The World Bank Group
Social Protection and Labor Global Practice
Europe & Central Asia Region
PORTRAITS OF LABOR MARKET EXCLUSION 2.0
Country Policy Paper (CPP) for Romania
Lead Authors: Aylin Isik-Dikmelik, Natalia Millán and Mirey Ovadiya
Project team: Aylin Isik-Dikmelik (Team Leader), Mirey Ovadiya (Team
Leader), Sandor Karacsony, Natalia Millán, and Frieda Vandeninden
July 2017
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Portraits of Labor Market Exclusion 2.0 2
Acknowledgements
This report was produced by a World Bank team co-led by Aylin Isik-Dikmelik (Senior Economist) and
Mirey Ovadiya (Senior Social Protection Specialist) including Sandor Karacsony (Social Protection
Specialist), Natalia Millán (Economist), and Frieda Vandeninden (Economist). The team wishes to
thank Alina Nona Petric (Operations Analyst) for her contributions and advice.
This report is one of the twelve country specific papers produced under a joint European Commission
(EC) World Bank and Organisation for Economic Cooperation and Development (OECD) project and
applies a joint methodology on country specific cases as developed in OECD-World Bank (2016). This
report would not have been possible without the financial and technical support of the EC’s
Directorate General of Employment, Social Affairs and Inclusion. Katalin Szatmari (Policy Officer,
Directorate C1-Social Investment Strategy), led the efforts from the Directorate General of
Employment, Social Affairs and Social Inclusion. Herwig Immervoll (Senior Social Policy Economist,
ELS/SPD) led the OECD team to undertake the activities under the project in six countries. The
European Commission team included Suzanna Conze (Policy Officer, formerly Directorate C1-Social
Investment Strategy), Manuela Geleng (Head of Unit, Directorate C1-Social Investment Strategy),
Ioana-Maria Gligor (Deputy Head of Unit, B5-Employment), Georgi Karaghiozov (Policy Officer,
Directorate C1-Social Investment Strategy), Dora Krumova (Programme Manager, B5-Employment),
Katharina Muhr (Policy Officer-Directorate C5-Employment), Raya Raychinova (Program Assistant,
B5-Employment), Alexandra Tamasan (Policy Officer, formerly Directorate C1-Social Investment
Strategy), Georgios Taskoudis (Policy Officer, C4-Employment), Miriam Toplanska (Policy Analyst,
Directorate C1-Social Investment Strategy), and Iva Zelic (Policy Officer, Directorate C5-Employment).
The OECD team included James Browne, Nicola Düll, Rodrigo Fernandez, Daniele Pacifico, and Céline
Thévenot. The team is grateful to the EC and OECD teams for the close collaboration exhibited under
this project.
Andrew D. Mason (Practice Manager, Europe and Central Asia Social Protection and Jobs Practice),
Arup Banerji (Regional Director, European Union) and Cem Mete (Practice Manager, Europe and
Central Asia Social Protection and Jobs Practice) provided overall guidance to the study. Peer review
comments were received at various stages from Christian Bodewig (Program Leader), Aline Couduel
(Lead Economist), Victoria Levin (Senior Economist), Matteo Morgandi (Senior Economist), Cristobal
Ridao-Cano (Lead Economist), Victoria Strokova (Economist), Ramya Sundaram (Senior Economist);
and Trang Van Nguyen (Senior Economist).
The team benefitted from extensive interaction and consultations with thank representatives of
Ministry of Labor and Social Justice and National Employment Agency. In particular, The team would
like to thank Adrian Dobre, Cristiana Barbu, Elena Baboi, Liana Mostenescu, Mihaela Ana Bujor, Ioan
Cristian Raileanu, and Petrica Gavrila, who provided guidance, data and specific inputs towards the
finalization of the report.
Finally, the team is grateful to Eurostat for the provision of the EU-SILC micro data used in the analysis
in this report.
Portraits of Labor Market Exclusion 2.0 3
Contents
Acknowledgements ........................................................................................................................................ 3
1. Introduction ........................................................................................................................................ 6
2. Country Context ................................................................................................................................. 7
3. Understanding Employment Barriers — Framework and Methodology ......................................... 16
4. Results: Portraits of Labor Market Exclusion in Romania ................................................................ 25
5. Priority Groups in the Romanian Target Population ........................................................................ 34
6. Policies and Programs Targeting Priority Groups in Romania .......................................................... 43
6.1 Framework and Approach ................................................................................................................ 43
6.2 Overview of Activation and Employment Support Programs and Policies in Romania ................... 44
6.3 Activation and Employment Support Policies Vis-à-vis Priority Groups Needs ............................... 52
7 Conclusions and Policy Directions .................................................................................................... 58
References ................................................................................................................................................ 61
Annex 1. Advantages and Disadvantages of EU-SILC Survey Data ........................................................... 63
Annex 2. Description of Employment Barrier Indicators.......................................................................... 65
Annex 3. Latent Class Analysis Model Selection for Romania .................................................................. 68
Annex 4. Characterization of Latent Groups Among the Target Population in Romania ........................ 71
Annex 5. Characterization and Definitions of Labor Market Programs Based on Eurostat ..................... 76
Figures
Figure 1: Employment and unemployment (aged 15 to 64) in Romania and EU-28 .............................. 8
Figure 2: Unemployment among population aged 15 to 64 by education level in Romania and EU-28 9
Figure 3: Long-term unemployment as a percentage of unemployment in Romania and EU-28, 2006-
2015 ............................................................................................................................................................. 10
Figure 4: Youth (15-24) Unemployment in Romania and EU-28............................................................ 11
Figure 5: Activity rates by sex and age in Romania and EU-28 ............................................................... 12
Figure 6: Part-time employment as a percentage of total employment by sex, EU Member States,
2015 ............................................................................................................................................................. 13
Figure 7: At-risk-of-poverty and in-work at-risk-of-poverty rates in EU Member States, 2015.......... 14
Figure 8: Age composition of the Romanian population in 2013 and 2050 .......................................... 15
Figure 9: Composition of working-age* population (left) and out of work (right) in Romania ........... 18
Figure 10: Labor market attachment status of working-age* population, Romania and other EU
countries under study (percent) ............................................................................................................... 19
Figure 11: Composition of the persistently out of-work population by labor market status, Romania
and other EU countries under study (as a percentage of working age) ................................................. 20
Figure 12: Employment barrier framework ............................................................................................. 21
Figure 13: Latent groups within the Romanian target population ......................................................... 26
Portraits of Labor Market Exclusion 2.0 4
Figure 14: Distribution of number of barriers faced by individuals in each latent group in Romania 28
Figure 15: Organizing framework for policy analysis.............................................................................. 43
Figure 16: Labor market spending as percent of GDP (left axis) and share of active labor market
program spending as share of labor market expenditure (right axis) ................................................... 48
Figure 17: Detailed composition of labor market programs in Romania, in percent of total labor
market expenditure in 2015 ...................................................................................................................... 49
Figure 18: Profile of ALMP and labor market service beneficiaries employed in 2016........................ 51
Tables
Table 1: Characterization of Romanian target and working-age population according to barrier
indicators (percent) .................................................................................................................................... 23
Table 2: Characterization of target population according to barrier indicators (percent):
international comparison........................................................................................................................... 24
Table 3: Result of latent class analysis for population with labor market difficulties in Romania: labor
market barriers ........................................................................................................................................... 27
Table 4: Employment barriers and demographic and socioeconomic characteristics of priority
groups in Romania ...................................................................................................................................... 39
Table 5: Status of the National Employment Agency’s Employment Program by type of measure,
2016 ............................................................................................................................................................. 50
Boxes
Box 1. Definition of target population ....................................................................................................... 17
Box 2. Definitions of employment barrier indicators used for Romania ............................................... 22
Box 3: Description of main ALMPs in Romania ........................................................................................ 50
Portraits of Labor Market Exclusion 2.0 5
1. Introduction
Successful labor market inclusion requires a better understanding of who the labor market
vulnerable are. People who are out of work are not all the same: they can be middle-aged individuals
and early retirees, as well as young adults neither working nor receiving education. At the same time,
there may be other types of vulnerability in the labor market: some people take part in temporary or
unstable employment, work a reduced number of hours, or earn very low incomes despite being
engaged in full time work. Considering the priorities of the inclusive growth pillar of the Europe 2020
Strategy1, and potential negative impacts of labor market vulnerability on long-term growth, it is
worth examining who the labor market vulnerable in Europe are and why they are out of work or are
precariously employed. While some statistics on broad groups (e.g. youth) exist, deeper analysis, in
particular on the diverse barriers faced by the labor market vulnerable in conjunction with other
characteristics, is needed and would constitute an important step forward towards better labor
market inclusion.
In this context, Portraits of Labor Market Exclusion-2 — a joint study between the European
Commission (EC), the World Bank, and the Organization for Economic Cooperation and
Development (OECD)2 — aims to inform employment support, activation, and social inclusion
policy making, through an improved understanding of labor-market barriers. Covering 12
countries3, the study builds on the previous joint EC and World Bank study to map the diversity of
profiles for the out of work in six countries (Sundaram et al., 2014) and other analyses that
characterize people with labor market difficulties (European Commission, 2012; Ferré et al., 2013;
Immervoll, 2013). The study expands the previous analysis by considering a broader group of labor
market vulnerable beyond the out of work to include: those in unstable employment, those with
restricted hours, and those with near-zero incomes (i.e. marginally employed individuals). It also
refines the analytical methodology by applying an employment barriers framework to facilitate policy
making and country-specific application, and to provide a reference point for future methodological
extensions.
Utilizing an advanced statistical method (latent class analysis), the study separates individuals
who are out of work or marginally employed into distinct groups with respect to types of
employment barriers faced. This approach facilitates discussions on the strengths and limitations
of existing policy interventions for concrete groups of beneficiaries, and helps inform policy decisions
on whether and how to channel additional efforts towards specific groups.
Addressing the same barrier may require a different set of policies according to the
characteristics of the identified groups. For example, while not having recent work experience may
be an employment barrier faced by many individuals, it may require a different approach for inactive
mothers compared to young unemployed men. It is therefore important to relate each barrier to
1 Where all European governments have committed to increasing the employment rate (European Commission,
2010).
2 The activities of the “Understanding Employment Barriers�? are financed through separate agreements between
the EC and the World Bank and the EC and the OECD respectively. The respective agreements with the EC are
titled “Portraits of Labor Market Exclusion 2.0�? (EC-World Bank) and “Cooperation with the OECD on Assessing
Activating and Enabling Benefits and Services in the EU�? (EC -OECD).
3 The existing analysis in Bulgaria, Estonia, Greece Hungary, Lithuania, and Romania is updated, broadened, and
refined with the new methodology; Croatia, Ireland, Italy, Poland, Portugal, and Spain are analyzed for the first
time.
Portraits of Labor Market Exclusion 2.0 6
specificities of each group. Thus, the study further delves into the results of the latent class analysis
(LCA) for the priority groups that are identified in close collaboration with the corresponding country
counterparts. Consequently, the study presents a richer and deeper understanding of the barriers,
beyond what could be glimpsed through traditional statistics. It also provides an assessment of the
adequacy of the policies and programs that are available to respond to the needs of the priority groups.
The analysis focuses primarily on the supply-side constraints and corresponding policies.
While the study recognizes the essential role demand plays in improving labor market outcomes,
analysis of these constraints — which requires a comprehensive approach across multiple facets of
the economy — is beyond the scope of this study.
The study provides a snapshot of the needs of the labor market vulnerable and relevant
policies to inform strategic policy choices and directions. Operationalization of these policy
directions (such as improvements in existing programs) requires a sequence of activities including
further in-depth analysis using program-level administrative and expenditure data as well as the more
commonly used profiling methods. Thus, the conclusions should be interpreted in this light.
This Country Policy Paper is one of twelve that is under study4, and analyzes the out of work
and marginally employed population in Romania along with existing activation and
employment support policies and programs. The paper consists of seven sections including this
introduction. Section 2 provides background on the Romanian labor market. Section 3 describes the
framework and the statistical clustering methodology. Section 4 presents the results, including a
description of the identified clusters according to labor market barriers and demographic and socio-
economic characteristics. Section 5 expands on this information with a more detailed analysis of the
groups that, together with the Government of Romania, have been selected as priority groups for
policy and program interventions. Section 6 analyzes the current policies and programs that address
the needs of the prioritized groups. Finally, section 7 presents conclusions along with policy directions.
2. Country Context
Although Romania was not severely affected by the financial crisis of 2008, structural issues in
the labor market persist, and given demographic trends, their consequences are expected to
grow. Unemployment, while low in broad terms, remains high among youth and a significant
proportion of the unemployed can be characterized as long-term unemployed. Women and younger
and older age cohorts have considerably low activity rates. There is also a stark urban-rural divide,
with a large proportion of working poor. Added to this is the fact that the work-able population is
aging and shrinking, further reducing the size of the Romanian workforce. (European Commission,
2016a).
The Romanian labor market was only modestly affected by the global financial crisis of 2008
and has been gradually recovering since then. The employment rate, though still below the EU
average and the national EU 2020 target, reached 61.4 percent by 2015, up from 59.0 percent in 2008
(see Figure 1, panel a). The activity rate followed a similar trajectory, reaching 66.1 percent by 2015,
up from its 2008 level of 62.9 percent. Romania’s unemployment rate has also been recovering, falling
4Six Country Policy Papers are led by the World Bank and include: Bulgaria, Croatia, Greece, Hungary, Poland, and
Romania. The Country Policy Papers led by OECD include: Estonia, Ireland, Italy, Lithuania, Portugal, and Spain.
Portraits of Labor Market Exclusion 2.0 7
to 6.8 percent in 2015 after peaking at 7.2 percent in 2011 (Figure 1, panel b). Although it has yet to
fully revert to pre-crisis levels, unemployment is expected to decrease further by 2017, aided by
sustained economic growth (European Commission, 2016a). In general, the crisis affected the labor
market only mildly, with subsistence agriculture acting as a buffer (Ibid.).
Low activity, low employment, and low unemployment rates characterize the Romanian labor
market. The activity rate in Romania remains over 6 percentage points lower than the EU average;
among EU Member States, only Italy has a lower activity rate than Romania. Such low labor market
activity may in part explain Romania’s low unemployment rate, one of the lowest unemployment rates
in the EU. At the same time, employment rates in Romania are low by EU standards, trailing the EU
average by over 4 percentage points. The Government of Romania has identified increasing the
employment rate as its main labor market objective. It hopes to reach 70 percent employment among
the population aged 20 to 64 by 2020.
Figure 1: Employment and unemployment (aged 15 to 64) in Romania and EU-28
a. Employment b. Unemployment
70 12
65.2 10
65 64.8 9.4
8
60 61.4 7.0
58.8 6 6.8
55 5.8
4
50
2
45 0
2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2007 2008 2009 2010 2011 2012 2013 2014 2015
EU-28 Romania EU (28 countries) Romania
Note: The EU-28 average is weighted.
Source: Eurostat LFS.
Unemployment is lowest among individuals with tertiary education; however, these
individuals were also the ones most affected by the crisis. Within the EU, individuals with lower
secondary education or less were generally the most affected by rising unemployment following the
crisis. In contrast, in Romania, it was individuals with tertiary education who saw a more pronounced
rise in unemployment. Nonetheless, unemployment is lowest among the high skilled —i.e., those with
tertiary education—, and unemployment remains lower than EU average among even among the low
Portraits of Labor Market Exclusion 2.0 8
skilled —i.e., those with lower secondary education or less. This represents an important divergence
from the weighted average for EU-28 where the low skilled face unemployment rates over 10
percentage points higher than the high skilled (Figure 2). Very low activity rates for low-skilled
individuals may (partly) explain their low unemployment rates: in 2015, only 46.9 percent of the low-
skilled population aged 15 to 64 participated in the labor market, versus 66.1 percent among the
general population. Recent increases in the Romanian minimum wage, however, may hinder job
prospects among the low skilled (European Commission, 2016a), as can already been seen given the
rise in unemployment among the least educated in 2015.
Figure 2: Unemployment among population aged 15 to 64 by education level in Romania and EU-28
a. Romania b. EU-28
20 20
18 18
16 16
14 14
12 12
10 10
8 8
6 6
4 4
2 2
0 0
2010
2007
2008
2009
2010
2011
2012
2013
2014
2015
2007
2008
2009
2011
2012
2013
2014
2015
Note: The EU-28 average is weighted.
Source: Eurostat LFS.
Despite the relatively low unemployment rate, long-term unemployment — as a percentage of
unemployment — is still of concern in Romania. Long-term unemployment5 as a percentage of
total unemployment in Romania, at 50 percent prior to the crisis, was markedly above the EU-28
average. After falling to just over 30 percent in 2009, it has been slowly rising, similarly to the rise
among the average for EU-28 countries. In 2015, 43.9 percent of the unemployed were long-term
unemployed. Although the indicator is lower than the EU-28 average of 48.1 percent, it is still a cause
for concern. The long-term unemployed are much less likely to eventually find employment due to
skills deterioration and stigma. Aside from putting a strain on household finances, long-term
unemployment can also lead to deteriorating health conditions and eventual exit from the labor
5
Defined as unemployment lasting 12 months or more.
Portraits of Labor Market Exclusion 2.0 9
market altogether as a result of discouragement. In 2014, only 15 percent of those unemployed for
one to two years found a job, one of the lowest rates in the EU (European Commission, 2016a).
Figure 3: Long-term unemployment as a percentage of unemployment in Romania and EU-28, 2006-2015
55 50.0
50
48.1
45
43.9
40
42.6
35
30
25
20
2007 2008 2009 2010 2011 2012 2013 2014 2015
EU (28 countries) Romania
Note: The EU-28 average is weighted. Long-term unemployment is defined as unemployment lasting 12 months or longer.
Source: Eurostat LFS.
Youth unemployment is also a significant concern in Romania, and disproportionately affects
young women. Unemployment among youth aged 15 to 24, at 21.7 percent in 2015, stands above the
EU-28 average, and has yet to revert to pre-crisis levels of 18.6 percent (Figure 4). Unlike in the rest of
the EU, young Romanian women have a higher unemployment rate than their young male
counterparts.
Portraits of Labor Market Exclusion 2.0 10
Figure 4: Youth (15-24) Unemployment in Romania and EU-28
30
25
21.4 21.7
20 20.4
17.4
15
10
5
0
2006 2007 2008 2009 2010 2011 2012 2013 2014 2015
European Union (28 countries) Romania
Note: The EU-28 average is weighted.
Source: Eurostat LFS.
The percentage of young Romanians that are neither in education, employment or training
(NEET) has been rising and is higher than the EU average. In 2015, the percentage of NEETs
reached 18.1 percent, markedly above the pre-crisis rate of 11.6 percent and above the EU average of
14.8 percent. The NEET rate is especially high among young women, having reached 21.4 percent in
2015 (versus 15.0 among young men). Early school leaving is one of the factors leading to high NEET
and unemployment rates among youth. At 19.1 percent, the early school leaving rate in Romania is the
second highest in the EU (after Spain), and is especially for rural residents, Roma, and children with
special needs (European Commission, 2016a). The composition of NEETs in Romania also differs from
that of the average for the EU. Although the percentage of NEETs with family responsibilities, at 21.8
percent in 2013, does lie close to the EU average, a relatively high percentage of NEETs fall under the
category of “other inactive�? (28.5 percent in 2013, versus only 11.8 percent among the EU). The
proportion of discouraged workers among NEETs is also high at 13.5 percent (versus just 5.9 percent
for the EU). In contrast, the percentage of Romanian NEETs who are unemployed is relatively low.
(Eurofound, 2016).
