018): A felsőfokú végzettségűek foglalkozási mobilitása [Occupational mo-
bility of individuals with a higher education qualification]. Doctoral thesis, Hun-
garian Academy of Sciences.

149
Ágnes Hárs & Dávid Simon

8.2 OUTWARD MIGRATION OF YOUTH – YOUNG PEOPLE

WORKING ABROAD
Ágnes Hárs & Dávid Simon

The young and entrepreneurial are assumed to be more mobile and more like-
ly to migrate. This seems to be evident, and, based on human capital theory,
outward migration during a long career is a better investment for younger
people (Becker, 1975). At the same time, their entrance to the labour market,
their initial insecure and marginal situation and their exploration within the
labour market may also encourage them to take up employment abroad (Os-
terman, 1979). If the prospects of young people finding a good job in Hun-
gary are poor, an alternative may be to work abroad for some time. However,
in the case of job search abroad, often only secondary workplaces are acces-
sible for new arrivals (Piore, 1979). Finding employment abroad may also be
1 Data from the LFS are weight- a response to social-political dissatisfaction and despair at home. The nature
ed based on mirror statistics,
using the characteristics of the of finding employment abroad varies over time according to the combina-
main host countries, assum-
ing that the characteristics of
tion of these factors.
leavers closely match those of This subchapter investigates who among young people look for employment
the sub-population defined
by the variable “place of work abroad and how this has changed in the past more than ten years since Hun-
abroad”. Estimation concern- gary’s EU accession, when working abroad became easier and more frequent,
ing outward migration is made
using the weighted database. and then accelerated and became part of everyday life after 2011 (Hárs, 2018).
Weighting was based on the The analysis includes young people aged 18–29 who work in Hungary vs. those
annual population data of the
mirror statistics of major host who have worked in Hungary and work abroad in the next quarter, according
countries, broken down by to the Labour Force Survey (LFS) and it covers the time period 2006–2017.1
gender, which enabled us to
estimate changes in the num- The sample shows that nearly half of the active age workers (aged 18–64) tak-
ber of Hungarians working ing up employment abroad are below age 30 (48.5 per cent), while more than
abroad (relying on Eurostat
EU LFS employment data on one-third of them (35 per cent) are of prime working age (aged 30–44).2 This
Hungarians living abroad) dur- study does not look into their return but according to the relevant literature
ing the periods 2006–2010 and
2011–2017 in the major host we assume that migration is not unidirectional. The proportion of returnees
countries (Germany, Austria, and repeated leavers is also considerable (Horváth, 2016, Hárs–Simon, 2018).
United Kingdom and other
countries). Data on outward
migration were fit to the above The empirical study of young people finding employment abroad
estimated change, using the
flow data from the LFS. Based on LFS data, we measured factors that influence outward migration us-
2 Recent estimates of migra- ing demographic, family status, labour market activity and regional explana-
tion intentions reveal similar
proportions. The probability tory variables. In addition to the usual impact of gender and age on working
of young people aged 18–28 abroad, the family situation is also expected to reveal motivations: in the case
taking up employment abroad
is especially high: it is nearly of youth living together with their parents it may indicate difficulties in en-
two and a half times higher for tering the labour market, while in the case of those living independently or
short-term intentions, com-
pared with the age group of with a partner (or especially those raising children) it may indicate a lack of
29–38, and nearly the same
for long-term intentions (Sik–
subsistence and prospects. The impact of educational attainment depends on
Szeitl, 2016). the domestic and host country labour market, where educational attainment

150
 8.2 Outward migration of youth...

does not necessarily yield the expected status and income. The labour market
activity prior to migration may reveal motivations: it may suggest exploration 3 The analysis included C-sta-
in the case of students and young graduates, unfavourable prospects at home tistic and link test instead of
the usual Hosmer–Lemeshow
in the case of the unemployed, while the migration of those permanently in test because with a large num-
ber of items a small difference
employment may point to labour market causes. The region of residence and from the expected distribution
type of municipality primarily reveal the impact of labour market opportu- would have been significant,
while it would not have influ-
nities in the Hungarian labour market. enced the readability of the
Logistic regression was used to explain employment abroad, with the out- model. The value of C-statistic
was 0.8, which is satisfactory;
put variable of entering the labour market abroad (a young person not living the linear term of the link was
abroad in one quarter and working abroad in the next quarter as opposed to significant (p = 0.001), while
the squared term was not
those who did not enter the labour market abroad). The model tested quar- (p = 0.751).
terly effects, which were expressed as annual effects for better interpretability. 4 Change over time was meas-
ured by including an interac-
Based on the applied fit test, the model proved satisfactory.3 tion using a variable in quar-
Independent effects were evaluated and the marginal effect of each variable terly breakdown and it is pre-
sented by marginal estimates
(or estimated marginal probability for continuous variables), and their change given for the first quarters of 7
over time, on the employment of youth aged 18–29 abroad is presented.4 Re- selected years at evenly spaced
intervals. The significance of
sults are shown in Figure 8.2.1 (the effects of gender and municipality type the marginal effect was esti-
are included in the analysis but not in the Figure). mated at certain selected dates.

Figure 8.2.1: The effect of factors influencing the employment of youth abroad
Age Region
3.5 5
3.0
4
2.5
2.0 3

1.5 2
1.0
1
0.5
0.0 0
2006 2008 2010 2012 2014 2016 2018 2006 2008 2010 2012 2014 2016 2018
18 years old 21 years old Central Western Southern
24 years old 27 years old Transdanubia Transdanubia Transdanubia
Northern Northern Great Southern Great
Hungary Plain Plain
Note: A variable estimated for single-year of age. It Note: Reference category: Central Hungary. The
is not significant at the measurement point for the Region variable is not significant at the measure-
18-year-olds in the first quarter of 2006. The effect of ment points of the first quarters of 2006–2010 in
the variable does not change significantly over time. Central Transdanubia, the first quarter of 2006 in
Western Transdanubia, the first quarters of 2006–
2008 in Northern Hungary, the first quarters of
2014–2018, at any of the measurement points of
the Northern Great Plain and the first quarters of
2006–2012 and 2016–2018 of the Southern Great
Plain. The effect of the variable does not change
significantly over time.

151
Ágnes Hárs & Dávid Simon

Family status Children below six years of age

6 0.8
5 0.6
4 0.4
3 0.2
2 0.0
1 –0.2
0 –0.4
–1 –0,6
2006 2008 2010 2012 2014 2016 2018 2006 2008 2010 2012 2014 2016 2018
Lives alone Lives with parents Other With a child below six years of age

Note: Reference category: lives with a partner. The Note: Reference category: no children. The variable
Family status variable is not significant in the Having a child below six years of age is not signifi-
first quarters of 2010–2014 of the Lives alone cat- cant at the measurement points of the first quar-
egory, in the first quarter of 2006 of the Lives with ters of 2014–2018 in the category With a child
parents category and at any of the measurement below six years of age. The effect of the variable
points of the Other category. does not change significantly over time.

Educational attainment Labour market activity prior to migration

2.5 10

2.0 8

1.5 6

1.0 4

0.5 2

0.0 0

–0.5 –2
2006 2008 2010 2012 2014 2016 2018 2006 2008 2010 2012 2014 2016 2018
Secondary voca- Matura obtained in general Young graduate Unemployed
tional qualification secondary schools Student in employmen young graduate
Matura obtained in vocatio- Higher educa- On parental Newly Permanently One year in
nal secondary schools tion college University leave employed unemployed employment

Note: Reference category: lower-secondary qualifi- Note: Reference category: uninterrupted employ-
cation at most. The Educational attainment vari- ment. The Labour market activity prior to migra-
able is not significant at the measurement points tion variable is not significant at the measurement
in the first quarters of 2006–2008 in the catego- points in the first quarters of 2014–2018 in the
ry Secondary vocational qualification, in the first category Student, in the first quarters of 2006–
quarters of 2016–2018 in the category Matura 2014 and 2018 in the category Young graduate in
obtained in general or vocational secondary schools employment, in the first quarter of 2006 in the
and in the first quarters of 2006 and 2014–2018 in category Unemployed young graduate, in the first
the category University or higher education college. quarters of 2006–2010 in the category On paren-
tal leave and at any of the measurement points in
the category Newly employed.

The acceleration of outward migration after 2010 was due to several factors,
including the prolonged impact of the economic crisis and the measures

152
 8.2 Outward migration of youth...

adopted after the change of government in 2010, gradually modifying the

probability of youth taking up employment abroad. The effect of age did not
change over time: this variable increases the impact of migration below 24
but above this age there is no difference between the age groups and this is
significant throughout the period. The effect of gender is not relevant among
youth. Overall, the significance of socio-demographic factors decreases with
the acceleration of migration, that is the population of outward migrants is
wider and more diverse. At the same time, the effect of labour market activ-
ity has increased.
Before the acceleration of outward migration, the effect of the region of resi-
dence was relatively strong: compared with a residence in Central Hungary,
living in Western or Southern Transdanubia significantly and to a large ex-
tent increased the probability of taking up employment abroad, while living
in a county town or Budapest slightly decreased it relative to living in a village
(during 2006–2012), whereas the effect of a residence in other towns was not
significant. The effect of family status was also of relevance: compared with
those living with a partner, those living with parents were slightly but signifi-
cantly more likely to find employment abroad, which may be due to young peo-
ple wishing to gain experience, while those living alone were significantly less
likely to work abroad during 2006–2008. A child below six living in the fam-
ily also significantly reduced the probability of working abroad (until 2012),
which suggests a more stable family background. The labour market influenc-
es the probability of working abroad through educational attainment and la-
bour market activity. Compared with lower-secondary education (ISCED2),
the effect of university qualification in this period was significant and strong,
while the effect of college and upper-secondary qualifications was less marked
but still significant. However, vocational school qualification (ISCED3C)
had no significant effect prior to 2010. Compared to those permanently in
employment, those who recently lost or quit their jobs were more likely to
work abroad,5 followed by unemployed fresh graduates probably motivated
by gaining experience and looking for the right career. The probability of find-
ing employment abroad was similar in the case of the long-term unemployed,
presumably because of looking for a labour market alternative. The effect of
being in education was significantly negative, whereas being on parental leave
and having found a job after graduation had no significant effect.
Following the acceleration of migration the significance of the region of one’s
residence barely changed. After 2010, compared with Central Hungary, a resi-
dence in Central Transdanubia or in Western Hungary significantly increased
the probability of working abroad. During the increasing intensity of migra- 5 Persons who were not in em-
tion in the period 2010–2012, living in Northern Hungary, the most disad- ployment in Hungary in the
quarter prior to starting a job
vantaged region, also significantly increased the probability but living in the abroad but had been in employ-
other regions did not and the type of municipality also lost its significance. ment a year prior.

153
Ágnes Hárs & Dávid Simon

The effect of family status changed markedly: compared with those living with
a partner, those living with their parents have been significantly and increas-
ingly more likely to take up employment abroad possibly due to labour market
pressure in addition to wishing to gain experience, while there has been a sig-
nificant increase among those living alone (since 2016).6 Having a child below
six years of age does not significantly hold back employment abroad any more,
probably due to labour market reasons and unfavourable prospects as well
as social and political dissatisfaction. The impact of educational attainment
on labour market significance has also altered considerably: compared with
primary education, the effect of tertiary education (after 2012) and second-
ary education (after 2014) has not been significant, while having vocational
school qualifications has significantly and steeply increased the probability of
working abroad since 2010. At the end of the period only vocational school
qualifications have a significant effect, indicating that educational attainment
hardly influences employment abroad.
Overall, labour market activity prior to migration had the strongest effect:
those unemployed in Hungary are increasingly more likely to go abroad and
fresh graduates also leave at an increasing rate. Compared with those perma-
nently in employment, the strongest significant effect was seen among those
who had been working a year prior but had become unemployed or inactive,
6 Results concerning people liv- followed by those permanently unemployed. After 2012 young graduates un-
ing alone need to be interpreted
with caution, since reaching able or unwilling to find a job in Hungary found employment abroad at a rap-
this group is uncertain due idly increasing rate and at the end of the period this effect was nearly as strong
to the nature of the survey.
Nevertheless, looking at those as that of quitting a job. We suspect that the motivation of young people has
dropping out of the Labour changed: the effect of deteriorating labour market prospects and expectations
Force Survey due to panel at-
trition, we found that although has grown. Since 2012, being on parental leave has significantly reduced the
the survey underestimates this probability of working abroad but being in education has not and neither has
group, the association found is
relevant. taking up employment in Hungary after graduation.

References
Becker, G. (1975): Human Capital. The University of and Regional Studies, Hungarian Academy of Sci-
Chicago Press. Chicago. ences, Budapest, pp. 110–116.
Hárs, Á. (2018): Növekvő elvándorlás – lehetőségek, re- Osterman, P. (1979): The Structure of the Labor Mar-
mények, munkaerőpiaci hatások. In: Kolosi, T.–Tóth, ket for Young Men. In: Piore, M. J. (ed.): Institutional
I. Gy. (eds.): Társadalmi riport, 2018. Tárki, Buda- and Structural Views of Unemployment and Inflation.
pest, pp. 81–105. Sharpe, New York, pp. 186–196.
Hárs, Á.–Simon, D. (2018): Labour Emigration and La- Piore, M. J. (1979): Birds of passage. Migrant labour and
bour Shortage. In: Fazekas, K.–Köllő, J. (eds.): The industrial society. Cambridge University Press, Cam-
Hungarian Labour Market, 2017. Institute of Econom- bridge.
ics, Centre for Economic and Regional Studies, Hun- Sik, E.–Szeitl, B. (2016): Migration intentions in contem-
garian Academy of Sciences, Budapest, pp. 94–108. porary. In: Blaskó, Zs.–Fazekas, K. (eds.): The Hun-
Horváth, Á. (2016): Returning Migrants. In: Blaskó, Zs– garian Labour Market, 2016. Institute of Economics,
Fazekas, K. (eds.): The Hungarian Labour Market, Centre for Economic and Regional Studies, Hungar-
2016. Institute of Economics, Centre for Economic ian Academy of Sciences, Budapest, pp. 55–59.

154
LABOUR MARKET
POLICY TOOLS
(JUNE 2018
– MAY 2019)

Miklós Hajdu
Ágnes Makó
Fruzsina Nábelek
Zsanna Nyírő
This Chapter overviews major changes in the regulation of labour market
policy tools between June 2018 and May 2019.

1 INSTITUTIONAL CHANGES
1.1 The system of vocational education and training
The amendment of Act CLXXXVII on Vocational Education and Training
in December 20181 introduced the possibility to provide distance e-learning
in a closed system in vocational education and training from 1 January 2019,
which imparts theoretical knowledge as a digital curriculum through an IT
network.
In January 2019, modelled on the system introduced at universities, chan-
cellors were appointed at vocational education and training centres of the
Ministry for Innovation and Technology. The director general continues to be
the chief manager responsible for a vocational training centre, whose primary
task is to manage the educational activities of the institution and supervise the
quality of education. The chancellor is a senior manager of the institution, in
charge of economic, financial, employment, legal and administrative activities
as well as the asset management of the institution. Chancellors are appointed
by the Minister responsible for vocational education and training. The director
general will be the employer of teachers and staff directly supporting educa-
tion but their wages will have to be approved by the chancellor. All other staff
will be appointed and employed by the chancellor. Measures of the director
general affecting the management, organisation or operation of the institu-
tion with financial implications will have to be approved by the chancellor:
his consent is a prerequisite of these decisions to take effect or be effective.
The Vocational Education and Training Innovation Council was established
in September 2018 on the initiative of the Ministry for Innovation and Tech-
nology.2 The most important goal of the council is to provide a regular fo-
rum for dialogue between key stakeholders of the vocational sector and the
government. The task of the council is to determine future developmental
trends and to formulate recommendations for infrastructural developments 1 Act CIV of 2018 on the
amendment of certain Acts
and for the revision of vocational and adult training curricula. In addition to on research and development
the government, the members of the Council include chambers, enterprises, and vocational education and
training.
trade unions, advocacy organisations, educational organisations, maintainers 2 See kormany.hu for more
of institutions and delegates of student councils. about the Council.

157
Hajdu, Makó, Nábelek & Nyírő

The Government discussed and adopted the strategic document “Vocational

Education and Training 4.0” in March 2019.3 According to this, four-plus-
one-grade vocational secondary schools will be replaced by five-grade poly-
technics in September 2020. After graduating from a polytechnic, pupils get
preferential treatment at entrance examinations to related programmes of
higher education institutions. Vocational schools will have three grades, with
practical dual training in the last two grades. So-called workshop schools offer-
ing partial qualifications are established for pupils who are unable to complete
lower-secondary education or cannot perform well in a traditional education
setting.4 In the dual training offered by polytechnics and vocational schools,
pupils’ contracts will be replaced by a pupils’ work contract. The National
Qualification Register will be completely dropped and a new qualification
register will be published in 2020 in order to better differentiate between ini-
tial vocational training programmes offering comprehensive vocational skills
and course-like, short programmes.
1.2 Pensioner cooperatives
In order to promote the employment of pensioners, the public interest pen-
sioner cooperative was introduced as a new type of cooperative on 1 July 2017.
Those employed by such cooperatives were exempt from paying social security
3 Government Resolution
1168/2019. (III. 28.) on adopt- contributions.5 However, the pensioner cooperative became pointless from
ing the strategy “Vocational a financial point of view because of a new regulation taking effect on 1 Janu-
education and training 4.0
– The mid-term policy strategy ary 2019 (Wiedemann, 2018): Act CXV of 20186 amended the rules for the
for renewing vocational edu- social security status of employees who are pensioners in their own right. Ac-
cation and training and adult
training: the response of the cordingly, pensioners in their own right employed under the Labour Code
VET system to the challenges have been exempt from insurance obligations since 1 January 2019, that is,
of the fourth industrial revolu-
tion” and on measures required they are exempt from paying pension contribution (10 per cent) and in-kind
for its implementation.
health insurance contribution (4 per cent). They only have to pay a personal
4 National Institute of Voca-
tional and Adult Education: income tax (15 per cent) on their wages – in the same way as if they were re-
First day of school in vocational munerated as members of a public interest pensioner cooperative. Pursuant
education in Nyíregyháza.
5 Act LXXXIX of 2017 on the to the new law, pensioners with employment contracts are not entitled to so-
amendment of certain Acts cial security benefits as per their employment contract, including any increase
with regard to establishing
public interest pensioner co- in pension otherwise applicable to pensioners engaged in other forms of em-
operatives. ployment. Under the new law, employers become exempt from paying social
6 Act CXV of 2018 on the
amendment of Act XLII of contribution tax (19.5 per cent) and vocational training contribution (1.5 per
2015 on the Service Status of cent) on their employees who are pensioners in their own right.7
Professional Members of Law

2 BENEFITS
Enforcement Agencies and
other related Acts.
7 Publication about this con-
tribution. 2.1 Unemployment benefits
8 Government Decree
324/2018. (XII. 30.) on the
minimum wage and the guar- As a result of raising the minimum wage in 20198 (see section 5.1), the max-
anteed minimum wage in 2019. imum amount of the unemployment benefit (so-called job-seekers’ allow-

158
 Labour Market Policy Tools

ance) also increased. The maximum of the job-seekers’ monthly allowance

has been HUF 149,000 since 1 January 2019, while the unemployment as-
sistance granted before retirement has been HUF 59,600 a month. The so-
called activity compensation of job seekers participating in intensive training
acknowledged by job centres ranges between HUF 48,918–81,530 a month.
2.2 Rehabilitation and disability benefits
The amount of rehabilitation and disability benefits increased by 2.7 per cent
in January 2019: the base allowance is now at HUF 101,560.9
The Constitutional Court found an omission concerning Act CXCI of
2011 on disability provisions10 during the autumn of 2018. According to the
Court, the law, which took effect in 2012, adversely affected those already
receiving provisions, whose benefits decreased or terminated, even though
their health did not improve – this violates an international treaty on the
protection of human rights. The law should have been amended prior to 31
March 2019 with a modification that links the granting of provision not
to the legal status of recipients but to aspects of their health affecting their
living conditions. However, the Government disputed the decision of the
Constitutional Court and did not amend the law until the deadline set by
the Constitutional Court.
2.3 Child benefits
As a result of raising the minimum wage in 201911 (see section 5.1), the maxi-
mum amount of the insured parental leave benefit (which equals 70 per cent
of double the amount of the minimum wage) increased to HUF 208,600
a month in 2019. The parental leave benefit for graduates otherwise not enti-
tled to one increased to HUF 104,300 for Bachelor degree holders and HUF
136,500 for Master degree holders. The maximum net amount of the insured
parental leave benefit is also linked to the minimum wage: it increased to
HUF 156,450.

3 SERVICES
3.1 Improving adaptability to labour market changes
The call for proposals of the programme “Thematic projects aiming at improv-
ing adaptability to labour market changes EDIOP-5.3.5-18” was published
in September 2018: enterprises can apply for a non-refundable grant of HUF 9 About provisions available
10–50 million. The projects improve labour market adaptability, strength- for disabled employees.
10 Resolution 21/2018. (XI. 14.)
en corporate social responsibility, promote service provider roles, reinforce AB of the Constitutional Court.
the engagement of social partners in society and the labour market, develop 11 G o v e r n m e n t D e c r e e
324/2018. (XII. 30.) on the
their capacities and improve their representative power. The total budget to minimum wage and the guar-
be granted to applicants amounts to HUF 4 billion. anteed minimum wage in 2019.

159
Hajdu, Makó, Nábelek & Nyírő

4 ACTIVE LABOUR MARKET POLICIES AND

COMPREHENSIVE PROGRAMMES
4.1 Defining the goals of public works schemes and preparations
for modifying the law on cooperatives
Government resolution 1497/2018. (X. 12.)12 sets out the objectives of public
works schemes for 2019 and provides for preparations for the amendment of
Act X of 2006 on cooperatives.
According to the government resolution, the primary aim of public works
schemes in 2019 is to encourage job seekers to exit public works, meet the
demands for seasonal labour in agriculture and improve the employment rate
and employability of those excluded from the primary labour market. Fur-
ther objectives include promoting the housing and social integration of the
Roma, supporting the social inclusion of ex-convicts, remedying labour mar-
ket disparities as well as improving the quality of life of participants of public
works schemes. Additionally, enhancing rural population retention capacity
and assisting self-sufficient and self-sustainable municipalities and social co-
operatives are also included among the aims.
4.2 Increasing the amount of the employment allowance
The amount of employment allowance provided as part of the “From public
works to the business sector” programme increased to HUF 45,600 in No-
vember 2018. It is payable to public works participants who successfully take
up employment in the business sector.
4.3 Supporting services that promote legal employment
The call for proposals for EDIOP-5.3.3-18 (Supporting services that support
promote legal employment) was published in October 2018, which aims at
providing services that promote legal employment; supporting the legal as-
sistance, mediation, conciliation, intermediary, consultation and arbitration
services of advocacy organisations and using alternative dispute resolution
tools. The total amount of grants is HUF 2.4 billion.
4.4 Supporting job-seekers and youth in becoming entrepreneurs
The programmes EDIOP-5.2.7-18 (Supporting youth in becoming entre-
preneurs) and EDIOP-5.1.10-18 (Supporting job-seekers in becoming en-
trepreneurs) were launched in November 2018 as the continuation of EDI-
OP-5.1.9-17. (Supporting job-seekers and youth in becoming entrepreneurs
– training and mentoring). The Hungarian State Treasury, applying simplified
cost options, provides grants for young people aged 18–30 and for job-seekers
12 Government Resolution
1497/2018. (X. 12.) on certain
above 30, who participated in training in business creation and development
aspects of public works. in EDIOP-5.1.9-17 and have an approved business plan, to set up a business.

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 Labour Market Policy Tools

The total amount of grants available in EDIOP-5.2.7-18 is HUF 26.7 billion

and that of grants in EDIOP-5.1.10-18 is HUF 13.4 billion.
4.5 Supporting the return of parents with young children to the
labour market
The programme EDIOP-5.3.11-18 (Supporting the return of parents with
young children to the labour market) was launched in November 2018, which
promotes the labour market return of parents with young children by offer-
ing targeted support to cover family day-care or workplace nursery fees. The
total amount of grants is HUF 9.8 billion.
4.6 Supporting the investment of micro-, small and medium-sized
enterprises in job creation
The call for proposal “NFA-2018-KKV Supporting the investment of micro-,
small and medium-sized enterprises in job creation” was published in July
2018, while “NFA-2019-KKV Supporting the investment of micro-, small
and medium-sized enterprises in job creation” was published in March 2019.
In order to improve the development, significance and market position of
small and medium-sized enterprises, these programmes aim at supporting
investment in job creation, reducing regional disparities, supporting region-
al cohesion, reinforcing local economies and expanding employment by pro-
moting the employment of persons disadvantaged in the labour market. The
total amount of grants in NFA-2018-KKV is HUF 6 billion, while in NFA-
2019-KKV it is HUF 5 billion.
4.7 Central labour market programme “Providing workers’
accommodation”
The central labour market programme titled “Providing workers’ accommoda-
tion” was published for the third time in October 2018: applicants can apply
for grants for building workers’ accommodation for at least 80 employees or
renovating buildings which may be converted into workers’ accommodation.
The aim of the programme is to facilitate the mobility of the labour force and
improving housing conditions in regions affected by labour shortage. The to-
tal amount of grants is HUF 5 billion.
4.8 Improving adaptability to labour market changes
The call for proposals of the programme EDIOP-5.3.5-18 (Thematic projects
aiming at improving adaptability to labour market changes) was published in
October 2018, which aims at reinforcing the engagement of social partners
in society and the labour market, improving their representative power and
supporting activities that efficiently contribute to improving the adaptability
of employees, employers and enterprises to labour market changes and rein-

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Hajdu, Makó, Nábelek & Nyírő

forcing their corporate social responsibility. The total budget to be granted

to applicants amounts to HUF 4 billion.

5 POLICY TOOLS AFFECTING THE LABOUR MARKET

5.1 Changes in the minimum- and the guaranteed minimum wage
The minimum amount of the base salary of full-time employees increased from
HUF 138 thousand to HUF 149 thousand gross on 1 January 2019, while
the guaranteed minimum wage of full-time employees in jobs requiring at
least an upper-secondary qualification and/or mid-level vocational qualifica-
tion increased from HUF 180,500 to HUF 195 thousand.13
5.2 Changes in the system of taxes and contributions
5.2.1 Act on the new social contribution tax
On 1 January 2019 an act on the new social contribution tax took effect,
which eliminated the healthcare contribution tax and introduced a uniform
social contribution tax (SCT) of 19.5 per cent. Instead of the earlier HUF
100 thousand, the new act specifies the amount of SCT reductions in terms
of the minimum wage. In the case of part-time employment, the reductions
do not have to be proportionate to the working time.14
Reductions for employees below 25 or over 55 and for the so-called Ca-
reer Bridge programme have been eliminated, while reductions for employ-
ing young graduates, long-term unemployed persons or those on maternal
leave will be phased out, similarly to the reduction for enterprises located in
free enterprise zones.
A new reduction is introduced for employing new entrants to the labour
market (that is employees who had a maximum of 92 days in insurance during
the 275 days prior to their employment): employers are exempt from paying
SCT and vocational training contribution payable on the minimum wage in
the first two years of employment and entitled to a 50 percent reduction in
the third year (Kiss, 2018).
The reduction for employees with a rehabilitation card was eliminated and
13 Govern ment D ecree
324/2018. (XII. 30.) on the replaced by another one with a wider range of beneficiaries, which may be
minimum wage and the guar- claimed by entrepreneurs with disabilities. Since 26 July 2018, and also under
anteed minimum wage in 2019.
14 Act LII of 2018 on the social the new SCT Act, persons with a lower than 60 percent health status score
contribution tax. based on a comprehensive rating but not eligible to rehabilitation benefit or
15 Act XLI of 2018 on the
amendment of certain tax laws disability benefit have been eligible to the reduction.15 On July 1 2019 the
and related Acts and on the spe- social contribution tax decreased by 2 percentage points to 17.5 per cent.16
cial immigration tax.
16 Act XLVIII of 2019 on re-
ducing the rate of the social
5.2.2 Exemption of working pensioners from contributions
contribution tax and on the
amendment of related legisla- In order to promote the employment of pensioners, since 1 January 2019,
tion. pensioners in their own right, employed under the Labour Code, have been

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 Labour Market Policy Tools

exempt from the 10 percent pension contribution and from the 4 percent in-
kind health insurance contribution stipulated by the Act on Social Security
Contributions17 previously.18 Such employment is also exempt from the social
contribution tax and vocational training tax.19
5.2.3 Changes in the cafeteria system
The cafeteria system was significantly modified on 1 January 2019. Due to
the amendment of the Act on the Personal Income Tax, only up to HUF 450
thousand per year may be given to employees on SZÉP-cards as a non-cash
fringe benefit from 2019 on. The tax rate on this benefit slightly grew because
of modification of the social contribution tax (from 34.22 per cent to 34.5
per cent). Additionally, the sub-accounts of the SZÉP-cards of each employee
are allocated separate bank account numbers. The cash benefit of HUF 100
thousand per year no longer qualifies as a benefit in kind.
From 2019 on, only benefits specified in the Act may qualify as certain
specific benefits. In the future these will not include benefits which qualified
as benefits in kind until 2017, such as the school start allowance, local travel
allowance, Erzsébet voucher and contributions to voluntary pension insur-
ance funds.
The maximum amount of the “allowance for cultural services”, exempt from
personal income tax, has increased from HUF 50 thousand to HUF 149 thou-
sand, equal to the minimum wage. However, the tax exemption of several al-
lowances ceased: assistance for housing, housing assistance for mobility, risk
insurance premium and assistance for repaying student loans will be subject
to tax as earned income.
5.3 Overtime Act
The amendment of the Labour Code adopted in December 2018 took effect
on 1 January 2019.20 The amendment increased the maximum timeframe of
working time from 12 months to 36 months in the case of collective agree-
ments. Employers may continue to require 250 hours of overtime annually; 17 Act LXXX of 1997 on the
however, under the amendment, based on a written agreement concluded by beneficiaries of social security
provisions and private pension
the employer and the employee, an additional 150 hours of overtime (volun- and on the contributions pay-
tary overtime) per calendar year at most may be required by the employer. In able to cover these.
18 Act XLI of 2018 on the
the case of collective agreements, an additional 100 hours of voluntary over- amendment of certain tax laws
time may be required annually at most, based on a written agreement, in ad- and related Acts and on the spe-
cial immigration tax.
dition to a maximum of 300 hours of overtime annually. 19 Act LII of 2018 on the social
The amended law also legislates on the rules of allocating rest days: two rest contribution tax.
days a week must be allocated but not necessarily evenly. After six consecu- 20 Act CXVI of 2018 on the
amendment of Acts related to
tive working days a minimum of one resting day a week must be provided. In the organisation of working
time and the minimum fee
the case of an uneven work schedule, uninterrupted, seasonal or shift work, payable for temporary employ-
at least one of the weekly resting days must be provided every month. At least ment.

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Hajdu, Makó, Nábelek & Nyírő

one of the weekly resting days per month must be provided on Sundays, ex-
cept for part-time employees working on Saturday and Sunday.
5.4 Expanding the programme on building workers’
accommodation
Since the autumn of 2019, employers are also eligible to grants for building
workers’ accommodation, in addition to municipalities. Due to the expansion
of the programme, business organizations can apply for regional investment
aid for building workers’ accommodation, with a 50 percent rate in Northern
Hungary, Northern Great Plain, Southern Great Plain and Southern Trans-
danubia, a 35 percent rate in Central Transdanubia and a 25 percent rate in
Western Transdanubia, while small enterprises may secure an additional 20
percent and medium-sized enterprises an additional 10 percent investment
aid. The grant is for establishing workers’ accommodation for at least 80 per-
sons and eligible costs include construction and refurbishment costs and the
costs of new tangible assets required for the investment. The beneficiary must
undertake to operate the facility for at least ten years.21
5.5 Summer student work
The Ministry of Finance has also published its summer student work pro-
gramme in 2019, with a budget of HUF 3.6 billion. This time, however, youth
aged between 16 and 25 may not only be employed by regional and local mu-
nicipalities or municipality institutions but also by enterprises active in ag-
riculture, tourism or catering. According to the estimates of the Ministry,
21 A new economy protection from the budget available in 2019 it is possible to provide work for about 30
measure: enterprises are also
eligible to a workers’ accom- thousand students between 1 July and 31 August, with a daily maximum
modation grant. working time of 6 hours. Employers are reimbursed for 75 per cent of wages
22 Up to 30 thousand students
may work in the summer stu-
and related social contribution tax, while for municipalities the rate is one
dent work programme. hundred percent.22
References
Kiss, Z. (2018): Az új szociális hozzájárulási adóról szóló törvény [The Act on the new
social contribution tax]. Adóváltozások. Adó szaklap, 2018/10.
Wiedemann, T. (2018): Mire igazán beindult volna, már át is alakul a nyugdíjasok fog-
lalkoztatása. G7, 21 November 2018.

164
 Labour Market Policy Tools

Appendix
Table A1: Expenditures and revenues of the employment policy section of the national budget,
2013–2019 (million HUF)*
2013 2014 2015 2016 2016 2017 2017 2018 2019
Expenditures actual actual actual plan actual plan actual plan plan
1. Active subsidies
Employment and training subsi-
25,105.9 28,120.8 12,302.4 16,172 27,503.9 16,172.0 27,238.9 35,000.0 35,000.0
dies
Co-financing EU-funded employ-
ability (and adaptability) pro- 16,279.6 17,130.1 11,064.6 3,808.7 3,808.7 84,300.0
jects
8. Public works (SART work
171,053.4 225,471.1 253,723.3 340,000.0 267,965.7 325,000.0 265,837.2 225,000.0 180,000.0
programme)
SROP 1.1. Labour market ser-
33,804.9 35,790.1 12,305.1 54.5 79.5
vices and support
SROP 1.2. Normative support for
14,477.3 1,080.1
promoting employment
EDIOP 5. Employment priority –
81,600.0 7,800.0 28,000.0
annually published budget
Of which CCHOP funding 1,000.0
EDIOP 6. Competitive workforce
74,380.0 9,770.0
– annually published budget
Reimbursement of social security
3,277.5 551.5
contribution relief
Pre-financing labour market
0.0 13,654.9 54,700.0 50,101.3 74,116.4 70,995.3 84,300.0
programmes 2014–2020
2. Vocational and adult training
18,736.2 24,725.9 30,084.7 13,819.0 27,872.0 20,000.0 29,919.4 29,930.0
subsidies
4. Passive expenditures
Job seekers’ allowances 51,819.9 49,235 49,657.7 47,000.0 53,454.1 47,000.0 59,674.0 55,000.0 75,000.0
Transfer to Pension Insurance
961.3 451.6 309.1 0.0
Fund
5. Payments from Wage Guaran-
5,487.8 4,178.5 3,790.7 4,950.0 3,994.3 4,000.0 3,341.2 4,000.0 4,500.0
tee Fund
6. Operational expenditures 1,472.8 2,418.3 2,816.0 3,283.4 2,899.3 3,500.0 2,785.6 2,900.0 4,310.0
7. Other budget contribution 70,000.0
15. Headline stability reserves 389.5 389.5
Supplementary subsidies for employers
16. Sectoral subsidy for mini-
7,000.0 9.1
mum wage increase
17. Other expenditures 22.3
Total expenditures 349,498.9 389,162.1 389,708.5 484,177.1 438,068.3 645,768.4 459,791.6 443,930.0 522,574.8

165
Hajdu, Makó, Nábelek & Nyírő

2013 2014 2015 2016 2016 2017 2017 2018 2019

Revenues actual actual actual plan actual plan actual plan plan
25. Revenues from SROP meas-
51,276.1 39,776.7 22,466.1 51,700.0 46,365 60,000.0 64,512.6 70,400.0 70,000.0
ures**
26. Other revenues
Other revenues, regional 602.3 1,507.8 1,290.8 1,000.0 1,839.5 1,000.0 2,188.1 1,000.0 1,000.0
Other revenues, national 1,376.8 2,537.1 901.5 1,000.0 1,745.6 1,000.0 2,013.8 1,000.0 1,000.0
Other revenues from vocational
692.6 216.8 10,147.6 800.0 2,169.2 800.0 1,643.1 800.0 800.0
and adult training
31. Vocational training contribu-
60,398.7 60,910.8 65,308.2 56,996.1 70,327.6 60,706.7 80,074.5 74,436.3 95,490.6
tion
33. Redemption of wage guaran-
1,046.1 934.5 663.6 1,000.0 424.6 1,000.0 783.0 1,000.0 1,000.0
tee subsidies
34. Debt management revenues (technical)
35. Part of health and labour
market contributions payable to 125,614.6 135,819.4 144,953.2 150,476.4 155,369.2 165,801.9 176,338.0 194,169.2 216,621.9
the National Employment Fund
36. Funding from the national
20,000.0 8,449.0 95,000.0 31,023.3 25,000.0
budget
38. Part of the social contribu-
tion tax payable to the National 68,605.5 217,539.6 194,435.5 0.0 68,001.0
Employment Fund
Contribution to the Job Protec-
91,542.7 95,936.7 100,541.7 105,769.9 52,884.9
tion Action Plan
Total revenues 352,549.9 337,639.8 354,721.7 463,742.4 430,754.4 507,848.2 521,988.5 367,805.5 453,913.2
Pending items –964.6
Changes in deposits –2,086.4
Total 351,560.1 389,162.1 354,721.7 484,177.1 430,754.4 507,848.2 521,988.5 367,805.5 453,913.2
At 2013 prices (deflated by
351,560.1 389,942.0 355,788.4 483,698.2 430,328.4 495,455.0 509,250.2 349,056.2 430,774.5
a consumer price index)
*
The ordinal numbers in the table correspond to the title numbers identifying the headlines of the national budget.
**
Regarding 2017, 2018 and 2019 it includes the revenue “Reimbursement of the expenditures of the pre-financed
EU programmes”.
Source: The act on the national budget of Hungary (plan) and the act on the implementation of the national budget
of the given year (actual); regarding the plan of 2013, the figure of 153,779.8 was modified by Government Deci-
sions No. 1507/2013 of 1st August and 1783/2013 of 4th November with an additional budget of 26,118 million
HUF to public works; regarding the plan of 2014, the original figure of 183,805.3 was modified by Govern-
ment Decision 1361/2014 of 30th June (allocating an additional budget of 47,300 million HUF to public works).
Regarding the plan of 2017, the figure was modified by Act LXXXVI on the modification of Act XC of 2016
on the 2017 Central Budget of Hungary’. The source of the expenses of EDIOP is Government Resolution No.
1006/2016 of 18th January on the annual development budget of the Economic Development and Innovation
Operational Programme and further Government Decisions on its modification.