Although both Romanian men and women have relatively low rates of economic activity, the
gender gap in labor market participation is particularly striking. In 2015, only 56.7 percent of
women 15 to 64 participated in the labor market, almost 20 percentage points below the male
participation rate.6 Moreover, this gap has been rising over the years as activity rates among men have
followed an upward trend but have remained flat for women. The latter also translates into a
significant gender gap in employment: in 2015, just 53.2 percent of females (ages 15 to 64) were
employed; among males of the same age group the employment rate stood at 69.5 percent (Eurostat).
6 In comparison, in 2015, the gender gap for EU-28 was 11.5 percentage points.
Portraits of Labor Market Exclusion 2.0 11
Activity rates are also especially low among youth and those nearing retirement age7 (ages 50
to 64). Only 42.7 percent of Romanians aged 50 to 64 participated in the labor market in 2015 versus
the EU-28 average of 66.5 percent. The activity rates of Romanians approaching retirement have
largely remained flat over the 10-year period under analysis; this is in sharp contrast to rising activity
rates among older individuals in the EU, especially among women aged 50 to 64. Contributing to the
low participation rates among older women is a young retirement age of 59(men also retire relatively
young, albeit at the older age of 64). Without equalization of the retirement age of men and women,
older women’s participation rates are expected to remain low (European Commission, 2016a). Among
Romanian youth aged 15 to 24, activity rates are lower than those of the EU average for both gender,
but the gap in activity rates vis-à-vis the EU is especially high for women, at 13.5 percentage points in
2015. (Figure 5)
Figure 5: Activity rates by sex and age in Romania and EU-28
a. Males b. Females
100 100
90 90
80 80
70 70
60 60
50 50
40 40
30 30
20 20
2007 2008 2009 2010 2011 2012 2013 2014 2015 2007 2008 2009 2010 2011 2012 2013 2014 2015
Note: The EU-28 average is weighted.
Source: Eurostat LFS.
In part, low activity rates among women, youth, and older individuals may reflect labor market
legislation and/or cultural norms that are not conducive to voluntary part-time work.
Voluntary part-time work, especially when under equal treatment vis-à-vis full-time work, can be a
means for otherwise excluded groups to participate or remain in paid work (ILO, 2016). Part-time
work allows older or disabled individuals to accommodate physical limitations, younger retirees to
continue to be engaged in work while pursuing more leisure activities, youth to prolong their
education while gaining work experience, and women (and sometimes men) to participate in care
bearing or other domestic responsibilities. Further, part-time work can also help attract and retain
workers for specific schedules in difficult jobs (Kjeldstad and Nymoen, 2012, as cited in ILO, 2016). In
Romania, individuals who may be interested in working but cannot take on a full-time job may be
7 The retirement age in Romania is 59 for women and 64 for men.
Portraits of Labor Market Exclusion 2.0 12
excluded from the labor market altogether, as shown by the very low percentage of part-time work
activity in comparison to other EU Member States, especially those in northern Europe where part-
time work is explicitly encouraged by government policies. Interestingly, part-time work is not used
by Romanian women as a way to combine work with family and child care responsibilities: Unlike in
most EU countries, part-time work is more common among men in Romania. Lastly, it must also be
noted that although part-time work in Romania has increased since the crisis, there has been a noted
increase in involuntary part-time work8 (European Commission, 2016c).
Figure 6: Part-time employment as a percentage of total employment by sex, EU Member
States, 2015
90.0
80.0
70.0
60.0
50.0
40.0
30.0
20.0
10.0
0.0
EU-28
Denmark
Italy
France
Finland
Netherlands
Austria
Cyprus
Latvia
Romania
Hungary
Germany
Spain
Belgium
Luxembourg
Ireland
Slovenia
Greece
Poland
Croatia
Bulgaria
Malta
Estonia
Czech Republic
Slovakia
Portugal
Lithuania
United Kingdom
Sweden
Females Males
Note: The EU-28 average is weighted.
Source: Eurostat LFS.
Much of the working Romanian population receives very low earnings from work, and having
a job is not enough to rise out of poverty. Romania has the highest in-work at-risk-of-poverty rate
in the EU, as well as the highest at-risk-of-poverty rate for the population as a whole (Figure 7). As
documented by the World Bank (2015), in-work poverty is a direct result of low productivity and
scarce formal employment opportunities. A weak safety net is also behind high at-risk-of-poverty and
in-work poverty in Romania. In particular, the income composition of working-age individuals in the
poorest income quintiles shows that 68 percent of income comes from labor and just 31 percent comes
from social benefits (European Commission, 2016c). In contrast, the simple average for the EU
indicates that social benefits represent 39 percent of the incomes of the working-age population in the
lowest income quintile (Ibid.). Low coverage of means-tested benefits is expected to improve with the
introduction of the Minimum Social Insertion Income (MSII) (World Bank, 2016; European
Commission, 2016a.). Through this program, it is expected that the working poor will be well-targeted,
given the introduction of 50 percent disregards of earned income from agricultural activities (Ibid.).
8Here, involuntary part-time work refers to individuals who, when asked why they work part-time, respond
that they ‘couldn’t find full-time work.’
Portraits of Labor Market Exclusion 2.0 13
Figure 7: At-risk-of-poverty and in-work at-risk-of-poverty rates in EU Member States, 2015
30.0
25.0
20.0
15.0
10.0
5.0
0.0
EU-28
Finland
France
Austria
Hungary
Cyprus
Latvia
Italy
Romania
Denmark
Netherlands
Croatia
Bulgaria
Poland
Germany
Spain
Greece
Czech Republic
Malta
Slovakia
Belgium
Slovenia
Lithuania
Estonia
Portugal
Luxembourg
Sweden
United Kingdom
In-work at risk of poverty 18-64 At risk of poverty total pop.
Note: The EU-28 average is weighted; it does not include Ireland, for which data were not available at the time the
data were extracted.
Source: Eurostat EU-SILC.
The Romanian labor market has a clear urban-rural9 divide, with the majority of the working
poor and self-employed concentrated in rural areas. In 2014, salaried work accounted for only 39
percent of employment in rural areas, compared to 92 percent in urban areas (European Commission,
2016a). Most of the working poor can be found in rural areas, where they mainly work as self-
employed farmers and face low productivity, low enterprise density, an absence of local markets, and
limited income support (World Bank, 2015). Their precarious income situation also puts them at risk
for poverty for years to come, as many do not contribute to the health insurance or pension systems
(Ibid.). Further, rural communities are not only physically distanced from higher-productivity labor
markets, they also lag behind in education terms of access, completion, and performance (European
Commission, 2016a). Such low educational attainment represents a barrier to employment
opportunities outside of their communities. This is particularly problematic as, in the long run, job
creation is expected to occur only in urban areas, with an expected decline in agricultural jobs (Ibid.).
In the short run, rural areas are expected to continue to concentrate low-productivity subsistence
agriculture (Ibid.).
A lack of employment opportunities is strongly associated with living in poverty in Romania.
The World Bank’s analysis — conducted in the context of support for the National Social Inclusion and
Poverty Reduction Strategy (World Bank, 2015) — finds a pronounced and persistent employment
gap between the work-able population in the poorest quintile and those in the top three quintiles.10
Among prime-aged men (aged 35 to 44 years old), the employment rate is 16 percentage points lower
9 According to EU-SILC 2014 data, 42 percent of Romanians of working-age (not in full-time education or
serving in the military) live in thinly populated areas.
10 The definition of quintiles used in these estimations is different than the ones used with data from the EU-
SILC. It is based on consumption rather than income. However, the results are similar regardless of the definition
used.
Portraits of Labor Market Exclusion 2.0 14
among those in the lowest quintile than among those in the top three. This gap is even larger for
women, at about 30 percentage points.
The Roma population is largely excluded from the labor market in Romania. Unlike the overall
Romanian population, the Roma population is a young population, and the share of Roma among the
working age-population is expected to grow (Ibid.). However, this population is largely excluded from
labor market opportunities. Roma have lower employment rates than their non-Romanian
counterparts living in the same neighborhoods. They also have lower labor force participation rates
—reflecting possible discouragement about limited prospects for work —, in addition to higher
unemployment rates. When they do find jobs, they tend to be unstable, informal, low-skilled, low-
paying jobs, often in agriculture. Discrimination and low skills (due to lower educational attainment)
are among the most prominent barriers to employment faced by Roma. (Ibid.).
Roma women are particularly excluded from the labor market, in part due to early marriage
and childbearing. About 28 percent of the Roma between the ages of 15 and 19 are married, versus
just 2 percent of the general population, and the desired age to start having children is 21, in
comparison to 26 for non-Roma women (Ibid.). Early marriage and childbearing results in higher
dependency rates among Roma and are a likely factor behind early school leaving and low labor force
participation rates among Roma women.
Low employment rates, high informality and low productivity are particularly worrisome
given Romania’s rapidly aging population. Rising life expectancy, decreased fertility rates, and
emigration,11 particularly among the younger population, are making Romania one of the most rapidly
aging societies in the European Union. Romania is projected to experience one of the sharpest
increases in the old-age dependency rate — defined as the ratio of people older than 65 to the
population of working age (20 to 64) — among the EU Member States. By 2020, the old-age
dependency rate is expected to reach 33 percent, up from 22 percent in 2014 (World Bank, 2015). By
2050, it will reach 55 percent (Ibid.). The result will be increased pressure on public budgets, with
increased pension and long-term care expenditures and declining growth and income tax revenues. In
order to face these challenges, Romania will have to mobilize all of its potential workers and address
the employment barriers they face.
Figure 8: Age composition of the Romanian population in 2013 and 2050
11In 2013 2.5 million Romanians (about 12.5 percent of the Romania’s population) were estimated to be living
abroad (European Commission, 2016a).
Portraits of Labor Market Exclusion 2.0 15
Source: World Bank, 2015
3. Understanding Employment Barriers — Framework and Methodology
Given that there are now fewer workers and more old-age dependents, labor productivity
improvements to increase employability and skill sets are key to growing the economy. Growth
policies must place at the forefront the need to better utilize Romania’s human capital. Although
statistics based on labor force surveys are categorized in broad groups such as “youth,�? “older
workers,�? and “retirees,�? these groups are not homogenous within themselves; members of each group
presumably face a variety of different employment barriers. Details on the characteristics of these
groups, and the obstacles they face, are difficult to pinpoint. An effective strategy is to identify groups
that share similar employment constraints and socioeconomic characteristics in an effort to design
tailored policy interventions.
Fundamental to crafting a holistic approach to policymaking for the labor market vulnerable
is gaining a deep understanding of their characteristics and their barriers for entering the
labor market. The analysis yields distinct subgroups in terms of barriers to employment as well as
socioeconomic characteristics. Developing narrower and more distinct categories of individuals who
share similar characteristics and face similar constraints provides a stronger evidence base to guide
the design of activation and employment support policies. This process also helps policymakers view
more critically the existing policies and assess their relevance and appropriateness in light of the
needs of the target population and priorities.
The rationale behind this exercise is thus to offer governments — in particular, ministries and
agencies in charge of labor and employment policy — an advanced statistical tool that will shed
light on the characteristics of individuals who are out of work or marginally employed. Simply
put, this tool will support the design of policies and programs that are suited to the distinct needs of
vulnerable individuals with no or low labor market attachment.
3.1 Target population: individuals with potential labor market difficulties
The target population — the focus of the current analysis — encompasses individuals who are
out of work or marginally employed. It is a subset of the Romanian population of working age; this
latter is the population 18 through 64 years old, and it excludes full-time students and those serving
compulsory military service. The target population comprises individuals who self-reported being out
of work during the entire survey reference period in addition to individuals who had weak labor
market attachment due to marginal employment (unstable jobs, restricted working hours, or very low
earnings).12 As such, the analysis offers a broader perspective than common profiling exercises, which
use administrative data collected on registered jobseekers.
12The survey data used were EU-SILC 2013 data, where the reference period is equal to the previous calendar
year, i.e., 2012. EU-SILC data is used rather than the LFS due to the opportunity to observe the labor market
status of each individual over the course of an entire calendar year as well as the richness of this data on
socioeconomic characteristics. The delay in data availability indicates that certain changes in the structure of the
labor market may have occurred since then. For a detailed discussion on the advantages and disadvantages of
EU-SILC data, see Annex 1. The data used on the policy section is the most recent data available.
Portraits of Labor Market Exclusion 2.0 16
This analysis expands upon the scope of traditional profiling exercises by including individuals
who face difficulties entering the labor market as well as those who are not working at an
optimal level (in terms of number of hours or job quality), those not covered by any activation
measures, and those registered as unemployed. Set out in Box 1 is the definition of different labor
market attachment categories for the target population of this analysis.
Box 1: Definition of target population
The target population consists of working-age individuals (ages 18 to 64, excluding full-time
students and individuals in compulsory military service) who are entirely out of work (either
actively searching for a job or inactive) or who are marginally employed, specifically:
o Persistently out-of-work: These individuals report being unemployed, retired, or inactive
throughout the survey reference period (previous calendar year). These individuals were also
not working at the time of the survey interview.
Individuals that are marginally employed can be categorized into the following three non-
mutually exclusive groups:*
o Unstable jobs: identified as those reporting work activity but only for a limited number of
months during the reference period (maximum 45 percent of potential working time) and
those who report no work activity during the reference period, but do report being employed
at the time of the interview;
o Restricted working hours: identified as individuals reporting less than 20 hours of work a
week, for most or all the reference period. Excluded from the target population are
individuals working 20 hours or less because they were in school or in training programs or
because the number of hours they were working is considered to be a full-time job in their
field of work.
o Negative, zero or near-zero labor incomes: identified as individuals reporting some work
activity during the income reference period but negative, zero or near zero earnings.
Specifically, to allow comparison across countries, we adopt the same low-earnings
threshold for all countries at EUR 120/month in purchasing power parities with EU-28 as
the reference. This translates to EUR 55 per month for Romania.
Note: The data source is Eurostat EU-SILC 2013. More detailed information on the definition of each group is available in the
background methodological paper (OECD and World Bank, 2016).
*There are several reasons why the three groups are not mutually exclusive. For example, an individual in an unstable job
could be working restricted hours and could be earning a very low income. However, individuals are assigned to a category,
starting with unstable jobs and ending with negative, zero, or near-zero labor incomes as a residual category.
The target population represents 40 percent of the population of working age (not including
full-time students or those in compulsory military service); the remainder — 60 percent —
comprises individuals with no potential labor market difficulties or “good jobs�? (left panel
Figure 9). In numbers, the target population represents 5.2 million individuals; the population with
“good�? jobs represents 7.8 million individuals.
The target population is heterogeneous and can be categorized into those who are persistently
out of work (29 percent of the working-age population) and those who are marginally
employed (11 percent). The marginally employed can further be disaggregated into (i) those who
Portraits of Labor Market Exclusion 2.0 17
have unstable jobs (1 percent); (ii) those who have restricted working hours (0.4 percent); and (iii) a
remainder category which, despite not having unstable jobs or restricted incomes, have near zero
earnings (10 percent). Similarly, the population that is persistently out of work can be broken down
into unemployed (3 percent)13, retired (13 percent), disabled (1 percent), engaged in domestic tasks
(11 percent), or inactive due to other reasons (1 percent) (right panel in Figure 9).
Figure 9: Composition of working-age* population (left) and out of work (right) in Romania
No labor market 30
10 1
difficulties (outside
1 0 of target pop.) 25 Other
Out of work 11 inactive
20 Domestic
29 tasks
1
Unstable jobs 15 Disabled
60 10 13 Retired
Restricted hours
5 Unemployed
Near zero earnings 3
0
Out of work
* The reference population (working-age population) refers to population aged 18-64 not studying full time or serving compulsory military
service. It represents 12.98 million individuals; of these, 5.2 million, or 40 percent, make up the target population of individuals who are out
of work or are marginally employed. The remaining 60 percent are considered as not having labor market difficulties , i.e., having “good�?
jobs.
Source: World Bank staff calculations based on EU-SILC 2013.
A unique characteristic of the Romanian population of working age is the relatively large
percentage of individuals who have near-zero earnings despite working full time. At first glance,
the make-up of the working-age population in Romania appears similar to that of the average for the
12 countries that are part of this broader study (Figure 10). On average, the target population makes
up 40 percent of the working-age population of the 12 countries, as is the case in Romania. Moreover,
the out of work also make up around 30 percent of the population. However, Romania has a
particularly large percentage of individuals who have near-zero earnings. Ten percent of the
population of working-age is working in stable, full-time jobs but earning near-zero earnings, versus
only 1 percent among the 12 countries under study. The high proportion of workers with near-zero
earnings is a reflection of persistent in-work poverty in Romania, especially among agricultural
subsistence workers. In contrast, Romania has a small percentage of individuals with “unstable jobs,�?
i.e., individuals who report working for a limited number of months during the reference period. This
finding implies that individuals in Romania are less likely to drift into and out of unemployment.
13
The share of unemployed here is calculated using the working age population which includes those who are
inactive. This is not directly comparable to the national unemployment rate where the denominator includes
only unemployed and employed.
Portraits of Labor Market Exclusion 2.0 18
Figure 10: Labor market attachment status of working-age* population, Romania and other EU countries under
study (percent)
100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%
Average**
Greece
Spain
Romania
Italy
Hungary
Ireland
Croatia
Poland
Bulgaria
Lithuania
Estonia
Portugal
No labor market difficulties Persistently out of work Unstable jobs
Restricted working hours Near-zero earnings
* Aged 18-64 and not studying full time or serving compulsory military service.
**Weighted average.
Source: Authors’ calculations based on EU-SILC 2013.
Disaggregating the population that is persistently out of work by labor market status reveals
that the Romanian out-of-work population is disproportionately retired, while a relatively
small proportion is unemployed.14 Only 3 percent of the Romanian population of working age is
classified as unemployed; the proportion classified as unemployed over the 12 countries under study
was over three times as high, at 10 percent (Figure 11). Such a low proportion of unemployed is to be
expected given Romania’s low unemployment rate. Moreover, 13 percent of the Romanian working-
age population reported being retired, versus just 7 percent for the 12 countries. In part, this may
reflect the young retirement age among women of just 59 years, as well the aging of the Romanian
population, resulting in a relatively high concentration of older individuals among the population of
working age. Lastly, it must be noted that the percentage of working-age individuals engaged in
domestic tasks in Romania is also relatively high at 11 percent, reflecting traditional gender roles and
low labor market participation among Romanian women.
14 The out of work refer to individuals who report being unemployed or inactive over the entire reference period
as well as at the time of the survey interview. Labor market status refers to the main activity reported during
the reference period.
Portraits of Labor Market Exclusion 2.0 19
Figure 11: Composition of the persistently out of-work population by labor market status, Romania and other EU
countries under study (as a percentage of working age)
45
40
35
30
25
20
15
10
5
0
Romania
Hungary
Italy
Greece
Estonia
Bulgaria
Poland
Spain
Ireland
Croatia
Lithuania
Portugal
Average*
Unemployed Retired Disabled Domestic tasks Other inactive
*Weighted average.
Notes: 1. Working-age population refers to the population 18-64 years of age who are not studying full time or serving compulsory
military service. 2. Out of work individuals report being unemployed or inactive during each of the 12 months of the reference period and
at the time of the survey interview. Labor market status refers to the main activity reported during the reference period.
Source: Authors’ calculations based on EU-SILC 2013.
3.2 Employment Barrier Indicators
In order to segment the target population into distinct groups according to labor market barriers and
socioeconomic characteristics, a set of indicators has been formulated to capture the employment
barriers that prevent individuals from being partially or fully active within the labor market. These
indicators represent the following three types of employment barriers, as defined below and
illustrated in Figure 12.
1. Insufficient work-related capabilities include factors that may limit an individual’s ability to
perform certain tasks. These include, for example, low education (as a proxy for skills); low
work experience; care responsibilities; or limitations in daily activities due to health status;
2. Weak economic incentives to look for or accept a “good�? job: an individual may decide not
to participate in the labor market if they could potentially lose social benefits when taking up
work or a higher-earning jobs (substitution effect) or if they already have a high standard of
living due to other income sources and can therefore consume more leisure (income effect);
and
Portraits of Labor Market Exclusion 2.0 20
3. Scarce employment opportunities: opportunities for employment may be scarce due to a
shortage of vacancies in the relevant labor market segment (geographical area or sector);
friction in the labor market due to information asymmetries, skills mismatches,
discrimination, or lack of social capital among other frictions present in labor markets.
Figure 12: Employment barrier framework
Source: OECD and World Bank (2016).
The three types of barriers described above cannot be directly observed using survey data.
Thus, a set of eight indicators have been constructed using EU-SILC 2013 data in order to proxy
for broad measures for each of the three different types of employment barriers. Together, the
eight indicators serve as a starting point for identifying and characterizing the target population
according to the barriers they face. However, bear in mind that while these indicators are able to
capture broad aspects of the three main types of employment barriers identified in this framework,
they do not offer a comprehensive view of labor market barriers. The indicators represent the barriers
that we are able to capture using EU-SILC data. Moreover, employment barriers are complex and are
often the result of the interaction of different individual and household characteristics including
gender, age, socioeconomic status, ethnicity, social and cultural norms, as well as frictions in the labor
market that we are unable to capture with household data. The indicators used for Romania are
outlined in Box 2. Additional information on the definitions and construction of each indicator is
available in Annex 2, as well as in the joint methodological paper (OECD and World Bank, 2016).
Portraits of Labor Market Exclusion 2.0 21
Box 2: Definitions of employment barrier indicators used for Romania
The indicators represent the three broad types of employment barriers and are constructed from EU-SILC
2013 data as follows:
Five indicators are used to proxy for capabilities barriers:
1. Low education: if an individual has an education level lower than upper secondary education in
the International Standard Classification of Education (ISCED)-11 classification);
2. Care responsibilities: if an individual lives with someone who requires care (i.e., children 12 and
under receiving under 30 hours of non-parental care a week, incapacitated household members
or elderly with health limitations) and is either the only potential care giver in the household or is
reported as inactive or working part time because of care responsibilities;
3. Health limitations: if an individual reports some or severe self-perceived limitations in daily
activities due to health conditions;
4. Low relative work experience: if an individual has worked less than 60 percent of the time since
they left full-time education;
5. No recent work experience:
▪ The indicator may represent two situations:
(i) those who have worked in the past but have no recent work experience (have not
worked for at least 1 month in the last semester of the reference period or at the
month of the interview);
(ii) those who have never worked;
Two indicators are used to proxy for incentives barriers:
6. High non-labor income: if household income (excluding that from the individual’s work-related
activities) is more than 1.6 times higher than the median value in the population of working age;
7. High replacement benefits: if earnings-replacement benefits (excluding categorical social
benefits) are more than 60 percent of an individual’s estimated potential earnings in work ;
One indicator is used to proxy for scarce employment opportunities:
8. Scarce employment opportunities*: if an individual is estimated to have a high probability of
being unemployed or involuntarily working part time due to their age, gender, education, and
region of residence.
*The scarce employment opportunities indicator does not take into account the fact that individuals who are not unemployed but are
inactive may nonetheless face scarce opportunities if they were to search for a job.
The target population is more likely to face almost all of the identified employment barriers
than the population of working age. It is clear that, with the exception of the scarce opportunities
and high non-labor income barriers, the target population is more likely to face each employment
barrier (Table 2).15 The most common barriers among the target population are having no recent work
experience (in total 73 percent do not have recent work experience, with 28 percent having never
worked); low relative work experience (48 percent) and low education (45 percent). In contrast, only
15The care responsibilities barrier, by definition, does not affect individuals who are not members of the target
population, since they are not considered potential caregivers (see definition of care responsibilities barrier
above). The barriers associated with recent work experience only affect the out-of-work population, as the
population with stable jobs, by definition, has recent work experience since they have all worked for at least 1
month during the last semester of the reference year or at the month of the interview. All other barriers can
equally affect all individuals who are part of the reference (working-age population) and the target population.
Portraits of Labor Market Exclusion 2.0 22
27 percent of the working-age population have low education and only 29 percent have low relative
work experience.16 One-third of the target population reports having health limitations in their daily
activities, much higher than the proportion for the working-age population, and also relatively high
considering that only 4 percent of the out-of-work report “disabled (unfit to work)�? as their main
activity during the reference period. Over one-quarter of the target population was found to have
scarce job opportunities due to their gender, age, education, and the region where they reside;
however, the figure for the working-age population is even higher, at 32 percent. Work disincentives
due to non-labor income is also a barrier that is more likely faced by the working-age population. In
other words, individuals among the target population are less likely to have high-earning spouses or
otherwise have high incomes not coming from their own labor. Nonetheless, the target population is
much more likely to receive a high level of social benefits that may be reduced when working full-time
in a high quality job. It must be noted, however, that only 10 percent of the target population faces
such a barrier, meaning that high incomes in general do not seem to be deterring this population from
taking up employment. The share that faces care responsibilities among the target population is also
relatively low, at just 13 percent.
Within the target population, the persistently out of work are much more likely to face each
employment barrier than the marginally employed population, with the exception of low
education and scarce opportunities. The marginally employed population, by definition, is not
likely to face the care responsibilities or the no recent work experience barrier; this is because they
are likely to be found in employment at the time of the interview, and are thus not considered as facing
these barriers. Interestingly, they are more likely to face the scarce opportunities barrier, which may
be partly explained by the fact that they also tend to have a higher incidence of the low education
barrier. Those who are marginally employed have a much lower incidence of health limitations, low
relative work experience, or incentives barriers.
Table 1: Characterization of Romanian target and working-age population according to barrier
indicators (percent)
Working-age Target population
population* Persistently Marginally
INDICATOR All out of work employed
Capabilities barriers
1- Low education 27 45 41 55
2- Care responsibilities** 5 13 17 2
3- Health limitations 19 33 39 16
4- Low relative work experience (WE) 29 48 56 27
No recent WE - Has worked in the past** 18 45 61 1
5-
No recent WE - Has never worked** 11 28 39 0
Incentives barriers
6- High non-labor income 24 19 23 6
7- High earnings-replacement benefits 4 10 13 1
Opportunity barrier
8- Scarce job opportunities 32 26 24 31
16The figures for the no recent work experience barrier are also significantly lower when the entire working-
age population is taken into account; however, by definition, individuals who are not part of the target
population do have recent work experience since they are in “good�? jobs.
Portraits of Labor Market Exclusion 2.0 23
Source: World Bank staff calculations based on EU-SILC 2013.
Note: The target population makes up 40 percent of the working-age population.
*Excludes individuals who are studying full time or in compulsory military service.
**By definition, this barrier does not affect individuals who are not members of the target population.
Compared to other countries under study, the target population in Romania stands out as
having a high percentage of individuals with low education and who have never worked before.
A cross-country comparison of the employment barrier indicators among the target groups in six EU
countries (Table 2) reveals that the target population in Romania has a very high share of individuals
who face the low education barrier.17 Almost one-half of the target population has not completed
upper secondary school, placing them at a disadvantage in a labor market that increasingly demands
a higher skilled workforce. The percentage of individuals who have never worked is also relatively
high for Romania, at 28 percent. It follows that the Romanian target population also stands out as
having a relatively low percentage of individuals with no recent work experience who have worked in
the past. This is the case for only 45 percent of the target population in Romania, versus between 59
and 73 percent for the other countries under study. These findings regarding work experience may be
a reflection of a large percentage of youth and women who have never entered the labor force.
Although the Romanian target population does include a relatively high percentage of women
who are dedicated to domestic tasks, in relative terms, the percentage of individuals facing a
care responsibilities barrier (13 percent) is similar to the average across the six countries
under study. This indicates that many women who report being inactive due to engagement in
domestic tasks do not have young children or other family members who are in need of care. Thus,
factors other than lack of childcare or elderly care, such as lack of skills and entrenched gender roles,
may be playing a more significant role in holding Romanian women back from the labor force.
Finally, the scarce employment opportunities barrier appears to have a low prevalence in
Romania when compared to other countries. This may be because Romania has a very small
percentage of unemployed or involuntary part-time workers. As such, a small percentage of the
population is statistically likely to resemble the population in these categories, given their age, gender,
education level or region.
Table 2: Characterization of target population according to barrier indicators (percent): international comparison
Country Bulgaria Croatia Greece Hungary Poland Romania Average
Share of target group facing each barrier by country (percent)
Capabilities barriers
1- Low education 38 30 81*** 31 19 45 33
2- Care responsibilities 13 12 16 15 15 13 14
3- Health limitations 19 33 19 37 30 33 29
4- Low relative work experience (WE) N/A* 59 57 N/A* 43 48 52
No recent WE - Has worked in the
5- past 58** 65 59 73 66 45 62
No recent WE - Has never worked 19** 20 26 9 10 28 19
Incentives barriers
17 The figure for Greece, at 81 percent, encompasses individuals who have not completed tertiary education.
Portraits of Labor Market Exclusion 2.0 24
6- High non-labor income 18 20 23 19 19 19 20
7- High earnings-replacement benefits 6 3 12 14 9 10 9
Opportunity barrier
8- Scarce employment opportunities 47 35 45 41 32 26 38
* In Bulgaria and Hungary, a significant share of observations on work experience was missing from the EU-SILC 2013 dataset: as a result,
the low relative work experience indicator could not be constructed for these countries.
** In Bulgaria, a significant share of observations was missing from the data on activities conducted in the reference year: as a result, the
indicator was constructed differently than in the other countries.
*** In the case of Greece, the cut-off for low education has been set at post-secondary rather than lower secondary level. The reason for the
change in the cut-off is that a look at unemployment (employment) rates by education level shows that unemployment (employment) only
falls (rises) significantly among individuals who have completed tertiary education.
Note: Average figures do not include figures marked with asterisks.
Source: World Bank staff calculations based on EU-SILC 2013 for Bulgaria, Croatia, Hungary, Poland, and Romania, and on EU-SILC 2014 for
Greece.
The statistical clustering method utilized in this note to analyze the target population is Latent
Class Analysis (LCA). This method exploits the observed proxies of the different categories of
employment barriers as captured by the employment barrier framework (Figure 12). LCA is a statistical
segmentation technique that enables a characterization of a categorical latent variable (unobserved;
in this case labor market vulnerability) starting from an analysis of relationships among several
observed variables (“indicators�? as defined above). It allows the statistical segmentation of the target
population into distinct but homogenous groups with similar barriers to employment in each group,
while across groups the profile of employment barriers would differ. In contrast to traditional
regression analysis, which identifies the effect of one barrier while assuming all the other barriers stay
constant, LCA exploits the interrelations of the employment barriers and the joint determination of
the observed outcome (Further details on LCA, and selection of indicators is provided in the OECD-
World Bank Joint Methodology Paper, 2016).
4. Results: Portraits of Labor Market Exclusion in Romania
Applying the above clustering methodology results in the classification of the target population
into six distinct groups. The six groups vary in terms of size (as shown in Figure 13), demographic
and socioeconomic characteristics, and the mix of barriers faced. The groups have been named
according to their most salient characteristics. This naming, however, is subjective in nature,
necessitating a closer look at the mix of barriers faced by each group in addition to the fuller list of
individual and household socioeconomic characteristics that are also pertinent for the design and
tailoring of active labor market policies. Table 3 elucidates the most salient barriers for each population
group emerging from the analysis. Annex 4 provides additional lists of details of group characteristics
that provide the basis for the group names. In order to put the characteristics of the groups in context,
they are also shown for the target population as a whole and for the working-age population. Details
on the selection of the model can be found in Annex 3.
Portraits of Labor Market Exclusion 2.0 25
Figure 13: Latent groups within the Romanian target population
Group 1. Higher income (early)
Group 6, 6% retirees with health limitations
Group 5, 8%
Group 2. Low-educated inactive
middle-aged women with no
work experience
Group 3. Middle-aged less
Group 4, Group 1, 36% educated rural working poor
12%
Group 4. Young mostly female
NEETs with no work experience
Group 5. Young less educated
Group 3, 18% rural working poor
Group 2, 20% Group 6. Mostly male relatively
educated long-term unemployed
or inactive with past work
experience
Source: World Bank staff calculations based on EU-SILC 2013.
The largest of the six identified groups, comprising over one-third of the target population, is
made up of retirees. Group 1: Higher income (early) retirees with health limitations is the largest
identified group. Comprising retirees, it is reflective of a low retirement age (especially for women)
and an aging population. On the other hand, none of the identified groups are made up of exclusively
or predominantly unemployed persons, either at the time of the interview or during most of the
reference period. This reflects the fact that the unemployed population in Romania is heterogeneous
and also relatively small. Two groups stand out as being made up primarily of women: the low-
educated inactive women in Group 2 make up 20 percent of the population, whereas an additional 12
percent are mostly female young NEETs (Group 4).
Portraits of Labor Market Exclusion 2.0 26
Table 3: Result of latent class analysis for population with labor market difficulties in Romania: labor market
barriers
Group 2. Group 6.
Low- Mostly male
Group 1. educated Group 4. relatively
Higher inactive Group 3. Young educated long-
income middle- Middle- mostly Group 5. term
(early) aged aged less female Young less unemployed
retirees women educated NEETs educated or inactive
with with no rural with no rural with past
health work working work working work Target
limitations experience poor experience poor experience pop.
Percent of target population 36 20 18 12 8 6 100
Thousands of individuals 1,880 1,023 943 645 417 307 5,215
Share of individuals facing each barrier, by class
Capabilities barriers
1- Low education 30 61 56 51 56 16 45
2- Care responsibilities 10 25 0 25 0 19 13
3- Health limitations 53 33 17 14 8 28 33
Low relative work
4- experience (WE) 21 100 24 100 31 29 48
No recent WE - Has worked
in the past 98 15 0 8 1 90 45
5-
No recent WE - Has never
worked 0 85 0 92 0 0 28
Incentives barriers
6- High non-labor income 26 21 6 16 5 25 19
High earnings-replacement
7- benefits 24 2 0 2 0 12 10
Opportunity barrier
Scarce employment
8- opportunities 0 0 0 100 100 100 26
Notes: Color shadings identify categories with high (darker) frequencies. See Box 1 for a brief explanation of the indicators. Only categories
depicting barriers to employment are included; complementary categories are omitted. See Annex 1 for full list of active covariates and
demographic and socioeconomic descriptives.
*Selected as priority groups for policy intervention (see section 5).
Source: World Bank staff calculations based on EU-SILC 2013.
The groups also vary in terms of the average number of barriers faced, as well as the prevalence
of simultaneous barriers. Figure 14 shows the distribution of the number of barriers faced by
individuals in each group (left axis), as well as the average number of barriers faced (right axis). On
average, all individuals in the target population face a total of 2.7 barriers;18 more than half (55
percent) face three or more barriers. Across groups, Group 4 stands out as having a particular high
average number of barriers (4.1), with all members of this group facing three or more barriers. At the
other end of the spectrum, Group 3 has only 1.1 barriers on average. It is important to note, however,
that the number of barriers on its own hides important information concerning the nature of such
barriers.
18
The highest possible number of barriers that an individual can face is eight.
Portraits of Labor Market Exclusion 2.0 27
Figure 14: Distribution of number of barriers faced by individuals in each latent group in Romania
100% 4.5
90% 4.1 4.0
80% 3.4 3.5
70% 3.2
3.0
60% 2.6 2.7
2.5
50%
2.0 2.0
40%
1.5
30%
1.1 1.0
20%
10% 0.5
0% 0.0
Group 4 Group 2 Group 6 Group 1 Group 5 Group 3 Target pop.
12% 20% 7% 36% 7% 17% 100%
5 or more barriers 4 barriers
3 barriers 2 barriers
1 barrier No major barriers
Average no. of barriers (right axis)
Note: Relative group sizes below the groups. Groups are ordered according to average number of barriers.
Source: World Bank staff calculations based on EU-SILC 2013.
The boxes that follow provide short descriptions of each of the six groups, focusing on the most salient
employment barriers faced and characteristics such as age, gender, geographical location, at-risk-of-
poverty, among others. For each group, we highlight those characteristics that set them apart.
Portraits of Labor Market Exclusion 2.0 28
Group 1. Higher income (early) retirees with health limitations (36 percent of the target
population)
➢ 81 percent retired*; 11 domestic tasks*
➢ 71 percent aged 56 to 64; average age is 57
➢ 64 percent female
36%
➢ 53 percent report health limitations
➢ 26 percent have high non-labor income
➢ 24 percent have high earnings replacement
➢ 47 percent in top two income quintiles
➢ 74 percent are married
➢ Average number of simultaneous barriers: 2.0
*Refers to main activity during the reference period
Group 1: Higher income (early) retirees with health limitations, the largest of the six identified
groups, comprises over one-third of the target population, or 1.9 million individuals. With
retirees making up 81 percent of this group, it is not surprising that 98 percent report having
worked in the past, yet not having recent work experience. Seventy-one percent of this group is
between 56 to 64 years of age; as such, though a large percentage may have already reached
retirement age, many may be considered early retirees.19 Most of the members of this group are
female (64 percent), in part reflecting the fact that Romanian women face a lower retirement
age than men. Over 50 percent of this group also reports having some or severe limitations in
their daily activities due to health status. Though these tend to be non-severe — only 18 percent
report severe limitations — this group is the most likely to face a health limitations barrier.
Group 1 also stands out as having a relatively high earnings replacement barrier: 24 percent
face this barrier (mainly due to the receipt of old-age benefits), compared to only 10 percent of
the target population. Twenty-six percent also have relatively high non-labor income that may
discourage participation in the labor market (versus 19 percent for the target population).
Related to this is the fact that only 16 percent of this group is at risk of poverty and 47 percent
are in the top two income quintiles (versus 36 and 25 percent of the target population,
respectively). Compared to the target population, the group is relatively urban, with 35 percent
living in densely populated areas; nonetheless, a significant proportion (almost 40 percent) does
live in thinly populated areas. Finally, although the group is older and does not have recent work
experience, none face scarce job opportunities; this is because their characteristics do not
mirror those who declare themselves as unemployed or who involuntarily work part-time;
however, they may nonetheless have a difficult time finding a job if they were to enter the labor
market due to their relatively older age and lack of recent work experience, as well as health
limitations (the latter faced by half of the members of this group). In terms of average number
of barriers, at 2.6, this group is close to the average for the target population. The two most
common barriers faced are no recent work experience (but having worked in the past) (98
percent) and health limitations (53 percent).