166
 STATISTICAL
DATA

Edited by
Éva Czethoffer

Compiled by
János Köllő
Judit Lakatos
József Tajti
Statistical data

Statistical tables on labour market trends that have been published in The Hungarian La-
bour Market Yearbook since 2000 can be download in full from the website of the Research
Centre for Economic and Regional Studies: http://adatbank.krtk.mta.hu/tukor_kereso

1. Basic economic indicators

2. Population
3. Economic activity
4. Employment
5. Unemployment
6. Wages
7. Education
8. Labour demand indicators
9. Regional inequalities
10. Industrial relations
11. Welfare provisions
12. The tax burden on work
13. International comparison
14. Description of the main data sources

DATA SOURCES
CIRCA Communication & Information Resource Centre Administrator
KSH Table compiled from regular Central Statistical Office publications [Központi
Statisztikai Hivatal]
KSH IMS CSO institution-based labour statistics [KSH intézményi munkaügyi
statisztika]
KSH MEF CSO Labour Force Survey [KSH Munkaerő-felmérés]
KSH MEM CSO Labour Force Account [KSH Munkaerő-mérleg]
NAV National Tax and Customs Administration [Nemzeti Adó- és Vámhivatal]
NEFMI Ministry of National Resources [Nemzeti Erőforrás Minisztérium]
NEFMI EMMI STAT Ministry of National Resources, Educational Statistics [Nemzeti Erőforrás
Minisztérium, Oktatásstatisztika]
NFA National Market Fund [Nemzeti Foglalkoztatási Alap]
NFSZ National Employment Service [Nemzeti Foglalkoztatási Szolgálat]
NFSZ BT National Employment Service Wage Survey [NFSZ Bértarifa-felvétel]
NFSZ IR NFSZ integrated tracking system [NFSZ Integrált (nyilvántartási) Rendszer]
NFSZ PROG National Employment Service Short-term Labour Market Projection Survey
[NFSZ Rövid Távú Munkaerőpiaci Prognózis]
NFSZ REG National Employment Service Unemployment Register [NFSZ regisztere]
NGM Ministry of National Economy [Nemzetgazdasági Minisztérium]
NMH National Labour Office [Nemzeti Munkaügyi Hivatal]
NSZ Population Census [Népszámlálás]
NYUFIG Pension Administration [Nyugdíjfolyósító Igazgatóság]
ONYF Central Administration of National Pension Insurance [Országos
Nyugdíjbiztosítási Főigazgatóság]
TB Social Security Records [Társadalombiztosítás]

EXPLANATION OF SYMBOLS
(–) Non-occurrence.
( .. ) Not available.
( n.a.) Not applicable.
( ... ) Data cannot be given due to data privacy restrictions.

168
 1 Basic economic indicators

Table 1.1: Basic economic indicators

Industrial Real Employ- Consumer Unemploy-
GDPa Exportc Importc
Year productionb earnings ment price index ment rate
1990 96.5 90.7 95.9 94.8 94.3 97.2 128.9 ..
1995 101.5 104.6 108.4 96.1 87.8 98.1 128.2 10.2
2000 104.2 118.1 121.7 120.8 101.5 101.0 109.8 6.4
2001 103.8 103.7 107.7 104.0 106.4 100.3 109.2 5.7
2002 104.5 103.2 105.9 105.1 113.6 100.1 105.3 5.8
2003 103.8 106.9 109.1 110.1 109.2 101.3 104.7 5.9
2004 105.0 107.8 118.4 115.2 98.9 99.4 106.8 6.1
2005 104.4 106.8 111.5 106.1 106.3 100.0 103.6 7.2
2006 103.9 109.9 118.0 114.4 103.6 100.7 103.9 7.5
2007 100.4 107.9 115.8 112.0 95.4 99.3 108.0 7.4
2008 100.9 100.0 104.2 104.3 100.8 98.6 106.1 7.8
2009 93.4 82.2 87.3 82.9 97.7 97.4 104.2 10.0
2010 100.7 110.6 116.9 115.1 101.8 99.6 104.9 11.2
2011 101.7 105.6 109.9 106.7 102.4 100.7 103.9 11.0
2012 98.4 98.2 100.7 99.9 96.6 101.8 105.7 11.0
2013 102.1 101.1 104.2 105.0 103.1 101.7 101.7 10.2
2014 104.2 107.7 106.9 108.8 103.2 105.3 99.8 7.7
2015 103.5 107.4 107.8 106.3 104.4 102.7 99.9 6.8
2016 102.3 100.9 104.4 104.9 107.4 103.4 100.4 5.1
2017 104.1 104.6 105.9 108.3 110.3 101.6 102.4 4.2
2018 104.9 103.6 104.2 106.3 108.3 101.1 102.8 3.7
a After 1996 there was a change in the methodology for accounting the undivided service fee
of financial intermediation. The method of measurement changed in 2014 with the adoption
of ESA2010 (European System of National and Regional Accounts). Unadjusted data. Previ-
ous year = 100.
b 1990–2000: those with more than 5 employees, 2001–: excluding water and waste manage-

ment, including businesses with fewer than 5 employees.

c Volume index.

Note: Previous year = 100, except for unemployment rate.

Source: GDP: STADAT (2019.03.01. version). Industrial production index: 2001–: STADAT
(2019.04.12. version). Export and import: 2001–: STADAT (2019.03.04. version). Real earn-
ings: 1995–: STADAT (2019.02.21. version). Employment: 1990: KSH MEM; 1995–: KSH
MEF (2019.03.13. version). Consumer price index: STADAT (2019.01.15. version). Unem-
ployment rate: STADAT (2019.03.13. version). Other data: KSH.
Online data source in xls format: http://www.bpdata.eu/mpt/2019ent01_01

Figure 1.1: Annual changes of basic economic indicators

15
12
9
6
Real earnings
3
Per cent

0 Employment
–3
GDP
–6
–9
–12
–15
1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014 2016 2018
Source: KSH.
Online data source in xls format: http://www.bpdata.eu/mpt/2019ena01_01

169
Statistical data

Figure 1.2: Annual GDP time series (2000 = 100%)

200

Slovakia
175
Poland
Per cent

150 Hungary

Czech Republic
125
EU-15

100
2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018
Source: Eurostat.
Online data source in xls format: http://www.bpdata.eu/mpt/2019ena01_02

Figure 1.3: Employment rate of population aged 15 –64

75

70 Slovakia

Poland
65
Per cent

Hungary

60
Czech Republic

55 EU-15

50
2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018
Source: Eurostat.
Online data source in xls format: http://www.bpdata.eu/mpt/2019ena01_03

170
 2 Population

Table 2.1: Populationa

Population Demographic dependency rate
Annual
In thousands 1992 = 100 age 15 –64, Total Old
changes
Year in thousands populationb agec
2000 10,221 98.5 –0.3 6,961.3 0.47 0.21
2005 10,098 97.3 –0.2 6,940.3 0.45 0.23
2006 10,077 97.1 –0.2 6,931.8 0.45 0.23
2007 10,066 97.0 –0.1 6,932.4 0.45 0.23
2008 10,045 96.8 –0.2 6,912.7 0.45 0.24
2009 10,031 96.7 –0.1 6,898.1 0.45 0.24
2010 10,014 96.5 –0.1 6,874.0 0.46 0.24
2011 9,986 96.3 –0.2 6,857.4 0.46 0.24
2012 9,932 95.7 .. 6,815.7 0.46 0.25
2013 9,909 95.5 –0.2 6,776.3 0.46 0.25
2014 9,877 95.2 –0.3 6,719.7 0.47 0.26
2015 9,856 95.0 –0.2 6,664.2 0.48 0.27
2016 9,830 94.7 –0.3 6,609.4 0.49 0.27
2017 9,798 94.4 –0.3 6,546.7 0.50 0.28
2018 9,778 94.2 –0.5 6,504.5 0.50 0.28
a January 1st. The data for 2000 –2011 are estimates based on the 2001 census and demograph-
ic data (reference date 2001.02.01.). Those for 2012 –2016 are estimates based on the 2011
census (reference day 2011.10.01.) and demographic data.
b (population age 0–14 + 65 and above) / (population age 15–64)
c (population age 65 and above) / (population age 15–64)

Source: KSH STADAT (2018.06.29. version)

Online data source in xls format: http://www.bpdata.eu/mpt/2019ent02_01

Table 2.2: Population by age groups, in thousandsa

0–14 15–24 25–54 55–64 65+
Total
Year years old
2000 1,729.2 1,526.5 4,291.4 1,143.4 1,531.1 10,221.6
2005 1,579.7 1,322.0 4,409.1 1,209.2 1,577.6 10,097.6
2006 1,553.5 1,302.0 4,399.8 1,230.0 1,590.7 10,076.6
2007 1,529.7 1,285.9 4,393.9 1,251.5 1,605.1 10,066.1
2008 1,508.8 1,273.3 4,377.1 1,262.3 1,623.9 10,045.4
2009 1,492.6 1,259.9 4,346.1 1,292.0 1,640.3 10,030.9
2010 1,476.9 1,253.4 4,293.7 1,326.9 1,663.5 10,014.4
2011 1,457.2 1,231.7 4,257.7 1,367.8 1,671.3 9,985.7
2012 1,440.3 1,214.1 4,164.6 1,437.0 1,675.9 9,931.9
2013 1,430.9 1,196.4 4,144.8 1,435.0 1,701.7 9,908.8
2014 1,425.8 1,172.8 4,123.8 1,423.2 1,731.8 9,877.4
2015 1,427.2 1,147.1 4,112.6 1,404.5 1,764.2 9,855.6
2016 1,424.4 1,120.1 4,109.6 1,379.7 1,796.6 9,830.4
2017 1,422.9 1,089.7 4,105.3 1,351.4 1,828.3 9,797.6
2018 1,421.9 1,068.0 4,118.7 1,317.8 1,852.0 9,778.4
aJanuary 1st. The data for 2000 –2011 are estimates based on the 2001 census and demograph-
ic data (reference date 2001.02.01.). Those for 2012 –2016 are estimates based on the 2011
census (reference day 2011.10.01.) and demographic data.
Source: KSH STADAT (2018.06.29. version)
Online data source in xls format: http://www.bpdata.eu/mpt/2019ent02_02

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Statistical data

Figure 2.1: Age structure of the Hungarian population, 1980, 2018

1980 2018
90+ 90+

Males Females Males Females

80 80

70 70

60 60

50 50

40 40

30 30

20 20

10 10

0 0
90,000 60,000 30,000 00 30,000 60,000 90,000 90,000 60,000 30,000 0 30,000 60,000 90,000
Source: KSH.
Online data source in xls format: http://www.bpdata.eu/mpt/2019ena02_01

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 2 Population

Table 2.3: Male population by age groups, in thousandsa

0–14 15–24 25–59 60–64 65+
Total
Year years old
2000 885.0 780.9 2,403.8 224.8 570.8 4,865.2
2005 809.5 674.6 2,480.0 252.2 576.8 4,793.1
2006 796.7 664.0 2,493.7 249.3 580.9 4,784.6
2007 784.5 655.4 2,503.7 249.4 586.1 4,779.1
2008 773.9 649.2 2,501.3 252.5 592.8 4,769.6
2009 765.8 642.7 2,497.0 258.4 599.2 4,763.1
2010 757.7 640.4 2,488.8 261.7 608.3 4,756.9
2011 747.6 629.7 2,480.4 274.7 611.5 4,743.9
2012 739.5 623.1 2,449.9 294.1 617.9 4,724.6
2013 734.7 614.4 2,439.4 297.0 630.5 4,716.0
2014 732.2 602.1 2,419.1 305.3 644.7 4,703.4
2015 732.8 589.1 2,395.1 319.1 659.7 4,695.8
2016 731.3 575.8 2,379.0 327.1 675.3 4,688.5
2017 730.4 560.3 2,365.0 330.8 688.9 4,675.4
2018 730.0 549.2 2,365.5 327.0 699.9 4,671.6
aJanuary 1st. The data for 2000 –2011 are estimates based on the 2001 census and demo-
graphic data (reference date 2001.02.01.). Those for 2012 –2016 are estimates based on
the 2011 census (reference day 2011.10.01.) and demographic data.
Source: KSH STADAT (2018.06.29. version)
Online data source in xls format: http://www.bpdata.eu/mpt/2019ent02_03

Table 2.4: Female population by age groups, in thousandsa

0–14 15–24 25–54 55–59 60+
Total
Year years old
2000 844.3 745.6 2,170.5 334.8 1,261.3 5,356.5
2005 770.2 647.4 2,221.9 341.7 1,323.1 5,304.3
2006 756.8 638.6 2,213.0 356.6 1,327.0 5,292.0
2007 745.1 630.6 2,206.8 369.6 1,335.0 5,287.1
2008 734.9 624.1 2,194.5 373.2 1,349.1 5,275.8
2009 726.8 617.2 2,176.0 381.8 1,366.1 5,267.9
2010 719.2 613.1 2,145.5 396.8 1,382.8 5,257.4
2011 709.6 601.9 2,124.0 404.4 1,401.9 5,241.8
2012 700.8 590.9 2,079.5 416.2 1,419.9 5,207.3
2013 696.2 582.0 2,066.5 411.2 1,436.9 5,192.8
2014 693.6 570.7 2,052.7 395.5 1,461.5 5,174.0
2015 694.4 558.0 2,043.2 370.2 1,494.0 5,159.8
2016 693.1 544.3 2,037.9 347.4 1,519.2 5,142.0
2017 692.5 529.4 2,032.5 327.9 1,539.9 5,122.3
2018 691.9 518.8 2,035.0 314.1 1,547.0 5,106.8
aJanuary 1st. The data for 2000 –2011 are estimates based on the 2001 census and demo-
graphic data (reference date 2001.02.01.). Those for 2012 –2016 are estimates based on
the 2011 census (reference day 2011.10.01.) and demographic data.
Source: KSH STADAT (2018.06.29. version)
Online data source in xls format: http://www.bpdata.eu/mpt/2019ent02_04

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Statistical data

Table 3.1: Labour force participation of the population over 14 years, in thousandsa
Population of males 15–59 Population of males over 59
and females 15–54 and females over 54
Inactive Pensioner,
Unem- Unem-
Employed Full time On child Other Inactive Total Employed other Total
ployed Pensioner ployed
Year student care leave inactive total inactive
1980 4,887.9 0.0 300.8 370.1 259.0 339.7 1,269.6 6,157.5 570.3 0.0 1,632.1 2,202.4
1990 4,534.3 62.4 284.3 548.9 249.7 297.5 1,380.4 5,977.1 345.7 0.0 1,944.9 2,290.6
1991 4,270.5 253.3 335.6 578.2 259.8 317.1 1,490.7 6,014.5 249.5 0.0 2,045.2 2,294.7
1992 3,898.4 434.9 392.7 620.0 262.1 435.9 1,710.7 6,044.0 184.3 9.8 2,101.7 2,295.8
1993 3,689.5 502.6 437.5 683.9 270.5 480.1 1,872.0 6,064.1 137.5 16.3 2,141.2 2,295.0
1994 3,633.1 437.4 476.5 708.2 280.9 540.7 2,006.3 6,076.8 118.4 11.9 2,163.8 2,294.1
1995 3,571.3 410.0 495.2 723.4 285.3 596.1 2,100.0 6,081.3 107.5 6.4 2,180.6 2,294.5
1996 3,546.1 394.0 512.7 740.0 289.2 599.4 2,141.2 6,081.3 102.1 6.1 2,184.6 2,292.8
1997 3,549.5 342.5 542.9 752.0 289.0 599.9 2,183.8 6,075.8 96.9 6.3 2,189.0 2,292.2
1998 3,608.5 305.5 588.8 697.0 295.5 565.7 2,147.0 6,061.0 89.3 7.5 2,197.6 2,294.4
1999 3,701.0 283.3 534.7 675.6 295.3 549.8 2,055.4 6,039.6 110.4 1.4 2,185.2 2,297.0
2000 3,745.9 261.4 517.9 721.7 281.4 571.4 2,092.4 6,099.7 130.3 2.3 2,268.0 2,400.6
2001 3,742.6 231.7 516.3 717.9 286.6 601.6 2,122.4 6,096.7 140.7 2.4 2,271.8 2,414.9
2002 3,719.6 235.7 507.1 738.3 286.8 593.0 2,125.2 6,080.5 164.1 3.2 2,263.9 2,431.2
2003 3,719.0 239.6 485.0 730.7 286.9 595.0 2,097.6 6,056.2 202.9 4.9 2,245.6 2,453.4
2004 3,663.1 247.2 480.5 739.8 282.4 622.4 2,125.1 6,035.4 237.3 5.7 2,236.1 2,479.1
2005 3,653.9 296.0 449.7 740.8 278.6 590.3 2,059.4 6,009.3 247.6 7.9 2,258.3 2,513.8
2006 3,680.1 309.9 416.1 811.4 261.1 524.3 2,012.9 6,002.9 248.3 8.4 2,270.2 2,526.9
2007 3,649.5 303.7 413.2 822.7 273.9 519.7 2,029.5 5,982.7 252.5 8.4 2,292.9 2,553.8
2008 3,596.3 315.5 394.7 814.3 282.2 549.0 2,040.2 5,952.0 252.0 10.9 2,323.6 2,586.5
2009 3,480.9 403.0 360.3 805.7 282.0 578.4 2,026.4 5,910.3 266.9 14.8 2,345.7 2,627.4
2010 3,435.8 450.1 336.6 805.4 275.9 558.1 1,976.0 5,861.9 298.5 19.3 2,353.3 2,671.1
2011 3,430.1 440.9 296.4 783.8 280.7 557.9 1,932.0 5,789.8 328.9 25.1 2,366.3 2,720.3
2012 3,498.6 447.0 260.1 769.6 263.2 484.3 1,777.2 5,722.8 328.6 26.1 2,407.2 2,761.9
2013 3,551.1 415.7 247.6 737.3 255.4 466.4 1,706.7 5,673.5 341.6 25.2 2,424.5 2,791.3
2014 3,720.7 317.5 222.3 701.2 237.8 412.5 1,573.8 5,612.0 380.0 25.8 2,419.0 2,824.8
2015 3,782.1 281.3 197.3 688.8 240.0 368.1 1,494.2 5,557.6 428.4 26.5 2,400.8 2,855.7
2016 3,860.6 211.3 181.6 656.3 242.4 361.2 1,441.5 5,483.8 491.0 23.3 2,364.1 2,878.4
2017 3,909.9 172.2 164.1 636.5 233.1 362.0 1,362.5 5,444.7 511.4 19.6 2,356.7 2,887.7
2018 3,933.9 158.3 140.9 627.6 232.1 368.4 1,369.0 5,461.2 535.6 13.6 2,339.2 2,888.4
a Annual average figures.

Note: Up to the year 1999, weighting is based on the 1990 population census. From 2000 to
2011, weighting is based on the 2001 population census. From 2012 onwards population
weights are based on the 2011 population census. To ensure comparability, the estimates for
2006 –2011 have been modified by the new weighting scheme.
Data on ‘employed’ includes conscripts and those working while receiving pension or child
support. The data on students for 1995–97 are estimates.
’Other inactive’ is a residual category calculated by deducting the sum of the figures in the
indicated categories from the mid-year population, so it includes the institutional popula-
tion not observed by MEF. The population weights have been corrected using the 2011 Cen-
sus data.
Source: Pensioners: 1980–91: NYUFIG, 1992–: KSH MEF. Child care recipients: up to the
year 1997 TB and estimation, after 1997 MEF. Unemployment: 1990–91: NFSZ REG, 1992–:
KSH MEF.
Online data source in xls format: http://www.bpdata.eu/mpt/2019ent03_01

174
 3 Economic activity

Table 3.2: Labour force participation of the population over 14 years, males, in thousandsa
Population of males 15–59 Population of males 60 and over
Inactive Pensioner,
Unem- Unem-
Employed Full time On child Other Inactive Total Employed other Total
ployed Pensioner ployed
Year student care leave inactive total inactive
1980 2,750.5 0.0 173.8 196.3 0.0 99.1 469.2 3,219.7 265.3 0.0 491.8 757.1
1990 2,524.3 37.9 188.4 284.2 1.2 80.3 554.1 3,116.3 123.7 0.0 665.5 789.2
1991 2,351.6 150.3 218.7 296.5 1.5 115.0 631.7 3,133.6 90.4 0.0 700.7 791.1
1992 2,153.1 263.2 252.0 302.4 1.7 174.8 730.9 3,147.2 65.1 3.2 722.1 790.4
1993 2,029.1 311.5 263.2 346.9 2.0 203.3 815.4 3,156.0 47.9 4.5 735.7 788.1
1994 2,013.4 270.0 277.6 357.1 3.7 239.6 878.0 3,161.4 41.6 3.8 740.0 785.4
1995 2,012.5 259.3 282.2 367.4 4.9 237.8 892.3 3,164.1 37.1 2.1 742.6 781.8
1996 2,007.4 242.4 291.9 372.8 3.3 248.3 916.3 3,166.1 28.9 1.3 746.3 776.5
1997 2,018.0 212.2 306.0 377.6 1.5 251.6 936.7 3,166.9 25.5 1.9 743.5 770.9
1998 2,015.5 186.5 345.4 350.4 1.0 264.2 961.0 3,163.0 26.2 2.8 737.3 766.3
1999 2,068.4 170.3 312.7 338.8 4.2 261.5 917.2 3,155.9 34.7 0.4 727.2 762.3
2000 2,086.0 158.2 315.2 358.2 4.1 261.7 939.2 3,183.4 39.8 0.7 758.8 799.3
2001 2,087.6 141.6 311.0 353.4 4.3 283.2 951.9 3,181.1 41.1 0.9 763.0 805.0
2002 2,080.4 137.3 307.5 370.3 5.0 273.4 956.2 3,173.9 45.2 0.7 764.4 810.3
2003 2,073.5 137.6 293.6 367.9 4.3 288.1 953.9 3,165.0 53.0 0.9 762.5 816.4
2004 2,052.7 136.2 293.5 371.2 4.6 300.2 969.5 3,158.4 64.6 0.6 758.8 824.0
2005 2,050.7 158.2 278.8 375.4 5.8 288.8 948.8 3,157.7 65.4 0.9 763.9 830.2
2006 2,078.4 163.4 258.9 404.1 4.0 249.6 916.6 3,158.4 60.2 1.1 771.5 832.8
2007 2,067.4 162.5 261.8 410.2 4.1 248.8 924.9 3,154.8 61.9 1.0 777.5 840.4
2008 2,033.6 172.7 261.2 408.3 4.7 264.6 938.8 3,145.1 60.0 1.0 790.4 851.4
2009 1,961.9 230.3 240.1 409.0 4.4 288.7 942.2 3,134.4 63.1 1.6 798.9 863.6
2010 1,929.5 259.5 228.7 410.3 4.6 287.1 930.7 3,119.7 63.0 2.2 812.9 878.1
2011 1,950.9 248.7 203.7 397.9 3.6 286.8 892.0 3,091.6 70.1 2.9 826.2 899.2
2012 1,979.2 257.9 187.7 395.6 4.2 238.8 826.3 3,063.4 69.6 4.1 846.1 919.8
2013 2,022.2 234.4 169.5 375.6 3.8 232.0 780.9 3,037.5 81.5 4.8 852.4 938.7
2014 2,120.3 173.1 151.3 352.5 3.0 200.9 707.7 3,001.1 100.1 8.6 855.6 964.3
2015 2,152.1 152.1 133.7 345.1 3.1 181.4 663.3 2,967.5 131.4 9.8 849.3 990.5
2016 2,192.4 119.0 119.6 332.3 3.8 173.6 629.3 2,940.7 170.1 8.5 832.5 1,011.1
2017 2,228.9 89.8 107.3 322.9 1.9 169.2 601.2 2,920.0 188.4 6.0 828.8 1,023.2
2018 2,245.4 83.9 94.2 315.9 1.3 171.0 582.4 2,911.7 200.8 4.1 824.4 1,029.3
a Annual average figures.

Note: Up to the year 1999, weighting is based on the 1990 population census. From 2000 to
2011, weighting is based on the 2001 population census. From 2012 onwards population
weights are based on the 2011 population census. To ensure comparability, the estimates for
2006 –2011 have been modified by the new weighting scheme.
Data on ‘employed’ includes conscripts and those working while receiving pension or child
support. The data on students for 1995–97 are estimates.
’Other inactive’ is a residual category calculated by deducting the sum of the figures in the
indicated categories from the mid-year population, so it includes the institutional popula-
tion not observed by MEF. The population weights have been corrected using the 2011 Cen-
sus data.
Source: Pensioners: 1980–91: NYUFIG, 1992–: KSH MEF. Child care recipients: up to the
year 1997 TB and estimation, after 1997 MEF. Unemployment: 1990–91: NFSZ REG, 1992–:
KSH MEF.
Online data source in xls format: http://www.bpdata.eu/mpt/2019ent03_02

175
Statistical data

Table 3.3: Labour force participation of the population over 14 years, females, in thousandsa
Population of females 15–54 Population of females 55 and above
Inactive Pensioner,
Unem- Unem-
Employed Full time On child Other Inactive Total Employed other Total
ployed Pensioner ployed
Year student care leave inactive total inactive
1980 2,137.4 0.0 127.0 173.8 259.0 240.6 800.4 2,937.8 305.0 0.0 1,140.3 1,445.3
1990 2,010.0 24.5 95.8 264.7 248.5 217.3 826.3 2,860.8 222.0 0.0 1,279.4 1,501.4
1991 1,918.9 103.1 116.9 281.8 258.3 201.9 858.9 2,880.9 159.1 0.0 1,344.5 1,503.6
1992 1,745.3 171.7 140.8 317.6 260.4 261.1 979.9 2,896.9 119.2 6.6 1,379.6 1,505.4
1993 1,660.4 191.1 174.3 337.0 268.5 276.8 1,056.6 2,908.1 89.6 11.8 1,405.5 1,506.9
1994 1,619.7 167.4 198.9 351.1 277.2 301.1 1,128.3 2,915.4 76.8 8.1 1,423.8 1,508.7
1995 1,558.8 150.7 213.0 356.0 280.4 358.3 1,207.7 2,917.2 70.4 4.3 1,438.0 1,512.7
1996 1,538.7 151.6 220.7 367.2 285.9 351.1 1,224.9 2,915.2 73.2 4.8 1,438.3 1,516.3
1997 1,531.5 130.3 236.9 374.4 287.5 348.3 1,247.1 2,908.9 71.4 4.4 1,445.3 1,521.1
1998 1,593.0 119.0 243.4 346.6 294.5 301.5 1,186.0 2,898.0 63.1 4.7 1,460.3 1,528.1
1999 1,632.6 113.0 222.0 336.8 291.1 288.3 1,138.2 2,883.8 75.8 1.0 1,458.0 1,534.8
2000 1,659.9 103.2 202.7 363.5 277.3 309.7 1,153.2 2,916.3 90.5 1.6 1,509.2 1,601.3
2001 1,655.0 90.1 205.3 364.5 282.3 318.3 1,170.4 2,915.5 99.6 1.5 1,508.8 1,609.9
2002 1,639.2 98.4 199.6 368.0 281.8 319.6 1,169.0 2,906.6 118.9 2.5 1,499.5 1,620.9
2003 1,645.6 102.0 191.4 362.8 282.6 306.9 1,143.7 2,891.2 149.9 4.0 1,483.2 1,637.1
2004 1,610.2 111.0 186.8 368.6 277.8 322.2 1,155.4 2,876.6 172.8 5.1 1,477.3 1,655.2
2005 1,603.2 137.8 170.9 365.4 272.8 301.5 1,110.6 2,851.6 182.2 7.0 1,494.4 1,683.6
2006 1,601.7 146.5 157.2 407.3 257.1 274.7 1,096.3 2,844.5 188.1 7.3 1,498.7 1,694.1
2007 1,582.1 141.2 151.4 412.5 269.8 270.9 1,104.6 2,827.9 190.6 7.4 1,515.4 1,713.4
2008 1,562.7 142.8 133.5 406.0 277.5 284.4 1,101.4 2,806.9 192.0 9.9 1,533.2 1,735.1
2009 1,519.0 172.7 120.2 396.7 277.6 289.7 1,084.2 2,775.9 203.8 13.2 1,546.8 1,763.8
2010 1,506.3 190.6 107.9 395.1 271.3 271.0 1,045.3 2,742.2 235.5 17.1 1,540.4 1,793.0
2011 1,479.2 192.2 92.7 385.9 277.1 271.1 1,040.0 2,698.2 258.8 22.2 1,540.1 1,821.1
2012 1,519.4 189.1 72.4 374.0 259.0 245.5 950.9 2,659.4 259.0 22.0 1,561.1 1,842.1
2013 1,528.9 181.3 78.1 361.7 251.6 234.4 925.8 2,636.0 260.1 20.4 1,572.1 1,852.6
2014 1,600.4 144.4 71.0 348.7 234.8 211.6 866.1 2,610.9 279.9 17.2 1,563.4 1,860.5
2015 1,630.0 129.2 63.6 343.7 236.9 186.7 830.9 2,590.1 297.0 16.7 1,551.5 1,865.2
2016 1,668.2 92.3 62.0 324.0 238.6 187.6 812.2 2,543.1 320.9 14.8 1,531.6 1,867.3
2017 1,681.0 82.4 56.8 313.6 231.2 192.8 761.3 2,524.7 323.0 13.6 1,527.9 1,864.5
2018 1,688.5 74.4 46.8 311.7 230.7 197.4 786.6 2,549.5 334.8 9.5 1,514.8 1,859.1
a Annual average figures.

Note: Up to the year 1999, weighting is based on the 1990 population census. From 2000 to
2011, weighting is based on the 2001 population census. From 2012 onwards population
weights are based on the 2011 population census. To ensure comparability, the estimates for
2006 –2011 have been modified by the new weighting scheme.
Data on ‘employed’ includes conscripts and those working while receiving pension or child
support. The data on students for 1995–97 are estimates.
’Other inactive’ is a residual category calculated by deducting the sum of the figures in the
indicated categories from the mid-year population, so it includes the institutional popula-
tion not observed by MEF. The population weights have been corrected using the 2011 Cen-
sus data.
Source: Pensioners: 1980–91: NYUFIG, 1992–: KSH MEF. Child care recipients: up to the
year 1997 TB and estimation, after 1997 MEF. Unemployment: 1990–91: NFSZ REG, 1992–:
KSH MEF.
Online data source in xls format: http://www.bpdata.eu/mpt/2019ent03_03

176
 3 Economic activity

Table 3.4: Labour force participation of the population over 14 years, per cent
Population of males 15–59 Population of males over 59
and females 15–54 and female over 54
Inactive Pensioner,
Unem- Unem-
Employed Full time On child Other Inactive Total Employed other Total
ployed Pensioner ployed
Year student care leave inactive total inactive
1980 79.4 0.0 4.9 6.0 4.2 5.5 20.6 100.0 25.9 0.0 74.1 100.0
1990 75.9 1.0 4.8 9.2 4.2 5.0 23.1 100.0 15.1 0.0 84.9 100.0
1995 58.7 6.7 8.1 11.9 4.7 9.8 34.5 100.0 4.7 0.3 95.0 100.0
2000 61.4 4.3 8.5 11.8 4.6 9.4 34.3 100.0 5.4 0.1 94.5 100.0
2001 61.4 3.8 8.5 11.8 4.7 9.9 34.8 100.0 5.8 0.1 94.1 100.0
2002 61.2 3.9 8.3 12.1 4.7 9.8 35.0 100.0 6.7 0.1 93.1 100.0
2003 61.4 4.0 8.0 12.1 4.7 9.8 34.6 100.0 8.3 0.2 91.5 100.0
2004 60.7 4.1 8.0 12.3 4.7 10.3 35.2 100.0 9.6 0.2 90.2 100.0
2005 60.8 4.9 7.5 12.3 4.6 9.8 34.3 100.0 9.8 0.3 89.8 100.0
2006 61.3 5.2 6.9 13.5 4.3 8.7 33.5 100.0 9.8 0.3 89.8 100.0
2007 61.0 5.1 6.9 13.8 4.6 8.7 33.9 100.0 9.9 0.3 89.8 100.0
2008 60.4 5.3 6.6 13.7 4.7 9.2 34.3 100.0 9.7 0.4 89.8 100.0
2009 58.9 6.8 6.1 13.6 4.8 9.8 34.3 100.0 10.2 0.6 89.3 100.0
2010 58.6 7.7 5.7 13.7 4.7 9.5 33.7 100.0 11.2 0.7 88.1 100.0
2011 59.2 7.6 5.1 13.5 4.8 9.6 33.1 100.0 12.1 0.9 87.0 100.0
2012 61.1 7.8 4.5 13.4 4.6 8.5 31.1 100.0 11.9 0.9 87.2 100.0
2013 62.6 7.3 4.4 13.0 4.5 8.2 30.1 100.0 12.2 0.9 86.9 100.0
2014 66.3 5.7 4.0 12.5 4.2 7.3 28.0 100.0 13.5 0.9 85.6 100.0
2015 68.1 5.1 3.6 12.4 4.3 6.6 26.9 100.0 15.0 0.9 84.1 100.0
2016 70.4 3.9 3.3 12.0 4.4 6.6 26.3 100.0 17.1 0.8 82.1 100.0
2017 71.8 3.2 3.0 11.7 4.3 6.6 25.0 100.0 17.7 0.7 81.6 100.0
2018 72.0 2.9 2.6 11.5 4.2 6.7 25.1 100.0 18.5 0.5 81.0 100.0
Source: Pensioners: 1980–90: NYUFIG, 1995–: KSH MEF. Child care recipients: up to the
year 1995 TB and estimation, after 1995 MEF. Unemployment: 1990: NFSZ REG, 1995–:
KSH MEF.
Online data source in xls format: http://www.bpdata.eu/mpt/2019ent03_04
Figure 3.1: Labour force participation of population
for males 15–59 and females 15–54, total
100 100
Other inactive
80 80
On child care leave
60 60 Student
Per cent

40 40 Pensioner
Unemployed
20 20
Employed
0 0
1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014 2016 2018
Source: Pensioners: 1990–91: NYUFIG, 1992–: KSH MEF. Child care recipients: up to the
year 1997 TB and estimation, after 1997 MEF. Unemployment: 1990 –91: NFSZ REG,
1992–: KSH MEF.
Online data source in xls format: http://www.bpdata.eu/mpt/2019ena03_01

177
Statistical data

Table 3.5: Labour force participation of the population over 14 years, males, per cent
Population of males 15–59 Population of males 60 and above
Inactive Pensioner,
Unem- Unem-
Employed Full time On child Other Inactive Total Employed other Total
ployed Pensioner ployed
Year student care leave inactive total inactive
1980 85.4 0.0 5.4 6.1 0.0 3.1 14.6 100.0 35.0 0.0 65.0 100.0
1990 81.0 1.2 6.0 9.1 0.0 2.6 17.8 100.0 15.7 0.0 84.3 100.0
1995 63.6 8.2 8.9 11.6 0.2 7.5 28.2 100.0 4.7 0.3 95.0 100.0
1996 63.4 7.7 9.2 11.8 0.1 7.8 28.9 100.0 3.7 0.2 96.1 100.0
1997 63.7 6.7 9.7 11.9 0.0 7.9 29.6 100.0 3.3 0.2 96.4 100.0
1998 63.7 5.9 10.9 11.1 0.0 8.4 30.4 100.0 3.4 0.4 96.2 100.0
1999 65.5 5.4 9.9 10.7 0.1 8.3 29.1 100.0 4.6 0.1 95.4 100.0
2000 65.5 5.0 9.9 11.3 0.1 8.2 29.5 100.0 5.0 0.1 94.9 100.0
2001 65.6 4.5 9.8 11.1 0.1 8.9 29.9 100.0 5.1 0.1 94.8 100.0
2002 65.5 4.3 9.7 11.7 0.2 8.6 30.1 100.0 5.6 0.1 94.3 100.0
2003 65.5 4.3 9.3 11.6 0.1 9.1 30.1 100.0 6.5 0.1 93.4 100.0
2004 65.0 4.3 9.3 11.8 0.1 9.5 30.7 100.0 7.8 0.1 92.1 100.0
2005 64.9 5.0 8.8 11.9 0.2 9.1 30.0 100.0 7.9 0.1 92.0 100.0
2006 65.8 5.2 8.2 12.8 0.1 7.9 29.0 100.0 7.2 0.1 92.6 100.0
2007 65.5 5.2 8.3 13.0 0.1 7.9 29.3 100.0 7.4 0.1 92.5 100.0
2008 64.7 5.5 8.3 13.0 0.1 8.4 29.8 100.0 7.0 0.1 92.8 100.0
2009 62.6 7.3 7.7 13.0 0.1 9.2 30.1 100.0 7.3 0.2 92.5 100.0
2010 61.8 8.3 7.3 13.2 0.1 9.2 29.8 100.0 7.2 0.3 92.6 100.0
2011 63.1 8.0 6.6 12.9 0.1 9.3 28.9 100.0 7.8 0.3 91.9 100.0
2012 64.6 8.4 6.1 12.9 0.1 7.8 27.0 100.0 7.6 0.4 92.0 100.0
2013 66.6 7.7 5.6 12.4 0.1 7.6 25.7 100.0 8.7 0.5 90.8 100.0
2014 70.7 5.8 5.0 11.7 0.1 6.7 23.6 100.0 10.4 0.9 88.7 100.0
2015 72.5 5.1 4.5 11.6 0.1 6.1 22.4 100.0 13.3 1.0 85.7 100.0
2016 74.6 4.0 4.1 11.3 0.1 5.9 21.4 100.0 16.8 0.8 82.3 100.0
2017 76.3 3.1 3.7 11.1 0.1 5.8 20.6 100.0 18.4 0.6 81.0 100.0
2018 77.1 2.9 3.2 10.8 0.0 5.9 20.0 100.0 19.5 0.4 80.1 100.0
Source: Pensioners: 1980–90: NYUFIG, 1995–: KSH MEF. Child care recipients: up to the
year 1997 TB and estimation, after 1997 MEF. Unemployment: 1990: NFSZ REG, 1995–:
KSH MEF.
Online data source in xls format: http://www.bpdata.eu/mpt/2019ent03_05
Figure 3.2: Labour force participation of population for males 15–59
100 100
Other inactive
80 80
On child care leave
60 60
Per cent

Student

40 40 Pensioner
Unemployed
20 20
Employed
0 0
1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014 2016 2018
Source: Pensioners: 1990–91: NYUFIG, 1992–: KSH MEF. Child care recipients: up to the
year 1997 TB and estimation, after 1997 MEF. Unemployment: 1990 –91: NFSZ REG,
1992–: KSH MEF.
Online data source in xls format: http://www.bpdata.eu/mpt/2019ena03_02

178
 3 Economic activity

Table 3.6: Labour force participation of the population over 14 years, females, per cent
Population of females 15–54 Population of females 55 and above
Inactive Pensioner,
Unem- Unem-
Employed Full time On child Other Inactive Total Employed other Total
ployed Pensioner ployed
Year student care leave inactive total inactive
1980 72.8 0.0 4.3 5.9 8.8 8.2 27.2 100.0 21.1 0.0 78.9 100.0
1990 70.3 0.9 3.3 9.3 8.7 7.6 28.9 100.0 14.8 0.0 85.2 100.0
1995 53.4 5.2 7.3 12.2 9.6 12.3 41.4 100.0 4.7 0.3 95.1 100.0
1996 52.8 5.2 7.6 12.6 9.8 12.0 42.0 100.0 4.8 0.3 94.9 100.0
1997 52.6 4.5 8.1 12.9 9.9 12.0 42.9 100.0 4.7 0.3 95.0 100.0
1998 55.0 4.1 8.4 12.0 10.2 10.4 40.9 100.0 4.1 0.3 95.6 100.0
1999 56.6 3.9 7.7 11.7 10.1 10.0 39.5 100.0 4.9 0.1 95.0 100.0
2000 56.9 3.5 7.0 12.5 9.5 10.6 39.5 100.0 5.7 0.1 94.2 100.0
2001 56.8 3.1 7.0 12.5 9.7 10.9 40.1 100.0 6.2 0.1 93.7 100.0
2002 56.4 3.4 6.9 12.7 9.7 11.0 40.2 100.0 7.3 0.2 92.5 100.0
2003 56.9 3.5 6.6 12.5 9.8 10.6 39.6 100.0 9.2 0.2 90.6 100.0
2004 56.0 3.9 6.5 12.8 9.7 11.2 40.2 100.0 10.4 0.3 89.3 100.0
2005 56.2 4.8 6.0 12.8 9.6 10.6 38.9 100.0 10.8 0.4 88.8 100.0
2006 56.3 5.2 5.5 14.3 9.0 9.7 38.5 100.0 11.1 0.4 88.5 100.0
2007 55.9 5.0 5.4 14.6 9.5 9.6 39.1 100.0 11.1 0.4 88.4 100.0
2008 55.7 5.1 4.8 14.5 9.9 10.1 39.2 100.0 11.1 0.6 88.4 100.0
2009 54.7 6.2 4.3 14.3 10.0 10.4 39.1 100.0 11.6 0.7 87.7 100.0
2010 54.9 7.0 3.9 14.4 9.9 9.9 38.1 100.0 13.1 1.0 85.9 100.0
2011 54.8 7.1 3.4 14.3 10.3 10.0 38.1 100.0 14.2 1.2 84.6 100.0
2012 57.1 7.1 2.7 14.1 9.7 9.2 36.0 100.0 14.1 1.2 84.7 100.0
2013 58.0 6.9 3.0 13.7 9.5 8.8 35.1 100.0 14.0 1.1 84.9 100.0
2014 61.3 5.5 2.8 13.4 9.0 8.1 33.2 100.0 15.0 0.9 84.0 100.0
2015 62.9 5.0 2.5 13.3 9.1 7.2 32.1 100.0 15.9 0.9 83.2 100.0
2016 65.6 3.6 2.4 12.7 9.4 7.4 31.9 100.0 17.2 0.8 82.0 100.0
2017 66.6 3.3 2.3 12.4 9.2 7.6 30.2 100.0 17.3 0.7 81.9 100.0
2018 66.2 2.9 1.8 12.2 9.1 7.7 30.9 100.0 18.0 0.5 81.5 100.0
Source: Pensioners: 1980–90: NYUFIG, 1995–: KSH MEF. Child care recipients: up to the
year 1997 TB and estimation, after 1997 MEF. Unemployment: 1990: NFSZ REG, 1995–:
KSH MEF.
Online data source in xls format: http://www.bpdata.eu/mpt/2019ent03_06
Figure 3.3: Labour force participation of population for females 15–54
100 100
Other inactive
80 80
On child care leave
60 60
Per cent

Student
40 40 Pensioner
Unemployed
20 20
Employed
0 0
1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014 2016 2018
Source: Pensioners: 1990–91: NYUFIG, 1992–: KSH MEF. Child care recipients: up to the
year 1997 TB and estimation, after 1997 MEF. Unemployment: 1990 –91: NFSZ REG,
1992–: KSH MEF.
Online data source in xls format: http://www.bpdata.eu/mpt/2019ena03_03

179
Statistical data

Table 3.7: Population aged 15–64 by labour market status (self-categorised), in thousands
2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018
Together
In work 3,862.5 3,831.6 3,769.3 3,681.5 3,660.3 3,690.1 3,748.4 3,824.5 4,039.5 4,159.5 4,298.5 4,366.9 4,401.6
Unemployed 470.4 450.2 476.7 591.3 670.7 675.8 700.4 666.5 538.8 454.6 366.3 314.0 284.1
Students, pupils 846.3 861.1 863.7 854.8 854.6 842.2 811.2 772.5 733.5 710.3 675.6 650.4 644.2
Pensioner 622.9 592.2 635.6 627.6 599.3 582.0 630.3 613.6 557.5 477.5 420.1 392.6 364.9
Disabled 506.8 554.4 525.8 498.9 488.4 455.1 356.7 335.7 317.7 318.0 303.1 285.7 253.4
On child care
275.5 286.2 295.0 293.0 289.3 290.2 265.0 259.1 237.0 236.9 236.4 227.5 228.6
leave
Dependent 115.2 111.9 104.0 101.9 95.3 104.3 93.1 96.9 85.3 91.7 93.7 93.2 106.2
Out of work for
107.7 101.8 101.7 104.9 78.2 78.9 89.1 78.0 78.4 81.9 84.1 84.9 86.4
other reasons
Total 6,807.3 6,789.4 6,771.6 6,753.8 6,736.0 6,718.5 6,694.1 6,646.8 6,587.7 6,530.4 6,477.9 6,415.2 6,369.5
Males
In work 2,106.3 2,095.3 2,056.8 1,993.3 1,958.0 1,985.4 2,009.3 2,065.1 2,186.4 2,256.0 2,331.6 2,384.2 2,407.8
Unemployed 251.6 242.0 255.8 333.6 375.6 372.2 382.9 364.4 283.7 241.4 198.9 159.4 146.9
Students, pupils 418.3 428.4 431.7 430.6 432.7 427.2 416.1 393.4 366.9 354.3 338.2 329.1 322.6
Pensioner 234.9 217.4 243.4 246.2 245.6 243.7 254.9 236.7 209.7 167.1 133.1 118.3 109.4
Disabled 243.0 269.4 257.9 238.2 234.6 215.7 177.1 161.6 152.5 152.0 149.4 137.8 123.1
On child care
5.6 4.3 5.6 5.7 6.7 4.5 4.1 4.1 3.1 2.9 3.8 1.9 1.4
leave
Dependent 5.4 6.3 6.8 6.8 9.6 10.0 7.0 9.8 8.3 9.4 8.9 7.8 9.9
Out of work for
55.1 51.8 51.6 49.8 36.1 35.8 40.8 37.1 36.0 39.8 39.2 38.4 40.1
other reasons
Total 3,320.2 3,314.9 3,309.6 3,304.2 3,298.9 3,294.4 3,292.2 3,272.1 3,246.7 3,222.9 3,203.1 3,176.9 3,161.2
Females
In work 1,756.3 1,736.3 1,712.4 1,688.2 1,702.2 1,704.7 1,739.1 1,759.4 1,853.1 1,903.6 1,967.0 1,982.7 1,993.9
Unemployed 218.8 208.3 220.9 257.6 295.1 303.6 317.5 302.1 255.0 213.2 167.4 154.5 137.2
Students, pupils 428.0 432.7 432.0 424.2 421.9 415.0 395.1 379.0 366.6 356.0 337.4 321.3 321.6
Pensioner 388.0 374.8 392.2 381.4 353.7 338.2 375.4 376.9 347.8 310.3 287.0 274.3 255.5
Disabled 263.9 285.0 267.9 260.7 253.8 239.5 179.6 174.1 165.2 166.0 153.7 147.9 130.3
On child care
269.9 281.9 289.4 287.3 282.6 285.7 260.9 255.0 233.8 233.9 232.6 225.6 227.2
leave
Dependent 109.7 105.6 97.2 95.1 85.7 94.3 86.1 87.2 77.0 82.3 84.7 85.4 96.3
Out of work for
52.6 50.0 50.1 55.1 42.1 43.1 48.3 40.9 42.4 42.2 44.9 46.5 46.3
other reasons
Total 3,487.1 3,474.5 3,462.1 3,449.6 3,437.1 3,424.1 3,401.9 3,374.7 3,341.1 3,307.5 3,274.8 3,238.2 3,208.3
Source: KSH MEF.
Online data source in xls format: http://www.bpdata.eu/mpt/2019ent03_07

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 3 Economic activity

Table 3.8: Population aged 15–64 by labour market status (self-categorised), per cent
2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018
Together
In work 56.5 56.7 56.4 55.7 54.5 54.3 54.9 56.0 57.5 61.3 63.7 66.4 68.1 69.1
Unemployed 7.2 6.9 6.6 7.0 8.8 10.0 10.1 10.5 10.0 8.2 7.0 5.7 4.9 4.5
Students, pupils 11.6 12.4 12.7 12.8 12.7 12.7 12.5 12.1 11.6 11.1 10.9 10.4 10.1 10.1
Pensioner 11.1 9.2 8.7 9.4 9.3 8.9 8.7 9.4 9.2 8.5 7.3 6.5 6.1 5.7
Disabled 5.3 7.4 8.2 7.8 7.4 7.3 6.8 5.3 5.1 4.8 4.9 4.7 4.5 4.0
On child care leave 4.0 4.0 4.2 4.4 4.3 4.3 4.3 4.0 3.9 3.6 3.6 3.6 3.5 3.6
Dependent 2.0 1.7 1.6 1.5 1.5 1.4 1.6 1.4 1.5 1.3 1.4 1.4 1.5 1.7
Out of work for other
2.3 1.6 1.5 1.5 1.6 1.2 1.2 1.3 1.2 1.2 1.3 1.3 1.3 1.4
reasons
Total 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0
Males
In work 62.7 63.4 63.2 62.1 60.3 59.4 60.3 61.0 63.1 67.3 70.0 72.8 75.0 76.2
Unemployed 8.0 7.6 7.3 7.7 10.1 11.4 11.3 11.6 11.1 8.7 7.5 6.2 5.0 4.6
Students, pupils 12.0 12.6 12.9 13.0 13.0 13.1 13.0 12.6 12.0 11.3 11.0 10.6 10.4 10.2
Pensioner 9.1 7.1 6.6 7.4 7.4 7.4 7.4 7.7 7.2 6.5 5.2 4.2 3.7 3.5
Disabled 5.4 7.3 8.1 7.8 7.2 7.1 6.5 5.4 4.9 4.7 4.7 4.7 4.3 3.9
On child care leave 0.2 0.2 0.1 0.2 0.2 0.2 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.0
Dependent 0.2 0.2 0.2 0.2 0.2 0.3 0.3 0.2 0.3 0.3 0.3 0.3 0.2 0.3
Out of work for other
2.4 1.7 1.6 1.6 1.5 1.1 1.1 1.2 1.1 1.1 1.2 1.2 1.2 1.3
reasons
Total 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0
Females
In work 50.6 50.4 50.0 49.5 48.9 49.5 49.8 51.1 52.1 55.5 57.6 60.1 61.2 62.1
Unemployed 6.4 6.3 6.0 6.4 7.5 8.6 8.9 9.3 9.0 7.6 6.4 5.1 4.8 4.3
Students, pupils 11.3 12.3 12.5 12.5 12.3 12.3 12.1 11.6 11.2 11.0 10.8 10.3 9.9 10.0
Pensioner 12.9 11.1 10.8 11.3 11.1 10.3 9.9 11.0 11.2 10.4 9.4 8.8 8.5 8.0
Disabled 5.2 7.6 8.2 7.7 7.6 7.4 7.0 5.3 5.2 4.9 5.0 4.7 4.6 4.1
On child care leave 7.6 7.7 8.1 8.4 8.3 8.2 8.3 7.7 7.6 7.0 7.1 7.1 7.0 7.1
Dependent 3.7 3.1 3.0 2.8 2.8 2.5 2.8 2.5 2.6 2.3 2.5 2.6 2.6 3.0
Out of work for other
2.3 1.5 1.4 1.4 1.6 1.2 1.3 1.4 1.2 1.3 1.3 1.4 1.4 1.4
reasons
Total 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0
Source: KSH MEF.
Online data source in xls format: http://www.bpdata.eu/mpt/2019ent03_08

181
Statistical data

Table 4.1: Employment

Year In thousands 1992 = 100 Annual changes Employment ratioa
1990 4,880.0 119.5 .. 59.0
1991 4,520.0 110.7 –7.4 54.4
1992 4,082.7 100.0 –9.7 49.0
1993 3,827.0 93.7 –6.2 45.8
1994 3,751.5 91.9 –2.0 44.8
1995 3,678.8 90.1 –1.9 43.9
1996 3,648.2 89.4 –0.9 43.6
1997 3,646.4 89.3 0.0 43.6
1998 3,697.8 90.6 1.4 44.3
1999 3,811.4 93.4 3.2 45.7
2000 3,849.1 94.3 1.0 46.2
2001 3,883.3 95.1 0.3 45.6
2002 3,883.7 95.1 0.0 45.6
2003 3,921.9 96.1 1.2 46.2
2004 3,900.4 95.5 –0.5 45.8
2005 3,901.5 95.6 0.0 45.7
2006 3,928.4 96.2 0.7 46.0
2007 3,902.0 95.6 –0.7 45.7
2008 3,848.3 94.3 –1.4 45.0
2009 3,747.8 91.8 –2.6 43.9
2010 3,732.4 91.4 –0.4 43.7
2011 3,759.0 92.1 0.7 44.2
2012 3,827.2 93.7 1.8 45.1
2013 3,892.8 95.3 1.7 46.0
2014 4,100.9 100.4 5.3 48.6
2015 4,210.5 103.1 2.7 50.0
2016 4,351.7 106.7 3.4 51.9
2017 4,421.4 108.3 1.6 52.9
2018 4,469.5 109.4 1.1 53.6
a Per cent of the population over 14 years of age.

Source: 1990–91: KSH MEM, 1992–: KSH MEF.

Online data source in xls format: http://www.bpdata.eu/mpt/2019ent04_01

Figure 4.1: Employed

Employed Employment ratio
5,000 60

4,500 55
In thousands

Per cent

4,000 50

3,500 45

3,000 40
1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014 2016 2018
Source: 1990 –91: KSH MEM, 1992–: KSH MEF.
Online data source in xls format: http://www.bpdata.eu/mpt/2019ena04_01

182
 4 Employment

Table 4.2: Employment by gender

Males Females Share of females
Year In thousands 1992 = 100 In thousands 1992 = 100 (%)
1990 2,648.0 119.4 2,232.0 119.7 45.7
1991 2,442.0 110.1 2,078.0 111.5 46.0
1992 2,218.2 100.0 1,864.5 100.0 45.7
1993 2,077.0 93.6 1,750.0 93.9 45.7
1994 2,055.0 92.6 1,696.5 91.0 45.2
1995 2,049.6 92.4 1,629.2 87.4 44.3
1996 2,036.3 91.8 1,611.9 86.5 44.2
1997 2,043.5 92.1 1,602.9 86.0 44.0
1998 2,041.7 92.0 1,656.1 88.8 44.8
1999 2,103.1 94.8 1,708.4 91.6 44.8
2000 2,122.4 95.7 1,726.7 92.6 44.9
2001 2,128.7 96.0 1,754.6 94.1 45.2
2002 2,125.6 95.8 1,758.1 94.3 45.3
2003 2,126.5 95.6 1,795.4 96.2 45.8
2004 2,117.3 95.5 1,783.1 95.6 45.7
2005 2,116.1 95.4 1,785.4 95.8 45.8
2006 2,138.6 96.4 1,789.8 96.0 45.6
2007 2,129.3 96.0 1,772.7 95.1 45.4
2008 2,093.6 94.4 1,754.7 94.1 45.6
2009 2,025.1 91.3 1,722.8 92.4 46.0
2010 1,992.5 89.8 1,739.8 93.3 46.6
2011 2,021.0 91.1 1,738.0 93.2 46.2
2012 2,048.8 92.4 1,778.4 95.4 46.5
2013 2,103.7 94.8 1,789.0 96.0 46.0
2014 2,220.5 100.1 1,880.4 100.9 45.9
2015 2,283.5 103.0 1,927.0 103.4 45.8
2016 2,362.5 106.5 1,989.1 106.7 45.7
2017 2,417.3 109.0 2,004.1 107.5 45.3
2018 2,446.2 110.3 2,023.3 108.5 45.3
Source: 1990–91: KSH MEM, 1992–: KSH MEF.
Online data source in xls format: http://www.bpdata.eu/mpt/2019ent04_02

Figure 4.2: Employment by gender

3,000

2,500

2,000
In thousands

1,500
Females
1,000
Males
500

0
1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014 2016 2018
Source: 1990–91: KSH MEM, 1992–: KSH MEF.
Online data source in xls format: http://www.bpdata.eu/mpt/2019ena04_02

183
Statistical data

Table 4.3: Composition of the employed by age groups, males, per cent
15–19 20–24 25–49 50–54 55–59 60+
Total
Year years old
2000 1.5 12.4 67.3 10.6 6.4 1.8 100.0
2001 1.2 10.4 68.6 11.1 6.7 2.0 100.0
2002 0.9 9.4 69.4 11.3 6.9 2.1 100.0
2003 0.7 8.6 69.1 11.8 7.3 2.5 100.0
2004 0.7 7.4 69.5 12.0 7.3 3.0 100.0
2005 0.6 6.8 68.9 12.7 7.9 3.1 100.0
2006 0.6 6.7 71.1 10.3 8.5 2.8 100.0
2007 0.5 6.7 71.3 10.2 8.4 2.9 100.0
2008 0.5 6.4 71.2 10.6 8.5 2.8 100.0
2009 0.4 5.7 70.6 10.9 9.3 3.1 100.0
2010 0.3 5.8 70.5 10.8 9.8 2.8 100.0
2011 0.3 5.5 69.8 10.9 10.0 3.5 100.0
2012 0.3 5.5 69.4 10.7 10.7 3.4 100.0
2013 0.4 6.1 68.6 10.3 10.7 3.9 100.0
2014 0.5 6.4 68.2 9.9 10.5 4.5 100.0
2015 0.7 6.3 67.3 10.0 10.1 5.8 100.0
2016 0.7 6.7 66.1 9.9 9.5 7.2 100.0
2017 0.6 6.6 65.6 10.4 9.0 7.8 100.0
2018 0.7 6.5 64.9 10.7 9.0 8.2 100.0
Source: KSH MEF.
Online data source in xls format: http://www.bpdata.eu/mpt/2019ent04_03
Table 4.4: Composition of the employed by age groups, females, per cent
15–19 20–24 25–49 50–54 55+
Total
Year years old
2000 1.4 11.1 69.6 12.7 5.2 100.0
2001 1.1 9.6 70.5 13.1 5.7 100.0
2002 0.8 9.2 69.4 13.8 6.8 100.0
2003 0.5 8.2 68.8 14.0 8.5 100.0
2004 0.5 7.1 68.2 14.6 9.7 100.0
2005 0.4 6.3 67.7 15.4 10.2 100.0
2006 0.4 6.0 70.1 12.9 10.6 100.0
2007 0.3 5.8 70.0 13.1 10.8 100.0
2008 0.3 5.6 69.8 13.4 10.9 100.0
2009 0.2 5.4 69.1 13.5 11.8 100.0
2010 0.3 5.3 67.4 13.6 13.4 100.0
2011 0.2 5.1 66.4 13.4 14.9 100.0
2012 0.2 5.2 66.6 13.4 14.6 100.0
2013 0.3 5.1 67.1 13.1 14.4 100.0
2014 0.4 5.6 66.4 12.7 14.9 100.0
2015 0.4 6.1 65.6 12.5 15.4 100.0
2016 0.5 6.0 65.2 12.2 16.1 100.0
2017 0.5 5.8 65.4 12.2 16.1 100.0
2018 0.5 5.5 64.4 13.0 16.6 100.0
Source: KSH MEF.
Online data source in xls format: http://www.bpdata.eu/mpt/2019ent04_04

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Table 4.5: Composition of the employed by level of education, males, per cent
8 grades of primary Vocational Secondary College,
Total
Year school or less school school university
2001 15.6 42.8 26.0 15.6 100.0
2002 14.6 43.2 26.4 15.8 100.0
2003 14.0 41.3 27.7 17.0 100.0
2004 13.0 40.4 28.0 18.6 100.0
2005 13.0 40.8 27.7 18.5 100.0
2006 12.3 41.0 28.2 18.5 100.0
2007 11.7 40.7 28.8 18.8 100.0
2008 11.7 39.4 29.1 19.8 100.0
2009 10.9 38.7 30.1 20.3 100.0
2010 10.6 38.3 30.6 20.5 100.0
2011 10.7 37.2 30.2 21.9 100.0
2012 10.6 36.8 30.1 22.5 100.0
2013 10.2 37.1 30.1 22.6 100.0
2014 11.1 35.8 30.6 22.5 100.0
2015 11.8 34.5 31.0 22.7 100.0
2016 11.9 34.6 31.6 21.9 100.0
2017 11.5 35.4 31.0 22.1 100.0
2018 11.4 35.6 30.4 22.6 100.0
Source: KSH MEF.
Online data source in xls format: http://www.bpdata.eu/mpt/2019ent04_05

Table 4.6: Composition of the employed by level of education, females, per cent
8 grades of primary Vocational Secondary College,
Total
Year school or less school school university
2001 19.1 21.3 40.3 19.3 100.0
2002 18.5 21.5 40.2 19.8 100.0
2003 16.4 21.5 40.9 21.2 100.0
2004 15.9 20.5 40.2 23.4 100.0
2005 15.4 20.2 40.0 24.4 100.0
2006 14.2 20.7 40.0 25.1 100.0
2007 13.5 21.2 40.0 25.3 100.0
2008 13.3 20.3 39.2 27.2 100.0
2009 12.5 19.8 39.3 28.4 100.0
2010 12.3 20.3 38.8 28.6 100.0
2011 11.7 20.1 38.0 30.2 100.0
2012 11.0 19.5 38.4 31.1 100.0
2013 10.9 19.6 38.1 31.4 100.0
2014 11.4 19.4 37.8 31.5 100.0
2015 11.5 19.1 37.4 32.0 100.0
2016 12.0 18.4 38.3 31.3 100.0
2017 12.4 18.6 38.4 30.6 100.0
2018 11.5 19.0 37.5 32.0 100.0
Source: KSH MEF.
Online data source in xls format: http://www.bpdata.eu/mpt/2019ent04_06

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Statistical data

Table 4.7: Employed by employment status, in thousands

Self-employed
Member of Member of other
Employees and assisting Total
cooperatives partnerships
Year family members
2004 3,347.8 8.1 136.6 407.8 3,900.3
2005 3,367.3 5.8 146.7 381.7 3,901.5
2006 3,428.9 4.8 128.0 366.7 3,928.4
2007 3,415.5 4.7 123.9 357.9 3,902.0
2008 3,378.4 2.6 120.9 346.4 3,848.3
2009 3,274.9 2.5 131.7 338.7 3,747.8
2010 3,272.7 2.9 137.6 319.3 3,732.5
2011 3,302.5 2.0 133.3 321.2 3,759.0
2012 3,378.1 2.3 144.3 302.5 3,827.2
2013 3,453.9 3.3 156.6 279.0 3,892.8
2014 3,652.0 3.6 157.3 288.0 4,100.9
2015 3,753.8 1.7 150.3 304.7 4,210.5
2016 3,884.4 0.9 147.1 319.2 4,351.6
2017 3,964.4 0.4 156.4 300.2 4,421.4
2018 4,003.9 0.4 148.7 316.5 4,469.5
Note: Conscripts are excluded. The participants of winter-time training programs within the
Public Works Program are accounted as employees (contrary to the practice of STADAT).
There are differences in data for 2014 –2016.
Source: KSH MEF.
Online data source in xls format: http://www.bpdata.eu/mpt/2019ent04_07

Table 4.8: Composition of the employed persons by employment status, per cent
Self-employed
Member of Member of other
Employees and assisting Total
cooperatives partnerships
Year family members
2004 85.8 0.2 3.5 10.5 100.0
2005 86.3 0.1 3.8 9.8 100.0
2006 87.3 0.1 3.2 9.4 100.0
2007 87.6 0.1 3.1 9.2 100.0
2008 87.7 0.1 3.2 9.0 100.0
2009 87.5 0.1 3.6 8.8 100.0
2010 87.7 0.1 3.7 8.5 100.0
2011 87.9 0.0 3.5 8.5 100.0
2012 88.3 0.1 3.8 7.9 100.0
2013 88.9 0.1 4.0 7.0 100.0
2014 89.1 0.1 4.0 6.8 100.0
2015 89.1 0.0 3.6 7.3 100.0
2016 89.3 0.0 3.4 7.3 100.0
2017 89.7 0.0 3.5 6.8 100.0
2018 89.6 0.0 3.3 7.1 100.0
Note: Conscripts are excluded. The participants of winter-time training programs within the
Public Works Program are accounted as employees (contrary to the practice of STADAT).
There are differences in data for 2014 –2016.
Source: KSH MEF.
Online data source in xls format: http://www.bpdata.eu/mpt/2019ent04_08

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 4 Employment

Table 4.9: Composition of employed persons by sector, by gender, per cent

2014 2015 2016 2017 2018
Fe- To- Fe- To- Fe- To- Fe- To-
Males Males Males Males Males Males Males
males gether males gether males gether males gether
Agriculture, forestry and
5.0 1.7 3.5 5.3 1.9 3.7 5.4 1.9 3.8 5.5 1.8 3.8 5.1 2.0 3.7
fishing
Mining and quarrying 0.4 0.1 0.3 0.4 0.1 0.2 0.3 0.1 0.2 0.4 0.0 0.2 0.4 0.1 0.3
Manufacturing 28.1 18.0 23.3 27.4 18.0 23.0 27.5 18.1 23.1 28.4 18.6 23.8 28.0 18.4 23.6
Electricity, gas, steam and air
1.4 0.6 1.0 1.3 0.4 0.9 1.2 0.5 0.9 1.2 0.5 0.9 1.4 0.5 1.0
conditioning supply
Water supply; sewerage,
waste management and 2.2 0.7 1.5 2.1 0.7 1.5 2.3 0.7 1.5 2.1 0.6 1.4 2.1 0.6 1.4
remediation activities
Construction 10.0 1.0 5.7 10.2 0.9 5.8 10.1 0.9 5.8 10.5 1.1 6.2 11.7 1.3 7.0
Wholesale and retail trade;
repair of motor vehicles and 10.2 15.5 12.7 9.6 15.2 12.3 9.7 14.6 12.0 9.9 14.5 12.0 9.9 14.9 12.1
motorcycles
Transportation and storage 9.1 3.8 6.6 9.0 3.7 6.5 9.4 3.5 6.6 9.6 3.7 6.9 9.2 3.7 6.6
Accommodation and food
3.0 5.2 4.1 3.5 5.3 4.4 3.8 5.1 4.4 3.4 5.3 4.2 3.4 4.9 4.1
service activities
Information and communica-
3.0 1.8 2.4 3.1 1.5 2.4 3.3 1.7 2.6 3.3 1.5 2.4 3.6 1.4 2.6
tion
Financial and insurance
1.6 3.0 2.3 1.3 3.0 2.1 1.5 3.0 2.2 1.7 2.6 2.1 1.4 2.8 2.0
activities
Real estate activities 0.4 0.4 0.4 0.5 0.4 0.4 0.4 0.5 0.5 0.4 0.6 0.5 0.5 0.7 0.6
Professional, scientific and
2.0 3.5 2.7 1.9 3.5 2.7 1.8 3.3 2.5 1.8 3.5 2.6 2.2 3.8 3.0
technical activities
Administrative and support
4.1 3.0 3.6 4.3 2.9 3.6 4.2 3.2 3.7 3.7 3.1 3.5 3.6 3.1 3.4
service activities
Public administration and
defence; compulsory social 10.5 11.6 11.0 10.9 13.0 11.9 10.9 13.5 12.1 10.3 13.1 11.6 8.9 11.8 10.1
security
Education 3.8 14.1 8.7 3.6 13.6 8.3 3.2 13.7 8.1 3.5 13.4 8.0 3.5 13.5 8.1
Human health and social
2.5 11.9 7.0 2.5 11.6 6.8 2.4 11.7 6.8 2.2 12.1 6.8 2.7 12.3 7.1
work activities
Arts, entertainment and
1.5 1.6 1.5 1.7 2.0 1.8 1.4 2.1 1.7 1.4 1.8 1.6 1.4 2.0 1.7
recreation
Other services 1.2 2.4 1.8 1.2 2.3 1.7 1.2 2.1 1.6 1.1 2.1 1.5 1.1 2.2 1.6
Total 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0
Source: KSH MEF.
Online data source in xls format: http://www.bpdata.eu/mpt/2019ent04_09

Table 4.10: Employed in their present job for 0–6 months, per cent
2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018
Hungary 7.2 6.3 6.6 7.2 6.8 7.0 6.8 7.5 7.6 7.4 7.9 7.3 8.4 9.1 8.9 8.4 7.5 7.7 8.1
Source: MEF, IV. quarterly waves.
Online data source in xls format: http://www.bpdata.eu/mpt/2019ent04_10

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Statistical data

Table 4.11: Distribution of employees in the competitive sectora by firm size, per cent
Less than 20 20 –49 50–249 250–999 1000 and more
Year employees
2002 21.6 14.0 21.5 20.1 22.9
2003 23.0 15.3 20.5 19.3 21.8
2004 23.6 14.8 21.3 18.3 22.0
2005 27.0 15.0 20.5 17.5 20.0
2006 15.7 10.7 25.7 24.3 23.6
2007 25.2 14.2 20.0 18.4 22.2
2008 26.0 15.7 20.7 18.9 18.6
2009 23.4 15.7 19.7 18.4 22.8
2010 23.5 15.7 18.6 18.0 24.2
2011 24.9 15.6 18.5 17.7 23.4
2012 24.2 14.7 18.3 18.6 24.1
2013 23.2 14.5 18.1 19.0 25.2
2014 23.8 15.0 18.4 19.2 23.5
2015 24.0 15.4 18.5 17.9 24.2
2016 24.9 15.9 18.0 16.9 24.3
2017 24.4 16.1 17.4 16.6 25.5
2018 24.9 16.6 15.4 16.4 26.7
a Firms employing 5 or more workers.

Source: NFSZ BT.

Online data source in xls format: http://www.bpdata.eu/mpt/2019ent04_11
Table 4.12: Employees of the competitive sectora by the share of foreign ownership, per cent
Share of foreign
2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018
ownership
100% 17.7 16.5 17.7 18.6 19.0 19.4 20.4 17.5 19.2 20.2 21.1 21.8 22.9 20.6 20.8 20.8 20.6
Majority 9.2 8.8 7.8 8.5 7.5 7.4 6.4 6.3 5.4 5.7 6.5 7.8 5.1 5.6 4.7 3.8 3.3
Minority 3.6 3.9 3.8 3.1 2.2 2.9 2.2 1.7 1.9 1.6 1.5 2.9 2.2 1.9 1.8 1.7 1.6
0% 69.5 70.8 70.7 69.8 71.3 70.3 71.0 74.6 73.5 72.4 70.9 67.5 69.9 71.9 72.6 73.8 74.5
a Firms employing 5 or more workers.

Source: NFSZ BT.

Online data source in xls format: http://www.bpdata.eu/mpt/2019ent04_12
Figure 4.3: Employees of the corporate sector by firm size and by the share of foreign ownership
Firm size Share of foreign ownership
100 100

80 80
1000– Minority
60 250–999 60 Majority
Per cent

Per cent

40 50–249 40 100%

–49 0%
20 20

0 0
2002 2004 2006 2008 2010 2012 2014 2016 2018 2002 2004 2006 2008 2010 2012 2014 2016 2018
Source: NFSZ BT.
Online data source in xls format: http://www.bpdata.eu/mpt/2019ena04_03

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Table 4.13: Employment rate of population aged 15–74 by age group, males, per cent
Year 15–19 20–24 25–49 50–54 55–59 60–64 65–74 Total
1999 10.6 60.3 80.5 69.0 44.0 10.4 3.8 56.2
2000 8.4 58.9 80.9 69.6 49.6 11.8 3.8 56.8
2001 7.9 56.7 81.6 68.2 51.3 13.1 3.1 57.1
2002 5.6 53.1 81.9 68.6 52.8 14.4 3.4 57.1
2003 4.8 51.8 82.2 69.7 55.2 16.8 3.8 57.6
2004 4.5 46.5 82.7 69.7 54.0 20.1 4.3 57.5
2005 4.0 43.6 82.5 70.1 56.6 20.9 4.2 57.4
2006 4.1 44.0 83.1 70.7 58.5 18.9 4.2 58.0
2007 3.7 44.0 83.4 71.0 57.3 18.0 4.7 57.8
2008 3.5 42.0 82.9 71.6 54.5 16.5 4.8 56.9
2009 2.4 36.7 80.5 70.5 56.1 16.7 5.0 55.1
2010 2.2 36.7 79.6 69.0 56.3 16.5 4.7 54.2
2011 2.4 36.1 81.0 71.2 56.9 17.4 4.4 55.0
2012 2.2 35.9 81.5 73.1 61.2 17.0 5.2 55.7
2013 2.8 40.8 82.6 74.2 64.9 21.1 4.9 57.4
2014 3.8 45.6 86.6 76.9 70.6 26.9 4.4 60.8
2015 5.9 46.6 87.9 80.5 73.9 35.3 4.6 62.7
2016 6.2 52.7 89.0 83.0 76.2 44.7 5.9 65.0
2017 6.4 55.6 90.7 86.6 77.5 49.6 6.3 66.9
2018 6.9 56.6 91.0 87.1 80.6 52.5 7.8 67.9
Source: KSH MEF.
Online data source in xls format: http://www.bpdata.eu/mpt/2019ent04_13

Table 4.14: Employment rate of population aged 15–74 by age group, females, per cent
Year 15–19 20–24 25–49 50–54 55–59 60–64 65–74 Total
1999 8.7 48.1 67.3 59.4 16.2 5.5 1.6 42.3
2000 8.0 45.9 67.8 62.5 20.0 5.1 1.8 43.0
2001 6.3 44.2 68.0 62.1 23.2 5.5 1.3 43.1
2002 4.3 44.2 67.0 64.0 28.3 6.0 1.5 43.3
2003 3.1 41.9 67.8 65.8 35.1 7.3 2.0 44.3
2004 2.7 37.4 67.2 66.0 39.8 9.0 1.9 44.1
2005 2.6 34.7 67.4 66.6 41.7 9.6 1.5 44.2
2006 2.5 33.6 67.8 67.5 42.4 8.5 1.6 44.4
2007 2.0 32.4 67.8 68.1 40.0 9.4 2.2 44.1
2008 1.8 31.3 67.8 68.7 38.7 9.8 2.3 43.8
2009 1.5 30.0 66.7 68.3 40.7 9.7 2.2 43.1
2010 1.9 30.3 66.6 69.4 46.6 9.5 2.4 43.6
2011 1.5 30.0 66.2 68.8 49.9 11.0 2.6 43.7
2012 1.4 31.3 68.3 72.7 49.7 11.2 2.6 44.9
2013 1.7 30.5 69.3 74.0 51.4 11.1 2.4 45.4
2014 3.0 35.2 72.3 77.9 56.8 13.4 2.3 48.0
2015 2.9 39.9 73.4 80.3 60.0 17.3 2.6 49.5
2016 3.9 41.8 75.3 81.6 64.7 21.9 2.9 51.3
2017 4.3 42.2 76.5 81.1 66.1 23.3 3.3 52.1
2018 4.6 41.4 76.5 84.0 68.2 26.4 3.9 52.9
Source: KSH MEF.
Online data source in xls format: http://www.bpdata.eu/mpt/2019ent04_14

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Statistical data

Table 4.15: Employment rate of population aged 15–64

by level of education, males, per cent
8 grades of primary Vocational Secondary College,
Total
Year school or less school school university
1998 35.0 75.3 67.0 84.9 60.4
1999 33.6 76.8 68.3 86.8 62.4
2000 33.6 77.4 67.9 87.1 63.1
2001 33.0 77.6 67.3 87.4 62.9
2002 32.0 77.6 67.1 85.8 62.9
2003 32.4 76.5 67.8 86.4 63.4
2004 31.0 75.7 67.3 87.1 63.1
2005 31.6 74.7 66.9 86.9 63.1
2006 31.4 75.6 67.7 86.0 63.9
2007 31.0 74.4 67.3 85.6 63.7
2008 31.1 72.4 66.1 84.3 62.7
2009 28.8 69.5 64.6 82.8 60.7
2010 28.1 67.7 64.2 81.8 59.9
2011 29.0 68.0 64.5 83.7 60.7
2012 30.0 68.7 64.6 84.4 61.6
2013 30.8 70.9 67.1 85.3 63.7
2014 36.3 74.8 71.2 87.1 67.8
2015 39.9 77.1 73.2 88.6 70.3
2016 42.5 80.1 76.1 90.5 73.0
2017 44.2 82.6 77.8 91.6 75.2
2018 45.8 83.9 77.9 91.9 76.3
Source: KSH MEF.
Online data source in xls format: http://www.bpdata.eu/mpt/2019ent04_15

Figure 4.4: Activity rate by age groups, males aged 15 –64, quarterly
15–19 20–24 25–49 50–54 55–59 60–64
100

80

60
Per cent

40

20

0
1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018
Quarterly
Source: KSH MEF.
Online data source in xls format: http://www.bpdata.eu/mpt/2019ena04_04

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 4 Employment

Table 4.16: Employment rate of population aged 15–64

by level of education, females, per cent
8 grades of primary Vocational Secondary College,
Total
Year school or less school school university
1998 26.6 60.5 58.1 76.9 47.3
1999 26.1 61.4 59.0 77.5 49.0
2000 26.0 61.0 59.3 77.8 49.7
2001 26.1 60.8 59.2 77.8 49.8
2002 26.0 60.4 58.6 77.9 49.8
2003 25.3 59.7 59.5 78.3 50.9
2004 25.0 58.8 58.1 78.1 50.7
2005 25.1 57.6 57.9 78.9 51.0
2006 24.3 57.8 57.5 78.0 51.1
2007 23.6 57.2 57.2 75.5 50.7
2008 23.7 55.2 56.1 75.3 50.3
2009 22.7 54.0 54.6 74.2 49.6
2010 23.3 56.2 54.0 74.3 50.2
2011 22.5 56.1 53.9 74.6 50.3
2012 22.6 56.8 56.3 74.3 51.9
2013 23.7 57.1 56.6 74.2 52.6
2014 27.3 60.4 59.1 76.1 55.9
2015 28.7 62.3 61.3 77.3 57.8
2016 31.5 63.4 64.1 80.0 60.2
2017 33.7 64.6 65.2 78.9 61.3
2018 33.7 66.7 64.8 80.0 62.3
Source: KSH MEF.
Online data source in xls format: http://www.bpdata.eu/mpt/2019ent04_16

Figure 4.5: Activity rate by age groups, females aged 15 –64, quarterly
15–19 20–24 25–49 50–54 55–59 60–64
100

80

60
Per cent

40

20

0
1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018
Quarterly
Source: KSH MEF.
Online data source in xls format: http://www.bpdata.eu/mpt/2019ena04_05

191
Statistical data

Table 5.1: Unemployment rate by gender and share of long term unemployed, per cent
Unemployment rate Share of long term
Year Males Females Total unemployeda
1992 10.7 8.7 9.8 ..
1993 13.2 10.4 11.9 ..
1994 11.8 9.4 10.7 43.2
1995 11.3 8.7 10.2 50.6
1996 10.7 8.8 9.9 54.4
1997 9.5 7.8 8.7 51.3
1998 8.5 7.0 7.8 48.8
1999 7.5 6.3 7.0 49.5
2000 7.0 5.6 6.4 49.1
2001 6.3 5.0 5.7 46.7
2002 6.1 5.4 5.8 44.9
2003 6.1 5.6 5.9 43.9
2004 6.1 6.1 6.1 45.0
2005 7.0 7.5 7.2 46.2
2006 7.1 7.9 7.5 46.9
2007 7.1 7.7 7.4 48.1
2008 7.7 8.0 7.8 48.1
2009 10.3 9.7 10.0 42.9
2010 11.6 10.7 11.2 50.6
2011 11.1 11.0 11.0 49.4
2012 11.3 10.6 11.0 47.0
2013 10.2 10.1 10.2 50.4
2014 7.6 7.9 7.7 49.5
2015 6.6 7.0 6.8 47.6
2016 5.1 5.1 5.1 48.4
2017 3.8 4.6 4.2 42.6
2018 3.5 4.0 3.7 41.0
aLong term unemployed are those who have been without work for 12 months or more, ex-
cluding those who start a new job within 90 days.
Note: Conscripted soldiers are included in the denominator.
Source: KSH MEF.
Online data source in xls format: http://www.bpdata.eu/mpt/2019ent05_01

Figure 5.1: Unemployment rates by gender

15

12

Females
Per cent

9
Males

3
1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014 2016 2018
Source: KSH MEF.
Online data source in xls format: http://www.bpdata.eu/mpt/2019ena05_01