19 The current retirement age in Romania is 64 for men and 59 for women.
Portraits of Labor Market Exclusion 2.0 29
Group 2. Low-educated inactive middle-aged women with no work experience (20 percent of the
target population)
➢ 98 percent are inactive: 78 percent engaged in domestic tasks; 12
percent retired; 6 percent unfit to work; 3 percent other inactive
➢ 93 percent female
➢ 77 percent married
➢ 79 percent aged 30 to 55; average age is 45
➢ 100 have no recent work experience; 85 percent have never worked
➢ 61 percent have low education
➢ 25 percent have care responsibilities
➢ Average number of simultaneous barriers: 3.4
20%
Group 2: Low-educated inactive middle-aged women with no work experience makes up one-fifth of the
target population, or about 1 million individuals. The group is predominantly female (93 percent) and
almost entirely inactive (98 percent). The main type of inactivity among this group is engagement in
domestic tasks (78 percent), with an additional 12 percent in retirement. Most of the members of this
group are middle-aged (79 percent are aged 30 to 55) with the remainder in the older category (56 to
64 years of age); the average age is 45. They tend to be married (77 percent), and close to one-half (45
percent) live with a child under the age of 12. This group faces several capabilities barriers. First, 25
percent are considered to have care responsibilities – although this may represent only one-fourth of
the group, this is double the proportion found among the target population. Eighty-five percent of this
group has never held a job and none have recent work experience; it is thus not surprising that all of the
members of this group have low relative work experience. Among all the identified groups, this group
has the highest percentage of members who have not completed upper secondary and thus face a skills
barrier — 61 percent, versus 45 percent for the target population. One-third of the group also reports
having some or severe health limitations in their daily activities. Twenty-one percent of the group has
high non-labor income — a percentage that is very similar to that of the target population. Non-labor
income, however, tends to come from the labor of other household members, since only 2 percent of the
group faces a barrier due to high benefit receipt (though 71 percent do receive at least some form of
benefit). Forty-three percent are at risk of poverty — a similar proportion to that of the target
population (39 percent). This group does not face scarce job opportunities, but this may be because they
do not resemble the population that is unemployed or that is involuntarily working part time, not
necessarily because they have a high probability of finding employment. With an average of 3.4, this
group faces a relatively high average number of barriers. The most common barriers are: low relative
work experience (100 percent), no recent work experience (100 percent, with 81 percent having never
worked), and low education (61 percent).
Portraits of Labor Market Exclusion 2.0 30
Group 3: Middle-aged less educated rural working poor (18 percent of the target population)
➢ 95 percent working
➢ 81 percent live in thinly populated areas
➢ 53 percent live in the eastern part of the country (northeast
and southeast NUTS 1 region)
➢ 82 percent aged 30 to 55; average age is 45
➢ 66 percent at risk of poverty
➢ 62 percent female
➢ 56 have low education
➢ Average number of simultaneous barriers: 1.1
18%
Group 3: Middle-aged less educated rural working poor makes up 18 percent of the target population, or
about 943,000 individuals. Predominantly rural (81 percent living in thinly populated areas), middle-
aged (82 percent aged between 30 and 55), and mostly female (62 percent), this group was working
during most of the reference period and at the time of the interview, but has very low earnings and has
a high risk of poverty (66 percent). They do not tend to face income or substitution effect barriers, as
their non-labor and benefit income is relatively low. Sixty-three percent live in households that receive
at least one type of benefit, versus 77 percent of the target population. The group does not face a barrier
in terms of health limitations, as only 17 percent report some or severe limitations in their daily
activities due to health status (only 3 percent report severe limitations). By definition, because most of
the members of this group were working at the time of the interview, they are not considered as facing
a care responsibilities barrier (although over one-third live in a household with a child under the age of
12). They also do not resemble the population that is unemployed or involuntarily working part-time
and thus do not face scarce job opportunities. Likewise, because they are working, they do not face the
barrier related to lack of recent work experience; about a quarter, however, do have low relative work
experience. Just over one-half of this group lives in the (generally poorer) northeastern and
southeastern region of Romania. This group stands out as having the least number of barriers, with an
average of only 1.1. The most commonly faced barrier is low education (56 percent), followed by low
relative work experience (24 percent).
Group 4: Young mostly female NEETs with no work experience (12 percent of the target population)
Portraits of Labor Market Exclusion 2.0 31
➢ 88 percent aged 18 to 29; average age is 26
➢ 74 percent inactive (54 percent engaged in domestic tasks; 11
percent are other inactive; 8 percent unfit to work); 26 percent
unemployed.
12% ➢ 68 percent female
➢ 100 percent have no recent work experience (92 percent have never
worked)
➢ 100 percent have low relative work experience
➢ 26 percent have care responsibilities
➢ 57 percent live with at least one parent
➢ 37 percent live in the South Muntenia and Bucharest region
➢ Average number of simultaneous barriers: 4.1
Youth who are neither in employment, education or training (NEETs) and have no work experience
dominate in Group 4, making up 12 percent of the target population, or about 645,000 individuals. The
group is mostly female (68 percent) and young (88 percent between 18 and 29 years of age; average
age of 26). The group is also relatively concentrated in the South Muntenia and Bucharest region, with
37 percent residing here (versus 26 percent of the target population). Seventy-four percent report being
inactive (with 54 percent dedicated to domestic tasks), although over one-fourth is unemployed. About
half of this group has low education (55 percent have not completed upper secondary) and, as is often
the case among youth, almost all have never worked before (95 percent). A little over one-half live with
children that are under 12 yet only a quarter have been identified as facing the care responsibilities
barrier (double the proportion found among the target population). The majority (57 percent) also lives
with their parents (as may be expected given their young age and mostly single marital status (62
percent)). Almost none receive high benefits that would discourage them from working and only 16
percent have high non-labor incomes, despite the high proportion living with their parents. Almost half
are at risk of poverty (versus 39 percent of the target population). All the members of this group face
scarce job opportunities. Notably, this group faces the highest average number of simultaneous barriers:
4.1. The most commonly faced are low relative work experience (100 percent), scarce employment
opportunities (100 percent), no recent work experience (100 percent, with 95 having never worked),
and low education (52 percent).
Group 5: Young less educated rural working poor (8 percent of the target population)
➢ 90 percent aged 18 to 29; average age is 26
8% ➢ 94 percent working during the reference period
➢ 71 percent live in thinly populated areas;
➢ 52 percent live in the eastern part of the country (northeast
and southeast NUTS 1 regions)
➢ 74 percent live with at least one parent
➢ 62 percent at risk of poverty
➢ 57 percent are male
➢ Average number of simultaneous barriers: 2.0
Portraits of Labor Market Exclusion 2.0 32
Group 5: Young less educated rural working poor makes up just 8 percent of the target population, or
about 417,000 individuals. Like its middle-aged counterpart (Group 3), this group reported being
employed during most of the reference period (94 percent), yet its members have very low income (86
percent have near-zero income and 62 percent are at risk of poverty). Likewise, this group does not face
the care responsibilities barrier (due to its working status), yet a large percentage (43 percent) live with
children under 12 years of age. What distinguishes this group from Group 3 is its young age (90 percent
are between 18 and 29 years old, with an average age of 26) and the fact that all of its members face
scarce job opportunities, i.e., they mostly resemble the unemployed or those involuntarily working part-
time. This group is also more likely to be male (57 percent are male, versus only 48 percent of Group 3)
and more likely to be single and living with their parents (this is unsurprising giving their relatively
young age and unmarried status). Otherwise, this group is similar to Group 3 in terms of skills (56
percent face the low education barrier, as they have not completed upper secondary) and incentives
barriers (hardly any face negative incentives due to high non-labor income or high benefits).
Furthermore, like Group 3, a slight majority of this group (52 percent) resides in the (generally poorer)
northeast and southeastern region of the country; the great majority (71 percent) live in thinly
populated areas (less than Group 3 (81 percent)). On average, the members of this group face 2.0
barriers, less than the average of 2.7 for the target population. The most salient barriers for this group
are: scarce employment opportunities (100 percent), low education (56 percent), and low relative work
experience (31 percent).
Group 6: Mostly male relatively educated long-term unemployed or inactive with past work
experience (6 percent of the target population)
➢ 47 percent long-term unemployed*; 45 percent inactive
6% (retired/domestic tasks/unfit to work/other inactive)*; 7
percent working*
➢ 59 percent live in densely populated areas
➢ 76 percent are male
➢ 64 percent middle aged (30-55); average age is 44
➢ 84 percent have complete upper secondary or above
➢ 82 percent have no recent work experience but have worked in
the past
➢ 48 percent live in the South Mt. and Bucharest region
➢ Average number of simultaneous barriers: 3.2
*During the reference period
Portraits of Labor Market Exclusion 2.0 33
Group 6: Mostly male relatively educated long-term unemployed or inactive with past work experience
comprises just 6 percent of the target population, or about 307,000 individuals. This group is the most
urban (59 percent reside in densely populated areas) as well as the least female (only 24 percent). It is
also the group with the highest percentage of individuals who report being unemployed during the
reference period (48 percent, versus only 8 percent for the target population; the only other group with
a significant proportion of unemployed is the group of young NEETs (Group 4), with 26 percent
reporting unemployment as their main labor market status during the reference period). Almost all of
the members of this group who report unemployment as their main activity during the reference period
are long-term unemployed. An additional 45 percent are inactive (they report their main activity during
the reference period as retired, engaged in domestic tasks, unfit to work, or other inactive). As regards
the capabilities barriers faced by this group, their long-term unemployment status is partially reflected
in the fact that 90 percent lack recent work experience; however, all have worked in the past, and only
29 percent have low relative work experience. The group is also the most educated of the six groups —
only 16 percent face the low education barrier. Over one-fourth reports health limitations. Twelve
percent faces possible disincentives to work due to high benefits, whereas one quarter face possible
disincentives due to high non-labor income. The group is somewhat less poor than the target population
as a whole (35 percent are at risk of poverty, versus 39 percent of the target population). Age wise, they
are mostly middle-aged (64 percent aged 30 to 55, with an average age of 44). Finally, 100 percent face
scarce job opportunities. At 3.2, the average number of barriers faced by this group is above the average
of 2.7 for the target population. No recent work experience (although having worked in the past) (90
percent), low relative work experience (29 percent), and health limitations (28 percent) are the most
common barriers among this group’s members.
5. Priority Groups in the Romanian Target Population
Among the six identified groups in the target population in Romania, four groups were singled
out for prioritization for activation and employment support policies in consultation with the
Government of Romania. They consist of: two groups of out of work individuals — Group 2 (Low-
educated middle-aged women with no work experience) and Group 4 (Young mostly female NEETs
with no work experience) — together with the two groups of working individuals — Group 3 (Middle-
aged less educated rural working poor) and Group 5 (Young less educated rural working poor).
The rationale for selection of these groups is based on salient features of the Romanian labor market
that are also considered a priority for intervention: a low labor force participation rate among women,
a large percentage of youth who are neither in employment, education or training as well as a high
unemployment rate among youth, and a high proportion of in-work poor who are predominantly
engaged in subsistence or sub-subsistence agriculture. Together, these four groups represent 58
percent of the target population, or about 3 million individuals.
Group 1 (Higher income (early) retirees with health limitations) — the largest group, representing
36 percent of the target population— is not considered a priority for activation because its members
are predominantly retired. Even if not all have reached the statutory retirement age, their advanced
age indicates that they have relatively few working years left and are also not likely to find
employment, even if they do have high relative work experience and education.
Group 6 (Mostly male relatively educated long-term unemployed and inactive with past work
experience) was also not selected as a priority group; this is mainly due to its small size and low
Portraits of Labor Market Exclusion 2.0 34
poverty status. This does not imply, however, that activation and employment support policies
should not address long-term unemployment or inactivity among more educated, male individuals.
Such policies remain important as the country aims to increase employment and ensure
improvements in the quality of life of Romanians. Nonetheless, it is important to note that the group
represents only 6 percent of the target population and comprises highly educated mostly male (long-
term unemployed or inactive). Although about half of this group is long-term unemployed, its small
size, low poverty status in relation to the priority groups, and male, urban, relatively educated
composition makes the other four groups a higher priority for activation and social inclusion.
In what follows, we take a closer look at the employment barriers faced by the prioritized
groups, in addition to their socioeconomic characteristics, in order to provide more detailed
profiles that can be used to design and prioritize AESPs that address their needs. Given broad
similarities between the groups, we analyze Group 2 (Low-educated inactive middle-aged women)
and Group 4 (Young NEETs) together, and Group 3 and Group 5 (the working poor) together. Table 4
shows the labor market barriers and demographic and socioeconomic characteristics of the four
priority groups. In broad terms, Group 2 and Group 4, as seen in Figure 14 above, have a high number
of simultaneous barriers: on average, Group 2 faces 4.1 barriers, whereas Group 4 faces 3.4 (the
average for the target population is 2.7). Group 3 and Group 5, on the other hand, face a relatively
small number of simultaneous barriers: only 1.1 and 2.0 on average, respectively.
Both Group 2 and Group 4 are made up of predominantly inactive individuals who are
dedicated to domestic tasks; however, there are important differences with respect to age and
gender, with Group 4 also containing some active individuals. Group 2 is a group of mostly middle-
aged women who are all inactive and mostly dedicated to domestic tasks. Group 4, on the other hand,
is made up of younger individuals (42 percent are aged 18 to 24, and 46 percent are aged 25 to 29);
most of them are female, but an important proportion (32 percent) is also male. Like Group 2, it is a
predominantly inactive group with a concentration of individuals who are also dedicated to domestic
tasks; however, a little over one-fourth are part of the labor market and are for the most part long-
term unemployed. Given the fact that those who are unemployed have been unemployed for 12 more
months,20 it is possible that some of the inactive in this group may represent discouraged workers who
have left the labor force after a long period of unemployment. Likewise, in terms of ease of activation,
it is important to note that long-term unemployment places individuals at a particular disadvantage
in the labor market. Long spells without a job can lead to skill deterioration. Many employers also
prefer hiring workers with shorter gaps in their employment history. Finally, long-term
unemployment itself can lead to discouragement and a reduction in job search intensity.
There are some important similarities between Group 2 and Group 4 as regards the identified
barriers to employment. First, the two groups are made up entirely of individuals who have low
relative work experience, meaning that, in general, they have spent less than 60 percent of their
potential working lives at work.21 In fact, the great majority of them have never worked at all (85
20Long-term unemployment is defined as unemployment lasting 12 or more months.
21Although, on average, the members of Group 2 who have worked before have 13 years of work experience
(versus only 4 years among the members of Group 4), this is a relatively low number relative to their full
potential work experience given their age and education level. This means that they have significant gaps in their
work employment record, resulting in greater difficulty in finding employment due to disengagement from the
labor market and possible skill deterioration.
Portraits of Labor Market Exclusion 2.0 35
percent and 92 percent of groups 2 and 4, respectively). Their lack of work experience altogether may
disqualify them from a large number of jobs. Likewise, many of the members of both groups have low
education, especially in the case of Group 2. Sixty-one percent of Group 2 has not completed upper
secondary school; the corresponding figure for Group 4 is 51 percent. It is particularly striking that
about one out of six individuals in both of these groups, respectively, only has up to a primary level
education. Tertiary education is especially rare among Group 2 — only 3 percent have completed this
level. In contrast, among the group of young NEETs, 12 percent have reached this level, meaning that
a significant, albeit small proportion, of this group could be considered high skilled and may as a result
be closer to the labor market.
Among Group 2 and Group 4, 25 percent face the care responsibilities barrier, respectively,
versus just 13 percent of the target population. The relatively high incidence of the care
responsibilities barrier is in part related to the fact that these two groups are predominantly female,
especially Group 2. The women of Group 422 are more likely to face the care responsibilities barrier
than those of Group 2: 37 percent of Group 4 women face the care responsibilities barrier versus 27
percent of Group 2 women.23 Around half of both groups live with children under the age of 12. In
particular, 8 percent of Group 2 and 15 percent of Group 4 report that not all the children under 12 in
the household are enrolled in formal care. Group 4, likely due to its younger age, is also more likely to
have children under 6 or under 3 in the household. In both groups, about one out of six individuals
also lives with an elderly individual,24 which, in part, can also contribute to the care responsibilities
barrier.
A few of the individuals in Group 2 and Group 4 may choose to remain inactive due to income
they can draw upon independent of their own work effort. This could be the case for one-fifth and
one-sixth of Group 2 and Group 4, respectively, as signified by the high non-labor income barrier. In
particular, many of the individuals in these two groups have spouses who are working (59 and 37
percent of Group 2 and Group 4, respectively), while a significant proportion of Group 4 (57 percent)
is also still living with their parents.
On the other hand, the proportion of individuals in Group 2 and Group 4 who may be
discouraged from work due to high earnings-replacement benefits is negligible, at just 2
percent. Although about 70 percent of each of the two groups receive at least one kind of social benefit
(in particular, 56 and 60 percent of Group 2 and Group 4 receive family benefits, respectively, and 17
and 23 percent of Group 2 and Group 4 receive social exclusion benefits, respectively), the amount
received is low in comparison to the minimum wage or the wage that these individuals could expect
to receive in the labor market. It is also worth noting that despite benefit receipt, the at-risk-of-poverty
rate among the members of these groups is high at 43 percent of Group 2 and 49 percent of Group 4.
Likewise, a high proportion is living in severe material deprivation: 39 and 54 percent of Group 2 and
Group 4, respectively. As such, although a high proportion of the members of these groups are indeed
inactive and dedicated to domestic tasks, the choice to remain at home does not appear to be due to
benefit receipt. Their dedication to domestic tasks may stem from several factors, including gender
roles, care responsibilities, along with health limitations, low education and lack of work experience,
as well as relative concentration in rural areas (the latter for Group 2). Likewise, as mentioned above,
22 Group 4 is 68 percent female, whereas women make up almost all of Group 2 (93 percent).
23 Only 0.01 percent of the men in Group 4 face the care responsibilities barrier.
24 Elderly individuals are those aged 65 or older.
Portraits of Labor Market Exclusion 2.0 36
some of the inactive, especially those in Group 4 (which is younger and includes a significant
proportion of males) may in fact be discouraged former jobseekers (11 percent of Group 4 reported
their main activity during the reference period as “other inactive�?, versus 54 percent reporting
domestic tasks and 8 percent unfit to work).
The unemployed within Group 4 face fewer employment barriers, making them closer to the
labor market and more responsive to AESPs. Self-declared labor market status has important
implications for activation and employment support policies, so it is worth examining how the
unemployed and the inactive differ within Group 4. As mentioned above, over a quarter of this group
reports being unemployed (either during the reference period or at the time of the interview). And
within this group, the unemployed are different from the inactive as regards several employment
barriers. They are much less likely to face the care responsibilities barrier (only 4 percent, versus 34
percent among the inactive), the health limitations barrier (just 6 percent, versus 17 percent among
the inactive) and education barrier (32 percent, versus 59 percent among the inactive). Like the
inactive, however, 100 percent of the unemployed face the low relative work experience, no work
experience, and scarce job opportunities barriers, respectively. Interestingly, they are more likely to
face the high non-labor income barrier (24 percent, versus 12 percent of the inactive) (although
equally likely to face the high earnings-replacement barrier). They are also less likely to be female (35
percent, versus 80 percent of the inactive), and less likely to live with children under 12 (28 percent,
versus 63 percent of the inactive). Both the unemployed and the inactive in Group 4 have an average
age of 26.
A full third of Group 2 faces a significant health limitations barrier, and this barrier tends to
overlap significantly with the low education barrier. Twenty-one percent of the group reports
limitations in daily activities, whereas 12 percent reports severe health limitations. The relatively high
incidence of the health care barrier among this group may be related to the fact that one-fifth of this
group is 56-to–64 years of age. Likewise, it must be noted that 70 percent of the members of Group 2
who face the health barrier also face the low education barrier. Health limitations, coupled with low
education and no work experience (the latter is the case for all members of this group), make this
segment of Group 2 particularly distanced from the labor market.
The two groups encompassing the working poor, Group 3 and Group 5, together make up 26
percent of the target population, or 1.36 million individuals; like Group 2 and Group 4, one
group is more middle-aged and the other younger; however, the two groups they are similar to
each other as regards employment barriers and poverty status. Group 3 is composed of middle-
aged individuals (average age of 45), while Group 5 comprises youth (average age of 26). The middle-
aged group is mostly female (62 percent), whereas the young group is mostly male (57 percent). Both
groups live in predominantly rural (thinly populated) areas, and just over one-half live in the northeast
and southeast regions. Reliance on subsistence farming may be a possible explanation behind their
near-zero incomes despite the fact that they report being at work during most of the reference period.