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 5 Unemployment

Table 5.2: Unemployment rate by level of education, males, per cent

8 grades of primary Vocational Secondary College,
Total
Year school or less school school university
2000 13.4 7.7 4.8 1.6 7.0
2001 13.6 6.4 4.3 1.2 6.3
2002 14.1 6.2 4.0 1.4 6.1
2003 13.6 6.6 3.9 1.6 6.1
2004 14.3 6.4 4.1 1.7 6.1
2005 15.6 7.4 4.9 2.3 7.0
2006 17.3 7.0 5.1 2.6 7.1
2007 18.7 6.8 5.1 2.4 7.1
2008 20.2 7.7 5.2 2.3 7.7
2009 24.6 10.7 7.6 3.6 10.3
2010 27.2 12.2 8.3 4.9 11.6
2011 25.5 12.1 8.3 4.1 11.1
2012 25.3 12.0 9.6 4.2 11.3
2013 24.5 10.8 8.4 3.4 10.2
2014 18.4 7.8 6.2 2.8 7.6
2015 16.7 6.7 5.3 2.2 6.6
2016 13.7 4.9 4.0 1.8 5.1
2017 11.0 3.6 2.8 1.4 3.8
2018 10.3 3.2 2.9 1.5 3.5
Source: KSH MEF.
Online data source in xls format: http://www.bpdata.eu/mpt/2019ent05_02

Table 5.3: Composition of the unemployed by level of education, males, per cent
8 grades of primary Vocational Secondary College,
Total
Year school or less school school university
2000 32.9 45.8 17.9 3.4 100.0
2001 36.5 43.2 17.5 2.8 100.0
2002 36.7 43.3 16.7 3.3 100.0
2003 34.0 44.7 17.2 4.1 100.0
2004 33.9 42.6 18.6 4.9 100.0
2005 32.1 43.1 19.0 5.8 100.0
2006 33.4 40.3 19.9 6.4 100.0
2007 35.1 38.6 20.4 5.9 100.0
2008 35.9 39.4 19.2 5.5 100.0
2009 31.2 40.5 21.7 6.6 100.0
2010 30.3 40.5 21.1 8.1 100.0
2011 29.4 41.1 21.9 7.6 100.0
2012 28.1 39.3 24.9 7.6 100.0
2013 29.2 39.3 24.4 7.1 100.0
2014 30.6 37.0 24.5 7.9 100.0
2015 33.4 34.9 24.5 7.2 100.0
2016 34.9 33.2 24.6 7.3 100.0
2017 35.7 33.7 22.5 8.1 100.0
2018 35.6 32.8 24.2 7.4 100.0
Source: KSH MEF.
Online data source in xls format: http://www.bpdata.eu/mpt/2019ent05_03

193
Statistical data

Table 5.4: Unemployment rate by level of education, females, per cent

8 grades of primary Vocational Secondary College,
Total
Year school or less school school university
2000 9.1 7.4 4.9 1.5 5.6
2001 8.4 6.4 4.0 1.6 5.0
2002 9.3 6.5 4.4 2.4 5.4
2003 10.5 7.2 4.4 1.9 5.6
2004 10.3 8.0 5.3 2.9 6.1
2005 13.0 9.8 6.7 3.1 7.5
2006 16.2 10.4 6.5 2.7 7.9
2007 16.3 9.7 6.2 3.2 7.7
2008 17.4 9.6 6.8 3.1 8.0
2009 21.6 12.6 7.8 4.1 9.7
2010 22.8 12.6 9.6 4.3 10.7
2011 24.5 12.9 9.9 4.4 11.0
2012 24.4 12.7 9.4 4.7 10.6
2013 22.7 12.8 9.0 4.3 10.1
2014 18.7 9.3 7.1 3.4 7.9
2015 18.1 8.7 5.9 2.6 7.0
2016 12.7 6.8 4.3 1.8 5.1
2017 11.3 5.4 4.0 1.8 4.6
2018 11.7 4.3 3.6 1.8 4.0
Source: KSH MEF.
Online data source in xls format: http://www.bpdata.eu/mpt/2019ent05_04

Table 5.5: Composition of the unemployed by level of education, females, per cent
8 grades of primary Vocational Secondary College,
Total
Year school or less school school university
2000 31.8 28.2 35.0 5.0 100.0
2001 33.7 28.0 32.2 6.1 100.0
2002 33.2 26.0 32.2 8.5 100.0
2003 32.7 28.3 32.0 7.0 100.0
2004 27.8 27.4 34.2 10.6 100.0
2005 28.2 27.1 35.2 9.5 100.0
2006 31.8 27.9 32.3 8.0 100.0
2007 31.3 27.2 31.6 9.9 100.0
2008 32.3 24.7 33.0 10.0 100.0
2009 31.8 26.4 30.6 11.2 100.0
2010 30.5 24.4 34.3 10.7 100.0
2011 30.8 24.1 33.9 11.2 100.0
2012 29.8 23.8 33.5 12.9 100.0
2013 28.5 25.6 33.4 12.5 100.0
2014 30.5 23.1 33.4 13.0 100.0
2015 33.5 24.1 31.2 11.3 100.0
2016 32.4 24.9 31.8 10.9 100.0
2017 33.0 22.2 33.1 11.7 100.0
2018 32.8 20.8 33.0 13.4 100.0
Source: KSH MEF.
Online data source in xls format: http://www.bpdata.eu/mpt/2019ent05_05

194
 5 Unemployment

Figure 5.2: Intensity of quarterly flows between labour market status, population between 15–64 years
Employment Unemployment Inactivity
100 100 5 5 5 5

4 4 4 4
90 90

3 3 3 3
Employment 80 80
2 2 2 2

70 70
1 1 1 1

60 60 0 0 0 0
1996 1998 2000 2002 2004 2006 2008 2010 2012 2014 2016 2018 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014 2016 2018 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014 2016 2018
25 25 100 100 25 25

20 20 20 20
90 90

15 15 15 15
Unemployment 80 80
10 10 10 10

70 70
5 5 5 5

0 0 60 60 0 0
1996 1998 2000 2002 2004 2006 2008 2010 2012 2014 2016 2018 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014 2016 2018 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014 2016 2018
5 5 5 5 100 100

4 4 4 4
90 90

3 3 3 3

Inactivity 80 80
2 2 2 2

70 70
1 1 1 1

0 0 0 0 60 60
1996 1998 2000 2002 2004 2006 2008 2010 2012 2014 2016 2018 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014 2016 2018 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014 2016 2018

Note: The calculations were carried out for the age group between 15 –64 based on KSH la-
bour force survey microdata. The probability of transition is given by the number of people
who transitioned from one status to the other in the quarter, divided by the initial size of the
group in the previous quarter, which were then corrected to preserve the consistency of
stock flows. The red curves show the trend smoothed using a 4th degree polynomial.
Source: KSH MEF.
Online data source in xls format: http://www.bpdata.eu/mpt/2019ena05_02

195
Statistical data

Table 5.6: The number of unemployeda by duration of job search, in thousands

Length of job search, weeks [month]
1–4 5–14 15–26 27–51 52 53–78 79–104 105– Total
Year [<1] [1–3] [4–6] [7–11] [12] [13–18] [19–24] [>24]
1992 43.9 90.9 96.4 110.7 10.6 41.7 38.4 n.a. 432.6
1993 36.2 74.8 87.9 120.5 14.7 75.1 83.7 n.a. 492.9
1994 30.5 56.5 65.0 91.9 8.4 63.0 73.8 40.4 429.5
1995 23.0 51.0 56.5 69.4 20.2 57.2 34.3 93.2 404.8
1996 19.9 46.4 49.3 61.5 18.2 56.1 37.1 100.2 388.7
1997 16.1 43.7 45.9 54.4 15.7 44.5 31.1 77.3 328.7
1998 12.9 44.2 44.5 45.7 16.0 39.0 27.6 63.5 293.4
1999 15.4 44.1 38.8 46.0 13.2 38.1 26.8 62.3 284.7
2000 16.7 38.5 35.1 42.8 12.7 36.9 23.6 55.4 261.3
2001 14.9 37.0 33.2 38.6 11.5 31.6 20.9 44.2 231.9
2002 15.5 39.4 34.8 40.7 11.6 32.7 19.8 42.5 237.0
2003 15.9 42.1 38.9 42.0 14.5 27.6 17.6 43.0 241.6
2004 13.0 42.0 39.9 41.8 13.5 33.4 19.6 47.2 250.4
2005 14.8 48.9 44.1 51.3 14.1 41.0 27.4 54.3 295.9
2006 13.2 51.1 48.5 52.0 17.9 41.1 26.6 59.7 310.0
2007 13.9 49.5 44.2 50.5 12.8 42.8 26.2 65.1 304.9
2008 13.5 50.3 47.9 53.4 13.5 39.1 26.3 74.0 317.9
2009 18.7 71.4 66.6 77.5 18.4 51.3 27.1 79.0 410.0
2010 16.9 65.4 62.5 83.5 23.2 74.7 42.6 93.7 462.5
2011 28.9 70.7 62.8 70.0 18.0 64.7 40.1 103.7 458.9
2012 39.2 64.0 63.1 80.5 22.2 59.5 36.6 100.9 466.0
2013 48.2 49.4 53.7 62.1 25.3 49.8 45.0 97.1 430.7
2014 36.5 41.5 44.9 46.3 19.0 35.1 29.2 82.7 335.3
2015 30.9 43.0 38.6 44.0 18.2 30.0 23.7 69.6 298.0
2016 28.9 29.8 29.3 29.4 12.2 24.1 20.4 52.8 226.9
2017 24.2 29.9 26.0 25.2 9.2 19.0 14.0 35.8 183.3
2018 22.5 26.7 24.7 21.6 9.5 14.7 11.7 30.7 162.1
aNot including those unemployed who will find a new job within 30 days; since 2003: within
90 days.
Source: KSH MEF.
Online data source in xls format: http://www.bpdata.eu/mpt/2019ent05_06

196
 5 Unemployment

Figure 5.3: Unemployment rate by age groups, males aged 15 –59, quarterly
15–19 20–24 25–49 50–54 55–59
5

4
Log of the unemployment rate

0
1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014 2016 2018
Quarterly
Source: KSH MEF.
Online data source in xls format: http://www.bpdata.eu/mpt/2019ena05_03

Figure 5.4: Unemployment rate by age groups, females aged 15 –59, quarterly
15–19 20–24 25–49 50–54 55–59
5

4
Log of the unemployment rate

0
1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014 2016 2018
Quarterly
Source: KSH MEF.
Online data source in xls format: http://www.bpdata.eu/mpt/2019ena05_04

197
Statistical data

Table 5.7: Registered unemployeda and LFS unemployment

Registered unemployed LFS unemployed, total LFS unemployed, age 15 –24
Year In thousands rate in % In thousands rate in % In thousands rate in %
1995 507.7 11.9 416.5 10.2 114.3 18.6
1996 500.6 12.1 400.1 9.9 106.3 17.9
1997 470.1 11.6 348.8 8.7 95.8 15.9
1998 423.1 10.5 313.0 7.8 87.6 13.4
1999 409.5 10.2 284.7 7.0 78.6 12.4
2000 390.5 9.6 262.5 6.4 70.7 12.1
2001 364.1 8.8 232.9 5.7 55.7 10.8
2002 344.7 8.3 238.8 5.8 56.5 12.3
2003 357.2 8.7 244.5 5.9 54.9 13.4
2004 375.9 9.1 252.9 6.1 55.9 15.5
2005 409.9 9.8 303.9 7.2 66.9 19.4
2006 393.5 9.4 318.2 7.5 64.1 19.1
2007 426.9 10.1 312.1 7.4 57.4 18.0
2008 442.3 10.4 326.3 7.8 60.0 19.5
2009 561.8 13.5 417.8 10.0 78.8 26.4
2010 582.7 14.0 469.4 11.2 78.3 26.4
2011 582.9 14.0 466.0 11.0 74.5 26.0
2012 559.1 13.3 473.2 11.0 84.6 28.2
2013 527.6 12.4 441.0 10.2 83.5 26.6
2014 422.4 9.8 343.3 7.7 67.6 20.4
2015 378.2 8.6 307.8 6.8 58.9 17.3
2016 313.8 7.0 234.6 5.1 44.7 12.9
2017 283.0 6.1 191.7 4.2 36.3 10.7
2018 255.3 5.5 172.1 3.7 33.6 10.2
aSince 1st of November, 2005: database of registered jobseekers. From the 1st of November,
2005 the Employment Act changed the definition of registered unemployed to registered
jobseekers. After termination of compilation of Balance of Labour Force in 2016 the number
of economically active population – that was the base of the registered unemployment rate
– has been derived from the Labour Force Survey. At the same time data have been corrected
retrospectively.
Note: the denominator of registered unemployment/jobseekers’ rate in the economically ac-
tive population on 1st January the previous year.
Source: Registered unemployment/jobseekers: NFSZ; LFS unemployment: KSH MEF.
Online data source in xls format: http://www.bpdata.eu/mpt/2019ent05_07
Figure 5.5: Registered and LFS unemployment rates
20

15
LFS unemployed
Per cent

10
Registered unemployed

0
1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014 2016 2018
Note: Since 1st of November, 2005: database of registered jobseekers.
Source: Registered unemployment/jobseekers: NFSZ; LFS unemployment: KSH MEF.
Online data source in xls format: http://www.bpdata.eu/mpt/2019ena05_05

198
 5 Unemployment

Table 5.8: Composition of the registered unemployeda by educational attainment, yearly averages, per cent
Educational
2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018
attainment
8 grades of primary
42.7 42.3 41.9 42.0 42.4 43.3 40.1 39.3 40.3 40.3 40.5 41.0 42.4 42.2 43.4 43.7
school or less
Vocational school 32.9 32.3 32.4 32.1 31.5 30.9 32.5 31.4 29.8 29.2 29.0 28.3 27.1 27.0 26.2 25.6
Vocational second-
13.1 13.4 13.5 13.4 13.3 13.1 14.4 15.0 14.9 15.1 15.3 15.3 15.0 14.9 14.6 14.7
ary school
Grammar school 7.5 7.7 7.9 8.0 8.2 8.2 8.5 9.1 9.5 9.7 9.8 10.1 10.1 10.1 10.1 10.3
College 2.7 3.1 3.2 3.3 3.3 3.3 3.2 3.7 3.8 3.8 3.6 3.4 3.4 3.5 3.4 3.4
University 1.0 1.1 1.2 1.3 1.3 1.2 1.2 1.5 1.7 1.8 1.8 1.9 2.0 2.2 2.3 2.4
Total 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0
aSince 1st of November, 2005: registered jobseekers. From the 1st of November, 2005 the
Employment Act changed the definition of registered unemployed to registered jobseekers.
Source: NFSZ.
Online data source in xls format: http://www.bpdata.eu/mpt/2019ent05_08
Table 5.9: The distribution of registered unemployed school-leaversa
by educational attainment, yearly averages, per cent
Educational
2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018
attainment
8 grades of primary
34.7 35.2 36.1 38.2 40.1 41.3 37.7 35.2 35.6 34.9 35.5 39.4 43.8 44.9 45.8 45.1
school or less
Vocational school 20.4 20.2 20.5 19.7 18.1 17.3 18.9 18.9 18.5 19.8 20.1 18.3 16.9 16.6 16.4 15.7
Vocational second-
23.2 22.1 21.5 20.3 20.7 21.2 23.1 23.9 23.6 23.7 23.1 21.7 19.8 18.9 18.3 19.0
ary school
Grammar school 10.8 10.7 10.8 11.7 12.8 13.3 13.7 14.3 15.0 14.9 14.9 15.0 14.7 14.6 15.0 16.0
College 7.7 8.1 7.8 6.9 5.8 4.9 4.5 4.8 4.2 3.6 3.4 2.8 2.3 2.2 1.8 1.6
University 3.3 3.6 3.4 3.0 2.5 2.0 2.1 2.8 3.1 3.0 3.0 2.7 2.5 2.8 2.7 2.6
Total 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0
aSince 1st of November, 2005: registered school-leaver jobseekers. From the 1st of November,
2005 the Employment Act changed the definition of registered unemployed to registered
jobseekers.
Source: NFSZ.
Online data source in xls format: http://www.bpdata.eu/mpt/2019ent05_09

199
Statistical data

Table 5.10: Registered unemployed by economic activity as observed in the LFS, per cent
Year Employed LFS-unemployed Inactive Total Year Employed LFS-unemployed Inactive Total
2001 6.5 45.2 48.3 100.0 2010 3.2 70.4 26.4 100.0
2002 4.4 47.4 48.2 100.0 2011 3.5 66.7 29.8 100.0
2003 9.4 44.1 46.5 100.0 2012 3.4 64.9 31.7 100.0
2004 3.0 53.5 43.5 100.0 2013 4.9 61.6 33.4 100.0
2005 2.3 59.7 38.0 100.0 2014 6.2 60.5 33.2 100.0
2006 3,0 60.9 36.1 100.0 2015 3.9 67.1 29.0 100.0
2007 3.7 62.2 34.1 100.0 2016 4.9 61.7 33.4 100.0
2008 3.9 62.8 33.2 100.0 2017 6.7 57.8 35.5 100.0
2009 3.7 67.1 29.2 100.0 2018 6.6 55.0 38.4 100.0
Note: The data pertain to those who consider themselves registered jobseekers in the KSH
MEF. From 1999 those who reported that their last contact with the employment centre was
more than two months ago were filtered from among those who reported themselves as reg-
istered unemployed.
Source: KSH MEF.
Online data source in xls format: http://www.bpdata.eu/mpt/2019ent05_10

Table 5.11: Monthly entrants to the unemployment registera, monthly averages, in thousands
2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018
First time
11.2 10.4 10.0 10.5 10.8 8.6 8.0 7.1 8.3 7.2 6.6 7.5 7.3 6.3 5.5 5.0 4.6 4.4
entrants
Previously
45.8 45.6 44.8 47.3 50.0 42.2 43.4 46.9 60.7 58.1 64.3 62.0 58.2 63.1 52.1 46.5 43.3 39.8
registered
Together 57.0 56.0 54.8 57.8 60.7 50.8 51.4 54.0 69.0 65.3 70.9 69.5 65.5 69.4 57.6 51.5 47.9 44.2
aSince 1st of November, 2005: database of jobseekers. From the 1st of November, 2005 the
Employment Act changed the definition of registered unemployed to registered jobseekers.
Source: NFSZ REG.
Online data source in xls format: http://www.bpdata.eu/mpt/2019ent05_11

Figure 5.6: Entrants to the unemployment register, monthly averages, in thousands

First time entrants Previously registered
80
70
60
50
Per cent

40
30
20
10
0
1996 1998 2000 2002 2004 2006 2008 2010 2012 2014 2016 2018
Source: NFSZ REG.
Online data source in xls format: http://www.bpdata.eu/mpt/2019ena05_06

200
 5 Unemployment

Table 5.12: Selected time series of registered unemployment, monthly averages, in thousands and per cent
1999 2000 2001 2002 2003 2004 2005 2006 2007 2008
Registered unemploymenta 409.5 390.5 364.1 344.7 357.2 375.9 409.9 393.5 426.9 442.3
Of which: School-leavers 29.9 26.0 26.8 28.5 31.3 33.8 40.9 38.7 40.4 41.4
Non school-leavers 379.6 364.4 337.4 316.2 325.9 342.2 369.1 354.7 386.5 400.9
Male 221.4 209.7 196.4 184.6 188.0 193.3 210.4 200.9 219.9 228.3
Female 188.1 180.8 167.7 160.1 169.2 182.6 199.5 192.5 207.0 214.0
25 years old and younger 85.4 79.1 75.6 71.1 71.6 71.4 78.9 75.8 80.3 75.9
Manual workers 336.8 321.2 302.0 286.3 296.2 308.5 336.2 321.9 .. ..
Non manual workers 72.7 69.3 62.1 58.4 61.0 67.4 73.7 71.6 .. ..
Unemployment benefit recipientsb 140.7 131.7 119.2 114.9 120.0 124.0 134.4 151.5 134.6 136.5e
Unemployment assistance recipientsc 148.6 143.5 131.2 113.4 116.2 120.4 133.4 121.8 133.0 147.5
Unemployment rated 9.7 9.3 8.5 8.0 8.3 8.7 9.4 9.0 9.7 10.0
Shares within registered unemployed, %
School-leavers 7.3 6.7 7.3 8.3 8.8 9.0 10.0 9.8 9.5 9.4
Male 54.1 53.7 53.9 53.5 52.6 51.4 51.3 51.1 51.5 51.6
25 years old and younger 20.9 20.3 20.8 20.6 20.0 19.0 19.2 16.5 18.8 17.2
Manual workers 82.3 82.2 82.9 83.1 82.9 82.1 82.0 81.8 .. ..
Flows, in thousands
Inflow to the Register 57.2 54.1 57.0 56.0 54.8 57.8 60.7 50.8 51.4 54.0
Of which: school-leavers 9.3 8.0 7.8 7.8 7.7 7.6 8.2 7.0 6.2 6.3
Outflow from the Register 57.2 56.8 59.4 55.8 53.5 54.4 59.8 51.4 48.4 51.3
Of which: school-leavers 9.4 8.2 7.7 7.5 7.6 7.1 7.9 7.1 6.0 6.2
2009 2010 2011 2012 2013 2014 2015 2016 2017 2018
Registered unemploymenta 561.8 582.7 582.9 559.1 527.6 422.4 378.2 313.8 283.0 255.3
Of which: School-leavers 49.3 52.6 52.9 61.5 66.0 54.6 47.0 35.8 29.6 24.8
Non school-leavers 512.5 530.1 529.9 497.6 461.6 367.8 331.2 278.0 253.4 230.5
Male 297.9 305.0 297.1 275.8 267.7 214.2 187.5 156.0 137.9 122.4
Female 263.9 277.7 285.8 283.3 259.9 208.2 190.7 157.8 145.1 132.9
25 years old and younger 104.3 102.8 102.3 101.1 97.8 78.2 68.8 56.0 49.8 43.6
Manual workers .. .. .. .. .. .. .. .. .. ..
Non manual workers .. .. .. .. .. .. .. .. .. ..
Unemployment benefit recipientsb 202.1 187.7 159.9 71.1 61.2 56.4 57.1 60.2 63.1 64.0
Unemployment assistance recipientsc 156.0 167.8 182.1 200.3 184.4 132.4 126.2 99.8 87.4 75.7
Unemployment rated 12.8 13.3 13.2 12.6 11.9 9.5 8.5 6.9 6.1 5.5
Shares within registered unemployed, %
School-leavers 8.8 9.0 9.1 11.0 12.5 12.9 12.4 11.4 10.5 11.0
Male 53.0 52.3 51.0 49.3 50.8 50.7 49.6 49.7 48.7 47.9
25 years old and younger 18.6 17.6 17.5 18.1 18.5 18.5 18.2 17.8 17.6 17.1
Manual workers .. .. .. .. .. .. .. .. .. ..
Flows, in thousands
Inflow to the Register 69.0 65.3 70.9 69.5 65.5 69.4 57.6 51.5 47.9 44.2
Of which: school-leavers 7.5 7.9 8.2 10.0 10.8 11.2 9.0 7.7 6.7 5.9
Outflow from the Register 58.4 66.4 74.2 68.1 78.4 71.3 62.1 56.8 49.4 45.3
Of which: school-leavers 6.7 7.5 8.1 8.6 11.8 11.3 9.7 8.2 7.0 6.1
a Since 1st of November, 2005: registered jobseekers. (The data concern the closing date of
each month.) From the 1st of November, 2005 the Employment Act changed the definition
of registered unemployed to registered jobseekers.
b Since 1st of November, 2005: jobseeker benefit recipients. From September 1st, 2011, the

system of jobseeking support changed.

201
Statistical data
c Only recipients who are in the NFSZ register. Those receiving the discontinued income sup-
port supplement were included in the number of those receiving income support supplement
up to the year 2004, and in the number of those receiving regular social assistance from 2005
to 2008. From 2009, those receiving social assistance were included in a new support type,
the on call support. This allowance was replaced by the wage replacement support from Jan-
uary 1, 2011, then from September 1, 2011, the name was changed to employment substitu-
tion support.
d Relative index: registered unemployment rate in the economically active population. From

1st of November, 2005, registered jobseekers’ rate in the economically active population.
e The new IT system introduced at the NFSZ in 2008 made the methodological changes pos-

sible:
1) The filtering out of those returning after, or starting a break from, the number of those en-
tering or leaving the different types of jobseeking support. The main reasons for a break are,
work for short time periods, receipt of child support (GYES) or TGYÁS, or involvement in
training.
2) Taking into account in the previous period the number of those entrants, for whom the first
accounting of the jobseeking support was delayed due to missing documentation.
2008 data, comparable to 2009: 141.5 thousand people.
Source: NFSZ REG.
Online data source in xls format: http://www.bpdata.eu/mpt/2019ent05_12

Table 5.13: The number of registered unemployeda who became employed

on subsidised and non-subsidised employmentb
2012 2013 2014 2015 2016 2017 2018
Per Per Per- Per Per Per Per
Persons Persons Persons Persons Persons Persons Persons
cent cent sons cent cent cent cent
Subsidised
261,631 50.0 359,962 60.2 351,550 63.2 278,875 61.0 237,986 60.0 180,630 54.8 149,481 51.4
employment
Non-subsidised
261,581 50.0 237,795 39.8 204,887 36.8 177,960 39.0 158,391 40.0 149,244 45.2 141,214 48.6
employment
Total 523,212 100.0 597,757 100.0 556,437 100.0 456,835 100.0 396,377 100.0 329,874 100.0 290,695 100.0
a Since 1st of November, 2005: registered jobseekers. From the 1st of November, 2005 the
Employment Act changed the definition of registered unemployed to registered jobseekers.
b Annual totals, the number of jobseekers over the year who were placed in work. It reflects

the placements at the time of their exit from the registry.

Source: NFSZ.
Online data source in xls format: http://www.bpdata.eu/mpt/2019ent05_13

202
 5 Unemployment

Table 5.14: Benefit recipients and participation in active labour market programmes
Unemploy- Regular UA for Do not
Public Wage Other
ment social school- receive Retrainingc Total
workc subsidyc programmesc
Year benefita assistanceb leavers provision
In thousands 117.0 139.7 0.0 106.5 26.7 25.3 27.5 73.5 516.2
2000
Per cent 22.7 27.1 0.0 20.6 5.2 4.9 5.3 14.2 100.0
In thousands 111.8 113.2 0.0 105.2 29.0 30.0 25.8 37.2 452.2
2001
Per cent 24.7 25.0 0.0 23.3 6.4 6.6 5.7 8.2 100.0
In thousands 104.8 107.6 – 115.3 21.6 23.5 21.2 32.8 426.8
2002
Per cent 24.6 25.2 – 27.0 5.1 5.5 5.0 7.7 100.0
In thousands 105.1 109.5 – 125.0 21.2 22.5 20.1 36.6 440.0
2003
Per cent 23.9 24.9 – 28.4 4.8 5.1 4.6 8.3 100.0
In thousands 117.4 118.4 – 132.3 16.8 12.6 16.8 28.5 442.8
2004
Per cent 26.5 26.7 – 29.9 3.8 2.8 3.8 6.4 100.0
In thousands 125.6 127.8 – 140.2 21.5 14.7 20.8 31.0 481.6
2005
Per cent 26.1 26.5 – 29.1 4.5 3.1 4.3 6.4 100.0
In thousands 117.7 112.9 – 146.4 16.6 12.3 14.6 13.8 434.3
2006
Per cent 27.1 26.0 – 33.7 3.8 2.8 3.4 3.2 100.0
In thousands 128.0 133.1 – 151.8 19.3 14.6 23.4 6.8 477.0
2007
Per cent 27.6 28.7 – 32.7 2.7 2.3 3.7 2.3 100.0
In thousands 120.7d 145.7 – 158.2 21.2 21.2 25.0 14.1 506.1
2008
Per cent 23.8 28.8 – 31.3 4.2 4.2 4.9 2.8 100.0
In thousands 202.8 151.9 – 215.0 135.3 13.6 17.8 54.1 790.5
2009
Per cent 25.7 19.2 – 27.2 17.1 1.7 2.3 6.8 100.0
In thousands 159.6 163.5 – 222.4 164.5 17.8 26.7 40.3 794.8
2010
Per cent 20.1 20.6 – 28.0 20.7 2.2 3.4 5.1 100.0
In thousands 120.2 168.2 – 242.3 91.6 12.6 26.1 3.4 664.4
2011
Per cent 18.1 25.3 – 36.5 13.8 1.9 3.9 0.5 100.0
In thousands 54.0 185.6 – 283.4 134.1 28.6 25.7 2.9 714.3
2012
Per cent 7.6 26.0 – 39.7 18.8 4.0 3.6 0.4 100.0
In thousands 52.6 169.3 – 266.7 157.2 42.0e 31.7 3.9 723.4
2013
Per cent 7.3 23.4 – 36.9 21.7 5.8 4.4 0.5 100.0
In thousands 55.3 123.4 – 216.5 170.3 24.6 17.7 2.7 610.5
2014
Per cent 9.1 20.2 – 35.5 27.9 4.0 2.9 0.4 100.0
In thousands 55.0 110.6 – 168.7 224.9 11.0 9.1 2.1 581.4
2015
Per cent 9.5 19.0 – 29.0 38.7 1.9 1.6 0.4 100.0
In thousands 56.8 85.0 – 136.0 219.6 17.9 21.1 3.0 539.4
2016
Per cent 10.5 15.8 – 25.2 40.7 3.3 3.9 0.6 100.0
In thousands 59.5 80.8 – 120.0 171.0 17.2 30.9 4.2 483.6
2017
Per cent 12.3 16.7 – 24.8 35.4 3.6 6.4 0.9 100.0
In thousands 64.1 70.4 – 109.7 123.9 13.2 40.5 6.0 427.8
2018
Per cent 15.0 16.5 – 25.6 29.0 3.1 9.5 1.4 100.0
a Since 1st of November, 2005: jobseeker benefit recipients. From September 1, 2011, the system of jobseeking sup-
port changed.
b Only recipients who are in the NFSZ register. Those receiving the discontinued income support supplement were

included in the number of those receiving income support supplement up to the year 2004, and in the number of
those receiving regular social assistance from 2005 to 2008. From 2009, those receiving social assistance were in-
cluded in a new support type, the on call support. This allowance was replaced by the wage replacement support
from January 1, 2011, then from September 1, 2011., the name was changed to employment substitution support.
c Up to the year 2008 the number financed from the MPA Decentralized Base, since 2009 the number financed from

MPA, TAMOP.
Public-type employment: community service, public service, public work programmes.

203
Statistical data

Wage subsidy: wage subsidy, wage-cost subsidy, work experience acquisition assistance to career-starters, support
for employment of availability allowance recipients, part-time employment, wage support for those losing their
job due to the crisis.
Other support: job preservation support, support to would-be entrepreneurs, contribution to costs related to com-
muting to work, job creation support, jobseeker’s clubs.
d The new IT system introduced at the NFSZ in 2008 made the methodological changes possible:

1) The filtering out of those returning after a break or starting a break from the number of those entering or leaving
the different types of jobseeking support. The main reasons for a break are work for short time periods, receipt
of child support (GYES) or TGYÁS, or involvement in training.
2) Taking into account in the previous period the number of those entrants, for whom the first accounting of the
jobseeking support was delayed due to missing documentation.
2008 data, comparable to 2009: 134.1 thousand people.
e In 2013, 18.1 thousand trainees were simultaneously involved in public works programmes.

Note: The closing numbers from October of each year. For the percentage data, the sum of those registered and
those taking part in labour market programmes ≈100.0.
Source: NFSZ.
Online data source in xls format: http://www.bpdata.eu/mpt/2019ent05_14
Table 5.15: The ratio of those who are employed among the former participants of ALMPsa, per cent
Active labour mar-
2003b 2004b 2005b 2006b 2007b 2008b 2009c 2010c 2011c 2012c 2013c 2014c 2015c 2016c 2017c 2018c
ket programmes
Suggested training
43.0 45.5 43.8 41.1 37.5 42.2 40.4 49.4 42.6 44.9 55.1 61.4 54.8 47.8 48.2 44.2
programmesd
Accepted training
46.0 45.6 51.4 50.9 47.6 48.0 41.9 48.8 41.6 56.7 65.9 58.8 63.4 55.7 44.9 48.7
programmese
Retraining of those
93.3 92.1 90.4 .. 92.3 93.9 .. 59.9 75.0 65.7 72.7 61.4 87.7 41.7 92.2 93.8
who are employedf
Support for self-
89.6 90.7 89.6 86.4 87.6 83.6 73.1 76.4 71.5 72.6 74.1 76.3 81.0 40.0 30.8 33.7
employmentg
Wage subsidy
62.0 64.6 62.6 62.3 63.4 65.0 72.4 90.9 69.6 70.3 73.0 56.0 70.9 53.5 28.6 30.2
programmesh
Work experience
66.1 66.5 66.8 66.6 66.3 74.6 .. .. 72.0 69.9 68.5 – – – – –
programmesi
Further employment
78.2 71.5 70.9 65.0 77.5 – – – – – – – – – – –
programmej
a The data relate to people having completed their courses successfully.
b Three months after the end of programmes.
c Six months after the end of programmes.
d Suggested training: group training programmes for jobseekers organized by the NFSZ.
e Accepted training: participation in programmes initiated by the jobseekers and accepted by NFSZ for full or

partial support.
f Training for employed persons: training for those whose jobs are at risk of termination, if new knowledge allows

them to adapt to the new needs of the employer.

g Support to help entrepeneurship: support of jobseekers in the amount of the monthly minimum wage or maxi-

mum HUF 3 million lump sum support (to be repaid or not), aimed at helping them become individual entrepre-
neurs or self-employed.
h Wage support: aimed at helping the employment of disadvantaged persons, who would not be able to, or would

have a harder time finding work without support. The data on wage subsidies and labour cost subsidies exclude
the programs supporting job seeking school leavers and student work during summer vacation.
i Work experience-gaining support: the support of new entrants with no work experience for 6 –9 months, the

amount of the support is equal to 50 –80% of the wage costs. The instrument was discontinued after December
31, 2006.. In 2009 they reintroduced the work experience gaining support for skilled new entrants, for employers
who ensure employment of at least 4 hours a day and for 365 days. The amount of the support is 50 –100% of the
wage cost. Monitoring for the first exiters is available from 2011. The program supporting the school to work
transition of skilled school leavers was abolished in 2014.
j Further employment programmes: to support the continued employment of new entrants under the age of 25 for

9 months. Discontinued from December 31, 2006.

Source: NFSZ.
Online data source in xls format: http://www.bpdata.eu/mpt/2019ent05_15

204
 5 Unemployment

Table 5.16: Distribution of registered unemployeda, unemployment benefit recipientsb

and unemployment assistance recipientsc by educational attainment
Educational attainment 2008 2008e 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018
Registered unemployed
8 grades of primary school or less 43.8 – 40.0 39.2 39.9 40.1 40.1 42.4 42.4 41.2 43.4 43.5
Vocational school 30.7 – 33.1 31.4 29.8 29.1 28.9 27.6 27.1 27.3 26.2 25.8
Vocational secondary school 12.8 – 14.4 15.0 15.0 15.2 15.6 14.9 15.1 15.4 14.6 14.9
Grammar school 8.1 – 8.3 9.1 9.7 9.8 10.0 9.9 10.0 10.3 10.1 10.1
College 3.2 – 3.0 3.7 3.9 3.9 3.6 3.3 3.4 3.6 3.4 3.4
University 1.2 – 1.1 1.5 1.7 1.9 1.9 1.8 2.0 2.3 2.3 2.3
100.0 – 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0
Total
415.6 – 549.0 546.0 553.3 524.4 497.0 438.6 366.9 291.6 283.0 240.7
Unemployment benefit recipientsd
8 grades of primary school or less 24.4 26.3 25.7 24.1 23.4 20.2 21.8 27.8 24.8 26.7 31.4 31.7
Vocational school 37.0 39.2 39.4 36.2 34.5 34.5 34.8 33.3 33.1 32.8 31.4 31.1
Vocational secondary school 19.3 18.3 18.5 19.7 20.1 21.2 21.2 19.0 20.0 19.5 17.6 17.8
Grammar school 11.0 10.6 10.1 11.6 12.3 12.7 12.0 10.9 11.8 11.3 10.8 10.8
College 6.0 5.7 4.5 5.8 6.7 7.6 6.7 5.7 6.4 5.9 5.2 5.1
University 2.3 2.1 1.7 2.6 3.1 3.8 3.6 3.3 3.9 3.8 3.6 3.6
100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0
Total
92.5 126.9 200.5 165.8 145.9 53.1 53.0 60.0 50.0 53.8 63.1 57.4
Unemployment assistance recipientsc
8 grades of primary school or less 60.3 – 59.4 56.4 56.1 53.4 52.4 53.5 54.1 53.4 56.3 57.5
Vocational school 26.5 – 26.6 27.4 26.1 26.4 26.6 26.1 25.6 25.5 24.3 23.5
Vocational secondary school 6.8 – 7.5 8.6 9.0 10.3 10.9 10.5 10.4 10.7 9.8 9.4
Grammar school 4.7 – 4.8 5.6 6.3 7.1 7.3 7.2 7.3 7.6 7.1 7.1
College 1.2 – 1.2 1.5 1.8 2.1 2.0 1.8 1.8 1.9 1.7 1.6
University 0.4 – 0.4 0.5 0.6 0.8 0.8 0.8 0.8 0.9 0.9 0.8
100.0 – 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0
Total
145.8 – 144.1 161.7 174.7 193.5 177.4 138.8 130.8 94.4 87.4 73.1
a Since 1st of November, 2005: registered jobseekers. From the 1st of November, 2005 the Employ-
ment Act changed the definition of registered unemployed to registered jobseekers.
b Since 1st of November, 2005: those receiving jobseeking support. From the 1st of September 2011,

the system of jobseeking support changed.

c Only recipients who are in the NFSZ register. Those receiving the discontinued income support

supplement were included in the number of those receiving income support supplement up to the
year 2004, and in the number of those receiving regular social assistance from 2005 to 2008. From
2009, those receiving social assistance were included in a new support type, the on call support.
This allowance was replaced by the wage replacement support from January 1, 2011, then from
September 1, 2011, the name was changed to employment substitution support.
d After 1st of November, 2005: jobseeking support. Does not contain those receiving unemploy-

ment aid prior to pension in 2004. From the 1st of September 2011, the system of jobseeking sup-
port changed.
e The new IT system introduced at the NFSZ in 2008 made the methodological changes possible:

1) The filtering out of those returning after or starting a break from the number of those entering or
leaving the different types of jobseeking support. The main reasons for a break are, work for short
time periods, receipt of child support (GYES) or TGYÁS, or involvement in training.
2) Taking into account in the previous period the number of those entrants, for whom the first ac-
counting of the jobseeking support was delayed due to missing documentation.
The right-hand column of 2008 contains the 2008 data in a form comparable to the 2009 data.
Note: Data from the closing date of June in each year.
Source: NFSZ.
Online data source in xls format: http://www.bpdata.eu/mpt/2019ent05_16

205
Statistical data

Table 5.17: Outflow from the Register of Beneficiaries

Total number Of which: Total number Of which:
Year of outflows became employed, % benefit period expired, % Year of outflows became employed, % benefit period expired, %
2000 325,341 28.1 64.6 2009 345,216 37.9 56.0
2001 308,780 27.2 65.1 2010 352,535 38.9 55.8
2002 303,288 27.6 66.7 2011 329,728 39.2 55.7
2003 297,640 26.7 65.2 2012 368,803 21.9 77.8
2004 308,027 27.4 64.6 2013 328,508 21.3 75.6
2005 329,738 27.2 63.0 2014 300,516 27.0 67.4
2006 234,273 33.2 53.7 2015 296,171 32.5 63.4
2007 251,889 33.4 46.9 2016 287,062 35.9 60.5
2008 232,151 40.0 48.7 2017 284,284 34.9 61.4
2008a 261,573 43.4 48.9 2018 280,772 33.1 61.4
aThe new IT system introduced at the NFSZ in 2008 made the methodological changes pos-
sible:
1) The filtering out of those returning after or starting a break from the number of those enter-
ing or leaving the different types of jobseeking support. The main reasons for a break are,
work for short time periods, receipt of child support (GYES) or TGYÁS, or involvement in
training.
2) Taking into account in the previous period the number of those entrants, for whom the first
accounting of the jobseeking support was delayed due to missing documentation.
The row of 2008a contains the data from 2008 in the form comparable to the 2009 data.
Source: NFSZ.
Online data source in xls format: http://www.bpdata.eu/mpt/2019ent05_17
Table 5.18: The distribution of the total number of labour market training participantsa
Groups of training participants 2003 2004 2005 2006 2007 2008 2009 2010
Participants in suggested training 45,261 33,002 29,252 36,212 32,747 48,561 41,373 50,853
Participants in accepted training 28,599 19,406 9,620 7,327 5,766 4,939 8,241 6,853
One Step Forward (OFS) programme – – – – 270 59,347 11,169 2,316
Non-employed participants together 73,859 52,407 38,872 43,539 38,783 112,847 60,783 57,706
Of which: school-leavers 18,320 12,158 9,313 1,365 1,111 18,719 21,103 12,030
Employed participants 9,036 7,487 4,853 3,602 3,467 37,466 12,496 336
Total 82,895 59,894 43,725 47,141 42,250 150,313 73,279 60,358
2011 2012 2013b 2014b 2015b 2016b 2017b 2018b
Participants in suggested training 32,172 43,438 22,574 10,900 330 50,953 68,125 61,451
Participants in accepted training 2,495 2,446 22,574 1,275 1,189 1,410 1,370 241
One Step Forward (OFS) programme – – – – – – – –
Non-employed participants together 34,667 45,884 132,587 200,466 61,127 53,153 69,495 61,692
Of which: school-leavers 7,935 9,976 106,333 31,083 3,981 12,318 14,984 12,924
Employed participants 908 716 631 827 14,389 2,493 3,002 3,214
Total 35,575 46,600 133,218 201,293 75,516 55,646 72,497 65,176
a The data contain the number of those financed from the NFA decentralized employment
base, as well as those involved in training as a part of the HEFOP 1.1 and the TÁMOP 1.1.2
programmes.
b The data include public works participants simultaneously involved in training (88,004 pub-

lic works participants in 2013, 143,275 public works participants in 2014, 50,124 public
works participants in 2015, 29,686 public works participants in 2016, 40,432 public works
participants in 2017, 32,735 public works participants in 2018).
Source: NFSZ.
Online data source in xls format: http://www.bpdata.eu/mpt/2019ent05_18

206
 5 Unemployment

Table 5.19: Employment ratio of participants ALMPs by gender, age groups

and educational attainment for the programmes finished in 2018a, per cent
Non-employed participants Supported Wage
suggested accepted self-employ- subsidy
total mentb programme
training training
By gender
Males 50.7 53.3 50.8 57.1 61.0
Females 49.6 50.5 49.7 58.3 66.7
By age groups
–20 29.5 28.6 29.5 45.8 32.2
20–24 38.7 57.7 38.7 29.6 36.4
25–29 44.2 43.9 44.2 31.0 31.2
–29 together 38.5 47.3 38.5 30.8 34.8
30–34 46.3 44.0 46.3 27.4 29.5
35–39 47.9 48.0 47.9 28.2 34.2
40–44 48.9 53.3 48.9 29.6 30.9
45–49 48.0 55.2 48.1 32.4 34.0
50–54 49.7 35.0 49.6 34.0 35.3
55+ 45.6 57.1 45.6 33.8 32.8
By educational attainment
Less than primary school 39.4 – 39.4 5.0 19.2
Primary school 42.1 41.0 42.1 25.6 31.4
Vocational school for skilled workers 47.9 62.2 48.0 28.8 32.8
Vocational school 45.7 .. 45.7 29.0 33.3
Vocational secondary school 49.7 51.7 49.7 31.6 37.0
Technicians secondary school 51.1 60.0 51.2 36.1 31.5
Grammar school 47.6 44.4 47.6 30.5 32.2
College 46.8 40.0 46.8 33.6 42.9
University 49.6 .. 49.6 28.8 42.0
Total 44.2 48.7 44.2 30.2 33.7
a Includes all kinds of wage subsidies except financial support for student work during vaca-
tion.
b Survival rate.

Note: 6 months after the end of each programme.

Source: NFSZ.
Online data source in xls format: http://www.bpdata.eu/mpt/2019ent05_19
Table 5.20: Distribution of the average annual number of those with no employment status who participate in
training categorised by the type of training, percentage
Types of training 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018
Approved qualifi-
78.7 77.6 78.3 75.1 72.9 71.5 69.0 65.8 63.6 65.2 68.6 71.6 50.2 53.3 59.4 56.4 65.7 76.8
cation
Non-approved
14.0 13.6 12.6 15.0 14.5 16.9 19.9 22.8 26.4 25.4 21.1 19.0 44.2 43.2 37.9 40.6 30.8 20.1
qualification
Foreign language
7.3 8.8 9.1 9.9 12.6 11.5 11.1 11.4 10.0 9.4 10.3 9.4 5.6 3.5 2.7 3.0 3.5 3.1
learning
Total 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0
Source: NFSZ.
Online data source in xls format: http://www.bpdata.eu/mpt/2019ent05_20

207
Statistical data

Table 5.21: The distribution of those entering training programmes by age groups and educational level
Training Training for public works participants Together
2015 2016 2017 2018 2015 2016 2017 2018 2015 2016 2017 2018
Total number of entrants 12,016 17,312 18,958 32,171 28,036 26,361 31,508 32,735 40,052 43,673 50,466 64,906
By age groups, %
–20 11.5 5.7 7.5 7.4 4.8 7.1 6.3 5.5 6.8 6.5 6.7 6.4
20–24 39.3 15.1 17.7 16.4 15.8 11.4 10.7 9 22.8 12.9 13.3 12.7
25–44 35.8 56.4 51.4 52.2 49.5 47.5 47.1 47.8 45.4 51.0 48.7 50.0
45–49 6.0 10.8 10.4 10.8 10.5 12.2 12.9 13.1 9.2 11.6 12.0 12.0
50+ 7.4 12.0 13.0 13.1 19.4 21.9 23.0 24.7 15.8 17.9 19.2 18.9
Total 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0
By level of education, %
Less than primary school 0.8 1.1 2.2 2.2 6.9 15.6 16.0 16.3 5.1 9.9 10.8 9.3
Primary school 35.2 35.1 38.8 36.2 44.6 78.8 75.2 71.3 41.8 61.4 61.6 53.9
Vocational school 19.7 22.4 21.8 21.4 21.5 1.8 5.7 7.9 21.0 10.0 11.7 14.6
Vocational and technical
23.5 21.7 18.7 20.2 14.0 1.9 1.6 2.4 16.8 9.8 8.0 11.2
secondary school
Grammar school 17.8 15.1 14.9 15.8 9.4 1.6 1.3 1.9 11.9 7.0 6.4 8.8
College, university 3.0 4.6 3.6 4.2 3.6 0.2 0.1 0.1 3.4 2.0 1.4 2.2
Total 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0
Source: NFSZ.
Online data source in xls format: http://www.bpdata.eu/mpt/2019ent05_21

208
 6 Wages

Table 6.1: Annual changes of gross and real earnings

Gross Net Gross earnings Net earnings Consumer Real earnings
earnings earnings index index price index index
Year HUF previous year = 100
1995 38,900 25,891 116.8 112.6 128.2 87.8
1996 46,837 30,544 120.4 117.4 123.6 95.0
1997 57,270 38,145 122.3 124.1 118.3 104.9
1998 67,764 45,162 118.3 118.4 114.3 103.6
1999 77,187 50,076 116.1 112.7 110.0 102.5
2000 87,750 55,785 113.5 111.4 109.8 101.5
2001 103,554 64,913 118.0 116.2 109.2 106.4
2002 122,481 77,622 118.3 119.6 105.3 113.6
2003 137,193 88,753 112.0 114.3 104.7 109.2
2004 145,523 93,715 106.1 105.6 106.8 98.9
2005 158,343 103,149 108.8 110.1 103.6 106.3
2006 171,351 110,951 108.2 107.6 103.9 103.6
2007 185,018 114,282 108.0 103.0 108.0 95.4
2008 198,741 121,969 107.4 107.0 106.1 100.8
2009 199,837 124,116 100.6 101.8 104.2 97.7
2010 202,525 132,604 101.3 106.8 104.9 101.8
2011 213,094 141,151 105.2 106.4 103.9 102.4
2012 223,060 144,085 104.7 102.1 105.7 96.6
2013 230,714 151,118 103.4 104.9 101.7 103.1
2014 237,695 155,717 103.0 103.0 99.8 103.2
2015 247,924 162,400 104.3 104.3 99.9 104.4
2016 263,171 175,009 106.1 107.8 100.4 107.4
2017 297,017 197,516 112.9 112.9 102.4 110.3
2018 329,943 219,412 111.3 111.3 102.8 108.3
Note: Earnings data include payments to public works participants.
Source: KSH IMS (earnings) and consumer price accounting. Gross earnings, gross earnings
index: 2000 –: STADAT (2019.02.20. version). Net earnings, net earnings index: 2008 –:
STADAT (2019.02.20. version). Consumer price index: 1995 –: STADAT (2019.01.13. ver-
sion). Real earnings index: 1995 –: STADAT (2019.02.21. version).
Online data source in xls format: http://www.bpdata.eu/mpt/2019ent06_01

Figure 6.1: Annual changes of gross nominal and net real earnings
30
Gross earnings index Real earnings index
25
20
15
10
5
Per cent

0
–5
–10
–15
1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014 2016 2018
Source: KSH IMS (earnings) and consumer price accounting STADAT (2018. 02. 20. version).
Online data source in xls format: http://www.bpdata.eu/mpt/2019ena06_01

209
Statistical data

Table 6.2.a: Gross earnings ratios in the economy, HUF/person/month

2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018
Agriculture, forestry and
133,570 137,101 143,861 153,301 164,136 171,921 180,251 189,136 204,385 230,638 255,664
fishing
Mining and quarrying 225,650 244,051 234,243 254,607 271,012 279,577 287,036 289,665 299,354 332,985 375,494
Manufacturing 183,081 190,331 200,692 213,281 230,877 241,170 253,162 263,877 279,336 311,879 344,495
Electricity, gas, steam and
321,569 345,035 363,900 379,606 404,073 410,485 422,444 439,282 454,361 498,280 546,640
air conditioning supply
Water supply; sewerage,
waste management and 178,049 181,818 193,604 207,614 223,206 224,654 224,447 230,574 234,037 269,090 300,387
remediation activities
Construction 146,475 152,204 153,130 156,682 163,649 177,790 185,680 196,947 201,095 227,524 254,711
Wholesale and retail trade;
repair of motor vehicles 171,780 175,207 185,812 196,942 212,521 218,936 223,882 230,036 243,716 273,810 304,112
and motorcycles
Transportation and storage 186,376 196,350 200,129 210,146 217,794 223,410 230,138 239,147 247,562 279,507 310,196
Accommodation and food
120,600 122,561 122,699 125,757 139,731 147,023 152,874 157,560 165,969 189,489 211,984
service activities
Information and communi-
358,217 366,752 368,113 392,963 410,045 426,460 449,412 460,122 479,625 510,675 561,443
cation
Financial and insurance
431,601 427,508 433,458 456,980 459,744 470,966 486,054 493,956 519,027 561,576 608,234
activities
Real estate activities 169,845 177,747 182,903 184,829 219,287 212,391 214,163 221,125 239,317 281,502 316,079
Professional, scientific and
281,150 292,974 297,489 303,292 330,860 320,422 345,198 369,460 392,266 431,838 462,814
technical activities
Administrative and support
147,125 149,131 145,576 149,675 163,300 169,223 181,338 198,050 215,241 246,072 277,744
service activities
Public administration and
defence; compulsory 267,657 234,696 242,958 252,848 247,139 258,803 262,055 282,194 313,084 358,569 392,840
social security
Education 204,600 194,958 195,930 192,984 197,344 216,927 245,933 258,200 274,211 297,404 320,233
Human health and social
169,977 161,265 142,282 153,832 151,446 151,287 143,047 146,700 154,443 185,037 218,184
work activities
Arts, entertainment and
183,813 179,199 179,976 192,407 209,930 216,869 226,327 213,286 227,509 289,154 333,997
recreation
Other service activities 157,950 160,375 150,025 162,490 175,872 174,777 181,601 193,303 207,222 243,967 271,921
National economy, total 198,741 199,837 202,525 213,094 223,060 230,664 237,695 247,924 263,171 297,017 329,943
Of which:
– business sector 192,044 200,304 206,863 217,932 233,829 242,191 252,664 262,731 276,923 308,994 341,540
– budgetary institutions 219,044 201,632 195,980 203,516 200,027 207,191 209,706 220,210 237,494 275,251 308,508
Note: The data are recalculated based on the industrial classification system in effect from
2008. Earnings data include payments to public works participants.
Source: KSH mid-year IMS. Gross earnings, gross earnings index: STADAT (2019.02.21. ver-
sion).
Online data source in xls format: http://www.bpdata.eu/mpt/2019ent06_02a

210
 6 Wages

Table 6.2.b: Gross earnings ratios in the economy, per cent

2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018
Agriculture, forestry and fish-
67.2 68.6 71.0 72.0 73.6 74.5 75.8 76.3 77.7 77.7 77.5
ing
Mining and quarrying 113.5 122.1 115.5 119.5 120.9 121.2 120.7 116.8 113.7 112.1 113.8
Manufacturing 92.1 95.2 99.1 100.0 103.4 104.6 106.4 106.4 106.1 105.0 104.4
Electricity, gas, steam and air
161.8 172.7 179.6 178.2 181.1 178.0 177.8 177.2 172.6 167.8 165.7
conditioning supply
Water supply; sewerage,
waste management and 89.6 91.0 95.6 97.4 100.0 97.4 94.7 93.2 88.9 90.6 91.0
remediation activities
Construction 73.7 76.2 75.5 73.5 73.4 77.1 78.0 79.4 76.4 76.6 77.2
Wholesale and retail trade;
repair of motor vehicles and 86.4 87.7 91.7 92.4 95.3 94.9 94.3 92.8 92.6 92.2 92.2
motorcycles
Transportation and storage 93.8 98.3 98.9 98.6 97.8 96.9 96.9 96.5 94.1 94.1 94.0
Accommodation and food
60.7 61.3 60.6 59.0 62.7 63.7 64.4 63.6 63.1 63.8 64.2
service activities
Information and communica-
180.2 183.5 181.7 184.4 183.9 184.9 189.0 185.6 182.2 171.9 170.2
tion
Financial and insurance
217.2 213.9 214.0 214.5 206.2 204.2 204.1 199.2 197.2 189.1 184.3
activities
Real estate activities 85.5 88.9 90.2 86.8 98.3 92.1 90.5 89.2 90.9 94.8 95.8
Professional, scientific and
141.5 146.6 146.9 142.4 148.4 138.9 145.1 149.0 149.1 145.4 140.3
technical activities
Administrative and support
74.0 74.6 71.9 70.3 73.3 73.4 77.3 79.9 81.8 82.8 84.2
service activities
Public administration and
defence; compulsory social 134.7 117.4 120.2 118.7 110.8 112.2 110.2 113.8 119.0 120.7 119.1
security
Education 102.9 97.6 96.7 90.6 88.5 94.0 103.4 104.1 104.2 100.1 97.1
Human health and social
85.5 80.7 70.3 72.2 67.9 65.6 60.2 59.2 58.7 62.3 66.1
work activities
Arts, entertainment and rec-
92.5 89.7 88.8 90.3 94.1 94.0 95.0 86.0 86.4 97.4 101.2
reation
Other service activities 79.5 80.3 74.1 76.1 78.9 75.8 76.1 78.0 78.7 82.1 82.4
National economy, total 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0
Of which:
– business sector 96.6 100.2 102.1 102.3 104.8 105.0 106.3 106.0 105.2 104.0 103.5
– budgetary institutions 110.2 100.9 96.8 95.5 89.7 89.8 88.2 88.8 90.2 92.7 93.5
Note: The data are recalculated based on the industrial classification system in effect from
2008. Earnings data include payments to public works participants.
Source: KSH mid-year IMS. Gross earnings, gross earnings index: STADAT (2019.02.21. ver-
sion).
Online data source in xls format: http://www.bpdata.eu/mpt/2019ent06_02b

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Statistical data

Table 6.3: Regression-adjusted earnings differentials

2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016
Male 0.1500 0.1360 0.1680 0.1670 0.1440 0.1500 0.1550 0.1500 0.1420 0.1350 0.1520 0.1300
Less than primary
–0.4800 –0.3720 –0.4140 –0.3650 –0.5540 –0.4950 –0.5200 –0.4260 –0.4800 –0.5240 –0.5360 –0.5710
school
Primary school –0.3730 –0.3520 –0.4010 –0.3910 –0.4330 –0.4040 –0.3990 –0.3840 –0.3650 –0.3570 –0.3760 –0.4040
Vocational school –0.2750 –0.2710 –0.2750 –0.2690 –0.2860 –0.2660 –0.2470 –0.2490 –0.2030 –0.1910 –0.2170 –0.2260
College, university 0.5900 0.5900 0.5670 0.5610 0.5970 0.6020 0.5970 0.5570 0.5630 0.6060 0.6000 0.5750
Estimated labour
0.0238 0.0233 0.0243 0.0237 0.0262 0.0267 0.0256 0.0238 0.0227 0.0070 0.0245 0.0253
market experience
Square of esti-
mated labour –0.0004 –0.0003 –0.0004 –0.0004 –0.0004 –0.0004 –0.0004 –0.0004 –0.0004 0.0000 –0.0004 –0.0004
market experience
Public sector 0.1130 0.1530 0.0444 0.0500 –0.0665 –0.1060 –0.1240 –0.2480 –0.1900 –0.0843 –0.2030 –0.3060
Note: the results indicate the earnings differentials of the various groups relative to the refer-
ence group in log points (approximately percentage points). All parameters are significant at
the 0.01 level. The region parameters can be seen in Table 9.6.
Reference categories: female, with leaving certificate (general education certificate), not in the
public sector, working in the Central-Transdanubia region.
Source: NFSZ BT.
Online data source in xls format: http://www.bpdata.eu/mpt/2019ent06_03

Figure 6.2: The percentage of low paid workers by gender, per cent
Males Females Together
30

25

20
Per cent

15

10

5
1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014 2016 2018
Source: NFSZ BT.
Online data source in xls format: http://www.bpdata.eu/mpt/2019ena06_02

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 6 Wages

Table 6.4: Percentage of low paid workersa by gender, age groups, level of education and industries
2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018
By gender
Males 20.7 22.3 24.8 25.1 25.4 26.7 21.9 21.2 21.1 21.2 20.5 15.5 16.2 18.8 18.3 19.2 10.0 11.1
Females 25.0 22.5 21.6 22.8 22.9 21.9 21.3 20.8 21.7 21.2 20.8 18.2 17.0 17.6 20.0 19.8 9.8 12.2
By age groups
–24 35.5 37.6 39.9 43.9 44.2 46.3 40.1 34.6 38.9 38.2 36.6 26.4 30.9 29.7 31.2 31.7 16.4 16.4
25–54 21.9 21.8 22.3 23.6 24.0 24.2 21.4 20.6 21.0 20.9 20.4 16.3 16.3 18.0 18.5 19.0 9.3 10.6
55+ 18.1 16.2 15.3 16.5 16.5 16.4 15.8 15.5 17.6 18.1 17.6 17.0 14.3 16.4 18.5 18.7 10.7 14.0
By level of education
8 grades of primary
40.4 38.3 37.1 39.6 41.2 40.1 41.4 41.3 47.4 43.4 45.4 38.6 38.7 41.1 42.1 40.1 36.6 32.6
school or less
Vocational school 29.4 32.1 35.4 35.7 36.8 37.9 32.9 32.1 33.5 33.3 31.3 25.2 24.0 27.5 28.3 30.0 14.0 14.4
Secondary school 18.0 16.5 17.7 18.6 18.6 19.7 16.1 15.4 16.4 17.3 17.2 13.7 15.3 17.0 18.4 19.1 5.9 6.3
Higher education 4.7 3.6 3.5 3.9 3.8 4.3 2.5 2.4 2.3 2.9 2.7 2.0 2.5 3.0 2.9 3.9 0.9 1.4
By industriesb
Agriculture, forestry,
34.3 37.9 37.3 37.1 37.5 41.6 37.9 36.6 36.7 34.6 31.8 21.8 26.3 28.2 25.8 24.6 15.2 18.5
fishing
Manufacturing 19.1 19.4 25.4 24.7 22.1 24.1 20.8 23.5 23.0 20.5 19.4 13.7 14.1 16.7 15.1 15.9 10.9 9.8
Construction 41.7 44.8 49.8 51.2 50.2 55.2 43.1 37.5 38.1 43.0 41.9 31.8 35.9 43.8 41.0 44.7 22.8 24.0
Trade, repairing 41.3 44.0 49.0 49.3 51.5 49.4 40.9 35.9 35.2 36.4 35.2 24.2 27.3 28.9 31.3 31.8 13.5 12.2
Transport, storage,
10.6 10.5 13.6 12.6 13.8 15.1 13.2 14.6 11.2 13.3 13.1 10.1 11.6 14.9 13.8 13.6 8.7 10.5
communication
Financial interme-
22.6 20.7 23.1 23.9 24.6 26.2 20.9 20.0 20.5 20.7 19.6 15.0 16.6 19.0 16.5 18.7 9.8 9.2
diation
Public administra-
tion and defence,
13.8 9.3 6.6 8.2 6.0 6.3 7.4 6.7 8.7 8.8 9.8 13.4 9.1 11.8 15.3 13.2 3.9 11.0
compulsory social
security
Education 22.6 16.0 4.8 6.9 8.8 6.1 9.0 7.2 11.9 10.6 11.2 16.3 14.9 10.2 15.7 13.8 3.1 12.7
Health and social
19.9 16.1 6.3 8.4 10.3 8.6 12.6 11.1 14.5 13.8 14.3 18.2 13.6 9.2 14.6 14.8 8.0 11.3
work
Total 22.8 22.4 23.2 24.0 24.2 24.3 21.6 21.0 21.4 21.2 20.7 16.8 16.6 18.3 19.1 19.5 9.9 11.5
a Percentage of those who earn less than 2/3 of the median earning amount.
b 2001–2008: by TEÁOR’03, 2009: by TEÁOR’08.

Source: NFSZ BT.

Online data source in xls format: http://www.bpdata.eu/mpt/2019ent06_04

213
Statistical data

Figure 6.3: The dispersion of gross monthly earnings

D9/D5 D5/D1 D9/D1
5

1
1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014 2016 2018
Source: NFSZ BT.
Online data source in xls format: http://www.bpdata.eu/mpt/2019ena06_03
Figure 6.4: Age-income profiles by education level in 1998 and 2016, women and men
Males Females
Primary school Vocational school Secondary school College, university
700,000 700,000
1998 1998
600,000 600,000

500,000 500,000

400,000 400,000

300,000 300,000
200,000 200,000

100,000 100,000

Main gross real wage

Main gross real wage

0 0
20 30 40 50 60 20 30 40 50 60

700,000 700,000
2016 2016
600,000 600,000

500,000 500,000

400,000 400,000

300,000 300,000
200,000 200,000

100,000 100,000
0 0
20 30 40 50 60 20 30 40 50 60
Age
Source: NFSZ BT.
Online data source in xls format: http://www.bpdata.eu/mpt/2019ena06_04

214
 6 Wages

Figure 6.5: The dispersion of the logarithm of gross real earnings (2016 = 100%)
Males Females
1992 2.0 2.0

1.5 1.5

1.0 1.0

0.5 0.5

0.0 0.0
11 12 13 14 11 12 13 14

1996 2.0 2.0

1.5 1.5

1.0 1.0

0.5 0.5

0.0 0.0
11 12 13 14 11 12 13 14

2002 2.0 2.0

1.5 1.5

1.0 1.0

0.5 0.5

0.0 0.0
11 12 13 14 11 12 13 14

2018 2.0 2.0

1.5 1.5

1.0 1.0

0.5 0.5

0.0 0.0
11 12 13 14 11 12 13 14
Source: NFSZ BT.
Online data source in xls format: http://www.bpdata.eu/mpt/2019ena06_05

215
Statistical data

Table 7.1: Graduates in full-time education

Students passed final Students passed
Students finished Students graduated
examination at vocational
8th grade at tertiary education
Year secondary level examination
1990 169,059 53,039 61,099 15,963
1995 126,066 70,265 67,234 20,024
1996 124,115 73,413 65,022 22,147
1997 120,378 75,564 56,994 24,411
1998 117,190 77,660 54,115 25,338
1999 117,334 73,965 50,247 27,049
2000 121,100a 72,200a .. 29,843
2001 118,200 70,441 48,828 29,746
2002 118,038 69,612 56,235 30,785
2003 115,863 71,944 53,056 31,929
2004 117,093 76,669 54,912 31,633
2005 119,561 77,025 53,704 32,732
2006 118,223 76,895 51,040 29,871
2007 112,351 77,527 44,754 29,059
2008 109,680 68,453 44,831 28,957
2009 105,811 78,037 43,999 36,064
2010 106,626 77,957 45,437 38,456
2011 99,632 76,441 48,316 35,433
2012 94,852 73,845 56,404 36,262
2013 91,277 68,436 46,512 37,089
2014 89,176 69,176 43,498 39,226
2015 91,164 65,363 41,411 41,083
2016 89,786 62,099 40,772 39,962
2017 89,480 61,025 36,323 37,771
2018b 88,719 61,815 38,117 37,878
aEstimated data.
bPreliminary data.
Source: KSH STADAT (Education – Time series of annual data).
Online data source in xls format: http://www.bpdata.eu/mpt/2019ent07_01

Figure 7.1: Full time students as a percentage of the different age groups
100

80

60 2018
Per cent

40 1993

20

0
15 16 17 18 19 20 21 22
Age
Source: KSH STADAT (Education – Time series of annual data).
Online data source in xls format: http://www.bpdata.eu/mpt/2019ena07_01

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 7 Education

Table 7.2: Pupils/students entering the school system by level of education,

full-time education
Vocational Tertiary under-
schools and Secondary Secondary Vocational graduate (BA/
Primary
special skills vocational general grammar BSc) and post-
schools
development schoolsb schools schoolsc graduate (MA/
School year schoolsa MSc) trainingd
2005/2006 101,157 2,684 33,276 46,252 49,979 61,898
2006/2007 99,025 2,795 32,780 45,711 50,328 61,231
2007/2008 101,447 2,809 32,012 43,796 49,212 55,789
2008/2009 99,871 2,907 32,852 43,150 47,571 52,755
2009/2010 99,270 2,935 34,270 41,398 46,371 61,948
2010/2011 97,664 2,780 35,386 42,464 46,223 68,715
2011/2012 98,462 2,637 35,507 40,819 42,255 70,954
2012/2013 100,183 2,555 37,033 38,665 39,504 67,014
2013/2014 107,108 2,320 35,015 41,650 41,624 46,931
2014/2015 101,070 3,562 32,068 42,744 39,825 44,867
2015/2016 97,553 3,617 30,400 44,803 39,351 43,080
2016/2017 95,391 3,593 30,265 47,326 38,157 43,292
2017/2018 89,343 3,497 28,046 48,608 36,582 42,856
2018/2019e 90,990 3,576 26,358 48,140 37,520 44,449
aTill 2015/2016 school year students in special vocational schools.
Till 2015/2016 school year students in vocational schools.
b
c Till 2015/2016 school year students in secondary vocational schools.
d Including students in university and college level education and undivided training.
e Preliminary data.

Note: In secondary schools number of students in 9th grade. In tertiary education number of
students in 1st grade, from 2013/2014 school year number of new entrants.
Source: KSH STADAT (Education – Time series of annual data).
Online data source in xls format: http://www.bpdata.eu/mpt/2019ent07_02
Figure 7.2: Flows of the educational system by level
Outflow Inflow
200,000 200,000
Primary school Vocational school
150,000 150,000

100,000 100,000

50,000 50,000
Pupils/students
Pupils/students

0 0
1990 1994 1998 2002 2006 2010 2014 2018 1990 1994 1998 2002 2006 2010 2014 2018
200,000 200,000
Secondary school University, college
150,000 150,000

100,000 100,000

50,000 50,000

0 0
1990 1994 1998 2002 2006 2010 2014 2018 1990 1994 1998 2002 2006 2010 2014 2018
Source: KSH STADAT (Education – Time series of annual data).
Online data source in xls format: http://www.bpdata.eu/mpt/2019ena07_02

217
Statistical data

Table 7.3: Students in full-time education

Vocational Tertiary under-
schools and Secondary Secondary Vocational graduate (BA/
Primary
special skills vocational general grammar BSc) and post-
schools
development schoolsb schools schoolsc graduate (MA/
School year schoolsa MSc) trainingd
2003/2004 909,769 8,147 123,457 190,447 247,622 204,910
2004/2005 887,785 8,369 123,403 193,366 245,302 212,292
2005/2006 859,315 8,797 122,162 197,217 244,001 217,245
2006/2007 828,943 9,563 119,637 200,292 243,096 224,616
2007/2008 809,160 9,773 123,192 200,026 242,016 227,118
2008/2009 788,639 9,785 123,865 203,602 236,518 224,894
2009/2010 773,706 9,968 128,674 201,208 242,004 222,564
2010/2011 756,569 9,816 129,421 198,700 240,364 218,057
2011/2012
2012/2013 742,931 9,134 117,543 189,526 224,214 214,320
2013/2014 747,746 8,344 105,122 185,440 203,515 209,208
2014/2015 748,486 7,496 92,536 182,228 188,762 203,576
2015/2016 745,323 7,146 80,493 180,966 182,529 195,419
2016/2017 741,427 7,108 78,231 181,782 167,574 190,098
2017/2018 732,491 7,169 74,104 184,525 162,216 187,084
2018/2019e 726,266 7,159 68,863 187,599 152,793 185,278
a Till 2015/2016 school year students in special vocational schools.
b Till 2015/2016 school year students in vocational schools.
c Till 2015/2016 school year students in secondary vocational schools.
d Including students in university and college level education and undivided training.
e Preliminary data.

Note: In secondary schools number of students in 9th grade. In tertiary education number of
students in 1st grade, from 2013/2014 school year number of new entrants.
Source: KSH STADAT (Education – Time series of annual data).
Online data source in xls format: http://www.bpdata.eu/mpt/2019ent07_03

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 7 Education

Table 7.4: Students in part-time education

Vocational Tertiary under-
schools and Secondary Secondary Vocational graduate (BA/
Primary
special skills vocational general grammar BSc) and post-
schools
development schoolsb schools schoolsc graduate (MA/
School year schoolsa MSc) trainingd
2003/2004 3,190 – 3,216 48,639 44,683 162,037
2004/2005 2,766 – 3,505 45,484 44,837 166,174
2005/2006 2,543 – 4,049 46,661 43,289 163,387
2006/2007 2,319 – 4,829 45,975 45,060 151,203
2007/2008 2,245 – 5,874 43,126 39,882 132,273
2008/2009 2,083 24 4,983 39,175 34,833 115,957
2009/2010 2,035 49 6,594 38,784 31,340 105,511
2010/2011 1,997 35 8,068 43,172 33,232 99,962
2011/2012 2,264 13 10,383 41,538 32,666 98,081
2012/2013 2,127 – 12,776 38,789 34,019 85,316
2013/2014 2,587 – 12,140 35,032 35,556 73,088
2014/2015 2,548 – 9,946 34,140 32,382 67,904
2015/2016 2,293 3 9,685 32,103 31,242 64,110
2016/2017 2,410 1 27,511 32,682 37,488 60,609
2017/2018 2,405 18 27,584 31,537 34,348 59,924
2018/2019e 2,440 29 25,016 28,046 31,766 60,486
a Till 2015/2016 school year students in special vocational schools.
b Till 2015/2016 school year students in vocational schools.
c Till 2015/2016 school year students in secondary vocational schools.
d Including students in university and college level education and undivided training.
e Preliminary data.

Note: In secondary schools number of students in 9th grade. In tertiary education number of
students in 1st grade, from 2013/2014 school year number of new entrants.
Source: KSH STADAT (Education – Time series of annual data).
Online data source in xls format: http://www.bpdata.eu/mpt/2019ent07_04

219
Statistical data

Table 7.5: Number of applicants for full-time high school courses

Admitted as a Applying Admitted
Applying Admitted percentage of as a percentage of the secondary
Year applied school graduates in the given year
1980 33,339 14,796 44.4 77.2 34.3
1989 44,138 15,420 34.9 84.0 29.3
1990 46,767 16,818 36.0 88.2 31.7
1991 48,911 20,338 41.6 90.2 37.5
1992 59,119 24,022 40.6 99.1 40.3
1993 71,741 28,217 39.3 104.6 41.1
1994 79,805 29,901 37.5 116.3 43.6
1995 86,548 35,081 40.5 123.2 49.9
1996 79,369 38,382 48.4 108.1 52.3
1997 81,924 40,355 49.3 108.4 53.4
1998 81,065 43,629 53.8 104.4 56.2
1999 82,815 44,538 53.8 112.0 60.2
2000 82,957 45,546 54.9 114.9 63.1
2001 84,499 50,515 59.8 120.0 71.7
2002 89,131 53,420 59.9 128.0 76.7
2003 87,110 52,703 60.5 121.1 73.3
2004 95,871 55,179 57.6 125.0 72.0
2005 91,677 52,957 57.8 119.0 68.8
2006 84,269 53,990 64.1 109.6 70.2
2007 74,849 50,941 68.1 96.5 65.7
2008 66,963 52,081 77.8 97.8 76.1
2009 90,878 61,262 67.4 116.5 78.5
2010 100,777 65,503 65.0 129.3 84.0
2011 101,835 66,810 65.6 133.2 87.4
2012 84,075 61,350 73.0 113.9 83.1
2013 75,392 56,927 75.5 110.2 83.2
2014 79,765 54,688 68.6 115.3 79.1
2015 79,255 53,069 67.0 121.3 81.2
2016 79,284 52,913 66.7 127.7 85.2
2017 74,806 51,487 68.8 122.6 84.4
2018 75,434 52,356 69.4 122.0 84.7
Note: Including students applying and admitted to BA/BSc, MA/MSc and undivided (joint
bachelor and master courses) training. From 2008 students applying and admitted in repeat-
ed, spring and autumn admission procedures altogether.
Source: KSH STADAT (Education – Time series of annual data).
Online data source in xls format: http://www.bpdata.eu/mpt/2019ent07_05

220
 8 Labour demand indicators

Table 8.1: The number of vacanciesa reported to the local offices of the NFSZ
Number of vacancies at closing date Number of registered Vacancies
Of which: public unemployedb per 100 registered
Total at closing date unemployedb
Year works participants
1992 21,793 – 556,965 3.9
1993 34,375 – 671,745 5.1
1994 35,569 – 568,366 6.3
1995 28,680 – 507,695 5.6
1996 38,297 – 500,622 7.6
1997 42,544 – 470,112 9.0
1998 46,624 – 423,121 11.0
1999 51,438 – 409,519 12.6
2000 50,000 – 390,492 12.8
2001 45,194 – 364,140 12.4
2002 44,603 – 344,715 12.9
2003 47,239 – 357,212 13.2
2004 48,223 – 375,950 12.8
2005 41,615 – 409,929 10.2
2006 41,677 – 393,465 10.6
2007 29,933 – 426,915 7.0
2008 25,364 – 442,333 5.7
2009 20,739 – 561,768 3.7
2010 22,241 – 582,664 3.8
2011 41,123 – 582,868 7.1
2012 35,850 18,669 559,102 6.4
2013 51,524 27,028 527,624 9.8
2014 75,444 37,840 422,445 16.4
2015 73,122 34,591 378,181 19.3
2016 96,841 49,405 313,782 30.9
2017 88,243 43,659 282,970 31.2
2018 85,641 33,736 255,310 33.5
aMonthly average stock figures.
bSince 1st of November, 2005: registered jobseekers.
Source: NFSZ.
Online data source in xls format: http://www.bpdata.eu/mpt/2019ent08_01
Figure 8.1: The number of vacancies reported to the local offices of the NFSZ
100,000

80,000

60,000

40,000

20,000

0
1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014 2016 2018
Source: NFSZ.
Online data source in xls format: http://www.bpdata.eu/mpt/2019ena08_01

221
Statistical data

Table 8.2: The number of vacanciesa reported to the local offices

of the NFSZ, by level of education
Vocational Secondary Secondary College,
Primary school Total
Year school school general school university
2008 15,039 7,046 1,020 1,259 1,000 25,364
2009 13,191 4,134 1,289 1,228 897 20,739
2010 13,359 5,289 1,281 1,388 924 22,241
2011 29,121 6,890 2,379 1,627 1,106 41,123
2012 21,227 8,005 2,732 1,945 1,941 35,850
2013 30,673 11,750 3,881 3,023 2,197 51,524
2014 45,555 16,440 7,216 3,329 2,904 75,444
2015 42,152 18,480 6,006 3,036 3,448 73,122
2016 58,781 22,184 8,840 4,085 2,951 96,841
2017 51,923 19,229 7,250 4,883 4,958 88,243
2018 52,690 18,124 6,872 4,754 3,200 85,641
a Monthly average stock figures.