Almost all of the members of these two groups report being self-employed. Interestingly, 55 percent
of Group 5 and 42 percent of Group 3 report being self-employed part-time during the reference
period.25
25Only 2 percent of Group 3 and 5 percent of Group 5 fall under the category “restricted hours,�? i.e., they spent
most or all of the reference period working 20 hours or less a week for the following reasons: illness or
disability, family or care duties, absence of other job opportunities.
Portraits of Labor Market Exclusion 2.0 37
In terms of capabilities barriers, low education is predominant in Group 3 and Group 5,
affecting 56 percent, respectively. About 10 percent have only completed up to primary education.
About 41 percent, however, do have an upper secondary education, though tertiary education is very
rare among these two groups. Like their inactive counterparts, the group that is composed of middle-
aged individuals is more likely to have individuals who report health limitations in their daily activities
(17 percent of Group 3, versus 8 percent of Group 5). This, too, may be a reflection of the fact that some
of the members of Group 3 are aged 56 to 64 (13 percent). However, interestingly, despite the fact that
some do report health limitations, nearly all of the members of these two groups are engaged in work,
albeit in activities that provide very little in earnings. Relatively few of these groups’ members have
low relative work experience (24 percent of Group 3 and 31 percent of Group 5, versus 48 percent of
the target population), meaning that the majority of them have spent over 60 percent of their potential
working years in work. As expected, none of the members of these groups face a care responsibilities
barrier: since they are working, by definition, they do not face this barrier. Likewise, though an
important proportion of Group 5 (43 percent) and Group 3 (36 percent) has children aged 12 and
under living in their households, the percentage who report that not all of their children aged 12 and
under are in formal childcare is only between 4 and 6 percent.
Lastly, it is noteworthy that groups 3 and 5 do not have high non-labor income or high earnings
replacement benefits that could be discouraging work in more productive activities. Like their
inactive counterparts in the two other priority groups, the older group is more likely to be married
and a large percentage of their spouses are working, while a significant proportion of the younger
group is living with their parents. However, the two groups are particularly poor, with over 60 percent
at risk of poverty and between 42 and 53 percent living in severe material deprivation. Over 60
percent report receiving at least one type of social benefit (51 and 57 percent receive family benefits,
and 25 and 29 percent receive social exclusion benefits) but the amount received is too low to
discourage work or have a significant impact on at-risk-of-poverty rates.
In summary, the four groups that have been identified as priority groups for intervention
encompass two groups of out of work (largely inactive), mostly female individuals, and two
groups of working poor. In both cases, one group comprises middle-aged and some older individuals,
and the other is made up of younger individuals. In terms of employment barriers, there are
similarities among the two groups of largely inactive and the two groups of working poor. Low
education is a common barrier to all four groups, whereas lack of work experience, by construction, is
a barrier affecting only the out of work. Health limitations affect some in both of the groups that
encompass older individuals. Care responsibilities only affect some of the members of the two out-of-
work groups (namely females). However, entrenched gender roles are apparent, as evidenced by
women’s predominant dedication to domestic activities despite, for the most part, not having children
who lack formal care nor having particularly high incomes independent of their own work effort. More
importantly, none of the groups identified appear to be discouraged from productive work due to high
benefit receipt. A large percentage do receive social benefits — predominantly family benefits,
followed by social exclusion benefits — but the benefit amount appears to be too low to make work
not worthwhile. The two groups of largely inactive individuals face the most barriers, possibly making
activation and employment support more difficult. On the other hand, while the group of working poor
appear to face fewer barriers, their concentration in rural areas makes them particularly distant from
gainful full-time employment.
Portraits of Labor Market Exclusion 2.0 38
Table 4: Employment barriers and demographic and socioeconomic characteristics of priority groups in Romania
Group 2.
Low-
educated
inactive Group 4. Group 3.
middle- Young Middle-aged Group 5.
aged mostly less Young less
women female educated educated
with no NEETs with rural rural
work no work working working Target Reference
experience experience poor poor pop. pop.
Group size
Percent of target population 20 12 18 8 100 NA
Thousands of individuals 1,023 645 943 417 5,215 12,985
Employment barriers:
Capabilities barriers
1- Low education 61 51 56 56 45 27
2- Care responsibilities 25 25 0 0 13 5
3- Health limitations 33 14 17 8 33 19
4- Low relative work experience (WE) 100 100 24 31 48 29
No recent WE - Has worked in the past 15 8 0 1 45 18
5-
No recent WE - Has never worked 85 92 0 0 28 11
Incentives barriers
6- High non-labor income 21 16 6 5 19 24
7- High earnings-replacement benefits 2 2 0 0 10 4
Opportunity barrier
8- Scarce employment opportunities 0 100 0 100 26 32
Demographic and socioeconomic characteristics:
Group 2.
Low- Group 4.
educated Young Group 3. Group 5.
inactive mostly Middle-aged Young less
middle-aged female less educated educated
women with NEETs with rural rural
no work no work working working Target Reference
experience experience poor poor pop. pop.
Women* 93 68 62 43 66 50
Children under 12 in household* 45 53 36 43 35 35
Age group*
Youth (18-29) 0 88 0 90 19 17
Middle-aged (30-55) 79 12 82 9 47 64
Older (56-64) 21 0 18 1 34 19
Main activity during the reference period*
Employed 0 0 95 94 26 70
Unemployed 2 26 1 2 8 3
Retired 11 1 3 0 33 13
Domestic tasks 78 54 1 1 27 11
Other inactive or disabled 9 19 0 3 6 3
Degree of urbanization*
Densely populated 22 31 5 13 26 34
Intermediate 19 25 14 16 21 24
Thinly populated 59 44 81 71 53 42
Portraits of Labor Market Exclusion 2.0 39
Group 2.
Low- Group 4.
educated Young Group 3. Group 5.
inactive mostly Middle-aged Young less
middle-aged female less educated educated
women with NEETs with rural rural
no work no work working working Target Reference
experience experience poor poor pop. pop.
Region
NW & Central 27 22 10 15 21 25
NE & SE 29 25 53 52 34 28
South Muntenia & Bucharest 25 37 13 20 26 29
SW Oltenia & West 19 16 23 14 19 17
Target population category
Out of work 100 100 0 1 72 29
Unstable jobs 0 0 5 8 2 1
Restricted hours 0 0 2 5 1 0
Near-zero income 0 0 93 86 24 10
Main activity during reference period (more disaggregated)
Employed full time 0 0 3 5 1 52
Employed part time 0 0 0 1 0 0
Self-employed full time 0 0 50 33 12 11
Self-employed part time 0 0 42 55 13 7
Unemployed 2 26 1 2 8 3
Retired 11 1 3 0 33 13
Domestic tasks 78 54 1 1 27 11
Disabled (unfit to work) 6 8 0 0 3 1
Other inactive 3 11 0 3 3 1
Main activity at moment of interview
Employed 0 0 99 94 27 70
Unemployed 2 28 0 2 8 3
Retired 12 1 0 0 33 13
Domestic tasks 78 53 0 1 26 11
Other inactive or disabled 8 17 0 2 6 2
Months in unemployment
Zero months 97 70 98 96 91 96
1 to 11 months 1 5 1 4 2 1
12 or more 2 24 0 0 7 3
Actively searching for a job at time of
interview 4 35 0 3 9 4
At risk of poverty (60% of median income) 43 49 66 62 39 21
At risk of poverty (40% of median income) 23 27 46 42 23 11
Equivalized income quintile
Poorest 38 45 61 58 36 19
2 28 29 22 29 23 18
3 17 14 10 8 16 19
4 12 8 4 4 15 21
Richest 5 4 3 2 11 23
Severe material deprivation 39 54 42 53 39 21
Years of work experience
None 85 92 0 2 28 0
1 to 5 3 6 6 50 9 13
6 to 10 3 1 11 36 8 15
11 to 20 7 0 33 10 15 28
Portraits of Labor Market Exclusion 2.0 40
Group 2.
Low- Group 4.
educated Young Group 3. Group 5.
inactive mostly Middle-aged Young less
middle-aged female less educated educated
women with NEETs with rural rural
no work no work working working Target Reference
experience experience poor poor pop. pop.
21 to 30 2 0 27 2 17 25
More than 30 0 0 23 1 24 19
Average years of work experience** 13 4 21 6 19 19
Education level
Primary or less 16 15 9 13 9 4
Lower secondary 44 36 47 43 36 22
Upper secondary 35 36 41 40 47 54
Post-secondary 1 1 1 1 3 4
Tertiary 3 12 3 3 6 16
Age groups (more disaggregated)
18-24 years 0 42 0 45 9 6
25-29 years 0 46 0 46 10 11
30-44 years 53 11 50 7 26 41
45-54 years 24 1 29 2 18 21
55-59 years 13 0 13 1 16 11
60-64 years 10 0 8 0 21 10
Average age 45 26 45 26 45 42
Severe limitations in daily activities 12 7 3 0 11 5
At least one other household member 25 &
older working 75 81 79 90 69 73
Elderly in the household 16 14 20 17 17 14
Children under 6 in household 17 31 14 25 17 16
Children under 3 in household 5 16 6 10 7 6
Children under 13 in formal childcare
None 5 11 2 6 4 3
Some 3 4 2 0 2 1
All 37 38 32 37 29 30
NA 55 47 64 57 65 65
Household type
One person 3 1 4 1 6 5
Single parent 0 1 1 0 1 1
2+ adults, 0 children 35 33 40 39 45 41
2+ adults, 1 child 11 9 8 5 8 14
2+ adults, 2+ children 49 55 46 55 40 39
Average household size 4.1 4.8 4.1 4.9 3.9
Live with parents 11 57 14 74 21 23
Marital status
Married 77 36 79 26 67 69
Never married 12 62 10 72 20 21
Divorced/separated 3 2 6 2 5 5
Widow/er 8 0 5 0 8 4
Labor market status of spouse/partner
Working 59 37 64 25 41 50
Unemployed 2 2 0 0 2 1
Retired 15 0 11 0 20 10
Unfit to work 1 0 0 0 0 0
Portraits of Labor Market Exclusion 2.0 41
Group 2.
Low- Group 4.
educated Young Group 3. Group 5.
inactive mostly Middle-aged Young less
middle-aged female less educated educated
women with NEETs with rural rural
no work no work working working Target Reference
experience experience poor poor pop. pop.
Domestic tasks 1 2 6 3 5 9
Other inactive 3 1 0 0 1 1
No spouse/partner 19 58 19 72 31 28
Receives family benefits 56 60 51 57 44 46
Average annual value (€)† 286 310 362 457 327 292
Receives social exclusion benefits 17 23 25 29 18 11
Average annual value (€)† 246 365 232 325 245 206
Receives old-age benefits 6 0 4 0 25 10
Average annual value (€)† 1,482 NA 1,712 NA 2,322 2,319
Receives survivor benefits 4 4 1 1 6 3
Average annual value (€)† 714 718 707 472 486 469
Receives disability benefits 4 2 0 0 9 4
Average annual value (€)† 1,246 882 NA NA 1,573 1,570
Receives education benefits 5 5 0 0 2 1
Average annual value (€)† 707 884 NA NA 758 746
Receives any social benefits 71 69 63 65 77 61
Average annual household income (€) from:
Labor 3,302 3,369 1,997 2,491 2,755 5,534
Benefits 1,219 1,047 981 969 1,824 1,224
Other 34 16 17 12 23 21
Total 4,555 4,432 2,987 3,472 4,601 6,778
Average annual equivalized household
income (€) 1,596 1,398 1,105 1,089 1,778 2,479
Notes: This table draws on the summary data provided in Table 4 and Annex 2. Color shadings identify categories with high (darker)
frequencies. See Box 1 for a brief explanation of the indicators. Only categories depicting barriers to employment are included;
complementary categories are omitted. Income quintiles are for the entire population. Unemployment and sickness benefits not included
due to small number of observations. Housing benefits not included as no households report receiving said benefits. Months in
unemployment refers to the reference period.
*Included in the LCA model as active covariates.
**Refers only to individuals who have worked before.
†Only includes non-zero observations.
Source: World Bank staff calculations based on EU-SILC 2013.
Portraits of Labor Market Exclusion 2.0 42
6. Policies and Programs Targeting Priority Groups in Romania
6.1 Framework and Approach
The identified priority groups face multiple barriers simultaneously; hence, they require a
tailored mix of services to improve their employability. The menu of programs/services to
address their wide-ranging employment barriers fall under three main areas: (i) employment support,
(ii) social services, and (iii) social benefits (with the appropriate design elements). These tools support
and incentivize job search and finding, productive participation in society, and improving self-
sufficiency.
This section reviews the activation and employment support programs and policies (AESPs)
relevant for the identified priority groups, paying particular attention to programs congruent
with the identified employment barriers. More specifically, based on the organizing framework
presented in Figure 15, we review programs that address — either solely or in combination with other
programs — work-related capability barriers (skills and care responsibilities), and to the extent
possible assess whether or not existing programs have adverse incentives on work (incentive
barriers). In addition, we consider whether existing programs address the needs of the relevant
crosscutting groups such as youth, women, long-term unemployed, and those living in rural areas.
The capacity and adequacy of existing menu of services/programs are analyzed next. First, we
present a broad overview of existing AESPs and the policy environment, followed by additional details
on active labor market programs and their broad capacity and adequacy. Contrasting with the needs
of the selected priority groups based on their barriers, the capacity and adequacy of existing services
to deliver the right package of support to help them find employment are explored. This allows for
assessment of any gaps and indicating potential policy directions.
Figure 15: Organizing framework for policy analysis
Source: Authors’ elaboration.
Portraits of Labor Market Exclusion 2.0 43
6.2 Overview of Activation and Employment Support Programs and Policies
in Romania
6.2.1 Institutional and policy context
Overall, a range of activation and employment support programs/policies exist in Romania,
but they suffer from fragmentation and gaps in supply, accessibility, and delivery. The main
programs/policies examined include: (i) social benefits (cash and in kind); (ii) social services; and (iii)
employment support: passive and active labor market measures, with particular focus on employment
support programs. Romania has a range of policies and programs in place in all three domains, and
while significant steps were taken in the past few years to strengthen support to the vulnerable,
fragmentation in all three dimensions persists, with resulting gaps in either coverage, accessibility, or
the coordination of services. For instance, existing social assistance benefits, with the exception of the
upcoming minimum income program, while offering some protection, are not necessarily effective in
linking recipients with the labor market (World Bank, 2016). Social services do not appear to have
adequate coverage, and there are clear gaps in certain areas such as childcare. In contrast, employment
support programs are available though there are potential issues in the coverage and effectiveness of
the measures that are in place, as well as capacity constraints within public employment services in
delivering effective services. A wide range of measures are proposed to be introduced or implemented
during the 2017-2020 period to achieve compliance with the Europe 2020 Strategy social inclusion
and employment targets. These measures include improving the availability and access to part-
time/flex work/seasonal work, childcare services, family allowances, improving job search assistance
services and active labor market programs for youth, long-term unemployed, people with disabilities
as well as social assistance measures to promote active inclusion.
Social assistance is mostly limited to cash benefits. The Ministry of Labor and Social Justice
(MoLSJ) is preparing a major overhaul of its means-tested programs aimed at poverty alleviation. The
existing means-tested programs— three small and loosely coordinated cash transfer programs — will
be unified into a single anti-poverty program, the Minimum Social Insertion Income (MSII) program.
The new, consolidated means-tested program will become the flagship anti-poverty program in
Romania and is expected to come into effect in 2018. The most important feature is the introduction
of a benefit formula that stimulates work and allows poor families to combine formal earnings or
imputed agricultural incomes with social assistance receipts, thus increasing the total income of the
families.
The provision of social assistance services is insufficient. To deliver services, local authorities are
required to establish a public social assistance service at the community level. However, public social
assistance services have not been set up everywhere and in many instances they are understaffed.
Identification and early intervention services as well as referral systems for vulnerable groups are
insufficiently developed at the community level. The government intends to develop integrated
intervention teams for marginalized communities, subordinated to public social assistance service and
Portraits of Labor Market Exclusion 2.0 44
a holistic package of anti-poverty measures mainly financed through EU funds.26 The National Strategy
for Social Inclusion and Poverty Reduction, adopted in May 2015, provides a comprehensive
framework for poverty reduction, but its success depends on concrete and realistic planning, budget
availability and coordination within the central and local public administration and with civil society.
The Strategy outlines an intensified approach to delivery of services at the local level through
integrated teams under the public social assistance units which will collaborate with various local
level entities including employment, health, and education.
Maternal/paternal/parental leave schemes exist and they appear to be compatible with work
incentives. Even though the maternal leave scheme is one of the most generous and long term across
Europe, its length and generosity do not seem to represent work disincentives for women with
children. This is probably due to the fact that an additional work reinsertion bonus is also attached to
the parental leave scheme. Employment rates for women with children younger than 6 years old are
higher than the average employment rates across Europe.
A menu of labor market policies exists; however, overall spending on labor market policies is
low compared to EU-28, especially if not considering spending from the European Social Fund
(ESF). Based on comparable data from Eurostat, a large share of labor market policy spending is
dedicated to passive measures (60 percent) with a small share (10 percent) dedicated to funding for
active labor market measures (see section 6.2.2 for more details).27 This inevitably results in low
coverage and inadequate programs and services provided to employers and jobseekers.
Despite a high share of spending on passive measures, the majority of the short-term
unemployed are not covered by unemployment benefits. Even though passive policies absorb a
large share of the national spending on labor market policies, the coverage of the short-term
unemployed by unemployment benefits is estimated to be among the lowest in the EU. This low
coverage reflects both the eligibility conditions for entitlement to unemployment benefits in the case
of termination of employment and the large number of uninsured self-employed. The adequacy of
unemployment benefits is low and deteriorating (European Commission, 2016a).
National spending on labor market policies is low and not well coordinated with the ESF.
Eurostat data (not including ESF funds) shows that spending on labor market policies is less than one-
tenth of the EU average. Expenditure on active labor market policies has decreased to about 40
percent of its level before EU accession and it is overly reliant on the ESF (European Commission,
2016a). The low level of national spending might signal sustainability issues in the long run. In
addition, lack of coordination between nationally-funded and ESF-funded measures results in
competing schemes. The government’s employment policies for 2017-2020, to be financed from both
the ESF and the national budget, aim to develop a menu of services for different segments of the
unemployed and young people who are entering the labor market.
26 An additional 1,000 social assistants are planned to be hired to be part of the integrated community teams
under the EU funds. A protocol defining responsibilities for each ministry (i.e. Ministry of Education, Ministry of
Labor, and Ministry of Health,) has been signed under a joint ministerial order.
27 The figures do not take into account programs funded by ESF. According to European Commission estimates,
including the ESF budget would result in about three times as much spending on active labor market measures,
though still considerably lower than spending dedicated to passive measures.
Portraits of Labor Market Exclusion 2.0 45
The National Employment Agency (NEA) is lagging behind in offering personalized services to
jobseekers and employers. The operational autonomy of the public employment service is limited
by prescriptive legislation on active labor market policies. In spite of mandatory referral of vacancies
to the NEA, its capacity to attract vacancies or to offer attractive services to employers is limited.
Forecasts of labor market needs have been introduced in several regions but have not been updated,28
and while an electronic registration card is being rolled out, its impact remains to be seen. A
reintroduction of public works is envisaged, with unclear impact on raising employability and
sustainable labor market integration. A more integrated approach, offering pathways to the labor
market for NEETs and centered on the public employment service, is being developed with the support
of EU funds.
Nevertheless, reforms are underway to improve NEA counseling and case management
capacity. In 2016, the Agency implemented a thorough reform of service delivery and developed a
profiling procedure for jobseekers, subsequently enshrined in legislation. The profiling procedure,
which segments jobseekers into four categories (i.e. easy, medium, difficult, very difficult) based on a
set of characteristics, started implementation in the second half of 2016. The Agency plans to take
tailor-made action in close collaboration with social assistance and education counsellors. The Agency
started training its counsellors in case management and intends to hire professionals oriented
towards NEETs, the long-term unemployed and inactive groups.