Note: The data include vacancies posted in the Public Works program.
Source: NFSZ.
Online data source in xls format: http://www.bpdata.eu/mpt/2019ent08_02

Table 8.3: The number of vacancies

Year 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018
Number of
34,633 23,156 27,167 28,724 26,523 32,802 37,709 44,552 55,202 66,118 83,510
personsa
Per centb 1.3 0.9 1.0 1.1 1.0 1.2 1.4 1.5 1.9 2.2 2.7
aAnnual mean of the quarterly observations.
bPer cent of the filled and unfilled jobs.
Source: Eurostat. http://ec.europa.eu/eurostat/web/labour-market/job-vacancies/database
(jvs_q_nace2: 2019.09.16. version, downloaded: 2019.10.04.)
Online data source in xls format: http://www.bpdata.eu/mpt/2019ent08_03

222
 8 Labour demand indicators

Table 8.4: Firms intending to increase/decrease their staffa, per cent

Intending to Intending to Intending to Intending to
Year decrease increase Year decrease increase
1996 I. 32.9 33.3 2004 30.0 39.8
II. 29.4 30.4 2005 25.3 35.0
1997 I. 29.6 39.4 2006 26.6 36.2
II. 30.7 36.8 2007 20.4 27.0
1998 I. 23.4 42.7 2008 26.9 23.2
II. 28.9 37.1 2009 18.4 26.8
1999 I. 25.8 39.2 2010 15.4 26.0
II. 28.8 35.8 2011 17.2 25.5
2000 I. 24.4 41.0 2012 19.9 29.2
II. 27.2 36.5 2013 21.3 30.1
2001 I. 25.3 40.0 2014 19.3 27.7
II. 28.6 32.6 2015 18.6 31.2
2002 I. 25.6 39.2 2016 19.3 32.4
II. 27.9 35.4 2017 19.1 34.6
2003 I. 23.6 38.5 2018 19.5 37.7
II. 32.1 34.3
aIn the period of the next half year following the interview date, in the sample of NFSZ
PROG, since 2004: 1 year later from the interview date.
Source: NFSZ PROG.
Online data source in xls format: http://www.bpdata.eu/mpt/2019ent08_04

Figure 8.2: Firms intending to increase/decrease their staff

Intending to decrease Intending to increase
50

40
Per cent

30

20

10
1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014 2016 2018
Source: NFSZ PROG.
Online data source in xls format: http://www.bpdata.eu/mpt/2019ena08_02

223
Statistical data

Table 9.1: Regional inequalities: Employment ratea

Central Western Southern Northern Northern Southern
Budapest Pest Total
Year Transdanubia Transdanubia Transdanubia Hungary Great Plain Great Plain
1996 58.1 54.5 52.7 59.4 50.3 45.7 45.6 52.8 52.4
1997 57.8 54.7 53.6 59.8 50.0 45.7 45.2 53.6 52.5
1998 58.4 55.4 55.7 61.6 51.6 46.5 46.7 54.2 53.6
1999 60.2 57.7 58.2 63.1 52.7 48.3 48.8 55.2 55.4
2000 60.9 58.8 58.8 63.3 53.3 49.6 49.0 55.6 56.0
2001 61.3 59.4 59.3 63.1 52.3 49.7 49.5 55.8 56.2
2002 61.8 59.6 60.0 63.7 51.6 50.3 49.3 54.2 56.2
2003 63.3 59.3 62.3 61.9 53.4 51.2 51.6 53.2 57.0
2004 65.1 59.5 60.3 61.4 52.3 50.6 50.4 53.6 56.8
2005 65.3 60.2 60.2 62.1 53.4 49.5 50.2 53.8 56.9
2006 64.6 61.0 61.3 62.5 53.2 50.7 51.1 54.0 57.4
2007 64.1 61.2 61.4 62.8 51.0 50.4 50.3 54.5 57.0
2008 64.5 60.1 59.9 61.6 50.8 49.4 49.5 54.0 56.4
2009 63.1 58.8 57.3 59.2 51.7 48.2 48.0 52.9 55.0
2010 61.4 57.9 57.0 58.6 52.4 48.3 49.0 54.1 54.9
2011 61.7 58.2 59.1 59.9 51.1 48.4 49.9 54.1 55.4
2012 63.8 58.9 59.2 61.0 51.9 49.1 51.8 55.5 56.7
2013 64.2 60.6 60.7 61.8 54.8 51.6 53.2 56.3 58.1
2014 67.5 63.9 64.3 65.8 58.6 55.7 57.3 59.7 61.8
2015 69.2 65.4 67.9 67.5 60.2 59.0 58.9 62.2 63.9
2016 72.7 68.1 68.4 68.9 62.2 61.8 62.0 65.7 66.5
2017 74.0 69.2 70.5 71.0 63.0 63.5 64.4 67.4 68.2
2018 73.1 70.6 70.9 73.0 64.5 65.6 65.8 68.8 69.2
a Age: 15–64.

Note: The territorial code system was modified on 1 January 2018. The modification was justi-
fied by international and national legislative changes. Based on the changes, Budapest and
Pest county are also planning and statistical regions, while Central Hungary became exclu-
sively a statistical large region.
Source: KSH MEF.
Online data source in xls format: http://www.bpdata.eu/mpt/2019ent09_01
Figure 9.1: Regional inequalities: Labour force participation rates,
gross monthly earnings and gross domestic product in NUTS-2 level regions
Labour force participation (2018)
Gross domestic product (2017) Gross monthly earnings (2018)
200 400,000

150 350,000
Per cent

100 300,000
HUF

50 250,000

0 200,000
Central Hungary Western Transdanubia Nothern Hungary Southern Great Plain
Central Transdanubia Southern Transdanubia Nothern Great Plain
Source: Employment rate: KSH MEF; gross domestic product: KSH; earnings: NFSZ BT.
Online data source in xls format: http://www.bpdata.eu/mpt/2019ena09_01

224
 9 Regional inequalities

Table 9.2: Regional inequalities: LFS-based unemployment ratea

Central Western Southern Northern Northern Southern
Budapest Pest Total
Year Transdanubia Transdanubia Transdanubia Hungary Great Plain Great Plain
1998 5.5 6.0 6.8 6.1 9.4 12.2 11.1 7.1 7.8
1999 5.3 5.0 6.1 4.4 8.3 11.6 10.2 5.8 7.0
2000 5.2 5.1 4.9 4.2 7.8 10.1 9.3 5.1 6.4
2001 4.2 4.5 4.3 4.1 7.7 8.5 7.8 5.4 5.7
2002 3.7 4.3 5.0 4.0 7.9 8.8 7.8 6.2 5.8
2003 3.6 4.7 4.6 4.6 7.9 9.7 6.8 6.5 5.9
2004 4.4 4.7 5.6 4.6 7.3 9.7 7.2 6.3 6.1
2005 4.7 5.9 6.3 5.9 8.8 10.6 9.1 8.2 7.2
2006 4.9 5.5 6.0 5.8 9.2 10.9 10.9 8.0 7.5
2007 4.9 4.5 4.9 5.1 9.9 12.6 10.7 8.0 7.4
2008 4.2 5.0 5.8 5.0 10.3 13.3 12.1 8.7 7.8
2009 6.1 7.2 9.2 8.7 11.2 15.3 14.1 10.6 10.0
2010 9.0 8.8 10.0 9.3 12.4 16.2 14.4 10.4 11.2
2011 9.6 7.9 9.5 7.3 12.9 16.4 14.6 10.5 11.0
2012 9.6 9.3 9.9 7.5 12.1 16.1 13.9 10.3 11.0
2013 8.5 9.1 8.7 7.7 9.3 12.6 14.2 11.0 10.2
2014 6.0 6.5 5.6 4.6 7.8 10.4 11.8 9.0 7.7
2015 5.1 5.7 4.4 3.8 8.1 8.7 10.9 7.9 6.8
2016 4.3 3.1 3.0 2.7 6.2 6.3 9.3 5.6 5.1
2017 2.9 2.6 2.2 2.4 6.3 5.8 7.4 4.1 4.2
2018 3.1 2.2 2.2 2.0 5.6 4.7 6.6 3.3 3.7
a Age: 15–74.

Note: The territorial code system was modified on 1 January 2018. The modification was justi-
fied by international and national legislative changes. Based on the changes, Budapest and
Pest county are also planning and statistical regions, while Central Hungary became exclu-
sively a statistical large region.
Source: KSH MEF.
Online data source in xls format: http://www.bpdata.eu/mpt/2019ent09_02
Figure 9.2: Regional inequalities: LFS-based unemployment rates in NUTS-2 level regions

16.1

9.6 14.8 2018

10.6 4.7
12.6

9.0 6.6
3.1
2.2 2.2
12.4
12.8 2.0

3.3
1993 5.6

Source: KSH MEF.

Online data source in xls format: http://www.bpdata.eu/mpt/2019ena09_02

225
Statistical data

Table 9.3: Regional differences: The share of registered unemployeda

relative to the economically active populationb, per cent
Central Central Western Southern Northern Northern Southern
Total
Year Hungary Transdanubia Transdanubia Transdanubia Hungary Great Plain Great Plain
1999 4.5 8.7 5.9 12.1 17.1 16.1 10.4 9.7
2000 3.8 7.5 5.6 11.8 17.2 16.0 10.4 9.3
2001 3.2 6.7 5.0 11.2 16.0 14.5 9.7 8.5
2002 2.8 6.6 4.9 11.0 15.6 13.3 9.2 8.0
2003 2.8 6.7 5.2 11.7 16.2 14.1 9.7 8.3
2004 3.2 6.9 5.8 12.2 15.7 14.1 10.4 8.7
2005 3.4 7.4 6.9 13.4 16.5 15.1 11.2 9.4
2006 3.1 7.0 6.3 13.0 15.9 15.0 10.7 9.0
2007 3.5 6.9 6.3 13.6 17.6 16.6 11.7 9.7
2008 3.6 7.1 6.3 14.3 17.8 17.5 11.9 10.0
2009 5.4 11.5 9.5 17.8 20.9 20.2 14.4 12.8
2010 6.6 11.8 9.3 17.1 21.5 20.9 15.2 13.3
2011 6.8 10.9 8.0 16.6 21.5 22.0 14.5 13.2
2012 6.6 9.9 7.4 16.4 21.2 21.0 13.6 12.6
2013 6.4 9.5 7.4 15.4 19.5 19.4 19.0 13.0
2014 5.2 7.1 5.4 13.6 17.4 16.7 10.5 9.8
2015 4.6 6.1 4.4 11.8 15.4 14.2 8.9 8.5
2016 3.7 4.7 3.6 9.8 13.1 11.8 7.0 6.9
2017 2.9 4.1 3.2 9.1 12.2 10.7 6.1 6.2
2018 2.4 3.7 2.9 8.3 11.1 9.7 5.4 5.5
a Since 1st of November, 2005: the ratio of registered jobseekers. From the 1st of November, 2005
the Employment Act changed the definition of registered unemployed to registered jobseekers.
b The denominator of the ratio is the economically active population on January 1st of the previ-

ous year.
Source: NFSZ REG.
Online data source in xls format: http://www.bpdata.eu/mpt/2019ent09_03
Figure 9.3: Regional inequalities: The share of registered unemployed
relative to the economically active population, per cent, in NUTS-2 level regions

19.1

18.2
8.0
2018
12.8 13.1
11.8
9.1
3.7
4.7
14.7
13.1 3.6

7.0
1993 9.8

Source: NFSZ REG.

Online data source in xls format: http://.bpdata.eu/mpt/2019ena09_03

226
 9 Regional inequalities

Table 9.4: Annual average registered unemployment ratea by counties, per centb
County 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018
Budapest 3.0 2.6 2.2 2.4 2.8 2.9 2.6 3.0 3.1 4.6 5.9 6.2 6.1 5.8 4.5 4.0 3.0 2.2 1.8
Baranya 11.6 11.1 11.2 11.9 11.6 13.4 13.3 12.9 13.6 14.7 17.1 16.6 16.4 15.0 9.1 11.6 9.6 6.3 8.1
Bács-Kiskun 10.0 9.3 8.8 9.4 9.9 10.4 10.2 11.4 12.0 17.9 15.6 14.8 13.7 13.3 15.8 9.7 7.3 8.6 5.5
Békés 13.1 11.9 11.2 11.5 12.0 13.0 13.5 15.0 14.8 17.3 18.1 17.8 15.8 14.8 12.0 9.6 8.2 7.6 7.0
Borsod-Abaúj-Zemplén 20.3 19.0 19.1 19.6 18.3 18.9 18.0 19.9 20.1 23.1 23.7 23.5 22.9 20.9 19.6 16.6 14.0 13.2 12.3
Csongrád 8.6 8.3 8.1 8.5 9.7 10.7 8.8 9.2 9.3 11.6 12.4 11.5 11.5 11.0 8.5 7.2 5.6 4.6 3.9
Fejér 7.2 6.4 6.4 7.1 7.3 7.4 7.3 7.1 7.5 11.5 12.4 12.1 10.8 10.1 7.6 6.6 5.1 4.5 4.0
Győr-Moson-Sopron 4.6 4.1 4.0 4.1 4.6 5.4 4.6 4.1 4.1 6.9 6.8 5.7 5.0 4.6 2.9 2.4 1.9 1.6 1.3
Hajdú-Bihar 14.7 13.6 12.8 13.1 12.9 14.0 13.9 15.6 16.5 19.1 20.3 20.7 19.9 18.6 16.1 14.1 11.5 10.3 9.4
Heves 12.0 10.6 9.8 10.0 10.6 11.3 11.1 12.2 12.7 15.8 16.1 16.1 15.7 15.0 11.9 11.5 9.8 9.0 7.9
Jász-Nagykun-Szolnok 13.4 11.5 10.2 10.7 11.2 12.0 11.4 11.8 12.2 15.5 16.4 18.1 16.8 15.4 13.4 12.0 10.3 9.2 8.1
Komárom-Esztergom 8.3 7.0 6.7 6.0 5.8 6.8 5.8 5.4 5.5 10.2 10.4 9.5 8.9 8.7 6.5 5.7 4.1 3.8 3.3
Nógrád 14.9 14.3 13.8 14.6 14.6 16.1 16.1 17.7 17.8 21.2 22.0 22.9 23.9 21.7 19.1 17.4 15.3 13.9 12.0
Pest 5.2 4.4 3.7 3.7 3.8 4.2 3.9 4.3 4.4 6.7 7.7 7.6 7.4 7.2 6.2 5.5 4.7 3.9 3.2
Somogy 11.9 11.6 11.5 12.2 13.4 14.5 14.6 16.2 16.9 19.4 18.9 18.3 18.2 17.1 16.1 13.8 11.6 11.2 10.3
Szabolcs-Szatmár-Bereg 19.5 17.8 16.7 17.7 17.5 18.6 18.8 21.0 22.4 24.7 24.8 26.0 25.0 23.0 19.5 16.0 13.0 12.0 11.0
Tolna 11.8 11.0 10.0 10.7 11.6 11.8 10.5 11.5 12.1 15.2 14.7 14.2 13.7 13.7 11.1 9.3 7.7 7.2 6.0
Vas 5.2 4.9 4.5 5.0 6.0 6.8 6.1 6.2 6.1 9.8 9.6 7.7 6.7 6.9 5.1 4.3 3.5 3.5 3.3
Veszprém 7.2 6.9 6.6 7.0 7.3 8.0 7.7 8.0 8.2 12.6 12.3 10.8 9.6 9.4 6.9 5.9 4.5 3.9 3.6
Zala 7.2 6.5 6.4 7.0 7.4 9.3 9.0 9.3 9.4 13.0 12.9 11.7 11.6 12.3 9.6 7.8 6.3 5.8 5.2
Total 9.3 8.5 8.0 8.3 8.7 9.4 9.0 9.7 10.0 12.8 13.3 13.2 12.6 11.9 9.8 8.5 6.9 6.2 5.5
a Since 1st of November, 2005: the ratio of registered jobseekers. From the 1st of November,
2005 the Employment Act changed the definition of registered unemployed to registered
jobseekers.
b The denominator of the ratio is the economically active population on January 1st of the

previous year.
Source: NFSZ REG.
Online data source in xls format: http://www.bpdata.eu/mpt/2019ent09_04
Figure 9.4: Regional inequalities:
Means of registered unemployment rates in the counties, 2018

12.3
12.0 11.0
7.9
1.3 3.3
1.8 9.4
3.2
3.3 8.1
3.6 4.0

5.2 7.0
5.5
10.3 6.0
3.9

8.1

Source: NFSZ REG.

Online data source in xls format: http://www.bpdata.eu/mpt/2019ena09_04

227
Statistical data

Table 9.5: Regional inequalities: Gross monthly earningsa

Central Central Western Southern Northern Northern Southern
Total
Year Hungary Transdanubia Transdanubia Transdanubia Hungary Great Plain Great Plain
2002 149,119 110,602 106,809 98,662 102,263 98,033 97,432 117,672
2003 170,280 127,819 121,464 117,149 117,847 115,278 113,532 135,472
2004 184,039 137,168 131,943 122,868 128,435 124,075 121,661 147,111
2005 192,962 147,646 145,771 136,276 139,761 131,098 130,406 157,770
2006 212,001 157,824 156,499 144,189 152,521 142,142 143,231 171,794
2007 229,897 173,937 164,378 156,678 159,921 153,241 153,050 186,229
2008 245,931 185,979 174,273 160,624 169,313 160,332 164,430 198,087
2009 254,471 187,352 182,855 169,615 169,333 160,688 164,638 203,859
2010 258,653 194,794 183,454 171,769 173,696 162,455 169,441 207,456
2011 264,495 197,774 184,311 181,500 185,036 173,243 177,021 214,540
2012 279,073 215,434 202,189 208,895 196,566 191,222 187,187 230,073
2013 290,115 220,495 209,418 190,126 188,635 178,499 187,762 230,018
2014 296,089 228,974 219,727 200,359 204,472 194,654 196,667 240,675
2015 306,890 234,443 230,142 205,020 200,174 191,973 203,280 245,210
2016 332,046 258,131 244,828 219,194 205,679 198,726 216,677 263,317
2017 375,349 286,126 279,518 250,879 240,210 232,855 249,125 300,232
2018 393,854 319,102 296,756 272,186 264,661 256,392 271,062 324,719
a Gross monthly earnings (HUF/person), May.

Note: The data refer to full-time employees in the budgetary sector and firms employing at
least 5 workers, respectively.
Source: NFSZ BT.
Online data source in xls format: http://www.bpdata.eu/mpt/2019ent09_05
Table 9.6: Regression-adjusted earnings differentials
Central Western Southern Northern Northern Southern
Year
Hungary Transdanubia Transdanubia Hungary Great Plain Great Plain
2002 0.0903 –0.0378 –0.1120 –0.0950 –0.1170 –0.1070
2003 0.0493 –0.0542 –0.1220 –0.1220 –0.1400 –0.1410
2004 0.0648 –0.0313 –0.1410 –0.0953 –0.1400 –0.1270
2005 0.0291 –0.0372 –0.1310 –0.1010 –0.1450 –0.1390
2006 0.0478 –0.0170 –0.1640 –0.0922 –0.1480 –0.1130
2007 0.0528 –0.0926 –0.1520 –0.1340 –0.1610 –0.1420
2008 0.0438 –0.0751 –0.1730 –0.1320 –0.1780 –0.1630
2009 0.0766 –0.0377 –0.1250 –0.1170 –0.1380 –0.1500
2010 0.0704 –0.0758 –0.1450 –0.1200 –0.1620 –0.1500
2011 0.0893 –0.0604 –0.1020 –0.0863 –0.1340 –0.1170
2012 0.0664 –0.0361 –0.0750 –0.0947 –0.1140 –0.1170
2013 0.0267 –0.0605 –0.1120 –0.1140 –0.1540 –0.1320
2014 0.0203 –0.0474 –0.1250 –0.1150 –0.1390 –0.1330
2015 0.0303 –0.0145 –0.0990 –0.0920 –0.1290 –0.1180
2016 0.0414 –0.0321 –0.1420 –0.1670 –0.1900 –0.1410
Note: the results indicate the earnings differentials of the various groups relative to the refer-
ence group in log points (approximately percentage points). All parameters are significant at
the 0.01 level.
Reference category: women, with leaving certificate (general education certificate), not in the
public sector, working in the Central-Transdanubia region.
Source: NFSZ BT.
Online data source in xls format: http://www.bpdata.eu/mpt/2019ent09_06

228
 9 Regional inequalities

Table 9.7: Regional inequalities: Gross domestic product

Central Central Western Southern Northern Northern Southern
Total
Year Hungary Transdanubia Transdanubia Transdanubia Hungary Great Plain Great Plain
Thousand HUF/person/month
2004 3,347 1,963 2,148 1,464 1,364 1,371 1,459 2,088
2005 3,631 2,102 2,213 1,544 1,480 1,439 1,540 2,237
2006 3,968 2,190 2,426 1,619 1,553 1,533 1,617 2,409
2007 4,242 2,356 2,487 1,717 1,627 1,597 1,679 2,554
2008 4,492 2,441 2,622 1,839 1,687 1,696 1,814 2,709
2009 4,432 2,198 2,464 1,810 1,602 1,706 1,741 2,636
2010 4,515 2,358 2,696 1,838 1,636 1,725 1,765 2,722
2011 4,595 2,513 2,874 1,915 1,708 1,839 1,900 2,839
2012 4,718 2,538 2,938 1,972 1,724 1,872 1,980 2,901
2013 4,924 2,732 3,094 2,069 1,862 1,931 2,117 3,058
2014 5,148 2,990 3,535 2,207 2,081 2,132 2,345 3,302
2015 5,361 3,253 3,770 2,267 2,316 2,223 2,503 3,493
2016 5,512 3,427 3,943 2,376 2,369 2,286 2,569 3,615
2017 5,992 3,618 4,140 2,597 2,641 2,490 2,789 3,919
Per cent
2004 160.3 94.0 102.9 70.1 65.4 65.7 69.9 100.0
2005 162.4 94.0 98.9 69.0 66.2 64.4 68.9 100.0
2006 164.8 90.9 100.7 67.2 64.5 63.7 67.1 100.0
2007 166.1 92.3 97.4 67.2 63.7 62.5 65.7 100.0
2008 165.8 90.1 96.8 67.9 62.1 62.6 67.0 100.0
2009 168.0 83.4 93.5 68.7 60.8 64.8 66.1 100.0
2010 165.8 86.6 99.1 67.6 60.1 63.4 64.8 100.0
2011 161.8 88.6 101.3 67.5 60.2 64.8 67.0 100.0
2012 162.6 88.1 100.7 67.9 59.5 64.6 68.1 100.0
2013 161.0 89.4 100.8 67.7 61.0 63.2 69.3 100.0
2014 155.9 90.5 107.0 66.8 63.0 64.5 71.0 100.0
2015 153.5 93.1 107.9 64.9 66.3 63.6 71.7 100.0
2016 152.5 94.8 109.1 65.7 65.5 63.2 71.1 100.0
2017 152.9 92.3 105.6 66.3 67.4 63.5 71.2 100.0
Note: The data on 2004–2015 have been retrospectively revised following ESA2010 standards
(European System of National and Regional Accounts).
Source: KSH STADAT (2018.12.21. version).
Online data source in xls format: http://www.bpdata.eu/mpt/2019ent09_07
Table 9.8: Commuting
Working in the place of residence Commuter
Year in thousands per cent in thousands per cent
1980 3,848.5 76.0 1,217.2 24.0
1990 3,380.2 74.7 1,144.7 25.3
2001 2,588.2 70.1 1,102.1 29.9
2005 2,625.1 68.2 1,221.3 31.8
2011 2,462.8a 62.5 1,479.8 37.2
2017 2,374.0 61.5 1,485.2 38.5
a Includes those working abroad but classified by the respondents of LFS as household members.

Source: NSZ, microcensus.

Online data source in xls format: http://www.bpdata.eu/mpt/2019ent09_08

229
Statistical data

Figure 9.5: The share of registered unemployed relative to the population

aged 15–64, 1st quarter 2007, per cent

Note: The ratio of registered unemployed was calculated using the following method: number
of registered unemployed divided by the permanent population of age 15 –64. The number
of registered unemployed is a quarterly average. The permanent population data is annual.
Source: Registered unemployed: NFSZ IR. Population: KSH T-Star.
Online data source in xls format: http://www.bpdata.eu/mpt/2019ena09_05

Figure 9.6: The share of registered unemployed relative to the population

aged 15–64, 1st quarter 2018, per cent

Note: The ratio of registered unemployed was calculated using the following method: number
of registered unemployed divided by the permanent population of age 15 –64. The number
of registered unemployed is a quarterly average. The permanent population data is from the
year 2017 (since 2018 data is not yet available).
Source: Registered unemployed: NFSZ IR. Population: KSH T-Star.
Online data source in xls format: http://www.bpdata.eu/mpt/2019ena09_06

230
 9 Regional inequalities

Figure 9.7: The share of registered unemployed relative to the population

aged 15–64, 3rd quarter 2007, per cent

Note: The ratio of registered unemployed was calculated using the following method: number
of registered unemployed divided by the permanent population of age 15 –64. The number
of registered unemployed is a quarterly average. The permanent population data is annual.
Source: Registered unemployed: NFSZ IR. Population: KSH T-Star.
Online data source in xls format: http://www.bpdata.eu/mpt/2019ena09_07

Figure 9.8: The share of registered unemployed relative to the population

aged 15–64, 3rd quarter 2018, per cent

Note: The ratio of registered unemployed was calculated using the following method: number
of registered unemployed divided by the permanent population of age 15 –64. The number
of registered unemployed is a quarterly average. The permanent population data is from the
year 2017 (since 2018 data is not yet available).
Source: Registered unemployed: NFSZ IR. Population: KSH T-Star.
Online data source in xls format: http://www.bpdata.eu/mpt/2019ena09_08

231
Statistical data

Table 10.1: Strikes

Year Number of strikes Number of persons involved Hours lost, in thousands
2001 6 21,128 61
2002 4 4,573 9
2003 7 10,831 19
2004 8 6,276 116
2005 11 1,425 7
2006 16 24,665 52
2007 13 64,612 186
2008 8 8,633 ..
2009 9 3,134 9
2010 7 3,263 133
2011 1 .. ..
2012 3 1,885 5
2013 1 .. ..
2014 0 0 0
2015 2 .. ..
2016 7 39,101 271
2017 5 6,706 30
2018 6 15,535 289
Source: KSH STADAT strike statistics (2019.06.28. version).
Online data source in xls format: http://www.bpdata.eu/mpt/2019ent10_01
Table 10.2: National agreements on wage increase recommendationsa
OÉT – from 2013 VKF – Recommendations Actual indexes
Year Minimum Average Maximum Budgetary sector Competitive sector
2001 .. .. .. 122.9 116.3
2002 108.0 .. 110.5 129.2 113.3
2003 .. 4.5% real wage growth .. 117.5 108.9
2004 .. 107.0–108.0 .. 100.4 109.3
2005 .. 106.0 .. 112.8 106.9
2006 .. 104.0–105.0 .. 106.4 109.3
2007 .. 105.5–108.0 .. 106.4 109.1
2008 .. 105.0–107.5 .. 106.2 108.4
2009 .. 103.0–105.0 .. 92.1 104.3
2010 .. real wage preservation .. 100.5b 103.2
2011 .. 104.0–106.0 .. 99.3 105.3
2012 – no wage recommendations – 103.7 107.2
2013 .. real wage preservation .. 110.9 103.6
2014 .. 103.5 .. 105.9 104.2
2015 .. 103.0 –104.0 .. 106.2 103.9
2016 .. verbal recommendation was issued and accepted .. 109.6 105.4
2017 .. recommendation wasn’t accepted .. 113.0 111.6
2018 .. recommendation wasn’t accepted .. 109.0 110.9
a Average increase rates of gross earnings from recommendations by the National Interest
Reconciliation Council (OÉT) and the Permanent Consultation Forum of the Business Sec-
tor and the Government (VKF, from 2013 onwards). Previous year = 100.
b Mean real wage index.

Source: KSH, PM.

Online data source in xls format: http://www.bpdata.eu/mpt/2019ent10_02

232
 10 Industrial relations

Table 10.3: Single employer collective agreements in the business sector

2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018
Number of agree-
1,032 1,027 962 966 959 942 951 951 950 994 995 999
ments
Number of persons
532,065 467,964 432,086 448,138 448,980 442,723 448,087 443,543 458,668 463,823 386,947 388,996
covered
Source: PM, Employment Relations Information System.
Online data source in xls format: http://www.bpdata.eu/mpt/2019ent10_03

Table 10.4: Single institution collective agreements in the public sector

2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018
Number of agree-
1,711 1,710 1,737 1,751 1,744 1,735 1,736 1,734 798 800 804 819
ments
Number of persons
224,246 222,547 225,434 224,651 222,136 261,401 260,388 259,797 301,430 312,055 270,583 167,583
covered
Source: PM, Employment Relations Information System.
Online data source in xls format: http://www.bpdata.eu/mpt/2019ent10_04

Table 10.5: Multi-employer collective agreements in the business sector

2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018
Number of agree-
74 78 80 82 81 81 83 83 83 84 84 83
ments
Number of persons
83,117 80,506 222,236 221,627 202,005 204,585 173,614 219,050 299,487 313,044 266,212 230,938
covered
Source: PM, Employment Relations Information System.
Online data source in xls format: http://www.bpdata.eu/mpt/2019ent10_05

Table 10.6: Multi-institution collective agreements in the public sector

2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018
Number of agree-
2 1 1 1 1 0 0 0 0 0 0 1
ments
Number of persons
238 .. .. .. 320 0 0 0 0 0 0 55,979
covered
Source: PM, Employment Relations Information System.
Online data source in xls format: http://www.bpdata.eu/mpt/2019ent10_06

Table 10.7: The number of firm wage agreementsa, the number of affected firms,
and the number of employees covered
2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018
Number of agreements 214 202 785 905 888 863 874 876 867 878 873 874
Number of persons covered 171,259 100,206 377,677 414,522 416,562 415,751 422,887 384,182 424,914 437,238 368,021 336,288
aUntil 2008, the data relate to the number of ’wage agreements’ concerning the next year’s
average wage increase, in the typical case. In and after 2009, the figures relate to resolutions
within collective agreements, which affect the remuneration of workers (including long-
term agreements on wage supplements, bonuses, premia, non-wage benefits and rights and
responsibilities connected with wage payments).
Source: PM, Employment Relations Information System.
Online data source in xls format: http://www.bpdata.eu/mpt/2019ent10_07

233
Statistical data

Table 10.8: The number of multi-employer wage agreementsa, the number of affected firms,
and the number of covered companies and employees
2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018
Number of agreements 40 45 62 68 68 73 74 74 74 73 70 72
Number of companies 147 150 2,350 2,460 2,199 2,219 1,096 2,886 3,700 1,833 1,833 1,830
Number of persons covered 33,735 40,046 191,258 211,753 180,131 191,013 160,092 208,128 289,154 199,779 165,789 165,293
aUntil 2008, the data relate to the number of ’wage agreements’ concerning the next year’s
average wage increase, in the typical case. In and after 2009, the figures relate to resolutions
within collective agreements, which affect the remuneration of workers (including long-
term agreements on wage supplements, bonuses, premia, non-wage benefits and rights and
responsibilities connected with wage payments).
Source: PM, Employment Relations Information System.
Online data source in xls format: http://www.bpdata.eu/mpt/2019ent10_08
Table 10.9: The share of employees covered by collective agreements, percenta
Multi-employer collective agreements Single employer collective agreements
in the business sectorb in the national economy
Industries 2014 2015 2016 2017 2018 2014 2015 2016 2017 2018
Agriculture 21.12 40.83 36.90 35.88 37.33 9.87 21.81 15.77 14.34 14.99
Mining and quarrying 5.35 6.87 16.02 16.21 14.08 40.46 58.42 52.92 35.02 30.41
Manufacturing 11.94 10.82 11.15 8.96 8.73 25.86 27.28 27.14 21.61 21.16
Electricity, gas, steam and air condi-
73.69 78.50 89.54 84.24 87.06 53.19 58.00 55.15 52.27 55.21
tioning supply
Water supply; sewerage, waste man-
27.10 35.25 43.26 42.61 42.64 46.57 59.09 57.08 53.44 55.43
agement and remediation activities
Construction 98.00 98.91 98.54 98.56 98.89 6.65 6.63 5.57 3.80 3.47
Wholesale and retail trade; repair of
6.88 7.56 6.65 5.84 5.54 7.71 7.34 6.81 5.03 4.57
motor vehicles and motorcycles
Transportation and storage 37.38 42.22 50.17 57.91 33.59 54.40 59.69 61.93 69.12 66.06
Accommodation and food service
87.66 93.51 94.02 93.26 93.05 6.24 5.62 5.75 2.94 2.59
activities
Information and communication 0.81 0.74 0.58 0.28 0.26 19.19 20.81 17.64 15.04 13.69
Financial and insurance activities 5.36 5.85 5.94 6.05 5.95 32.89 37.50 37.05 36.78 36.72
Real estate activities 17.36 16.77 16.81 1.38 1.39 26.14 26.82 29.89 5.68 5.79
Professional, scientific and technical
4.49 5.39 4.20 0.85 0.80 12.78 10.37 7.45 4.71 4.16
activities
Administrative and support service
7.06 6.30 6.24 3.96 3.59 8.17 6.18 5.87 2.63 2.40
activities
Public administration and defence;
.. .. .. 0.00 0.00 15.55 7.27 9.75 3.82 12.62
compulsory social security
Education 4.81 5.43 2.27 2.32 2.65 44.98 70.79 68.30 61.75 15.55
Human health and social work activi-
.. .. .. 0.00 0.00 36.38 26.50 27.36 24.35 26.79
ties
Arts, entertainment and recreation 0.14 0.09 0.02 0.00 0.00 22.99 21.68 23.51 21.15 19.10
Other service activities 1.46 7.58 2.54 1.52 1.66 6.88 11.80 12.58 11.18 10.86
National economy, total 21.51 20.85 23.66 22.14 20.32 24.59 25.84 25.99 22.14 18.18
aPercentage share of employees covered by collective agreements.
bIn the observed period only a single multi-employer collective agreement was in effect in
the public sector.
Source: PM, Employment Relations Information System, Register of Collective Agreements.
Online data source in xls format: http://www.bpdata.eu/mpt/2019ent10_09

234
 10 Industrial relations

Table 10.10: Single employer collective agreements in the national economy

Number of The number of employees covered
collective agreements by collective agreements
Industries 2014 2015 2016 2017 2018 2014 2015 2016 2017 2018
Agriculture 66 66 66 65 65 7,680 17,603 12,263 10,990 10,990
Mining and quarrying 9 9 9 9 9 1,498 2,057 1,751 1,136 1,136
Manufacturing 355 353 346 343 346 157,178 174,379 180,257 148,315 149,136
Electricity, gas, steam and air
44 43 45 44 44 12,414 13,450 13,210 12,410 12,524
conditioning supply
Water supply; sewerage, waste
management and remediation 68 69 59 56 63 19,010 25,021 25,796 23,283 24,316
activities
Construction 46 47 45 46 45 7,488 7,540 6,358 4,511 4,510
Wholesale and retail trade;
repair of motor vehicles and 119 117 115 112 110 25,565 25,212 24,197 18,326 17,575
motorcycles
Transportation and storage 59 50 91 96 96 96,550 109,336 125,960 112,168 112,470
Accommodation and food ser-
35 34 36 36 37 4,986 4,969 5,127 2,805 2,699
vice activities
Information and communication 15 15 16 16 16 13,727 15,514 13,954 12,255 12,255
Financial and insurance activi-
26 26 27 29 29 20,892 22,476 22,882 22,285 22,672
ties
Real estate activities 32 32 43 49 50 7,079 7,367 8,152 1,446 1,672
Professional, scientific and
54 57 55 53 53 10,047 9,534 7,432 4,981 4,791
technical activities
Administrative and support
24 24 23 25 25 11,080 10,238 9,589 4,270 4,263
service activities
Public administration and de-
fence; compulsory social secu- 104 104 106 102 123 40,431 21,224 28,022 10,734 34,947
rity
Education 1,292 352 355 354 354 114,377 176,637 177,956 175,162 45,072
Human health and social work
228 226 227 226 228 95,961 94,549 98,399 81,037 84,116
activities
Arts, entertainment and recrea-
91 92 96 96 97 7,592 9,341 9,955 8,181 8,181
tion
Other service activities 18 19 21 20 22 1,474 2,283 2,552 2,311 2,330
National economy, total 2,685 1,735 1,781 1,777 1,812 655,029 748,730 773,812 656,606 555,655
Source: PM, Employment Relations Information System, Register of Collective Agreements.
Online data source in xls format: http://www.bpdata.eu/mpt/2019ent10_10

235
Statistical data

Table 10.11: Multi-employer collective agreements in the business sectora

The number of firms covered by the The number of employees covered by
multi-employerb collective agreements multi-employer collective agreements
Industries 2014 2015 2016 2017 2018 2014 2015 2016 2017 2018
Agriculture 41 706 673 678 667 17,002 32,822 28,586 27,359 27,182
Mining and quarrying 4 4 6 6 6 195 242 530 526 526
Manufacturing 174 231 237 240 244 72,623 67,668 72,432 60,161 60,291
Electricity, gas, steam and air
35 34 40 39 37 17,142 17,962 21,151 19,720 19,440
conditioning supply
Water supply; sewerage, waste
management and remediation 28 28 32 33 31 9,283 11,450 14,039 13,053 12,990
activities
Construction 510 555 558 549 558 110,173 112,034 112,352 116,659 128,317
Wholesale and retail trade;
repair of motor vehicles and 192 240 221 209 207 22,827 25,944 23,640 21,256 21,284
motorcycles
Transportation and storage 1,209 1,560 1,620 1,618 1,613 63,934 73,515 97,689 89,412 54,567
Accommodation and food ser-
37 35 39 39 40 63,526 73,759 75,848 79,360 86,972
vice activities
Information and communication 12 11 9 9 9 597 550 461 231 231
Financial and insurance activi-
9 12 12 13 12 3,269 3,499 3,662 3,652 3,652
ties
Real estate activities 34 40 42 47 48 4,055 4,030 4,255 330 365
Professional, scientific and
45 58 56 57 58 3,326 4,368 3,783 815 843
technical activities
Administrative and support
104 111 104 105 105 10,013 9,310 9,433 6,007 6,009
service activities
Public administration and de-
fence; compulsory social secu- 1 3 3 3 3 0 1,540 1,571 1,388 1,388
rity
Education 24 26 25 25 24 172 189 134 122 122
Human health and social work
2 0 0 0 0 .. 0 0 0 0
activities
Arts, entertainment and recrea-
4 2 1 0 0 13 10 2 0 0
tion
Other service activities 2 13 9 9 9 204 1,125 381 236 236
National economy, total 2,467 3,669 3,687 3,679 3,671 398,354 440,017 469,949 440,287 424,415
a In the observed period only a single multi-employer collective agreement was in effect in the
public sector.
b Multi-employer collective agreements are those concluded and/or extended by several em-

ployers or employer organizations.

Source: PM, Employment Relations Information System, Register of Collective Agreements.
Online data source in xls format: http://www.bpdata.eu/mpt/2019ent10_11

236
 11 Welfare provisions

Table 11.1: Family benefits

Family Child-care Child-rearing Child-care Infant-care
allowancea benefita supporta allowancea benefitb
Average Average
Average num- Average Average num- Average num- Average Average num- Average num-
monthly monthly
ber of recipi- monthly ber of recipi- ber of recipi- monthly ber of recipi- ber of recipi-
amount per amount per
ent families amount, HUF ents ent families amount, HUF ents ents
Year family, HUF family, HUF
2009 24,524 1,245,893 78,725 95,050 28,652 40,263 30,716 174,153 29,230
2010 24,442 1,224,042 81,356 94,682 .. 39,275 30,388 178,532 27,289
2011 24,528 1,190,707 83,959 87,717 .. 37,829 30,929 169,721 24,769
2012 24,491 1,167,640 91,050 81,839 .. 38,608 30,640 168,037 25,223
2013 24,257 1,149,796 96,661 81,234 .. 37,411 30,687 161,274 24,230
2014 23,674 1,134,556 104,547 83,701 .. 36,101 31,180 161,226 24,753
2015 23,902 1,108,302 110,896 85,970 .. 34,587 31,883 163,376 25,886
2016 23,849 1,094,004 118,607 91,126 .. 33,381 31,880 162,992 26,931
2017 23,678 1,090,651 130,087 97,470 .. 32,941 31,278 164,297 27,989
2018 23,681 1,082,791 142,084 102,512 .. 32,607 31,248 159,226 27,696
aAnnual mean.
bPregnancy and confinement benefit till 31st December 2014. Infant-care benefit is 70 per
cent of the recipient’s daily income. The amount is subject to personal income tax but ex-
empt from health and pension contributions.
Source: KSH STADAT.
Online data source in xls format: http://www.bpdata.eu/mpt/2019ent11_01
Table 11.2: Unemployment benefits and average earnings
Insured unemployment benefit Means tested
and other non-means tested benefitsa unemployment assistanceb Net monthly earnings,
Average monthly Average number of Average monthly Average number of HUFc
Year amount, HUF recipients amount, HUF recipients
2009 51,831 152,197 23,117 167,287 124,115
2010 50,073 125,651 27,574 174,539 132,604
2011 52,107 110,803 25,139 209,918 141,151
2012 63,428 62,380 21,943 236,609 144,084
2013 68,730 48,019 22,781 212,699 151,117
2014 69,720 42,423 22,800 160,858 155,689
2015 72,562 40,576 22,787 158,141 162,391
2016 75,183 41,521 22,874 115,568 175,009
2017 82,912 42,344 22,868 99,783 197,515
2018 93,276 42,258 22,800 75,665 219,412
a Average of headcount at the end of the month. Since 1st of November, 2005 insurance based
unemployment benefits are officially called “jobseeker’s allowance”.
b Persons receiving social assistance: registered job-seekers of working age, classified as vulner-

able by the PES. Since 1st of January 2009 two types of social assistance exist; group 1 receive
social benefit, while group 2 receive ‘availability assistance’, conditional on acceptance of job
offers provided by the PES. From the 1st of January 2011, the second type of benefit was re-
named as ‘wage replacement allowance’. On 1st of September 2011 the name changed again to
‘non-employment subsidy’. These welfare payments are regulated in Law 1993. III.
c The average net wage refers to the entire economy, competitive sector: firms with at least 4

employees.
Source: NFSZ: Labour Market Report, 2001. KSH: Welfare systems 2007, Welfare Statistics, Year-
book of Demographics. KSH Social Statistics Yearbooks. KSH STADAT.
Online data source in xls format: http://www.bpdata.eu/mpt/2019ent11_02

237
Statistical data

Table 11.3.a: Number of those receiving pensiona, and the mean sum of the provisions
they received in January of the given year
Old age pension Disability pension under and above retirement age
Number of Average amount before Average amount Number of Average amount before Average amount
Year recipients increase, HUF after increase, HUF recipients increase, HUF after increase, HUF
2000 1,671,090 33,258 35,931 762,514 29,217 31,556
2001 1,667,945 37,172 41,002 772,286 32,381 35,705
2002 1,664,062 43,368 47,561 789,544 37,369 40,972
2003 1,657,271 50,652 54,905 799,966 43,185 46,801
2004 1,637,847 57,326 60,962 806,491 48,180 51,220
2005 1,643,409 63,185 67,182 808,107 52,259 55,563
2006 1,658,387 69,145 72,160 806,147 56,485 58,935
2007 1,676,477 74,326 78,577 802,506 59,978 63,120
2008 1,716,315 81,975 87,481 794,797 65,036 69,160
2009 1,731,213 90,476 93,256 779,130 70,979 73,166
2010 1,719,001 94,080 98,804 750,260 73,687 77,500
2011 1,700,800 99,644 104,014 721,973 77,945 81,367
2012 1,959,202b 99,931 104,610 302,990c .. ..
a Pension: Excludes survivors pensions.
b From 2012 onwards, the disability pensions of persons older than the mandatory retirement
age are granted as old-age pensions.
c Excludes persons older than the mandatory retirement age.

Source: MÁK.
Online data source in xls format: http://www.bpdata.eu/mpt/2019ent11_03a
Table 11.3.b: Number of those receiving pensiona, and the mean sum of the provisions
they received in January of the given year
2015 2016 2017 2018
Average Average Average Average
Number of amount after Number of amount after Number of amount after Number of amount after
recipients increase recipients increase recipients increase recipients increase
Type of benefit (HUF/month) (HUF/month) (HUF/month) (HUF/month)
Old age pension 2,022,905 118,439 2,014,666 121,041 2,045,738 123,725 2,027,256 129,637
Of which:
–old age pension of persons
above the mandatory retire- 1,894,897 118,194 1,870,457 120,930 1,901,565 123,799 1,876,148 129,801
ment ageb
–pension for women entitled
to retire before the manda-
tory age after having accu- 122,253 117,926 139,639 119,457 141,904 121,184 149,971 126,797
mulated at least 40 accrual
years
–old age pension of persons
younger than the mandatory 5,755 210,014 4,570 215,017 2,269 220,526 1,137 233,700
retirement age
a Pension: Excludes survivors pensions. From 2012 onwards, no old-age pension is granted to
persons younger than the mandatory retirement age. Exceptions are pensions for women
having accumulated 40 or more accrual years.
b From 2012 onwards, the disability pensions of persons older than the mandatory retirement

age are granted as old-age pensions.

Source: MÁK.
Online data source in xls format: http://www.bpdata.eu/mpt/2019ent11_03b

238
 11 Welfare provisions

Table 11.4.a: Number of those receiving social annuities for people with damaged health, and the mean sum of
the provisions they received after the increase, in January of the given year
Temporary annuity Regular social annuity Health damage annuity for miners Total
Number of Average Number of Average Number of Average amount, Number of Average
Year recipients amount, HUF recipients amount, HUF recipients HUF recipients amount, HUF
2000 15,491 18,309 196,689 14,435 2,852 48,581 215,032 15,167
2001 15,640 20,809 198,820 15,610 3,304 53,379 217,764 16,556
2002 11,523 26,043 200,980 17,645 3,348 59,558 215,851 18,744
2003 12,230 30,135 203,656 19,907 3,345 65,380 219,231 21,171
2004 11,949 33,798 207,300 21,370 2,950 69,777 222,199 22,681
2005 13,186 36,847 207,091 22,773 2,839 74,161 223,116 24,259
2006 14,945 40,578 195,954 23,911 2,786 77,497 213,685 25,776
2007 19,158 42,642 184,845 25,050 2,693 80,720 206,696 27,406
2008 21,538 46,537 170,838 27,176 2,601 85,805 194,977 30,096
2009 21,854 46,678 159,146 27,708 2,533 86,165 183,533 30,774
2010 20,327 47,060 148,704 27,645 2,448 86,252 171,479 30,783
2011 16,448 47,096 139,277 27,588 2,371 86,411 158,096 30,500
Disability pensions and temporary provisions for disability groups 1 –2, granted prior to 2012,
have been transformed to ‘disability allotments’. The provisions for permanent social benefit
recipients born before 1955 have also been transformed to ‘disability allotments’. Disability
pensions and permanent social benefits granted before 2012 to the members of disability
group 3 have been transformed to ‘rehabilitation allotment’. The conditions of these provi-
sions will be set in the framework of a complex revision of entitlement and eligibility.
Source: MÁK.
Online data source in xls format: http://www.bpdata.eu/mpt/2019ent11_04a
Table 11.4.b: Number of those receiving social annuities for people with damaged health, and the mean sum of
the provisions they received after the increase, in January of the given year
2015 2016 2017 2018
Average Average Average Average
Number of amount after Number of amount after Number of amount after Number of amount after
Support for recipients increase recipients increase recipients increase recipients increase
disabled persons (HUF/month) (HUF/month) (HUF/month) (HUF/month)
Support for disabled persons 404,880 67,759 357,979 69,399 355,188 70,127 338,906 72,762
Of which:
–disability provision for persons
older than the mandatory 44,436 74,509 52,215 78,425 62,518 80,833 51,965 84,885
retirement age
–disability provision for persons
younger than the mandatory 217,625 74,463 228,730 73,215 249,909 71,199 250,062 73,696
retirement
–rehabilitation provision 140,658 54,810 92,951 54,282 40,741 45,604 34,955 46,292
–annuity for miners with dam-
2,161 96,567 2,038 98,621 2,020 100,817 1,924 104,818
aged health
Disability pensions and temporary provisions for disability groups 1 –2, granted prior to 2012, have been transformed to
‘disability allotments’. The provisions for permanent social benefit recipients born before
1955 have also been transformed to ‘disability allotments’. Disability pensions and perma-
nent social benefits granted before 2012 to the members of disability group 3 have been
transformed to ‘rehabilitation allotment’. The conditions of these provisions will be set in
the framework of a complex revision of entitlement and eligibility.
Source: MÁK.
Online data source in xls format: http://www.bpdata.eu/mpt/2019ent11_04b

239
Statistical data

Table 11.5: The median age for retirement and the number of pensioners
2009 2010 2011 2012 2013
Pension Age Persons Age Persons Age Persons Age Persons Age Persons
Females
Old age and similar pensions 59.9 15,243 60.7 13,617 58.5 84,922 59.2 51,011 59.6 40,032
Pension for women entitled to retire before
the mandatory age after having accumu- – – – – 57.6 54,770 57.8 26,554 58.0 24,033
lated at least 40 accrual years
Disability and accident-related disab. pens. 51.1 9,065 50.8 10,478 50.7 8,667 – – – –
Rehabilitation annuity 44.9 6,574 47.6 6,789 47.2 4,386 .. .. .. ..
Total 54.1 30,882 54.4 30,884 57.3 97,975 .. .. .. ..
Males
Old age and similar 59.7 37,116 60.2 37,219 60.3 43,240 61.8 20,411 62.2 21,525
Disability and accident-related disab. pens. 52.3 11,992 52.1 13,345 51.9 10,673 – – – –
Rehabilitation annuity 44.8 6,278 47.4 6,123 47.0 4,102 .. .. .. ..
Total 56.4 55,386 56.9 56,687 57.8 58,015 .. .. .. ..
Together
Old age and similar pensions 59.7 52,359 60.3 50,836 59.1 128,162 59.9 71,422 60.5 61,557
Disability and accident-related disab. pens. 51.8 21,057 51.5 23,823 51.4 19,340 – – – –
Rehabilitation annuity 44.9 12,852 47.5 12,912 47.1 8,488 .. .. .. ..
Total 55.6 86,268 56.0 87,571 57.5 155,990 .. .. .. ..
2014 2015 2016 2017 2018a
Females
Old age and similar pensions 59.6 38,911 60.0 41,558 61.1 55,288 61.0 46,372 61.2 48,436
Pension for women entitled to retire before
the mandatory age after having accumu- 58.3 27,450 58.7 28,537 59.0 28,126 59.3 28,500 59.5 29,009
lated at least 40 accrual years
Disability and accident-related disab. pens. – – – – – – – – – –
Rehabilitation annuity .. .. .. .. .. .. .. .. .. ..
Total .. .. .. .. .. .. .. .. .. ..
Males
Old age and similar pensions 62.7 18,634 62.7 22,195 63.1 49,831 63.5 31,822 63.6 33,851
Disability and accident-related disab. pens. – – – – – – – – – –
Rehabilitation annuity .. .. .. .. .. .. .. .. .. ..
Total .. .. .. .. .. .. .. .. .. ..
Together
Old age and similar pensions 60.6 57,545 60.9 63,753 62.0 105,119 62.0 78,194 62.2 82,287
Disability and accident-related disab. pens. – – – – – – – – – –
Rehabilitation annuity .. .. .. .. .. .. .. .. .. ..
Total .. .. .. .. .. .. .. .. .. ..
Note: The source of these statistics is data from the pension determination system of the
ONYF (NYUGDMEG), so these do not include the data for the armed forces and the police.
Data on MÁV is included from 2008. ’Old age pensions’ include some allowances of minor
importance paid to recipients younger than the mandatory retirement age. The data on
2012 –2016 have been revised and may differ from those in earlier publications.
a Preliminary data.

Source: MÁK.
Online data source in xls format: http://www.bpdata.eu/mpt/2019ent11_05

240
 11 Welfare provisions

Table 11.6: The number of those receiving a disability annuity and the mean sum of the
provisions they received after the increase, in January of the given year
Disability annuity Disability annuity
Number of Average amount, Number of Average amount,
Year recipients HUF Year recipients HUF
2003 27,058 23,884 2011 32,314 33,429
2004 27,923 25,388 2012 32,560 33,426
2005 28,738 27,257 2013 32,463 33,422
2006 29,443 28,720 2014 32,497 33,422
2007 30,039 30,219 2015 32,528 34,034
2008 30,677 32,709 2016 32,430 34,581
2009 31,263 33,434 2017 32,789 35,147
2010 31,815 33,429 2018 33,027 36,494
Source: MÁK.
Online data source in xls format: http://www.bpdata.eu/mpt/2019ent11_06

Table 11.7: Newly determined disability pension claims and detailed data
on the number of newly determined old-age pension claims
Disability and accident- Old-age and From the total: From the total:
related disability pen- old-age type pensionsa at the age limit under the age limit
Year sions (total number) Male Female Together Male Female Together Male Female Together
2005 41,057 33,175 48,771 81,946 4,035 6,721 10,756 27,719 40,142 67,861
2006 36,904 34,207 47,531 81,738 4,013 732 4,745 29,025 45,675 74,700
2007 34,991 51,037 62,168 113,205 3,722 6,660 10,382 45,731 54,177 99,908
2008 19,832 25,912 39,423 65,335 3,154 288 3,442 22,180 38,761 60,941
2009 21,681 37,468 15,468 52,936 4,193 6,692 10,885 32,452 8,289 40,741
2010 24,094 37,394 13,719 51,113 6,350 7,213 13,563 29,990 5,801 35,791
2011 19,340 43,240 84,922 128,162 9,058 7,938 16,996 32,400 76,019 108,419
2012 n.a. 20,411 51,011 71,422 8,173 7,601 15,774 7,507 40,512 48,019
2013 n.a. 21,525 40,032 61,557 15,948 11,281 27,229 513 25,493 26,006
2014 n.a. 18,634 38,911 57,545 10,537 6,996 17,533 1,756 28,617 30,373
2015 n.a. 22,195 41,558 63,753 11,735 7,760 19,495 2,434 29,773 32,207
2016 n.a. 49,831 55,288 105,119 32,787 21,078 53,865 1,611 28,782 30,393
2017 n.a. 31,822 46,372 78,194 16,506 11,920 28,426 2,084 29,004 31,088
2018b n.a. 33,851 48,436 82,287 17,029 12,079 29,108 1,963 29,393 31,356
a Before 2012 old-age type pensions include: old-age pensions given with a retirement age
threshold allowance (early retirement), artists’ pensions, pre-pension up until 1997, miners’
pensions. From 2012 onwards the data include the recipients of allowances substituting
(abolished) early retirement pensions.
b Preliminary data.

Note: These statistics exclude data for the armed forces and police, and those for the State
Railways (MÁV) until 2008. Pensions disbursed in the given year (determined according to
the given year’s rules). The data for old age pensions include some items paid to people retir-
ing before the mandatory age. The data on 2012–2016 have been revised and may differ
from those in earlier publications. The column for ‘of which in the year of reaching the man-
datory age’ exclude people, who retired before reaching the mandatory age but expected to
reach it in the given calendar year.
Source: MÁK.
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241
Statistical data

Table 11.8: Retirement age threshold

Calendar year
2009 2011 2013 2014 II. 2015 II. 2017 I. 2018 I. 2019 2020 II. 2021 II. 2023
Birth year 2010 2012 2014 I. 2015 I. 2016 2017 II. 2018 II. 2020 I. 2021 I. 2022 2024
1948 61 62 63 64 65 66 66 67 67 68 69 69 70 70 71 72 72 73 73 74 75 76
1949 60 61 62 63 64 65 65 66 66 67 68 68 69 69 70 71 71 72 72 73 74 75
1950 59 60 61 62 63 64 64 65 65 66 67 67 68 68 69 70 70 71 71 72 73 74
1951 58 59 60 61 62 63 63 64 64 65 66 66 67 67 68 69 69 70 70 71 72 73
1952 I. 57 58 59 60 61 62 62,5 63 63,5 64 65 65,5 66 66,5 67 68 68,5 69 69,5 70 71 72
1952 II. 57 58 59 60 61 61,5 62 62,5 63 64 64,5 65 65,5 66 67 67,5 68 68,5 69 70 71 72
1953 56 57 58 59 60 61 61 62 62 63 64 64 65 65 66 67 67 68 68 69 70 71
1954 I. 55 56 57 58 59 60 60 61 61,5 62 63 63,5 64 64,5 65 66 66,5 67 67,5 68 69 70
1954 II. 55 56 57 58 59 59,5 60 60,5 61 62 62,5 63 63,5 64 65 65,5 66 66,5 67 68 69 70
1955 54 55 56 57 58 59 59 60 60 61 61 62 63 63 64 65 65 66 66 67 68 69
1956 I. 53 54 55 56 57 58 58,5 59 59,5 60 61 61,5 62 62,5 63 64 64,5 65 65,5 66 67 68
1956 II. 53 54 55 56 57 57,5 58 58,5 59 60 60,5 61 61,5 62 63 63,5 64 64,5 65 66 67 68
1957 52 53 54 55 56 57 57 58 58 59 60 60 61 61 62 63 63 64 64 65 66 67
1958 51 52 53 54 55 56 56 57 57 58 59 59 60 60 61 62 62 63 63 64 65 66
1959 50 51 52 53 54 55 55 56 56 57 58 58 59 59 60 61 61 62 62 63 64 65
1960 49 50 51 52 53 54 54 55 55 56 57 57 58 58 59 60 60 61 61 62 63 64
Those persons are entitled to receive an old age pension who are at least of the age of the old
age pension threshold indicated in the legislature – marked grey in the table – relevant to
them (uniform for men and women), who have fulfilled the required number of years of
service, and who are not insured. In the case of old age pension, the minimum service time is
15 years. The table displays the old age pension age threshold in the case of a “representative
person”. The cells show the age, based on the calendar year, of a person born in the given
year.
Women who have accumulated at least 40 accrual years are entitled to a full old age pension,
regardless of their age. Following December 31, 2011 (legislature number CLXVII/2011) no
pension can be granted prior to the old-age threshold. At the same time, the legislature con-
tinues to provide previously determined allowances under different legal titles (pre-retire-
ment age provision, service salary, allotments for miners and ballet dancers).
Prior to 2012, early retirement pensions included the following allowances : early and re-
duced-amount early retirement pensions, pensions with age preference, miner’s pension,
artist’s pension, pre-retirement age old age pension of Hungarian and EU MPs and mayors,
pre-pension, service pension of professional members of the armed forces.
Source: 1997. legislature number LXXXI.; 2011. legislature number CLXVII., http://www.
ado.hu/rovatok/tb-nyugdij/nyudijkorhatar-elotti-ellatasok.
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242
 12 The tax burden on work

Table 12.1: The mean, minimum, and maximum value

of the personal income tax rate, per cent
The personal income tax rate projected
Mean tax burden, on the gross wage
per cent
Year minimum maximum
1990 .. 0 50
1991 .. 0 50
1992 .. 0 40
1993 .. 0 40
1994 .. 0 44
1995 .. 0 44
1996 .. 20 48
1997 .. 20 42
1998 .. 20 42
1999 .. 20 40
2000 .. 20 40
2001 .. 20 40
2002 .. 20 40
2003 .. 20 40
2004 .. 18 38
2005 18.89 18 38
2006 19.03 18 36
2007 18.63 18 36
2008 18.86 18 36
2009 18.10 18 36
2010a 16.34 21.59 40.64
2011a 13.78 20.32 20.32
2012b 14.90 16 20.32
2013 .. 16 16
2014 .. 16 16
2015 .. 16 16
2016 .. 15 15
2017 .. 15 15
2018 .. 15 15
2019 .. 15 15
a In 2010 the nominal tax rate was 17% for annual incomes lower than 5,000,000 HUF. For
incomes higher than 5,000,001 HUF it was 850,000 HUF plus 32% of the amount exceeding
5,000,000 HUF. In 2011, the nominal tax rate was 16%. The joint tax base is the amount of
income appended with the tax base supplement (equal to 27%).
b In 2012 the nominal tax rate was 16%. The joint tax base is the amount of income appended

with the tax base supplement.

The amount of the tax base supplement:
– does not need to be determined for the part of the income included in the joint tax base that
does not surpass 2 million 424 thousand HUF,
– should be determined as 27% of the part of the income included in the joint tax base that is
over 2 million 424 thousand HUF.
Source: Mean tax burden: http://nav.gov.hu/nav/szolgaltatasok/adostatisztikak/szemelyi_jo-
vedelemado/szemelyijovedelemado_adostatiszika.html. Other data: http://nav.gov.hu/nav/
szolgaltatasok/adokulcsok_jarulekmertekek/adotablak.
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243
Statistical data

Table 12.2: Changes in the magnitude of the tax wedge in the case of minimum wage and the temporary work
booklet (AMK)
Minimum Total wage cost in the AMK public bur- Total wage costa, AMK tax wedge,
wage case of minimum wage Minimum dena, HUF/day HUF/day %a
gross, net, wage tax registered registered registered
gross, net,
HUF/ HUF/ HUF/month HUF/day wedge, % general unem- general unem- general unem-
HUF/day HUF/day
Year month month ployed ployed ployed
1997 17,000 783 15,045 693 26,450 1,196 43.1 500 500 1,193 1,193 41.9 41.9
1998 19,500 899 17,258 795 30,297 1,369 43.0 500 500 1,295 1,295 38.6 38.6
1999 22,500 1,037 18,188 838 34,538 1,546 47.3 500 500 1,338 1,338 37.4 37.4
2000 25,500 1,175 20,213 931 38,963 1,746 48.1 800 800 1,731 1,731 46.2 46.2
2001 40,000 1,843 30,000 1,382 58,400 2,638 48.6 1,600 1,600 2,982 2,982 53.6 53.6
2002 50,000 2,304 36,750 1,694 71,250 3,226 48.4 1,000 500 2,694 2,194 37.1 22.8
2003 50,000 2,304 42,750 1,970 70,200 3,191 39.1 1,000 500 2,970 2,470 33.7 20.2
2004 53,000 2,442 45,845 2,113 74,205 3,376 38.2 1,000 500 3,113 2,613 32.1 19.1
2005 57,000 2,627 49,305 2,272 79,295 3,572 37.8 700 500 2,972 2,772 23.6 18.0
2006 62,500 2,880 54,063 2,491 85,388 3,910 36.7 700 700 3,191 3,191 21.9 21.9
2007 65,500 3,018 53,915 2,485 89,393 4,095 39.7 700 700 3,185 3,185 22.0 22.0
2008 69,000 3,180 56,190 2,589 94,065 4,310 40.3 900 900 3,489 3,489 25.8 25.8
2009 71,500 3,295 57,815 2,664 97,403b 4,464 40.6 900 900 3,564 3,564 25.3 25.3
2010 73,500 3,387 60,236 2,776 94,448 4,352 36.2 900 900 3,676 3,676 24.5 24.5
Minimum Total wage cost in the Simplified employ- Total wage cost, Tax wedge, simpli-
wage case of minimum wage mentc, Ft/day HUF/day fied employment,%
Minimum
wage tax seasonal seasonal seasonal
gross, net, wedge, tempo- agricul- tempo- agricul- tempo- agricul-
gross, net,
HUF/ HUF/ HUF/month HUF/day % rary tural/ rary tural/ rary tural/
HUF/day HUF/day
month month work tourism work tourism work tourism
work work work
2011 78,000 3,594 60,600 2,793 100,230 4,619 39.5 1,000 500 3,793 3,293 26.4 15.2
2012 93,000 4,280 60,915 2,803 119,505 5,500 49.0 1,000 500 3,383 2,883 29.6 17.3
2013 98,000 4,510 64,190 2,954 125,930 5,795 49.0 1,000 500 3,511 3,011 28.5 16.6
2014 101,500 4,670 66,483 3,059 130,428 6,001 49.0 1,000 500 3,600 3,100 27.8 16.1
2015 105,000 4,830 68,775 3,164 134,925 6,207 49.0 1,000 500 3,689 3,189 27.1 15.7
2016 111,000 5,110 73,815 3,398 142,635 6,566 48.2 1,000 500 3,888 3,388 25.7 14.8
2017 127,500 5,870 84,788 3,904 157,463 7,543 46.2 1,000 500 4,318 3,818 23.2 13.1
2018 138,000 6,603 91,770 4,391 167,670 8,022 45.3 1,000 500 4,732 4,232 21.1 11.8
2019 149,000 7,163 99,085 4,764 180,290 8,668 45.0 1,000 500 5,049 4,549 19.8 11.0
a Wage paid at the amount in accordance with the gross daily minimum wage column and in
the case of work performed with a temporary work booklet. The basis for the comparison
with the minimum wage is the assumption that employers pay temporary workers the small-
est possible amount.
b According to regulations pertaining to the first half of 2009.
c From April 1st, 2010. the temporary work booklets and the public contribution tickets were

discontinued, these were replaced by simplified employment.

Note: The tax wedge is the quotient of the total public burden (tax and contribution) and the
total wage cost, it is calculated as: tax wedge = (total wage cost – net wage)/total wage cost.
Source: Minimum wage: 1990 –91: http://www.ksh.hu/docs/hun/xstadat/xstadat_eves/i_
qli041.html. Public contribution ticket: 1997. legislation number LXXIV. Simplified em-
ployment: 2010. Legislation number LXXV. Data for 2014 –2015: http://www.afsz.hu/en-
gine.aspx?page=allaskeresoknek_ellatasok_osszegei_es_kozterhei, http://officina.hu/
gazdasag/93-minimalber-2015, http://nav.gov.hu. Based on calculations of Ágota Scharle.
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244
 12 The tax burden on work

Table 12.3: The monthly amount of the minimum wage, the guaranteed wage minimum,
and the minimum pension, in thousands of current-year HUF
Guaranteed
Monthly amount of As a percentage Minimum
As a ratio of APW, skilled workers
the minimum wage, of mean gross pension,
% minimum wage,
HUF earnings HUF
Date HUF
1990. II. 1. 4,800 .. 40.9 – 4,300
1991. IV.1. 7,000 .. .. – 5,200
1992. I. 1. 8,000 35.8 41.4 – 5,800
1993. II. 1. 9,000 33.1 39.7 – 6,400
1994. II. 1. 10,500 30.9 37.8 – 7,367
1995. III. 1. 12,200 31.4 37.0 – 8,400
1996. II. 1. 14,500 31.0 35.8 – 9,600
1997. I. 1. 17,000 29.7 35.1 – 11,500
1998. I. 1. 19,500 28.8 34.4 – 13,700
1999. I. 1. 22,500 29.1 34.6 – 15,350
2000. I. 1. 25,500 29.1 35.0 – 16,600
2001. I. 1. 40,000 38.6 48.3 – 18,310
2002. I. 1. 50,000 40.8 54.5 – 20,100
2003. I. 1. 50,000 36.4 51.5 – 21,800
2004. I. 1. 53,000 37.2 50.7 – 23,200
2005. I. 1. 57,000 33.6 49.2 – 24,700
2006. I. 1. 62,500 36.5 52.3 68,000 25,800
2007. I. 1. 65,500 35.4 49.3 75,400 27,130
2008. I. 1. 69,000 34.7 49.5 86,300 28,500
2009. I. 1. 71,500 35.8 50.0 87,500 28,500
2010. I. I. 73,500 36.3 48.6 89,500 28,500
2011. I. I. 78,000 36.6 49.8 94,000 28,500
2012. I. I. 93,000 41.7 54.3 108,000 28,500
2013. I. I. 98,000 42.5 55.1 114,000 28,500
2014. I. I. 101,500 42.7 56.9 118,000 28,500
2015. I. I. 105,000 42.4 54.0 122,000 28,500
2016. I. I. 111,000 42.2 53.5 129,000 28,500
2017. I. I. 127,500 42.9 .. 161,000 28,500
2018. I. I. 138,000 41.8 .. 180,500 28,500
2019. I. I. 149,000 .. .. 195,000 28,500
Notes: Up to the year 1999, sectors employing unskilled labour usually received an extension
of a few months for the introduction of the new minimum wage.
The guaranteed wage minimum applies to skilled employees, the minimum wage and the
skilled workers minimum wage are gross amounts.
The minimum wage is exempt from the personal income tax from September 2002. This policy
resulted in a 15.9% increase in the net minimum wage.
APW: mean wage of workers in the processing industry, based on the NFSZ BT. In 1990, the
data is the previous year’s data, indexed (since there was no NFSZ BT conducted in 1990).
Source: Minimum wage: 1990–91: http://www.mszosz.hu/files/1/64/345.pdf, 1992–: CSO.
Guaranteed wage minimum: http://www.nav.gov.hu/nav/szolgaltatasok/adokulcsok_jaru­
lekmertekek/minimalber_garantalt. Minimum pension: http://www.ksh.hu/docs/hun/
xtabla/nyugdij/tablny11_03.html. APW: NFSZ BT.
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Statistical data

Table 12.4: The tax burden on work as a ratio of tax revenue and earnings
Tax burden on work
Implicit Tax wedge on 67% Tax wedge on the
as a ratio of
tax rateb level of mean earnings minimum wagec
Year tax revenuea, %
1990 .. .. 38.2
1991 52.4 .. .. 40.4
1992 54.8 .. .. 40.9
1993 54.4 .. .. 42.3
1994 53.7 .. .. 41.2
1995 52.1 42.3 .. 44.2
1996 52.5 42.1 .. 41.8
1997 54.2 42.5 .. 43.1
1998 53.1 41.8 .. 43.0
1999 51.5 41.9 .. 47.3
2000 48.7 41.4 51.4 48.1
2001 49.8 40.9 50.9 48.6
2002 50.3 41.2 48.2 48.4
2003 48.7 40.0 44.6 39.1
2004 47.5 39.1 44.8 38.2
2005 48.6 37.6 43.1 37.8
2006 48.8 38.2 43.3 36.7
2007 49.3 40.6 46.1 39.7
2008 51.0 41.9 46.8 40.3
2009 47.9 39.9 46.2 40.6d
2010 46.7 38.1 43.8 36.2
2011 46.8 37.9 45.2 39.5
2012 46.0 39.2 47.9 49.0
2013 45.7 39.0 49.0 49.0
2014 45.3 39.6 49.0 49.0
2015 45.0 41.6 49.0 49.0
2016 45.6 40.9 48.3 48.3
2017 45.3 39.5 46.2 46.2
2018 .. .. 45.0 45.0
a Tax burden on work and contributions as a ratio of tax revenue from all tax forms.
b The implicit tax rate is the quotient of the revenue from taxes and contributions pertaining
to work and the income derived from work.
c The tax wedge is the quotient of the total public burden (tax and contribution) and the total

wage cost, it is calculated as: tax wedge = (total wage cost – net wage)/total wage cost.
d The tax wedge of the minimum wage is the 2009 annual mean (the contributions decreased

in June).
Source: 1991–1995: estimate of Ágota Scharle based on Ministry of Finance (PM) balance
sheet data. 1996–2002: http://ec.europa.eu/taxation_customs/taxation/gen_info/econom-
ic_analysis/tax_structures/index_en.htm. 2003 –: https://ec.europa.eu/taxation_customs/
business/economic-analysis-taxation/data-taxation_en, Eurostat online database. Implicit
tax rate: Eurostat online database (gov_a_tax_itr). 2003 –: https://ec.europa.eu/taxation_
customs/business/economic-analysis-taxation/data-taxation_en. Tax wedge on the 67 per-
cent level of the mean wage: OECD: Taxing wages 2010, Paris 2011, OECD Tax Statisctics/
Taxing wages/ Comparative tables. Tax wedge at the level of the minimum wage: calcula-
tions of Ágota Scharle.
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246
 13 International comparison

Table 13.1: Employment and unemployment rate of population aged 15–64

by gender in the EU, 2018
Employment rate Unemployment rate
Country males females together males females together
Austria 80.7 71.7 76.2 4.9 4.5 4.7
Belgium 73.9 65.5 69.7 6.2 5.4 5.8
Bulgaria 76.5 68.3 72.4 5.6 4.6 5.1
Cyprus 79.3 68.9 73.9 8.1 8.7 8.4
Czech Republic 87.4 72.2 79.9 1.7 2.8 2.2
Denmark 81.5 74.8 78.2 4.4 4.9 4.7
United Kingdom 83.7 73.8 78.7 3.6 3.5 3.6
Estonia 83.4 75.6 79.5 5.1 5.2 5.1
Finland 78.2 74.5 76.3 7.0 6.7 6.8
France 75.2 67.6 71.3 8.8 8.9 8.8
Greece 70.1 49.1 59.5 15.3 24.2 19.3
Netherlands 84.3 74.2 79.2 3.3 3.5 3.4
Croatia 70.3 60.1 65.2 7.4 9.0 8.2
Ireland 80.3 68.1 74.1 5.5 5.2 5.4
Poland 79.4 65.0 72.2 3.8 3.8 3.8
Latvia 79.0 74.8 76.8 8.5 6.6 7.5
Lithuania 79.0 76.7 77.8 7.0 5.6 6.3
Luxembourg 76.0 68.0 72.1 5.0 5.6 5.3
Hungary 82.1 66.8 74.4 3.3 3.9 3.6
Malta 85.7 63.4 75.0 3.5 3.2 3.4
Germany 83.9 75.8 79.9 3.8 2.9 3.3
Italy 72.9 53.1 63.0 9.7 11.6 10.5
Portugal 78.9 72.1 75.4 6.5 7.4 7.0
Romania 78.9 60.6 69.9 4.5 3.4 4.0
Spain 73.1 61.0 67.0 13.4 16.8 14.9
Sweden 84.7 80.4 82.6 5.8 5.6 5.7
Slovakia 79.2 65.5 72.4 5.9 6.9 6.4
Slovenia 79.0 71.7 75.4 4.6 5.8 5.1
EU-28 78.9 67.4 73.1 6.5 7.0 6.7
Source: Eurostat http://epp.eurostat.ec.europa.eu.
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Statistical data

Table 13.2: Employment composition of the countries in the EUa, 2018

Self Part Fixed term Market Non market
Agriculture Industry
Country employedb time contract services servicesc
Austria 10.4 27.3 8.1 3.3 25.7 38.6 32.5
Belgium 12.7 24.5 9.3 0.9 21.2 34.8 43.1
Bulgaria 10.6 1.8 3.6 6.4 30.4 38.1 25.1
Cyprus 11.7 10.8 12.2 1.7 16.7 48.7 32.8
Czech Republic 16.0 6.3 7.0 2.8 38.0 32.6 26.6
Denmark 7.2 24.8 10.3 2.1 18.7 37.8 41.4
United Kingdom 13.8 24.6 4.7 0.9 18.2 40.2 40.7
Estonia 10.4 11.1 3.1 3.2 30.3 37.5 29.0
Finland 11.6 15.1 14.2 3.3 22.5 35.2 39.0
France 11.0 18.0 14.8 2.4 20.4 36.0 41.1
Greece 29.1 9.1 7.6 11.7 15.4 43.1 29.9
Netherlands 15.4 50.1 17.8 2.0 16.3 41.3 40.5
Croatia 10.2 5.2 17.6 5.6 27.7 38.5 28.2
Ireland 12.9 19.5 8.6 3.8 19.1 41.9 35.1
Poland 17.4 6.4 19.5 9.4 32.1 32.4 26.0
Latvia 11.0 7.2 2.4 7.0 24.0 39.8 29.2
Lithuania 10.8 7.1 1.4 7.0 26.0 36.7 30.3
Luxembourg 7.5 17.7 8.9 1.0 11.7 42.0 45.3
Hungary 9.7 4.2 6.5 4.8 32.6 31.4 31.3
Malta 13.6 13.3 6.7 0.9 18.7 41.4 39.0
Germany 8.8 26.8 11.5 1.2 27.6 34.9 36.3
Italy 20.6 18.4 13.4 3.6 26.4 37.7 32.4
Portugal 13.1 8.1 19.0 3.8 25.7 35.9 34.6
Romania 15.5 6.5 0.9 19.8 31.1 29.8 19.4
Spain 15.2 14.5 22.7 4.2 20.4 40.6 34.9
Sweden 8.4 22.7 14.3 1.5 18.3 36.6 43.6
Slovakia 14.6 4.9 6.9 2.3 36.8 31.7 29.3
Slovenia 12.1 9.7 13.5 4.4 33.8 33.8 28.0
EU-28 13.5 19.2 12.1 3.7 24.3 36.7 35.2
a Per cent of employment, except for employees with fixed-term contracts: per cent of employ-
ees.
b Includes the members of cooperatives and business partnerships.
c One-digit industries O –U.

Source: Eurostat (Newcronos) Labour Force Survey.

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248
 13 International comparison

Table 13.3: The ration of vacancies, IV. quarter, 2018

Country Vacancy rate Country Vacancy rate
Bulgaria 0.9 Finland 2.2
Portugal 0.9 Norway 2.3
Poland 1.2 Latvia 2.4
Slovakia 1.2 Sweden 2.5
Romania 1.3 Slovenia 2.5
Lithuania 1.5 Hungary 2.7
North Macedonia 1.6 Netherlands 2.9
Luxembourg 1.7 Germany 3.1
Croatia 1.7 Czech Republic 5.7
Estonia 1.9
Source: Eurostat. http://ec.europa.eu/eurostat/web/labour-market/job-vacancies/database
(jvs_q_nace2: 2019.08.20. version, donwnloaded: 2019.08.24.)
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249
Statistical data

DESCRIPTION OF THE MAIN DATA SOURCES

The data have two main sources in terms of which of- Work providing income includes all activities that:
fice gathered them: the regular institutional and pop- – result in monetary income, payment in kind, or
ulation surveys of the Hungarian Central Statistical – that were carried out in the hopes of income realized
Office (CSO, in Hungarian: Központi Statisztikai Hi- in the future, or
vatal, KSH), and the register and surveys of the Na- – were performed without payment in a family business
tional Employment Service (in Hungarian: Nemzeti or on a farm (i.e. unpaid family workers).
Foglalkoztatási Szolgálat, NFSZ). From the survey’s point of view the activities below
MAIN DATA SOURCES OF THE KSH
are not considered as work:
– work done without payment for another household or
Labour Force Survey – KSH MEF institution (voluntary work),
The KSH has been conducting a new statistical survey – building or renovating of an own house or flat, intern-
since January 1992 to obtain ongoing information on ships tied to education (not even if it is compensated),
the labour force status of the Hungarian population. – housework, including work in the garden. Work on a
The MEF is a household survey which provides quar- person’s own land is only considered to generate in-
terly information on the non-institutional population come if the results are sold in the market, not pro-
aged 15–74. The aim of the survey is to observe employ- duced for self-consumption.
ment and unemployment according to international Persons on child-care leave are classified – based on
statistical recommendations based on the concepts and the 1995 ILO recommendations for transitional coun-
definitions recommended by the International Labour tries determined in Prague – according to their activity
Organization (ILO), independently from existing na- during the survey week.
tional labour regulations or their changes. Since, according to the system of national account-
In international practice, the labour force survey is ing, defense activity contributes to the national prod-
a widely used statistical tool to provide simultaneous, uct, conscripts are generally considered as economi-
comprehensive, and systematic monitoring of employ- cally active persons, any exceptions are marked in the
ment, unemployment, and underemployment. The sur- footnotes of the table. The data regarding the number
vey techniques minimise the subjective bias in classi- of conscripts comes from administrative sources. (The
fication (since people surveyed are classified by strict retrospective time-series based on CSO data exclude
criteria), and provide freedom to also consider national conscripted soldiers. This adjustment affects the data
characteristics. until 2003, when military conscription was abolished.)
In the MEF, the surveyed population is divided into Unemployed persons are persons aged 15–74 who:
two main groups according to the economic activity – were without work, i.e. neither had a job nor were at
performed by them during the reference week (up to work (for one hour or more) in paid employment or
the year 2003, this was always on the week contain- self-employment during the reference week,
ing the 12th of the month): economically active per- – had actively looked for work at any time in the four
sons (labour force), and economically inactive persons. weeks up to the end of the reference week,
The group of economically active persons consists of – were available for work within two weeks following
those in the labour market either as employed or unem- the reference week if they found an appropriate job.
ployed persons during the reference week. Those who do not have a job, but are waiting to start
The definitions used in the survey follow ILO rec- a new job within 30 days (since 2003 within 90 days)
ommendations. According to these, those designated make up a special group of the unemployed.
employed are persons who, during the reference week Active job search includes: contacting a public or
worked one hour or more earning some form of income, private employment office to find a job, applying to an
or had a job from which they were only temporarily ab- employer directly, inserting, reading, answering adver-
sent (on leave, illness, etc.). tisements, asking friends, relatives or other methods.