Activation beyond the efforts of the new MSII program are inadequate. Public employment
services do not offer tailor-made services for social assistance beneficiaries and do not enforce job
search requirements. Eighty percent of social assistance beneficiaries live in rural areas where public
employment services are limited and coordination among social assistance and employment offices is
weak and inconsistent and employment opportunities are more limited.
In recognition of diversity in labor markets and concentration of unemployment and poverty
in some parts of the country, new measures have been introduced to promote labor mobility.
The legislation concerning labor market policies has recently been amended. Aligned with the national
mobility plan, one particular area of focus was to address the mobility constraints jobseekers may face
when accepting a job. As of January 2017, the existing mobility premiums were adjusted to expand the
geographic eligibility and increase financial incentive levels.29
Romania has had low investment and weak policies for supporting early childhood
development (European Commission, 2015). One of the key weaknesses appears to be the lack of
an integrated approach and a tendency to take on a piecemeal approach to the development of
services. In this vein, the coverage and places offered for early childhood development are limited and
enrollment rates are low. According to data from the Single Electronic Registry of Social Services, there
were only a total of 398 daycare centers in the country (174 of which were publicly provided). In
28 The most recent labor market diagnostic mechanism was undertaken in 2015. The update is planned with
the use of EU funds in the current programming period (2017-2020).
29 Law 76 (2002); Ordinance number 60 of 2016.
Portraits of Labor Market Exclusion 2.0 46
November 2015, a new program has been introduced to improve access to kindergartens for 3-5-year-
old children from poor families.30
Coordination between agencies is weak although the 2016-2020 Social Inclusion and Poverty
Reduction Strategy foresees integrated delivery of services at the local level. The Strategy
outlines an intensified approach to delivery of services at the local level through integrated teams
under the public social assistance units which will collaborate with various local level entities
including those in charge of employment, health, and education.
6.2.2 Overview of Active Labor Market Policy (ALMP) programs
When not taking into account ESF, spending on labor market policies in Romania represents
one-tenth of the EU-28 average. In 2015, Romania spent 0.18 percent of GDP on labor market
policies, including active and passive measures as well as services (based on Eurostat comparable
data, see Annex 5 for programs classification). In comparison, the EU-28 average for 2011 (latest
available data) was ten times as high, at 1.8 percent. Active labor market policies represented a tiny
fraction of total labor market policy (LMP) spending in Romania (0.02 percent of GDP), well below
most West European countries (representing more than 1 percent of GDP in Denmark and Sweden),
and the lowest among the countries included in this study (Figure 16).31
Spending on active labor market policy programs (ALMPs) in relation to GDP represents less
than one-twentieth of the EU average. About 59 percent of labor market expenditure is dedicated
to passive measures, while active labor market measures absorb 10 percent and the remaining 31
percent goes to services (Figure 17). While the spending on passive measures is aligned with EU
countries where the majority of the spending is primarily dedicated to passive measures (representing
about 60 percent of total labor market spending for the EU-28 average), the level of spending on
ALMPs, at 0.02 percent of GDP is well below the EU-28 average (0.45 percent of GDP), even considering
additional spending by ESF funds not included in these figures.
30 http://www.togetherforbetterhealth.eu/news/news-romania-every-child-preschool-kindergarten-national-
program
31 The spending data is reported to Eurostat by Romanian authorities, and likely only reflect national spending
and do not include programs financed by ESF funds.
Portraits of Labor Market Exclusion 2.0 47
Figure 16: Labor market spending as percent of GDP (left axis) and share of active labor market program spending
as share of labor market expenditure (right axis)
2.00
1.80
1.60
1.40
1.20
1.00
0.80
0.60
0.40
0.20
0.00
EU-28 Hungary Poland Greece Croatia Bulgaria Romania
Services Active Passive
Note: Data for Bulgaria, Greece, Hungary, and Romania are for 2015; data for Croatia and Poland are for 2014; data for EU-28 are for 2011
(based on latest availability).
Source: Eurostat
Labor market services represent 31 percent of total labor market policies spending in
Romania. This spending represents the resources dedicated towards PES delivery systems, covering
the expenses related to the functioning of the employment offices, delivering ALMPs, and counseling
and intermediation. The general objective of employment offices’ services is to support clients during
unemployment and facilitate their transition to employment.
The resources dedicated to ALMPs appear to be low in comparison to spending on labor market
services. In 2015, total ALMP expenditure represented only one-third of spending on labor market
services. In countries with well-functioning PES delivery systems such as Denmark and the
Netherlands, expenditure on ALMPs represents almost twice the resources dedicated to services.
Across EU-28 countries, on average, countries tend to spend over two times as much on ALMPs as they
do on services. The relatively high level of spending on services delivered by PES suggests possible
inefficiency in service delivery, and a need to rethink counseling and intermediation services. More
importantly, the relatively low spending on ALMPs as a percentage of GDP appears to be a binding
constraint. Thus, it may point towards the need to increase spending on coverage and the range of
ALMPs offered to align counseling services offered and placement in programs.
The category “employment incentives�? represents the majority of spending on ALMP measures.
This category (see Annex 5 for the Eurostat classification) represents about 83 percent of total ALMPs.
About 10 percent is dedicated to direct job creation, another 6 percent to training, with a small share
dedicated to start up incentives (Figure 17).
Portraits of Labor Market Exclusion 2.0 48
Figure 17: Detailed composition of labor market programs (LMPs) in Romania, in percent of total labor market
expenditure in 2015
LM Services, Measures and Supports Categories of ALMPs as percentage of LMP
as percentage of Total LMP Spending spending
LM supports:
Unemployment
benefits, 58.7% Employment Direct job
incentives, 8.3% creation, 1%
LM measures
(ALMPs), 10.2% Startup
incentives,
0.01%
Training, 0.6%
LM services,
31.1%
Source: Eurostat
Coverage of ALMP programs remains relatively low; however, there are new efforts underway
to improve outreach and take up. In 2016, according to data received from the National
Employment Agency, 288,589 persons benefitted from labor market services and employment
programs, representing 38 percent of persons included in measures intended to stimulate
employment of the registered jobseekers (752,391).32 Labor mediation services, vocational
information and counseling measures made up a significant proportion of the total labor market
measures (Error! Reference source not found.). New measures including mobility premiums and t
ransitional grants for re-entry to the labor market have been introduced however remain limited.
It is not possible to assess the cost effectiveness of the existing ALMPs with available data. In
the absence of data on employment outcomes six or 12 months after program participation, as well as
for similar individuals who have not participated in these programs (i.e. control group), it is not
possible to assess the cost effectiveness of each of these programs.
32
All registered unemployed receive the core labor market service of mediation or counseling thus the number
of registered unemployed correspond to that of persons included in active measures intended to stimulate
employment of the labor force (i.e. 752,391 for 2016).
Portraits of Labor Market Exclusion 2.0 49
Box 3: Description of main ALMPs in Romania
Training: Training represented 5.6 percent of total 2015 ALMP expenditure. In 2015, the main training
program was a vocational training program (representing 5.1 percent of total ALMP spending). Registered
unemployed, those employed with less than gross minimum wage, and parents who wish to re-enter labor
market after 2 years of leave are entitled to vocational training, to re-qualification, or to acquire new skills.
The unemployment insurance fund finances the program.
Employment Incentives: This is the main category of ALMPs in Romania, representing 82 percent of
ALMP spending in 2015. There are in total 12 employment incentive programs, but the three major ones
are:
(i) ‘Subsidies for employers taking on registered unemployed’: This is the largest national
ALMP program, representing 50 percent of all ALMP spending. The program focuses on
two specific target groups, i.e. registered unemployed above 45 years old and ‘unique
bread earner’. The unemployment insurance fund finances up to 12 months o f monthly
subsidies to employers as well as reduced social security contribution. The subsidy is
provided given the employment relationship is maintained for at least 2 years.
(ii) ‘Subsidies for graduate recruitment’ representing 17 percent of ALMP spending. They
provide employers with a subsidy if they hire graduates for at least 18 months. The
amount of the subsidy varies according the type of diploma (increasing with level of
education).
(iii) ‘Income top-up for unemployed persons finding work before the expiry of the
unemployment benefit period’ represents 9 percent of ALMP spending. It provides a
monthly transfer of 30 percent of the unemployment allowance received the month
before taking up a job, if the unemployed individual takes up a job before the expiration
of their unemployment benefit entitlement.
Direct Job creation: There is one direct-job-creation program in Romania: ‘Solidarity contracts for young
people with difficulties and at risk of professional exclusion.’ This program represents 12 percent of total
ALMP expenditures. It aims at integrating youth (below 26 years old) at risk of professional exclusion or
with specific vulnerabilities (e.g. those with children and those coming from placement centers, with
disabilities, etc.). The social insurance fund finances these solidarity contracts for a maximum of two years.
The employer has to be approved by the public employment services.
Startup incentives: There is only one small startup incentives program in Romania, ‘Counselling for
business start-up,’ representing 1 percent of total ALMP expenditure. Under this program, registered
unemployed are provided with counseling and assistance for the startup of an independent activity. The
program is open to all unemployed but also those employed but who want to have a different job. It is
financed by the unemployment insurance fund.
Source: Based on Eurostat Labor Market Statistics Quality Report, 2015.
Table 5: Status of the National Employment Agency’s Employment Program by type of measure, 2016
Type of measure Number of beneficiaries
Labor mediation services 254,956
Vocational information and counseling services 41,552
Vocational training 11,742
Income top-up for unemployed persons finding work 16,973
before the expiry of the unemployment benefit period
Subsidies for employers taking on registered unemployed 17,962
Portraits of Labor Market Exclusion 2.0 50
Subsidies to the employers that hire persons that have 5 300
more years until retirement
Subsidies for graduate recruitment 4,898
Qualification premium to the graduates of education 2,544
Subsidies to the employers hiring persons with 141
disabilities
Solidarity contracts 297
Counselling for business start-up 100
Promotion of labor market mobility 1,106
Other ALMPs 3,280
Note: Individuals may have benefited from more than one type of service. The total number of beneficiaries who benefitted from at least
one program equals 288,589.
Source: National Employment Agency.
Individuals with upper secondary education or higher, those living in urban areas, and men
make up the majority of the 288,589 persons who were employed due to active measures in
2016. Figure 18 offers information on the profile of ALMP beneficiaries, as well as those benefiting
from labor mediation and counseling services in 2016. It shows that ALMPs and labor market services
mainly cover youth and middle-aged individuals, with only 9 percent of beneficiaries being older than
56 years old. Most beneficiaries have a relatively good level of education as only 28 percent have
reached only lower secondary education or below.33 Individuals living in urban areas represent the
majority of beneficiaries (57 percent). Similarly, most beneficiaries are male (57 percent).
Figure 18: Profile of ALMP and labor market service beneficiaries employed in 2016
Participants by age Participants by education
9% 15%
28%
44%
48%
57%
Low (lower secondary or below)
Youth (18-29) Middle-aged (30-55) Middle (upper secondary and vocational)
Older (56-64) High (post-seondary or higher)
33 Due to legislative restrictions established by Ministry of Education individuals who have not completed
compulsory/basic education cannot participate in professional training programs. However, no such restrictions
exist for other type of programs or services. Considering the very small share of training programs in the overall
programs and services, this restriction cannot explain the profile of highly educated beneficiaries.
Portraits of Labor Market Exclusion 2.0 51
Participants by urbanization Participants by gender
43% 43%
57% 57%
Male Female
urban rural
Note: The data include individuals who benefit from labor mediation services and vocational information and counseling services, in
addition to ALMPs.
Source: National Employment Agency
6.3 Activation and Employment Support Policies Vis-à-vis Priority Groups
Needs
This section reviews the main barriers faced by the prioritized groups and their consequent
needs and links the latter with available policies in order to evaluate potential gaps . The
previous section illustrated how the barriers are interconnected with the groups’ socioeconomic
characteristics. In other words, addressing the same barrier may require a different set of activation
policies according to the socioeconomic characteristics of the identified priority group. For example,
while low relative work experience may be an employment barrier faced by two different groups, it
may require a different approach for inactive mothers compared to young unemployed men. It is
therefore important to relate each barrier to the specificities of each group. This section focuses on
identifying the needs34 and corresponding policies for the four priority groups selected.
The existing programs/policies do not appear to be adequately capturing the four priority
groups or addressing their potentially simultaneous constraints. While a range of activation and
employment support policies and programs are available, they are fragmented with limited coverage
and coordination, and do not appear to have adequate capacity to address the needs of the selected
priority groups in (re)integration to the labor market. These constraints relate to their work
experience (in particular lack of (recent) work experience), education levels, opportunity to access
jobs (closely linked to where they reside) and in some cases care responsibilities. The institutional
34 Themain barriers are those (i) with a probability of occurrence higher than 50 percent in each group, (ii) with
a probability of occurrence of 10 percentage points higher than for the target population.
Portraits of Labor Market Exclusion 2.0 52
capacity constraints limit adequate coverage even if it is assumed that appropriate programs/services
exist, with adequate information, service levels, and affordability. This assumption is probably not in
line with the reality on the ground.
Group 2, largely made up of inactive women, likely does not have much access to existing
ALMPs. Almost all of the women in this group are inactive. Most of this group is therefore not likely to
be registered with NEA. The current set of ALMPs focuses on registered unemployed and thus the set
of training and subsidy programs are out of reach for this group. More explicit outreach and
information dissemination may be required link these women with at least the basic services such as
group counseling, job search assistance, etc.
Group 2: Low-educated inactive middle-aged women with no work experience
Considering the long-term inactivity (and hence little to no work experience) and low
education of the women in Group 2, multiple interventions would be needed to integrate them
to the labor market; among these, acquiring relevant skills, getting work experience, and
assisting with job search would play an important role. Assuming that public employment services
are able to reach out to these inactive women in order to activate them, international evidence can
provide insights regarding the targeting of ALMPs to those with low skills or those who lack work
experience. Training programs, especially those that respond to the needs of employers can be
effective if targeted to those lacking skills and if they combine institutional training with practical
training, mirroring a real job and workplace environment (European Commission, 2015). Similarly,
recent evidence from the United States indicates that sectoral training (i.e. focusing on training
workers for jobs in particular industries in partnership with employers) can have positive impacts for
the disadvantaged (Hendra et al., 2016). However, such programs can be difficult to implement and
the degree of diligence in analyzing demand is crucial to identify the relevant industries. International
Portraits of Labor Market Exclusion 2.0 53
evidence also indicates that employment subsidies can be effective if targeted to those who are far
away from the labor market (e.g. low-skilled, inactive) with a positive impact on post-intervention
employment (Almeida et al., 2014, and European Commission, 2014). While there are different design
elements that can determine the success of employment subsidies (e.g. targeting, level duration, etc.)
they broadly have the potential to improve the employability of disadvantaged workers and build
human capital (by providing work experience and/or specific training) and therefore mitigate the risk
of returning to inactivity after the subsidized job with a valued added that goes beyond employers and
employees (Almeida et al., 2014; World Bank, 2013). They are particularly successful if combined with
training — either as part of the employment subsidies or prior to recruitment (European Commission,
2014).
Access to childcare facilities, along with supportive work environments, may provide
additional support for the one-fourth that has care responsibilities in Group 2. International
evidence indicates that increased access to childcare services — through subsidized care, tax
allowances or vouchers for care, for instance — contributes to women’s labor market participation
(OECD, 2011 and Vuri, 2016). In fact, evidence from Romania shows that government subsidies for
childcare were an effective means of increasing the number of working hours among mothers who
work, increasing the incomes of poor households and lifting some families out of poverty (though the
effects of such policies are less significant for poorer households) (Fong and Lockshin, 2000). Evidence
from other countries such as the United States, Canada, and Spain (as cited in Vuri, 2016), and Israel
and Russia35 (as cited in Todd, 2013) also indicate large impacts, though context is also an important
factor. Policies to enable access to affordable childcare in turn should be supplemented by a potential
increase in the supply of care institutions to avoid capacity constraints. Measures that encourage
supportive work environments that can accommodate family life, such as telework and part-time
work, may further aid in connecting these women to employment. In Romania, very few women (about
10 percent of employed women) work in part-time jobs compared to the EU average (about 32
percent).
A differentiated approach would be needed to address the diverse barriers and characteristics
of Group 4, made up of mostly female NEETs with no work experience. While this group is quite
homogenous regarding low relative work experience, no recent work experience and a young age,
there are differences within the group with respect to other barriers and socioeconomic
characteristics. For instance, the unemployed within the group (representing over one-fourth) are
different from the inactive and thus will require a differentiated support. In particular, they are much
less likely to face the care responsibilities and education barriers and are also more predominantly
male. The unemployed in this group are potentially covered by measures offered by the NEA, while
the inactive will fall outside of the purview of NEA and hence may not have access to the assistance
they need.
35Studies in Argentina, Brazil, Guatemala, and Colombia have also shown a significant impact of childcare
provision on the labor force participation, working hours and earnings among mothers with young children.
Portraits of Labor Market Exclusion 2.0 54
Group 4: Young mostly female NEETs with no work experience
Job search assistance/counseling and employment subsidies would likely provide the needed
support for the members of Group 4, especially for those who are unemployed. International
evidence suggests that a combination of programs yield better results than single interventions. For
instance, the British New Deal program for young people, a program which offers a combination of job
search assistance (for four months) followed by a wage subsidy to employers, shows an economically
and statistically significant effect of the program on outflows to employment among men. The program
appears to have increased the probability of young men (who had been unemployed for six months)
finding a job in the next four months; and it is estimated that part of this overall effect is the job subsidy
element and part is the enhanced job search assistance (Blundell et al., 2004). Overall, job search
assistance is relatively more cost effective (compared to other ALMPs) and is proven to have large
positive short-term impacts on employment of jobseekers (Card et al., 2015). In addition, hiring
subsidies targeting low-skilled workers or youth (as e.g. in Colombia, South Africa and Turkey) may
improve their employability and build human capital by providing work experience and/or specific
training, and likely mitigate the risk of retuning to unemployment after the intervention (Betcherman
et al., 2010 and Levinsohn et al., 2014). Thus, a combined approach may help address the low relative
work experience and not having worked in the past and scarce job opportunities faced by the young
and the unemployed in this group.
Skill building activities, particularly training activities closely linked with employers, such as
apprenticeships/on the job training, would aid in obtaining the much-needed work experience
for both the inactive and unemployed in Group 4. International evidence suggests that training
and internship programs that are appropriately targeted to groups lacking skills and that adequately
respond to the needs of employers may have a strong impact on employment (European Commission,
2015). One key lesson coming out of the literature is that the training programs have higher impact
when combined with effective intermediation. Indeed, the meta-analysis study on youth employment
Portraits of Labor Market Exclusion 2.0 55
programs (Kluve et al., 2016) finds that when employment programs are comprehensive —
integrating multiple interventions (i.e. intermediation with other forms of support such as skills
training, wage subsidies, or self-employment support) — they are more likely to succeed. This is a
very important message, as improving the effectiveness of intermediation services not only helps save
government resources in the administration of public services but more importantly contributes to
increasing the success of ALMPs as well.
Enabling access to childcare facilities could aid in linking the inactive females with care
responsibilities within Group 4 to the labor market. About one-third of the inactive (mostly
women) within this group have care responsibilities. As mentioned previously evidence from
other countries indicates that increased childcare services positively contribute to women’s labor
market participation (OECD, 2011 and Vuri, 2016). These policies should be supplemented by a
potential increase in supply of care institutions to avoid capacity constraints. As outlined in the policy
context section, the supply of childcare facilities in Romania is limited. Measures to encourage part-
time work can further aid in connecting these women to the labor market.