250
 14 Description of the main data sources

The labour force (i.e. economically active population) Institution-Based Labour Statistics – KSH IMS
comprises employed and unemployed persons. The source of the earnings data is the monthly (annual)
Persons are defined economically inactive (i.e. not institutional labour statistical survey. The sample frame
in the labour force) if they were neither employed in covers enterprises with at least 5 employees, and public
regular, income-earning jobs, nor searching for a job, and social insurance and non-profit institutions irre-
or, if they had searched, had not yet started work. Pas- spective of the staff numbers of employees.
sive unemployed are included here – those who would The earnings data relate to the full-time employees
like a job, but have given up any active search for work, on every occasion. The potential elements of the pre-
because they do not believe that they have a chance of vailing monthly average earnings are: base wage, al-
finding any. lowances (including the miner’s loyalty bonus, and the
The Labour Force Survey is based on a multi-stage Széchenyi and Professor’s scholarships), supplementary
stratified sample design. The sample design strata were payments, bonuses, premiums, and wages and salaries
defined in terms of geographic units, size categories for the 13th and further months.
of settlements and area types such as city centres, out- Net average earnings are calculated by deducting
skirts, etc. The sample has a simple rotation pattern: any from the institution’s gross average earnings the em-
household entering the sample at some time is expected ployer’s contributions, the personal income tax, ac-
to provide labour market information at six consecu- cording to the actual rates (i.e. taking into account the
tive quarters, then leaves the sample forever. The quar- threshold concerning the social security contributions
terly sample is made up of three monthly sub-samples. and employee deductions). The personal income tax is
In each sampled dwelling, labour market information calculated based on the actual withholding rate applied
is collected from each household and each person aged by the employers when disbursing monthly earnings
15–74 living there. The number of addresses selected for in the given year.
the sample in a quarter is about 38 thousand. The size and direction of the difference between the
Grossing up of LFS data has been carried out monthly gross and the net (after-tax) income indexes depends
on the basis of the population number of the last Census on actual annual changes in the tax table (tax brack-
corrected with the extrapolated population numbers. ets) and in the tax allowances. Thus the actual size of
Estimated totals or levels based on the LFS sample are the differences are also influenced by the share of indi-
computed by inflating and summing the observations viduals at given firms that fall outside the bracket for
by suitable sample weights. The weights to the estima- employee allowances.
tion are made in two steps. First the primary weights The indexes pertain to the comparable sample, tak-
are calculated for the 275 strata of the sample, then ing changes in the definitions, and of the sample frame
these weights need to be adjusted for non-response by into account. The KSH traditionally publishes the main
updated census counts in cross-classes defined by age, average index as the earnings growth measure. Thus
sex and geographic units. In the correction procedure the indicator of change in earnings reflects both the
the further calculated population and dwelling num- changes in the number of observations and the actual
bers have a key role. earnings changes simultaneously. The change of net
Since 2003, the weights used to make the sample real earnings is calculated from the ratio of net income
representative are based on the 2001 census popula- index and the consumer price index in the same period.
tion record base. At the same time, the 2001–2002 data Non-manual workers are persons with occupa-
was recalculated and replaced as well. The LFS-based tions classified by the standardized occupational code
time series published in this volume use the following (FEOR) in major groups 1–4., manual workers are per-
weighting schemes: (i) in 1992 –1997 the weights are sons with occupations classified in major groups 5–9.
based on the 1990 Census (ii) in 1998 –2001 the weights
KSH Job vacancy statistics
based on the 1990 Census have been corrected using
data of the 2001 Census (iii) in 2002 –2005 the weights The Job Vacancies Survey is a firm-based survey of
are based on the 2001 Census (iv) from 2006 onwards quarterly frequency. The survey covers all corporations
the weights based on the 2001 Census have been cor- with more than 49 employees. Businesses with 5–49
rected using the 2011 Census. Due to correction, the employees are randomly sampled. Budgetary institu-
LFS statistics published earlier were modified. tions and non-profit ones with more than two employ-

251
Statistical data

ees are observed on a full-scope basis. In line with EU task they performed changed as well. The new name
recommendations, newly created, unfilled positions of special vocational schools is vocational school and
are those which are unfilled or about to become vacant special skills development school, the name of ear-
within 3 months, provided that the employer takes ac- lier vocational schools became secondary vocational
tive steps to find a suitable candidate for the job, and school and that of earlier secondary vocational schools
is in the position to fill the job. became vocational grammar school. In the new voca-
tional schools pupils with special educational need who
KSH Strike statistics
are unable to make progress with the other pupils are
The CSO data cover strikes with at least 10 participants prepared for vocational examinations; the special skills
and token strikes lasting for at least 2 hours. development schools provide preparation for SEN-stu-
dents with moderate disability for commencing inde-
Labour Force Accounting Census – KSH MEM
pendent life or the learning of work processes requiring
Before the publication of the MEF, the annual MEM simple training, which enable employment. In the new
gave an account of the total labour force in the time system secondary vocational schools students aquire
period between the two censuses. a vocational qualification during the first 3 years, af-
The MEM, as its name shows, is a balance-like ac- ter which they have the opportunity to complete two
count that compares the labour supply (human resourc- further years preparing for a final examination at sec-
es) to the labour demand at an ideal moment (1 Janu- ondary level then they can pass a maturity examina-
ary). Population is taken into account by economic tion. After completing the first four years of vocational
activity, with a differentiation between statistical data grammar schools, students pass a vocational grammar
of those of working age and the population outside of school-leaving examination, during an additional year
the working age. Source of data: Annual labour survey students prepare for the vocational examination. There
on employment since 1992 of enterprises and of all gov- was no change in the case of secondary general schools.
ernment institutions, labour force survey, census, na- The category of secondary school preparing students
tional healthcare records, social security records, and for final examination at secondary level (maturity ex-
company registry. Data on unemployment comes from amination) has changed. Earlier the secondary general
the registration system of the NFSZ. school and the secondary vocational school belonged
in this category, in the new system the secondary vo-
Source of educational data
cational school, the secondary general school and the
Data on educational institutions are collected and pro- vocational grammar school together are meant by it.
cessed by the Ministry of Human Capacities (or the at As a result, some of the education time series can no
all times ministry responsible for education). Data sur- longer be resumed in their earlier forms.
veys relating to education have undergone changes both till 2015/16 school year from 2016/17 school year
in content and in methodology since the 2000/2001 Vocational school
school-year (the paper-based questionnaires were re- Special
vocational school and special skills
placed by the electronic data collection system, which development school
in the year of transition temporarily has resulted in Vocational Secondary
lower reliability data); they follow the structural and school vocational school
secondary school

activity system laid down by Acts LXXIX. and LXXX.

secondary school

of 1993 on education. The observed units of the data Secondary Secondary

survey are the educational institutions, and the ac- general school general school
tivities and educational tasks within them. Since the
2000/2001 school-year October 1st and October 15th Secondary Vocational
vocational school grammar school
of every year was designated as the nominal date of the
data survey (before 2000 it was a similar date, which Former and current scheme of secondary education:
nevertheless varied by school-types).
In the 2016/2017 school year significant transfor- Other data sources
mations started in secondary education. In addition Census data were used for the estimation of the employ-
to changing the name of vocational institutions, the ment data in 1980 and 1990. The aggregate economic

252
 14 Description of the main data sources

data are based on national account statistics, the con- cial data of jobseeking benefits (for example, average
sumer’s and producer’s price statistics and industrial monthly amount, average support paid for the num-
surveys. A detailed description of the data sources are ber of participants on the closing date, for exiters, and
to be found in the relevant publications of the KSH. those who found placement).
MAIN NFSZ DATA SOURCES
The jobseeking benefit register can also monitor the
average duration of the period of benefit allocation and
Unemployment (Jobseekers’) Register Database the average monthly amount of the benefits allocated.
– NFSZ-REG For the period between 1991 and 1996, the register
The other main source of unemployment data in Hun- also contains the stock and flow data of the recipients
gary – and in most of the developed countries – is the of new entrant’s unemployment benefit. Between 1997–
huge database containing so called administrative re- 2005, the system also contained the recipients of pre-
cords which are collected monthly and include the in- retirement unemployment benefit.
dividual data of the registered unemployed/jobseekers. Jobseeking allowance recipients: from September 1,
The register actually includes all jobseekers, but from 2011 the conditions for determining and disbursing the
these, at a given point of time, only those are regarded jobseeking allowance changed. The two phases of the
as registered unemployed/jobseekers, who: jobseeking allowance were discontinued and the peri-
– had themselves registered with a local office of the od of entitlement decreased from 270 days to 90 days.
NFSZ as unemployed/jobseekers (i. e. he/she has no Jobseekers needed to have at least 360 days of work-
job but wishes to work, for which they seek assistance time counting towards entitlement in the 5 years prior
from the labour market organisation); to becoming a jobseeker (prior to September 1, 2011,
– at the time of the examination (on the final day of any this was 365 days in the previous 4 years). Its amount
month), the person is not a pensioner or a full-time is 60% of the allowance base, but the maximum is the
student, does not receive any rehabilitation provision amount of the smallest mandatory wage on the first day
or benefit, and is ready to co-operate with the local of the entitlement (allowance base: the monthly aver-
employment office in order to become employed (i. e. age amount from the four calendar quarters preceding
he/she accepts the suitable job or training offered to the submission of the application).
him/her, and keeps the appointments made with the Jobseeking assistance recipients: from September 1,
local employment office’s placement officer/counsel- 2011 the conditions for determining and disbursing the
lor/benefit administrator). jobseeking assistance changed. The “a” and “b” type of
If a person included in the register is working under benefit were discontinued, jobseekers can still request
any subsidised employment programme on the clos- the “c” type of benefit under the title of pre-retirement
ing day, or is a participant of a labour market training jobseeking benefit, but the period of entitlement (and
programme, her/his unemployed/jobseeker status is depletion) of at least 140 days decreased to 90 days.
suspended. Regular social assistance recipients: those from
If the client is not willing to co-operate with the lo- among the regular registered jobseekers who are of
cal office, he/she is removed from the register of the active age and are in a disadvantaged labour market po-
unemployed/ jobseekers. sition, and who receive social assistance to complement
The data – i. e. the administrative records of the reg- or substitute their income. From January 1, 2009, those
ister – allow not only for the identification of date-re- receiving regular social assistance were included in two
lated stock data, but also for monitoring flows, inflows categories: regular social assistance recipients, and re-
as well as outflows, within a period. cipients of on call support. This support was replaced by
The database contains the number of decrees per- a new type of assistance, the wage replacement support
taining to the removal or suspension of jobseeking from January 1, 2011, then from September 1, 2011, the
benefits, the number of those receiving monetary name was changed to employment substitution support.
support based on accounting items, support transac- (Legislation III. of 1993 pertaining to social manage-
tions, the exact date of entry and exit and the reason ment and social assistance).
for the exit (for example, job placement, the end of Based on the records of labour demand needs re-
entitlement, disqualification, entry into a subsidized ported to the NFSZ, the stock and flow data of vacan-
employment programme, etc.), as well as the finan- cies are also processed and published for each month.

253
Statistical data

Furthermore, detailed monthly statistics of partici- are likely to occur in the short term, and to effectively
pation in the different active programmes, number of meet the ever-changing needs of their clients.
participants, and their inflows and outflows are also The forecast is only one of the outputs of the survey.
prepared based on the assistance disbursed. Further very important “by-products” include regular
The very detailed monthly statistics – in a breakdown and personal liaison with companies, the upgraded
by country, region, county, local employment office ser- skills of the placement officers and other administra-
vice delivery area and community – build on the sec- tive personnel, enhanced awareness of the local circum-
ondary processing of administrative records that are stances, and the adequate orientation of labour market
generated virtually as the rather important and useful training programmes in view of the needs identified
“by-products” of the accomplishment of the NFSZ’s by the surveys.
main functions (such as placement services, payment The prognosis surveys are occasionally supplemented
of benefits, active programme support, etc.). by supplementary questions and sets of questions to ob-
The NFSZ (and its predecessors, i. e. NMH, OMK – tain some further useful information that can be used
National Labour Centre, OMMK and OMKMK) has by researchers and the decision-makers of employment
published the key figures of these statistics on a month- and education/ training policy.
ly basis since 1989. The denominators of the unemploy- From 2005, the surveys are conducted in cooperation
ment rates calculated for the registered unemployed/ with the Institute for Analyses of the Economy and En-
jobseekers are the economically active population data trepreneurship of the Hungarian Chamber of Industry
published by the KSH MEM. and Commerce (in Hungarian: Magyar Kereskedelmi
The figures of the number of registered unemployed/ és Iparkamara Gazdaság- és Vállalkozáskutató Intézet,
jobseekers and the registered unemployment rate are MKIK GVI), with one additional benefit being that
obviously different from the figures based on the KSH with the help of the surveyors of the Institute, the sam-
MEF. It is mainly the different conceptual approach, ple size has increased to nearly 8,000.
definition, and the fundamentally different monitor-
Wage Survey Database – NFSZ BT
ing/measuring methods that account for this variance.
The NFSZ (and its legal predecessors) has conducted
Short-Term Labour Market Projection Surveys
since 1992, once a year, a representative survey with a
– NFSZ PROG
huge sample size to investigate individual wages and
At the initiative and under the coordination of the earnings, at the request of the Ministry of National
NFSZ (and its legal predecessors), the NFSZ PROG Economy (and its legal predecessors).
has been conducted since 1991, twice a year, in March The reference month of data collection is the month
and September, by interviewing over 7,500 employers. of May in each year, but for the calculation of the
Since 2004 the survey is conducted once a year, in the monthly average of irregularly paid benefits (beyond
month of September. the base wage/salary), 1/12th of the total amount of
The interviews focus on the companies’ projections such benefits received during the previous year is used.
of their material and financial processes, their devel- In the competitive sector, the data collection only
opment and human resource plans, and they are also covered initially companies of over 20 persons; it was
asked about their concrete lay-off or recruitment plans, incumbent on all companies to provide information,
as well as their expected need for any active labour mar- but the sample includes only employees born on cer-
ket programmes. tain dates in any month of any year.
The surveys are processed from bottom up, from the Data collection has also covered companies of 10–19
service delivery areas, through counties, to the whole since 1995, and companies of 5–9 have been covered
country, providing useful information at all levels for since 2000, where the companies actually involved in
the planning activities of the NFSZ. data collection are selected at random (ca. 20 per cent),
The survey provides an opportunity and possibility and the selected ones have to provide information about
for the regions, the counties and Budapest to analyse all of their full-time employees.
in greater depth (also using information from other Data on basic wages and earnings structure can only
sources) the major trends in their respective labour mar- be retrieved from these surveys in Hungary, thus it is,
kets, to make preparations for tackling problems that in practice, these huge, annually generated databases

254
 14 Description of the main data sources

that can serve as the basis of the wage reconciliation – model calculations to determine the expected impact
negotiations conducted by the social partners. of the rise of the minimum wage;
In the budgetary sector, all budgetary institutions – analyses to meet the needs of the Wage Policy Depart-
provide information, regardless of their size, in such a ment, Ministry of National Resources, for the analy-
way that the decisive majority of the local budgetary sis and presentation of wage ratios;
institutions – the ones that are included in the TAKEH – analyses for the four volume statistical yearbook (to-
central payroll accounting system – provide fully com- tal national economy, competitive sector, budgetary
prehensive information, and the remaining budgetary sector, and regional volumes).
institutions provide information only about their em- The entire database is adopted every year by the KSH,
ployees who were born on certain days (regarded as which enables the Office to also provide data for certain
the sample). international organisations, (e. g. ILO and OECD). The
Data has only been collected on the professional NGM earlier the NMH also regularly provides special
members of the armed forces since 1999. analyses for the OECD.
Prior to 1992, such data collection took place in every The database containing the data by individual al-
third year, thus we are in possession of an enormous lows for a) the analysis of data for groups of people de-
database for the years of 1983, 1986 and also 1989. termined by any combination of pre-set criteria, b) the
Of the employees included in the sample, the follow- comparison of basic wages and earnings, with special
ing data are available: regard to the composition of the different groups ana-
– the sector the employer operates in, headcount, em- lysed, as well as c) the analysis of the dispersion of the
ployer’s local unit, type of entity, ownership struc- basic wages and earnings.
ture; Since 2002, the survey of individual wages and earn-
– employee’s wage category, job occupation, gender, age, ings was substantially developed to fulfill all require-
educational background. ments of the EU, so from this time on it serves also for
Based on the huge databases which include the data the purposes of the Structure of Earnings Survey (SES),
by individual, the data is analysed every year in the which is obligatory for each member state in every fourth
following ways: year. One important element of the changes was the in-
– standard data analysis, as agreed upon by the social clusion of part-time employees in the sample since 2002.
partners, used for wage reconciliation negotiations SES 2002 was the first, and recently the databases
(which is received by every confederation participat- of SES 2006 and 2010 were also sent to the Eurostat in
ing in the negotiations); anonymized form in accordance with EU regulations.

255
 index of tables and figures

INDEX OF TABLES AND FIGURES

Tables vocational secondary schools or Table 5.5.2: The number of youth

The Hungarian Labour Market vocational schools, among those between the ages of 16–19 entering
Table 1: Employee groups according continuing their education at a sec- public employment for the first time,
to their secondary status, 2018 ..... 20 ondary level, by vocational groups, and their share within the group of
Table 2: Changes in the number of 2007, 2017 .......................................... 61 all first entrants, 2011–2017 ......... 117
employees broken down by major Table 2.4.1: The effect of the vocation- Table 5.5.3: The share of those among
characteristics ................................... 21 al school reform on test score change the 16–19-year-olds registering in
Table 3: Employment rate of the between the 8th and 10th grade ..... 67 the public work scheme for the first
population aged 20–64, broken Table 2.5.1: The effect of the time who entered public employment
down by educational attainment school-leaving age reform on the within 90 days, 2011–2017 ............ 117
and gender, excluding public works status “not attending school and Table 5.5.4: The share of those among
participants, 2014, 2017, 2018 ....... 23 lacking a qualification” and on ac- youth entering public employment
Table 4: Employment rate of the quiring an upper-secondary qualifi- for the first time who, before doing
Roma and non-Roma population, cation, marginal effects .................. 75 so, spent not more than 30 days in
aged 20–64, broken down by educa- Table 2.5.2: The impact of the the unemployment registry, 2012–
tional attainment, 2017, 2018 ....... 24 school-leaving age reform on not 2017 .................................................. 118
Table 5: The number of employees attending school and lacking a qual- Table 5.5.5: The total distribution of
and the potential labour reserve .... 27 ification in two age groups ............ 76 educational attainment at the time of
Table 6: Net and real wages taking Table 3.3.1: Share of fixed-term entry into public employment in the
into account the family tax benefit, contracts in the year of graduation period between 2011 and 2016 ..... 119
2018 ..................................................... 31 among entrants ................................. 87 Table 5.5.6: The share of those among
In Focus Table 4.1.1: The effects of early-stage youth entering public employment
Table 1.1: New entrants’ monthly unemployment on wages ................ 92 with an educational attainment
gross real wages in the business sec- Table 4.2.1: Characteristics of young of the eighth grade of elementary
tor, by education, gender and work men by categories based on time school who also attended secondary
experience, 2012–2016 ................... 42 spent as registered jobseekers or in school, broken down by the year of
Tables 1.2: Decomposing the employ- public works in the two calendar entry, 2011–2017 ........................... 119
ment rate of the population aged years after graduation ..................... 96 Table 5.5.7: The share of those among
16–29, 2002–2017 ........................... 43 Table 4.2.2: The relationship between youth entering public employment
Table 1.3: Decomposing the NEET- unemployment in the first two years with an educational attainment of the
rate of the population aged 15–29, of the career and the labour market 8th grade of elementary school and
2002–2017 ........................................ 43 outcomes in the fifth year after fin- having started secondary school who
Table 2.1.1: The association between test ishing secondary school, finished obtained secondary-level qualification
scores in Grade 10 and the logarithm secondary school in 2011–2012 .... 97 within two years, 2011–2015 .......... 120
of earnings in young adulthood ....... 48 Table 5.1.1: Correlation of various Table 6.1.1: The 16–25 year-old popu-
Table 2.1.2: The impact of test scores factors with the time until exit from lation by ethnicity and labour mar-
in Grade 10 on the probability of un- NEET status to employment and to ket status, 2011 and 2016 ............. 123
employment in young adulthood ... 49 education, 2015–2018 ................... 101 Table 6.2.1: The impact of gender,
Table 2.1.3: The association between Table 5.1.2: Relationship between ethnic group and neighbourhood
test scores in grade 10 and the log- motivational factors and registration characteristics on participation in
arithm of earnings in young adult- with time until exit from NEET sta- education, 2011, 2016 ................... 127
hood by school attainment ............ 50 tus to employment, 2015–2018 .... 102 Table 6.2.2: The effect of gender, eth-
Table 2.1.4: The association between Table 5.2.1: Regression estimate of nicity and certain neighbourhood
test scores in Grade 10 and labour the factors determining the entry characteristics on NEET status (not
market status in young adulthood of registered jobseekers under the in education, employment or train-
by school attainment ...................... 50 age of 25 into the Youth Guarantee ing), 2011, 2016 .............................. 129
Table 2.2.1: Regression estimates Programme ..................................... 108 Table 7.1.1: The determinants of the
of the effect of school tracks on Table 5.4.1: The percentage of 16–29 probability of non-formal training
student performance in Grade 10; and 30–64 year-olds employed in among 16–34-year-olds, 2018 ..... 133
students in Grade 8 in 2014 ........... 56 the private sector who earn around Table 8.1.1: Factors influencing the
Table 2.2.2: Regression estimates of the the minimum wage or the guaran- probability of occupational change,
effects of school tracks on the proba- teed minimum wage ..................... 113 binary outcome probit estimates
bility of high and low student perfor- Table 5.5.1: The number of youth be- (changes occupation: yes/ no) ...... 147
mance; students in Grade 8 in 2014 .. 57 tween the ages of 16–19 registering Table 8.1.2: Factors influencing the
Table 2.3.1: The percentage of as unemployed for the first time, direction of occupational change,
those continuing their studies in 2011–2017 ....................................... 116 multinomial logit estimates ........ 148

257
The hungarian labour market

policy tools size, per cent ................................... 188 recipients and unemployment as-
Table A1: Expenditures and revenues Table 4.12: Employees of the compe- sistance recipients by educational
of the employment policy section of titive sector by the share of foreign attainment ...................................... 205
the national budget, 2013–2019 .. 165 ownership, per cent ....................... 188 Table 5.17: Outflow from the Register
Statistical Data Table 4.13: Employment rate of po- of Beneficiaries .............................. 206
Table 1.1: Basic economic pulation aged 15–74 by age group, Table 5.18: The distribution of the to-
indicators ........................................ 169 males, per cent ............................... 189 tal number of labour market train-
Table 2.1: Population ........................ 171 Table 4.14: Employment rate of po- ing participants .............................. 206
Table 2.2: Population by age groups, pulation aged 15–74 by age group, Table 5.19: Employment ratio of
in thousands .................................... 171 females, per cent ............................ 189 participants ALMPs by gender, age
Table 2.3: Male population by age Table 4.15: Employment rate of popu- groups and educational attainment
groups, in thousands .................... 173 lation aged 15–64 by level of educa- for the programmes finished in
Table 2.4: Female population by age tion, males, per cent ...................... 190 2018, per cent ................................. 207
groups, in thousands .................... 173 Table 4.16: Employment rate of popu- Table 5.20: Distribution of the aver-
Table 3.1: Labour force participation lation aged 15–64 by level of educa- age annual number of those with no
of the population over 14 years ... 174 tion, females, per cent .................... 191 employment status who participate
Table 3.2: Labour force participation Table 5.1: Unemployment rate by gen- in training categorised by the type
of the population over 14 years, der and share of long term unemp- of training, percentage ................. 207
males, in thousands ...................... 175 loyed, per cent ................................ 192 Table 5.21: The distribution of those
Table 3.3: Labour force participation Table 5.2: Unemployment rate by le- entering training programmes by age
of the population over 14 years, fe- vel of education, males ................. 193 groups and educational level ........ 208
males, in thousands ...................... 176 Table 5.3: Composition of the unemp- Table 6.1: Annual changes of gross
Table 3.4: Labour force participation loyed by level of education, males, and real earnings ........................... 209
of the population over 14 years ... 177 per cent ............................................ 193 Table 6.2.a: Gross earnings ratios in
Table 3.5: Labour force participation Table 5.4: Unemployment rate by le- the economy ................................... 210
of the population over 14 years, vel of education, females .............. 194 Table 6.2.b: Gross earnings ratios in
males, per cent ............................... 178 Table 5.5: Composition of the unem- the economy, per cent .................... 211
Table 3.6: Labour force participation ployed by level of education, females, Table 6.3: Regression-adjusted earn-
of the population over 14 years, fe- per cent ............................................ 194 ings differentials ............................ 212
males, per cent ............................... 179 Table 5.6: The number of unemployed Table 6.4: Percentage of low paid wor-
Table 3.7: Population aged 15–64 by by duration of job search ............. 196 kers by gender, age groups, level of
labour market status (self-categori- Table 5.7: Registered unemployed and education and industries ............. 213
sed), in thousands .......................... 180 LFS unemployment ....................... 198 Table 7.1: Graduates in full-time edu-
Table 3.8: Population aged 15–64 by Table 5.8: Composition of the regis- cation ............................................... 216
labour market status (self-categori- tered unemployed by educational Table 7.2: Pupils/students entering
sed), per cent ................................... 181 attainment, yearly averages ......... 199 the school system by level of educa-
Table 4.1: Employment .................... 182 Table 5.9: The distribution of regis- tion, full-time education ............... 217
Table 4.2: Employment by gender ... 183 tered unemployed school-leavers by Table 7.3: Students in full-time educa-
Table 4.3: Composition of the emp- educational attainment, yearly aver- tion ................................................... 218
loyed by age groups, males ........... 184 ages, per cent .................................. 199 Table 7.4: Students in part-time edu-
Table 4.4: Composition of the emp- Table 5.10: Registered unemployed by cation ............................................... 219
loyed by age groups, females, per economic activity as observed in the Table 7.5: Number of applicants for
cent ................................................... 184 LFS, per cent ................................... 200 full-time high school courses ...... 220
Table 4.5: Composition of the emp- Table 5.11: Monthly entrants to the Table 8.1: The number of vacancies
loyed by level of education, males, unemployment register, monthly reported to the local offices of the
per cent ............................................ 185 averages, in thousands ................. 200 NFSZ ................................................ 221
Table 4.6: Composition of the emp- Table 5.12: Selected time series of Table 8.2: The number of vacancies
loyed by level of education, females, registered unemployment, monthly reported to the local offices of the
per cent ............................................ 185 averages ........................................... 201 NFSZ, by level of education ........ 222
Table 4.7: Employed by employment Table 5.13: The number of registered Table 8.3: The number of
status, in thousands ...................... 186 unemployed who became employed vacancies ......................................... 222
Table 4.8: Composition of the emp- on subsidised and non-subsidised Table 8.4: Firms intending to increase/
loyed persons by employment status, employment .................................... 202 decrease their staff, per cent ......... 223
per cent ............................................ 186 Table 5.14: Benefit recipients and par- Table 9.1: Regional inequalities: Emp-
Table 4.9: Composition of employed ticipation in active labour market loyment rate .................................... 224
persons by sector, by gender, per programmes ................................... 203 Table 9.2: Regional inequalities:
cent ................................................... 187 Table 5.15: The ratio of those who are LFS-based unemployment rate ... 225
Table 4.10: Employed in their present employed among the former partici- Table 9.3: Regional differences: The
job for 0–6 months, per cent ....... 187 pants of ALMPs, per cent ............ 204 share of registered unemployed
Table 4.11: Distribution of employees Table 5.16: Distribution of registered relative to the economically active
in the competitive sector by firm unemployed, unemployment benefit population, per cent ...................... 226

258
 index of tables and figures

Table 9.4: Annual average registered pensioners ....................................... 240 by age group, 2002–2018 ............... 40
unemployment rate by counties ... 227 Table 11.6: The number of those re- Figure 1.4: Share of youth not in
Table 9.5: Regional inequalities: ceiving a disability annuity and the employment, education or training
Gross monthly earnings ............... 228 mean sum of the provisions they re- (NEET) by age group and gender,
Table 9.6: Regression-adjusted ear- ceived after the increase, in January 2002–2018 (percent) ........................ 41
nings differentials ......................... 228 of the given year ............................. 241 Figure 2.1.1: Association between test
Table 9.7: Regional inequalities: Table 11.7: Newly determined disa- scores in grade 10 and the logarithm
Gross domestic product ............... 229 bility pension claims and detailed of earnings ......................................... 47
Table 9.8: Commuting ..................... 229 data on the number of newly deter- Figure 2.1.2: Association between test
Table 10.1: Strikes ............................ 232 mined old-age pension claims ..... 241 scores in grade 10 and unemploy-
Table 10.2: National agreements on Table 11.8: Retirement age ment .................................................... 47
wage increase recommendations ... 232 threshold ......................................... 242 Figure 2.2.1: Skill levels of Grade 10
Table 10.3: Single employer collective Table 12.1: The mean, minimum, and students broken down by school
agreements in the business sector .. 233 maximum value of the personal in- track, 2017 (percentage) ................. 54
Table 10.4: Single institution collective come tax rate, per cent .................. 243 Figure 2.2.2: Test scores in Grade 10
agreements in the public sector ... 233 Table 12.2: Changes in the magnitude as a function of Grade 8 scores, bro-
Table 10.5: Multi-employer collective of the tax wedge in the case of mini- ken down by school track; students
agreements in the business sector .. 233 mum wage and the temporary work in Grade 8 in 2014 ........................... 54
Table 10.6: Multi-institution collective booklet (AMK) .............................. 244 Figure 2.2.3: Changes in average test
agreements in the public sector ... 233 Table 12.3: The monthly amount of scores between Grades 6 and 10,
Table 10.7: The number of firm wage the minimum wage, the guaranteed broken down by school track; stu-
agreements, the number of affected wage minimum, and the minimum dents in Grade 8 in 2014 ................. 55
firms, and the number of employees pension, in thousands of current- Figure 2.3.1: The proportion of those
covered ............................................ 233 year HUF ........................................ 245 continuing their education at the
Table 10.8: The number of multi- Table 12.4: The tax burden on work secondary level and those who were
employer wage agreements, the as a ratio of tax revenue and earn- not admitted into the school type
affected firms, and the covered com- ings ................................................... 246 designated as their first choice in
panies and employees ................... 234 Table 13.1: Employment and unemploy- their priority list, by school type,
Table 10.9: The share of employees cov- ment rate of population aged 15–64 2005–2017 ........................................ 60
ered by collective agreements .......... 234 by gender in the EU, 2018 .............. 247 Figure 2.3.2: The proportion of those
Table 10.10: Single employer collec- Table 13.2: Employment composition continuing their studies in second-
tive agreements in the national eco- of the countries in the EU, 2018 ... 248 ary education, by school type and
nomy ................................................ 235 Table 13.3: The ration of vacancies, gender, 2005–2017 ........................... 61
Table 10.11: Multi-employer collec- IV. quarter, 2018 ............................ 249 Figure 2.3.3: The proportion of those
tive agreements in the business sec- Figures continuing their studies after el-
tor ..................................................... 236 The Hungarian Labour Market ementary school, by gender ........... 62
Table 11.1: Family benefits ............. 237 Figure 1: The number of employees Figure 2.4.1: Average test score
Table 11.2: Unemployment benefits in the 15–74 age group and the change between the 8th and 10th
and average earnings .................... 237 employment rate among those aged grade for different school types.
Table 11.3.a: Number of those rece- 15–74, 2010–2018 ............................ 18 2010–2014 ......................................... 66
iving pension, and the mean sum Figure 2: Changes in the headcount of Figure 2.5.1: The share of early school
of the provisions they received in public works participants, leavers in the population aged 18–
January of the given year ............. 238 2010–2018 .......................................... 21 24 in Hungary, in the Visegrad
Table 11.3.b: Number of those rece- Figure 3: Employment rates of men countries and in the EU on average,
iving pension, and the mean sum and women aged 20–64, 2009–2018 ........................................ 70
of the provisions they received in 2014–2018 ......................................... 23 Figure 2.5.2: Changes in the propor-
January of the given year ............. 238 Figure 4: The number of vacancies in tion of those not in education and
Table 11.4.a: Number of those recei- the business sector, 2010–2018 ..... 25 lacking a qualification after finish-
ving social annuities for people with Figure 5: The average duration of job ing lower-secondary education ..... 73
damaged health, and the mean sum seeking and the share of the long- Figure 2.5.3: The proportion of those
of the provisions they received after term unemployed, 2010–2018 ...... 26 with an upper-secondary qualifica-
the increase, in January of the given Figure 6: Labour reserve and labour tion 4 and 5 years after completing
year ................................................... 239 demand by county, 2018 ................. 27 lower-secondary education ............ 73
Table 11.4.b: Number of those recei- Figure 7.: The rate of increase in gross Figure 2.5.4: The proportion of those
ving social annuities for people with earnings (2013 = 100 per cent) ..... 29 not attending school and lacking
damaged health, and the mean sum In Focus a qualification, by age ..................... 75
of the provisions they received after Figure 1.1: Employment rate of men Figure K2.5.1: The share of 17-year-
the increase, in January of the given and women aged 15–29 .................. 38 olds in employment, and not in
year ................................................... 239 Figure 1.2: Employment rate by age education, employment, or training
Table 11.5: The median age for and gender, 2002–2018 .................. 40 (NEET), 1992–2018 ........................ 78
retirement and the number of Figure 1.3: Share of full-time students, Figure 3.1.1: Share of those working

259
The hungarian labour market

while studying full-time by complet- Figure 7.1.1: The participation rates Figure 4.5: Activity rate by age groups,
ed education, 2002–2017 ............... 80 of 25–34-year-olds in non-formal females aged 15 –64 ....................... 191
Figure 3.1.2: Share of those working education and training, according Figure 5.1: Unemployment rates by
while studying full-time by complet- to the data of the AES surveys .... 132 gender .............................................. 192
ed education and sex, 2002–2017 ... 80 Figure 7.1.2: The participation rates Figure 5.2: Intensity of quarterly flows
Figure 3.2.1: The expected employ- of 25–34-year-olds in non-formal between labour market status, popu-
ment probability of students of vo- education and training, according lation between 15–64 years .......... 195
cational schools, 2011–2012 .......... 84 to the data of the labour force sur- Figure 5.3: Unemployment rate by age
Figure 3.3.1: The share of non fixed- vey, by educational attainment ... 132 groups, males aged 15 –59 ........... 197
term contracts among 15–29 years Figure 7.3.1: The distribution of Figure 5.4: Unemployment rate by age
old employees, by education, 2002– young graduates by labour market groups, females aged 15 –59 ........ 197
2017 (percent) ................................... 86 status and the level of qualification Figure 5.5: Registered and LFS
Figure 4.1.1: Youth employment in (MA/BA), 2004–2018 .................. 137 unemployment rates ..................... 198
terms of labour market experience, Figure 7.3.2: Wage return to BA/ Figure 5.6: Entrants to the unemploy-
in three graduation cohorts ........... 90 college and MA/university de- ment register, monthly averages .. 200
Figure 4.1.2: The effects of the unem- grees compared with a Matura, by Figure 6.1: Annual changes of gross
ployment rate in the year of gradua- quantiles, 2006–2016 ................... 138 nominal and net real earnings .... 209
tion on youth labour market status Figure 7.3.3: The proportions of those Figure 6.2: The percentage of low paid
in terms of labour market experi- employed in occupational groups workers by gender, per cent .......... 212
ence, 2002–2017 .............................. 90 among young graduates, by qualifi- Figure 6.3: The dispersion of gross
Figure 4.1.3: The effects of the cation level, 2004–2018 ............... 140 monthly earnings .......................... 214
unemployment rate in the year Figure 7.3.4: The proportion of well- Figure 6.4: Age-income profiles by edu-
graduation on youth employment, matched, overqualified and under- cation level in 1998 and 2016 ......... 214
by educational attainment groups, qualified graduates, by qualification Figure 6.5: The dispersion of the loga-
2002–2017 ......................................... 91 level, 2004–2016 (percentage) ..... 141 rithm of gross real earnings ......... 215
Figure 5.1.1: Changes in the number Figure 7.3.5: Wage return on required, Figure 7.1: Full time students as a percen-
of NEET groups in the 16–29 age surplus and missing years, 2004– tage of the different age groups ...... 216
group, 2013–2018 ............................ 99 2016 .................................................. 142 Figure 7.2: Flows of the educational
Figure 5.1.2: Number of year quarters Figure 8.1.1: The share of those chang- system by level ................................ 217
until exit from NEET status to emp- ing occupations among youth in em- Figure 8.1: The number of vacancies
loyment by type of unemployment, ployment and among older groups in reported to the local offices of the
2015–2018 ....................................... 100 employment, 2000–2004 ............. 145 NFSZ ................................................ 221
Figure 5.1.3: Registration rate by Figure 8.1.2: The share of youth chang- Figure 8.2: Firms intending to increa-
distance from the primary labor ing occupations among youth in em- se/decrease their staff ................... 223
market, 2013–2018 ....................... 102 ployment, by educational attainment Figure 9.1: Regional inequalities: La-
Figure K5.1.1: Search tools used by categories, 2000–2018 .................. 146 bour force participation rates, gross
unemployed young people who Figure 8.2.1: The effect of factors in- monthly earnings and gross domestic
completed vocational school, fluencing the employment of youth product in NUTS-2 level regions .. 224
2014–2018 ....................................... 104 abroad ............................................... 151 Figure 9.2: Regional inequalities:
Figure K5.1.2: Search tools used by Statistical Data LFS-based unemployment rates in
ILO unemployed young people who Figure 1.1: Annual changes of basic NUTS-2 level regions ................... 225
completed secondary education, economic indicators ...................... 169 Figure 9.3: Regional inequalities: The
2014–2018 ....................................... 104 Figure 1.2: Annual GDP time series .. 170 share of registered unemployed
Figure 5.2.1: What happens to young Figure 1.3: Employment rate of popu- relative to the economically active
people under the age of 25 regis- lation aged 15 –64 ......................... 170 population, per cent, in NUTS-2
tered as jobseekers in the first six Figure 2.1: Age structure of the Hun- level regions .................................... 226
months after registration? ........... 107 garian population, 1980, 2015 .... 172 Figure 9.4: Regional inequalities:
Figure 5.2.2: The number of entrants Figure 3.1: Labour force participation Means of registered unemployment
into the Youth Guarantee Pro- of population for males 15–59 and rates in the counties, 2018 ........... 227
gramme by active instruments ... 107 females 15–54, total ...................... 177 Figure 9.5: The share of registered
Figure 5.4.1: The distribution of wag- Figure 3.2: Labour force participation unemployed relative to the population
es of young people aged 16–25, by of population for males 15–59 .... 178 aged 15–64, 1st quarter 2007 ........ 230
education, 2002, 2006 .................. 112 Figure 3.3: Labour force participation Figure 9.6: The share of registered
Figure 6.1.1: Share of matriculated of population for females 15–54 ... 179 unemployed relative to the population
youth by age, 2011, 2016 .............. 122 Figure 4.1: Employed ....................... 182 aged 15–64, 1st quarter 2018 ........ 230
Figure 6.1.2: The share of full-time Figure 4.2: Employment by gender .. 183 Figure 9.7: The share of registered
students among Roma and non-Ro- Figure 4.3: Employees of the corpo- unemployed relative to the population
ma youth, 2011, 2016 .................... 122 rate sector by firm size and by the aged 15–64, 3rd quarter 2007 ........ 231
Figure 6.1.3: Employment rate of the share of foreign ownership .......... 188 Figure 9.8: The share of registered
16–25 year-old, not in education Figure 4.4: Activity rate by age unemployed relative to the population
population, 2011 and 2016 .......... 124 groups, males aged 15 –64 ........... 190 aged 15–64, 3rd quarter 2018 ........ 231

260
x!7HB7I5-iagcjd! ,L.T.k.k.m

THE HUNGARIAN LABOUR MARKET, 2019; IN FOCUS: YOUNG
THE HUNGARIAN
LABOUR MARKET
2019

EDITORS
KÁROLY FAZEKAS
MÁRTON CSILLAG
ZOLTÁN HERMANN
ÁGOTA SCHARLE

AND IN THE LABOUR MARKET

PEOPLE IN EDUCATION
INSTITUTE OF ECONOMICS, CENTRE
FOR ECONOMIC AND REGIONAL STUDIES
BUDAPEST, 2020