While the unemployed in Group 4 potentially have access to ALMPs, the inadequate coverage
and the range of measures offered may prevent the provision of effective support . Although
there have been noticeable spikes and expansions in coverage in ALMPs in 2009, 2010 and 2012, in
recent years this trend has been reversed and coverage of all programs have dropped. The training
programs that would address the needs of this group have constituted only a small share of overall
ALMP spending. Furthermore, inactive women are likely not registered in NEA and hence do not have
any access to the measures offered. More explicit outreach and information dissemination may be
required link these women with at least the basic services such as group counseling, etc.
Group 3 and Group 5, both comprising working poor, require similar support in many aspects
considering their barriers and characteristics, but with some differences. Low education is the
predominant barrier in both Group 3 and Group 5. Relatively few of these groups’ members have low
relative work experience. Given that almost all of the members of these two groups report being self-
employed, it is not surprising that they do not face the recent work experience barrier. Reliance on
subsistence farming may be a possible explanation behind their near-zero incomes despite the fact
that they report being at work during most of the reference period. About two-thirds of both groups
are at risk of poverty. The main difference is that Group 5 is much younger and faces scarce job
opportunities (i.e., they mostly resemble the unemployed or those involuntarily working part-time).
Portraits of Labor Market Exclusion 2.0 56
Group 3: Middle-aged less educated rural working poor
Considering their low education and self-employment (with near-zero incomes), training
focusing on building skills and linked with employers would help connect the younger group 5
to better quality employment. Lack of skills (as proxied by education) appears to be a binding
constraint for this group. Members of this group are likely trapped in subsistence agricultural
activities with near-zero incomes. One way to escape this trap, and transition to better employment,
would be for them to acquire specific skills that are needed by recruiters. In fact, evidence from the
European Commission (2015) and the United States (Hendra et al., 2016) indicates that a key element
of successful training and vocational programs is addressing sectoral mismatches through linking the
programs closely with the employers to ensure their needs are met.
Group 5: Young less educated rural working poor
Portraits of Labor Market Exclusion 2.0 57
Employment or self-employment subsidies may also offer opportunities to gain new relevant
skills for these groups and improve their chances of finding better employment. Studies indicate
that subsidies that compensate part of the salary costs could be effective with positive impact on
employment if targeted to those who are disadvantaged in the labor market (e.g. low skilled, inactive)
(Almeida et al., 2014, and European Commission, 2014). As mentioned previously, there are different
design elements that can determine the success of employment subsidies (e.g. targeting, level
duration, etc.) and broadly have the potential to improve the employability of disadvantaged workers
and build human capital (by providing work experience and/or specific training). They can be
particularly successful if combined with training — either as part of employment subsidies or prior to
recruitment (European Commission, 2014).
The concentration of these two groups in rural areas indicates measures encouraging mobility
may provide additional support. While the working poor groups appear to face fewer barriers, they
are likely to be particularly distant from gainful full-time employment due to their area of residence.
Even if they were to acquire skills demanded in particular industries, those jobs are likely to be in
densely populated areas. Hence, the members of these two groups may benefit from mobility
measures (e.g., financial incentives to cover transportation costs, etc.). Recently, financial incentives
covering relocation and transport costs were increased to improve internal labor mobility in line with
the national mobility plan.36 Roughly 1,100 individuals benefited from the promotion of labor market
mobility (Error! Reference source not found.). Although the composition of beneficiaries for these m
easures is unknown, it is clear that the number of beneficiaries of such measures is very small in
comparison to the more than one million individuals making up groups 3 and 5.
The access of Group 3 and Group 5 to labor market services might be constrained by their rural
location and distance to the nearest labor office. Offering the counseling and access to job offers
through online tools, as well as promoting the services provided by NEA among the younger (Group
5), could increase the chances that the individuals in these groups are covered by available labor
market services.
7 Conclusions and Policy Directions
The objective of this study has been to provide a snapshot of the multiple and simultaneous
constraints faced by the labor market vulnerable in Romania to inform policy decisions that
address the pressing needs of these groups. Policy makers are accountable for ensuring that
employment policy takes into account the different needs, challenges, and barriers faced by different
at-risk groups in the labor market when they develop policy tools or program-level interventions. To
this end, this paper categorized (through the use of an advanced statistical clustering technique)
traditionally known vulnerable groups into more distinct homogenous groups and identified their
most salient employment barriers and socioeconomic characteristics. Four priority groups were then
identified, and their key relevant characteristics for activation and social inclusion policies were
examined in depth. An overview assessment of the key features of ongoing (and some upcoming)
activation and employment support programs and policies (AESPs) in Romania were presented to
36 The recent amendments to the legislation on labor market measures expanded geographic eligibility and
financial incentives to promote mobility.
Portraits of Labor Market Exclusion 2.0 58
explore whether and to what extent the needs of selected priority groups were met with existing
programs/policies. While recognizing the essential role of labor demand to achieve good employment
outcomes, this study primarily focused on supply-side constraints and related policies. Further
analysis of demand-side constraints remains a topic for a different study.
Extended outreach, employment promotion, and individualized services are necessary to
reach, in particular, inactive women and youth who live in remote and rural areas. A first step
to activate these individuals is to ensure that they have information about and access to public
employment offices, and register as jobseekers to benefit from counseling services and available
programs. Close to 80 percent of the out of work and marginally employed receive social assistance
and more than half live in rural or remote areas which makes it imperative for the National
Employment Agency to have in place multiple outreach mechanisms which rely on mobile units and
communication strategies through traditional and social media channels. The government’s plans t o
expand the cadre of employment counselors, strengthen the network of local offices and introduce
improved methodologies to segment the client population will be welcome to ensure extended
outreach, individualized job search assistance and employment promotion for these difficult to
activate clients.
Operationalizing coordination among local-level agencies that provide services to vulnerable
populations and working poor is critical. Though collaboration between the National Employment
Agency and public social assistance and education units is foreseen at the local level, better incentives
and increased human and financial resources are required to improve collaboration. More than half
of the out of work and marginally employed live in rural areas and, in particular, among priority
groups of young inactive women and the working poor, more than half of them are at risk of poverty.
Given the profile of the priority groups and the multiple barriers faced, activating them will require a
concerted effort and an integrated package of benefits, programs, and services such as improved job
search assistance, mobility incentives, training, flexible work opportunities and access to care
facilities.
Access to affordable childcare services (in particular for children 0-6 years) will need to be
expanded especially in more remote areas. Among priority groups, care responsibilities are a
major constraint for women’s activity or access to the labor market. Inadequate levels of investment
in childcare facilities have translated into limited services offered in terms of geographic coverage and
space availability. Alternative mechanisms to achieve this objective could be through expanding access
to and availability of community-based care facilities and subsidizing childcare via vouchers or
providing larger employers with incentives to set up in-house childcare facilities.
A preliminary assessment indicates that the existing range of active labor market measures is
very limited in scope, spending, and coverage vis-à-vis the needs of the priority groups. Given
their limited scope and coverage, the existing range of interventions do not target, attend to the needs
or benefit those who are in most need of employment support. Therefore, there is scope to recalibrate
the spending on different measures in line with priority groups’ needs. On-the-job training
(employment subsidy) programs targeting youth and long-term unemployed can be expanded in light
of the relatively low levels of education among those who are out of work and marginally employed.
In addition, employment subsidy programs can also be modified to include part-time jobs to
encourage more women to join the labor force and/or to be able to switch from unstable often
informal jobs to secure, longer-term jobs. There is also scope to expand the startup incentives/self-
Portraits of Labor Market Exclusion 2.0 59
employment programs which might, in particular, be attractive to women who have care
responsibilities and live in remote areas.
Finally, in order to adjust targeting and design of AESPs, there will be need to invest in rigorous
evaluations. Currently there is no systematic impact evaluation process within the National
Employment Agency or under the Ministry of Labor; this prevents the identification of those measures
that are effective and better deployment of scarce resources. Better use of collected administrative
data (from different sources) can be made to conduct cost benefit and net impact analysis. Designing
interventions with a rigorous results evaluation framework will allow in identifying design and
implementation elements that work for particular target groups and adjusting existing programs
accordingly. Sharing and discussing the experience with other agencies within and outside of Europe
will have cross-country benefits.
Portraits of Labor Market Exclusion 2.0 60
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Portraits of Labor Market Exclusion 2.0 62
Annex 1. Advantages and Disadvantages of EU-SILC Survey Data
The data source for the analysis is the harmonized version of the European Union Statistics of Income
and Living Conditions (EU-SILC) survey. There are several reasons why the SILC survey was selected
instead of the European Union Labor Force Surveys (EU-LFS), which are made available to researchers
on a timelier basis. The SILC survey, as its full name implies, is a comprehensive survey of income and
living conditions that goes beyond standard labor market surveys. In addition to several
socioeconomic characteristics, the survey captures the incomes (from labor, social transfers, and other
sources) as well as the (self-reported) labor market status of individuals and households throughout
each month of the calendar year (reference period) prior to the interview. This level of comprehensive
data is necessary for this analysis. Had we used the LFS survey, we would only be able to identify the
target population of this study — those with no or with weak labor market attachment — according
to their labor market status at the time of the interview. Had we used the LFS survey, we therefore
would not have been able to identify the population that, although working at the time of the interview,
may have weak labor market attachment due to working in unstable jobs. Furthermore, because we
were able to capture the full income of individuals and their households (the LFS survey would only
have allowed us to capture earnings from labor and unemployment benefits), we are able to get a more
comprehensive view of the socioeconomic status of the target population of this study, which includes
income from social transfers other than unemployment benefits that may be denied or reduced when
accepting a job. Moreover, the SILC survey also includes information about access to childcare that is
necessary to identify caregiving responsibilities that present a barrier to work.
Although using SILC data provides many clear benefits for the present analysis, a few shortcomings of
this data collection method are worth mentioning.
First, the survey relies on self-reported labor market status, rather than a series of questions that lead
to standardized classification of employment status. Thus, it is possible that some individuals who
work do not self-identify as employed because they work very few hours. Thus, some of the population
identified as out of work may have been mischaracterized.
Second, among old-age and family/child social transfers, the survey does not distinguish between
those receiving social insurance and social assistance benefits. Being able to yield this type of
information would enrich the analysis of how social inclusion policies are targeted to specific groups,
as well as how social benefits may affect incentives to participate in the labor market.
Another drawback of the SILC survey vis-à-vis the LFS survey is that it does not yield detailed
information pertaining to an individual’s educational status. EU-SILC only includes information
regarding the highest International Standard Classification of Education (ISCED) level achieved. In
contrast, the LFS survey includes information on vocational versus general education, field of study,
and additional training or certifications. This information could be used to inform policies aimed at
addressing barriers to employment due to skills.
Another important dimension that is not captured by the SILC survey (or by the LFS survey) is
ethnicity. Ethnicity can play an important role in the labor market. For example, certain groups, such
as Roma, may have more difficulty finding jobs due to discriminatory practices by employers.
Information from other surveys shows that Roma are likely to be overrepresented among the
population that is out of work or marginally employed, at risk of poverty, and who have low levels of
education. It is therefore likely that some of the groups identified in this analysis comprise a large
proportion of the Roma. Being able to identify the Roma population would make the labor market
Portraits of Labor Market Exclusion 2.0 63
barriers they face more visible, allowing for the design of evidence-based policies, and perhaps
breaking down stereotypes of Roma as being out of work or marginally employed by choice. Designing
and prioritizing policies aimed at including the Roma population in the labor market — a group that
has historically suffered from social exclusion — is also increasingly important in the context of aging
and shrinking populations.
Finally, compared to the LFS survey, the SILC survey has a small sample size, totaling 4,684
observations for the target population of this study for the Romania 2013 survey. The statistical
methodology used in this study benefits significantly when there is a large sample size. Large sample
sizes can allow us to identify a greater number of groups of individuals that are more homogenous
within themselves and more heterogeneous among each other in terms of labor market barriers and
socioeconomic characteristics. In doing so, we could design more specific tailored policies.
Source: Based on Sundaram et al. (2014).
Portraits of Labor Market Exclusion 2.0 64
Annex 2. Description of Employment Barrier Indicators
Across the six countries that are analyzed by the World Bank, eight indicators37 are used in order to
proxy for broad measures of each of the three types of employment barriers: insufficient work-related
capabilities, weak economic incentives to look for a job, and scarce employment opportunities. The
definitions of the indicators are outlined below, with further details available in the joint
methodological paper (OECD and World Bank, 2016).
The following five indicators are used to capture different aspects of the insufficient work-
related capabilities barrier:
1. Low education: In the absence of data on the cognitive, socio-emotional, or technical skills of the
population, we use education as a proxy for skills. Even though education may not be a
comprehensive measure of the skills that individuals bring into the labor market, a high
correlation between education level and skill level is reasonable to assume. Similarly, the labor
market itself uses education to screen for skills. We consider an individual to have low education
if his or her education level is lower than upper-secondary (based on the International Standard
Classification of Education (ISCED)-11 classification). In other words, the population with this
barrier has only completed pre-primary, primary, or lower secondary schooling. In Greece, the
cut-off for low education has been set at the post-secondary level rather than the lower secondary
level. The reason for the change in the cut-off is that a look at unemployment (employment) rates
by education level shows that unemployment (employment) only falls (rises) significantly among
individuals who have completed tertiary education.
2. Care responsibilities: Caring for children or caring for incapacitated family members are
legitimate barriers to employment, because they reduce the time that an individual can spend on
paid work. To determine whether an individual faces a care-related employment barrier using EU-
SILC data, we rely on information regarding (i) household members who face some unmet care
need, such as young children, incapacitated family members, or elderly relatives with health
limitations and (ii) the availability of alternative care arrangements, namely the use of formal
childcare services38 and the availability of other potential caregivers in the household. We consider
an individual as having care responsibilities if he or she lives with someone who requires care and
is either the only potential caregiver in the household or if he or she reports being inactive or
working part time because of care responsibilities.
The individuals who require care are children 12 years or younger who receive 30 or fewer hours
of non-parental childcare a week. We also considered individuals of working age who (1) reported
severe long-lasting limitations in activities due to health problems and (2) reported a permanent
disability as the main reason of inactivity. Lastly, elderly household members are classified as
requiring care if they have long-lasting limitations in activities due to poor health and if they report
being inactive during each month of the SILC reference period. An individual is considered to be a
potential caregiver if he or she is an adult 18-75 years of age with no severe health-related
limitations and if during the SILC reference period he or she engaged in either part-time work,
unemployment, retirement, domestic responsibilities, and other types of inactivity and did not
have a permanent disability. Individuals who reported they were full-time workers, full-time
37For Hungary, only seven indicators are used due to data availability.
38 EU-SILC dataonly provides information with regard to access to non-parental formal or informal childcare for
children 12 and under. Information on access to formal or informal care services for incapacitated individuals
ages 13 and over is unavailable.
Portraits of Labor Market Exclusion 2.0 65
students, or participated in compulsory military service could not be considered potential
caregivers.
3. Health limitations: An individual is considered to have health limitations if they report having
moderate or severe self-perceived limitations carrying out daily activities due to health conditions
(physical or mental).
4. Low relative work experience: An individual is considered to have low relative work experience
if they have worked less than 60 percent of their total potential work life, measured by the number
of years since they left full-time education. Note that this indicator is not used in the analysis for
Hungary or Bulgaria due to missing data on work experience.
5. No recent work experience: This indicator may represent two situations: (i) individuals who
have worked in the past but have no recent work experience (i.e. have not worked for at least one
month in the last semester of the reference year or in the month of the interview); (ii) those who
are not working at the time of the interview and report having never worked in the past.
Individuals working at the time of the interview do not face this employment barrier.
Two indicators are used to capture the weak economic incentives to look for a job or accept a
job barrier by identifying individuals who could potentially draw on significant income
independently of their own work effort:
6. High non-labor income. In this scenario, an individual’s total household income (excluding
income from the individual’s work-related activities) is more than 1.6 times higher than the
median value among the population of working age.39
7. High earnings-replacement benefits: This indicator captures possible financial disincentives to
work that are based on the extent of the benefit reductions that an individual is likely to experience
if they were to engage in full-time employment. The indicator is constructed using the ratio
between the amount of earnings-replacement benefits received at the individual level and the own
shadow income or reservation wage.40 The following individual earnings-replacement benefits are
considered, as grouped by the EU-SILC survey: unemployment benefits, old-age benefits received
before the statutory retirement age, survivor benefits, sickness benefits, disability benefits, and
full-time education-related allowances. The adult-per-capita amounts of the following household-
level allowances — family/children related allowances, housing, and social exclusion not
elsewhere classified — are also added to the individual benefits, assuming that at least part of
these benefits would be withdrawn if the individuals increased their own labor supply. Based on
this resulting variable, an individual is considered to have high replacement benefits if their
earnings-replacement benefits are more than 60 percent of their estimated potential earnings in
work or shadow wage.
39 Specifically, we use the EU-SILC variable ‘gross household income’ (which includes pre -tax income from labor
and capital plus government transfers) minus the person of interest’s own income which is dependent on the
person’s own work efforts (i.e., employment income and earnings-replacement benefits, such as unemployment
benefits) and minus a share, proportional to the number of adults in the household, of social transfers awarded
at the household level (for instance, social assistance or rent allowances). The final indicator is the difference
between the total gross household income and the own labor-market contribution as defined above, divided by
the Eurostat equivalence scale and discretized in two categories. The individuals with high financial work
disincentives are those with a value of the indicator above 1.6 times the median of the resulting variable in the
reference population; the remainder in the target population is characterized as having no or low financial work
disincentives.
40 See OECD and World Bank, 2016 for details on how the reservation wage is calculated.
Portraits of Labor Market Exclusion 2.0 66
One indicator is used to capture the scarce employment opportunities barrier:
8. Scarce job opportunities: In general, this barrier relates to demand-related constraints in the
respective labor market segment. Although a number of indicators of labor demand exist at the
aggregate or semi-aggregate level, capturing the scarcity of job opportunities at the micro-level
would require the ability to describe the availability of vacancies in the labor-market segment that
are relevant for each individual given their skills set and job market characteristics. This type of
information is unavailable in EU-SILC data. In order to proxy individuals facing scarce employment
opportunities, we estimate risk of demand-side constraints (specifically the risk of being long-
term unemployed or working in a sub-optimal job) in standard labor-market segments in a
regression including age, gender, education level, and region (at the NUTS (Nomenclature of
Territorial Units for Statistics) 1 level) as independent variables and being long-term unemployed
or involuntarily working part-time as the dependent variable. In this way, we are able to calculate
different risks depending not only on the geographical location but also on the combination of
other observable characteristics within the same geographical area. The estimated parameters are
then used to predict at the local level the risk of becoming long-term unemployed or involuntarily
working part time conditional on individual circumstances. Importantly, the estimated risk will
depend on the empirically observed relation between covariates included in the regression model
and the variable describing labor-market tightness. We consider an individual to have scarce
employment opportunities if their estimated risk of being long-term unemployed or involuntarily
working part time is 1.6 times the median value. It is important to note, however, that the scarce
employment opportunities indicator may underestimate the risk of becoming long-term
unemployed or involuntarily working part-time among individuals who are inactive if they were
to undertake a job search. This is because many inactive individuals may not resemble the long-
term unemployed and involuntary part-time workers but they may still have a high probability of
unemployment. This does not imply, however, that they would be able to find a job without
difficulty if they were to enter the labor market. This is an important weakness of this indicator
that should be born in mind.
Portraits of Labor Market Exclusion 2.0 67
Annex 3. Latent Class Analysis Model Selection for Romania
A latent class model does not automatically provide an estimate of the optimal number of latent groups
of individuals. Instead, models with different numbers of classes must first be estimated sequentially
and the optimal model is then chosen based on a series of statistical criteria. The model selection
process starts with the definition of a baseline model (Step 1). In this case, the baseline model has been
defined based on a set of eight indicators representing the three main types of employment barriers
which are to be used as the main drivers of the segmentation of individuals into groups. Under Step 2,
the model with the optimal number of classes is selected, primarily based on the goodness-of-fit
statistics and classification-error statistics. Next, Step 3 examines misspecification issues, mostly
associated with the violation of the Local Independence Assumption (LIA) (see Box 9 of OECD and
World Bank, 2016). The final model is then further refined with the inclusion of the so-called active
covariates under Step 4. The following paragraphs describe the step-by-step process that was used to
select the final model for Romania starting with Step 2. For a general more detailed explanation of the
step by step process of model selection, see OECD and World Bank, 2016.
Figure A3.1 below graphically summarizes Step 2 outlined above for Romania. The blue bars show the
percentage variations of the Bayesian Information Criterion (BIC, Schwarz 1978)41 for increasing
numbers of latent groups for the baseline model; the green bars show the percentage variation of the
Akaike Information Criterion (AIC; Akaike, 1987);42 and the black line shows the classification error
statistics (Vermunt and Magdison, 2016).43 In general, smaller values of the BIC and AIC indicate a
more optimal balance between model fit and parsimony, whereas a smaller value of the classification
error statistics means that individuals are better classified into one (and only one) group. In Figure
A1.1 the BIC is minimized for a model with seven classes; the AIC criterion, on the other hand, is
minimized for a model with 14 classes. It must be noted that the difference between the BIC and AIC
statistics depends on the different penalty that the two measures apply to the increasing goodness-of-
fit: the AIC takes into account only the higher number of parameters whereas the BIC considers also
the overall sample size. Thus, in general, the BIC points to a more parsimonious specification than the
AIC. When the BIC and AIC point to different numbers of classes, as is the case here, the classification
error statistic provides further information for the selection of the optimal model. In this case, the
classification error would rule out the 14-class model in favor of the 7-class model. The classification
error itself is minimized under a 6-class model.
41 The BIC summarizes into a single index the trade-off between the model’s ability to fit the data and the model’s
parametrization: a model with a higher number of latent classes always provide a better fitting of the underlying
data but at the cost of complicating the model’s structure.
42 The BIC and the AIC are measures that capture the trade-off between the model’s ability to fit the data and the
model’s parametrization: a model with a higher number of latent classes always provide a better fitting of the
underlying data but at the cost of complicating the model’s structure. The BIC and the AIC s ummarize this trade-
off into a single index, which provides guidelines for choosing between an adequate representation of the
population into a finite number of sub-groups and an increasing complexity of the statistical model.
43 The classification error shows how-well the model is able to classify individuals into specific groups. To
understand the meaning of the classification error index it is important to keep in mind that LCA does not assign
individuals to specific classes but, instead, estimates probabilities of class membership. One has therefore two
options to analyses the results: allocate individuals into a given cluster based on the highest probability of class-
membership (modal assignment) or weighting each person with the related class-membership probability in the
analysis of each class (proportional assignment). The classification error statistics is based on the share of
individuals that are miss-classified according to the modal assignment.
Portraits of Labor Market Exclusion 2.0 68
Figure A3.1: Selection of the optimal number of latent classes
Percentage variation of goodness-of-fit criteria
0.4% 30.0%
0.2%
25.0%
0.0%
Classification error (%)
4 5 6 7 8 9 10 11 12 13 14 15 20.0%
% var. AIC and BIC
-0.2%
-0.4% 15.0%
-0.6%
10.0%
-0.8%
5.0%
-1.0%
-1.2% 0.0%
Var BIC Var AIC Class.Err.
Source: World Bank calculations based on EU-SILC 2013.
Step 3: Misspecification tests
The model selected through goodness-of-fit and classification statistics under Step 2 may not be
optimal due to misspecification issues, the most common of which being related to the violation of the
Local Independence Assumption (LIA). This assumption shapes the mathematical specification of the
statistical model and, in practice, requires the indicators to be pairwise independent within the latent
groups. When this requirement is not met the model is not able to reproduce the observed association
between the indicators, at least for the indicators showing some residual within-class (local)
dependency. Such violations of the LIA can be best addressed modelling explicitly the local
dependencies between pairs of indicators, via the so-called direct effects (Vermunt and Magdison,
2016; OECD and World Bank, 2016). The inclusion of direct effects in the model specification
eliminates any residual correlation between the indicators (by construction) but it also requires
repeating the model selection process from the beginning, as the new baseline model with local
dependencies may lead to a different optimal number of classes.
For Romania, both 7 and the 6-class model selected clear signs of misspecification, with bivariate
residuals significantly higher than 1 for several pairs of indicators.44 Eliminating the local
dependencies through the use of direct effects points to a 5-cluster model when minimizing the BIC
criterion and the classification error. However, the 5-class model shows new residual association
between other pairs of indicators. The 6-class model does not show misspecification issues and
remains a favorite option for Romania.
44 Results are available upon request.
Portraits of Labor Market Exclusion 2.0 69
Step 4: Model refinements – inclusion of active covariates
In most empirical applications the aim of latent class analysis is not just to build a classification model
based on a set of indicators but also to relate the class membership to other individual and household
characteristics identifying specific population sub-groups of interest, such as youth and women.
In order to further describe the identified groups according to specific population sub-groups that are
typically considered in the breakdown of common labor market statistics, we run the latent class
model again, this time with covariates actively contributing to the definition of the group-membership
probabilities. The inclusion of active covariates is primarily driven by the interest in specific
population sub-groups that are typically considered in the breakdown of common labor market
statistics. As such, different specifications of models with active covariates were estimated, including
different combinations age (3 categories), gender, presence of young children, activity, degree of
urbanization, and region at the NUTS 1 level. The choice of the active covariates also relies on practical
considerations, i.e. the relevance of these categories in the policy debate on AESPs and also on the
possibility for the public employment services to actually collect such information.
The inclusion of active covariates does produce misspecification once again (i.e. bivariate residuals
between combinations of indicators and covariates), which we, again, address by explicitly modeling
the associations between indicators and covariates with direct effects (as discussed in Step 3 above).
Culminating Step 4, a 6-cluster model with the combination of active covariates and direct effects that
brings the bivariate residuals down to zero and also has the lowest classification error is the final
model chosen.45 In the case of Romania, this model is a six-class model including age, gender, presence
of young children and degree of urbanization as active covariates. The model has a classification error
of only 3 percent. This is a significant reduction in the classification error, which was close to 15
percent when no active covariates are introduced to the model. A significant reduction of the
classification-error statistics in models with active covariates is the sign that, for some individuals, the
employment-barrier indicators alone do not produce a clear-cut latent-class assignment and that,
therefore, the covariates are playing an important role not only in improving the latent-class
membership but also in shaping the main barrier profile characterizing some of the latent groups.
While this does not typically affect the barrier profiles of the biggest groups (i.e. those with the biggest
shares in the target population) the barrier profiles of the smallest groups could be partially shaped
around the interaction between the information provided with the active covariates and the
indicators.46
45 Steps 3 and 4 were also carried out for a 7-cluster model, given that although this model does not initially
result in the lowest classification error, it is the model that would be chosen under the BIC criterion alone.
However, in the end, the 6-cluster model is chosen for two reasons. First, the classification error is considerably
lower. Second, the 6-cluster model shows the presence of a youth cluster. Youth have been shown to be
particularly vulnerable in the labor market, given their markedly high unemployment status; their participation
rates are also low, given rise to a high NEET rate. The 6-cluster model allows for the analysis of this important
labor market demographic without significantly compromising on the BIC criterion, while at the same time
reducing the classification error.
46 This should be considered as an improvement with respect to a model without covariates whose indicators do
not produce a clear-cut latent-class assignment for some individuals. In fact, without additional information, the
allocation of these individuals into a specific latent group would be done almost at random, whereas in models
with covariates the allocations of this individuals depends on the additional information provided to the latent
class model and how this interact with the indicators.
Portraits of Labor Market Exclusion 2.0 70
Annex 4. Characterization of Latent Groups Among the Target Population in
Romania
Group 2. Group 6.
Low- Mostly male
Group 1. educated Group 4. relatively
Higher inactive Group 3. Young Group 5. educated
income middle- Middle- mostly Young long-term
(early) aged aged less female less unemployed
retirees women educated NEETs educated or inactive
with with no rural with no rural with past
health work working work working work Target
limitations experience poor experience poor experience pop.
Group size (% of target population) 36 20 18 12 8 6 100
Thousands of individuals 1,880 1,023 943 645 417 307 5,215
Share of individuals facing each barrier, by class
Capabilities barriers
1- Low education 30 61 56 51 56 16 45
2- Care responsibilities 10 25 0 25 0 19 13
3- Health limitations 53 33 17 14 8 28 33
Low relative work experience
4- (WE) 21 100 24 100 31 29 48
No recent WE - Has worked in the
5- past 98 15 0 8 1 90 45
No recent WE - Has never worked 0 85 0 92 0 0 28
Incentives barriers
6- High non-labor income 26 21 6 16 5 25 19
High earnings-replacement
7- benefits 24 2 0 2 0 12 10
Opportunity barrier
8- Scarce employment opportunities 0 0 0 100 100 100 26
Average number of barriers 2.6 3.4 1.1 4.1 2.0 3.2 2.7
Socioeconomic and demographic of latent groups (percent)
Group 2. Group 6.
Low- Mostly male
Group 1. educated Group 4. relatively
Higher inactive Group 3. Young Group 5. educated
income middle- Middle- mostly Young long-term
(early) aged aged less female less unemployed
retirees women educated NEETs educated or inactive
with with no rural with no rural with past
health work working work working work Target Reference
limitations experience poor experience poor experience pop. pop.
Percent of target population 36 20 18 12 8 6 100 NA
Thousands of individuals 1,880 1,023 943 645 417 307 5,215 12,985
Women* 64 93 62 68 43 24 66 50
Children under 12 in household* 19 45 36 53 43 48 35 35
71
Group 2. Group 6.
Low- Mostly male
Group 1. educated Group 4. relatively
Higher inactive Group 3. Young Group 5. educated
income middle- Middle- mostly Young long-term
(early) aged aged less female less unemployed
retirees women educated NEETs educated or inactive
with with no rural with no rural with past
health work working work working work Target Reference
limitations experience poor experience poor experience pop. pop.
Age group*
Youth (18-29) 0 0 0 88 90 15 19 17
Middle-aged (30-55) 29 79 82 12 9 64 47 64
Older (56-64) 71 21 18 0 1 22 34 19
Main activity during the reference period*
Employed 1 0 95 0 94 7 26 70
Unemployed 3 2 1 26 2 48 8 3
Retired 81 11 3 1 0 24 33 13
Domestic tasks 11 78 1 54 1 9 27 11
Other inactive or disabled 3 9 0 19 3 13 6 3
Degree of urbanization*
Densely populated 35 22 5 31 13 59 26 34
Intermediate 26 19 14 25 16 22 21 24
Thinly populated 39 59 81 44 71 19 53 42
Region
NW & Central 26 27 10 22 15 9 21 25
NE & SE 27 29 53 25 52 32 34 28
South Muntenia & Bucharest 28 25 13 37 20 46 26 29
SW Oltenia & West 20 19 23 16 14 13 19 17
Target population category
Out of work 97 100 0 100 1 90 72 29
Unstable jobs 2 0 5 0 8 4 2 1
Restricted hours 0 0 2 0 5 3 1 0
Near-zero income 1 0 93 0 86 3 24 10
Main activity during reference period (more disaggregated)
Employed full time 0 0 3 0 5 0 1 52
Employed part time 0 0 0 0 1 1 0 0
Self-employed full time 1 0 50 0 33 2 12 11
Self-employed part time 1 0 42 0 55 5 13 7
Unemployed 3 2 1 26 2 48 8 3
Retired 81 11 3 1 0 24 33 13
Domestic tasks 11 78 1 54 1 9 27 11
Disabled 2 6 0 8 0 3 3 1
Other inactive 2 3 0 11 3 10 3 1
Main activity at moment of interview
Employed 2 0 99 0 94 9 27 70
Unemployed 4 2 0 28 2 46 8 3
Retired 80 12 0 1 0 24 33 13
Domestic tasks 11 78 0 53 1 8 26 11
Other inactive or disabled 3 8 0 17 2 12 6 2
Months in unemployment
72
Group 2. Group 6.
Low- Mostly male
Group 1. educated Group 4. relatively
Higher inactive Group 3. Young Group 5. educated
income middle- Middle- mostly Young long-term
(early) aged aged less female less unemployed
retirees women educated NEETs educated or inactive
with with no rural with no rural with past
health work working work working work Target Reference
limitations experience poor experience poor experience pop. pop.
Zero months 96 97 98 70 96 51 91 96
1 to 11 months 0 1 1 5 4 2 2 1
12 or more 3 2 0 24 0 47 7 3
Actively searching for a job at time
of interview 2 4 0 35 3 47 9 4
At risk of poverty (60% of median
income) 16 43 66 49 62 35 39 21
At risk of poverty (40% of median
income) 7 23 46 27 42 24 23 11
Income quintile
Poorest 14 38 61 45 58 34 36 19
2 18 28 22 29 29 21 23 18
3 21 17 10 14 8 14 16 19
4 25 12 4 8 4 17 15 21
Richest 22 5 3 4 2 14 11 23
Severe material deprivation 29 39 42 54 53 49 39 21
Years of work experience
None 0 85 0 92 2 0 28 0
1 to 5 3 3 6 6 50 16 9 13
6 to 10 4 3 11 1 36 18 8 15
11 to 20 13 7 33 0 10 25 15 28
21 to 30 29 2 27 0 2 19 17 25
More than 30 51 0 23 0 1 21 24 19
Average years of work experience** 29 13 21 4 6 19 19 19
Education level
Primary or less 4 16 9 15 13 1 9 4
Lower secondary 26 44 47 36 43 15 36 22
Upper secondary 57 35 41 36 40 72 47 54
Post-secondary 7 1 1 1 1 4 3 4
Tertiary 6 3 3 12 3 8 6 16
Age groups (more disaggregated)
18-24 years 0 0 0 42 45 2 9 6
25-29 years 0 0 0 46 46 13 10 11
30-44 years 8 53 50 11 7 33 26 41
45-54 years 17 24 29 1 2 25 18 21
55-59 years 27 13 13 0 1 26 16 11
60-64 years 48 10 8 0 0 0 21 10
Average age 57 45 45 26 26 44 45 42
Severe limitations in daily activities 18 12 3 7 0 15 11 5
At least one other household
member 25 & older working 51 75 79 81 90 67 69 73
Elderly in the household 17 16 20 14 17 11 17 14
Children under 6 in household 9 17 14 31 25 31 17 16
73
Group 2. Group 6.
Low- Mostly male
Group 1. educated Group 4. relatively
Higher inactive Group 3. Young Group 5. educated
income middle- Middle- mostly Young long-term
(early) aged aged less female less unemployed
retirees women educated NEETs educated or inactive
with with no rural with no rural with past
health work working work working work Target Reference
limitations experience poor experience poor experience pop. pop.
Children under 3 in household 3 5 6 16 10 15 7 6
Children under 13 in formal childcare
None 1 5 2 11 6 7 4 3
Some 1 3 2 4 0 3 2 1
All 16 37 32 38 37 38 29 30
NA 82 55 64 47 57 52 65 65
Household type
One person 11 3 4 1 1 4 6 5
Single parent 1 0 1 1 0 2 1 1
2+ adults, 0 children 59 35 40 33 39 37 45 41
2+ adults, 1 child 6 11 8 9 5 9 8 14
2+ adults, 2+ children 23 49 46 55 55 47 40 39
Average household size 3.1 4.1 4.1 4.8 4.9 3.9 3.9
Live with parents 6 11 14 57 74 26 21 23
Marital status
Married 74 77 79 36 26 69 67 69
Never married 3 12 10 62 72 23 20 21
Divorced/separated 7 3 6 2 2 7 5 5
Widow/er 15 8 5 0 0 2 8 4
Labor market status of spouse/partner
Working 26 59 64 37 25 43 41 50
Unemployed 1 2 0 2 0 6 2 1
Retired 40 15 11 0 0 10 20 10
Unfit to work 0 1 0 0 0 0 0 0
Domestic tasks 7 1 6 2 3 13 5 9
Other inactive 1 3 0 1 0 0 1 1
No spouse/partner 25 19 19 58 72 28 31 28
Receives family benefits 25 56 51 60 57 52 44 46
Average annual value (€)† 239 286 362 310 457 479 327 292
Receives social exclusion benefits 11 17 25 23 29 13 18 11
Average annual value (€)† 149 246 232 365 325 150 245 206
Receives old-age benefits 61 6 4 0 0 13 25 10
Average annual value (€)† 2,355 1,482 1,712 … … 3,093 2,322 2,319
Receives survivor benefits 11 4 1 4 1 6 6 3
Average annual value (€)† 367 714 707 718 472 828 486 469
Receives disability benefits 21 4 0 2 0 12 9 4
Average annual value (€)† 1,615 1,246 … 882 … 1,741 1,573 1,570
Receives education benefits 1 5 0 5 0 2 2 1
Average annual value (€)† … 707 … 884 … 713 758 746
Receives any social benefits 93 71 63 69 65 77 77 61
Average annual household income (€) from:
Labor 2,523 3,302 1,997 3,369 2,491 3,750 2,755 5,534
74
Group 2. Group 6.
Low- Mostly male
Group 1. educated Group 4. relatively
Higher inactive Group 3. Young Group 5. educated
income middle- Middle- mostly Young long-term
(early) aged aged less female less unemployed
retirees women educated NEETs educated or inactive
with with no rural with no rural with past
health work working work working work Target Reference
limitations experience poor experience poor experience pop. pop.
Benefits 3,031 1,219 981 1,047 969 1,839 1,824 1,224
Other 21 34 17 16 12 40 23 21
Total 5,575 4,555 2,987 4,432 3,472 5,630 4,601 6,778
Average annual equivalized
household income (€) 2,466 1,596 1,105 1,398 1,089 1,973 1,778 2,479
*Included in the LCA model as active covariates.
**Refers only to individuals who have worked before.
†Average across only positive value observations. Averages based on less than 30 observations are omitted.
Note: Unemployment and sickness benefits not included due to small number of observations. Housing benefits not included as no
households report receiving said benefits.
Source: Authors’ calculations based on EU-SILC 2013.
75
Annex 5. Characterization and Definitions of Labor Market Programs Based on
Eurostat
Labor market programs are government initiatives that include expenditure programs but also
foregone revenues (e.g. reductions in social security contributions) that aim to reduce disequilibria
and improve efficiency of the labor market (Eurostat 2013).
Eurostat classifies these labor market policies into three broad categories:
1. Labor Market Services. This covers all services and activities of the public employment
service together with any other publicly funded services for jobseekers, including their
administrative costs.
2. Active Labor Market Programs (ALMPs). These include all interventions where the
main activity of participants is “other than job-search�? related and where participation
usually results in a change in labor market status. With the exception of programs
supporting permanent reduced working capacity, measures are usually providing a
temporary support aimed at activating the unemployed, helping people move from
involuntary inactivity into employment, or maintaining the jobs of persons threatened by
unemployment. Since 2013, Eurostat classifies measures into five subcategories:
a) training,
b) employment incentives,
c) supported employment and rehabilitation,
d) direct job creation, and
e) startup incentives.
3. Passive Labor Market Programs. These usually provide financial assistance to those
who are out of work (unemployment benefits) or who retired early from the labor market.
Source: Adapted from Eurostat LMP database, Eurostat (2013).
76