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 Hungary Lead Authors: Sandor Karacsony and Natalia Millán Project team: Aylin Isik-Dikmelik (Team Leader), Mirey Ovadiya (Team Leader), Sandor Karacsony, Natalia Millán, and Frieda Vandeninden July 2017 © 2017 International Bank for Reconstruction and Development / The World Bank 1818 H Street, NW Washington, DC 20433 Telephone: 202-473-1000 Internet: www.worldbank.org The findings, interpretations, and conclusions expressed here do not necessarily reflect the views of the Executive Directors of The World Bank or the governments they represent. The World Bank does not guarantee the accuracy of the data included in this work. 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Any queries on rights and licenses, including subsidiary rights, should be addressed to the Office of the Publisher, The World Bank, 1818 H Street NW, Washington, DC 20433, USA; fax: 202-522-2422; e-mail: pubrights@worldbank.org. Photos: © World Bank Cover design/layout and editing: Nita Congress 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 Millan (Economist), and Frieda Vandeninden (Economist). 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), Balazs Rottek (Policy Officer, Directorate F4-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 representatives of the Ministry of Interior, the Ministry for National Economy and the Ministry of Human Capacities. In particular, the team would like to thank Iren Busch, Zsolt Ruskai, and Linda Niki Volosinovsky , 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. 3 Portraits of Labor Market Exclusion 2.0 Contents Acknowledgements....................................................................................................................................... 3 1. Introduction ..................................................................................................................................... 6 2. Country context ............................................................................................................................... 7 3. Understanding employment barriers – a framework ................................................................ 15 3.1. Population of analysis: Individuals with potential labor market difficulties.................................... 16 3.2. Employment barrier indicators ........................................................................................................ 20 4. Results of the analysis: portraits of labor market exclusion in Hungary .................................. 24 5. Priority groups in the Hungarian target population .................................................................. 33 6. Policies and measures targeting the employment barriers of priority groups ........................ 39 6.1. Framework and approach ................................................................................................................ 39 6.2. Overview of activation and employment support programs and policies ................................. 40 6.2.1 Institutional and policy context ..................................................................................................... 40 6.2.2 Overview of Active Labor Market Program (ALMP) Policies in Hungary ....................................... 43 6.3. Activation and employment support policies vis-à-vis priority groups’ needs................................ 49 7. Conclusions and policy recommendations .................................................................................. 55 References.................................................................................................................................................. 59 Annex 1. Advantages and disadvantages of the EU-SILC Data............................................................... 64 Annex 2. Description of employment barrier indicators ....................................................................... 66 Annex 3. Latent Class Analysis results of EU-SILC 2013 respondents who are out-of-work or marginally employed ................................................................................................................................ 69 Annex 4. Latent Class Analysis model selection for Hungary ................................................................ 76 Annex 5. Categorization and definitions of labor market programs based on Eurostat ..................... 79 Annex 6: An overview of proposed policy actions .................................................................................. 80 Tables Table 1. Characterization of target population according to barrier indicators (percent) ................. 23 Table 2. A cross-country comparison of barriers faced by the target population ............................... 23 Table 3. Employment barriers faced by excluded groups in the Hungarian labor market ................. 26 Table 4. The priority groups’ employment barriers and characteristics .............................................. 34 Table 5. Employment rate of former public workers on the 180th day after program exit, 2015 (percent) .................................................................................................................................................... 41 Table 6. Overview of Hungarian activation and employment support programs, 2015 ..................... 47 4 Portraits of Labor Market Exclusion 2.0 Table 7. An overview of barriers and their implications on service delivery and quality in Hungary .................................................................................................................................................................... 49 Figures Figure 1. Employment (ages 15 to 64) in Hungary and EU-28 ................................................................ 8 Figure 2. Unemployment and long-term unemployment (ages 15 to 64) in Hungary and EU-28 ....... 9 Figure 3. Unemployment among population (ages 15 to 64) by education level ................................ 10 Figure 4. Activity rates by sex and age in Hungary and EU-28 .............................................................. 11 Figure 5. Part-time employment as a percentage of total employment by sex, EU Member States, 2015............................................................................................................................................................ 12 Figure 6. At-risk-of-poverty and in-work at-risk-of-poverty rates in EU Member States, 2015 (percent) .................................................................................................................................................... 13 Figure 7. PISA mathematics scores by school type and students’ socioeconomic background, 2012 14 Figure 8. Age distribution and population dynamics in Hungary (2010–2050) .................................. 15 Figure 9. Composition of the working-age population in Hungary (left) and out of work (right) ..... 18 Figure 10. Labor market attachment status of working-age* population, Hungary and other EU countries under study (percent) .............................................................................................................. 19 Figure 11. Composition of the persistently out of-work population by labor market status, Hungary 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 Hungarian target population ....................................................... 25 Figure 14. Number of barriers faced by individuals in latent groups in the Hungarian target population .................................................................................................................................................. 27 Figure 15. Typical constraints faced by registered vulnerable jobseekers (Category 3), 2016 ......... 33 Figure 16. Organizing framework for policy analysis ............................................................................ 40 Figure 17. Composition of labor market spending as percentage of GDP ............................................ 44 Figure 18. Composition of labor market programs in Hungary, in percent of total labor market expenditure, 2015 ..................................................................................................................................... 45 Figure 19. Group 2: Main employment barriers and necessary activation and employment support programs .................................................................................................................................................... 52 Figure 20. Group 5: Main employment barriers and necessary activation and employment support programs .................................................................................................................................................... 53 Figure 21. Group 6: Main employment barriers and necessary activation and employment support programs ..................................................................................................................................................... 55 Boxes Box 1. Definition of target population ..................................................................................................... 16 Box 1. Definitions of employment barrier indicators used for Hungary .............................................. 21 Box 3. An assessment of the Hungarian public works program ............................................................ 42 Box 4. Highlights from an assessment of EU-funded social inclusion measures in Hungary .............. 43 5 Portraits of Labor Market Exclusion 2.0 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 (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 out of work or marginally employed individuals 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 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. 6 Portraits of Labor Market Exclusion 2.0 inactive mothers compared to young unemployed men. It is therefore important to relate each barrier to 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 Hungary along with existing activation and employment support policies and programs. The paper consists of seven sections. Section 2 provides background on the Hungarian 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 Hungary, 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 While employment, unemployment and activity has been gradually improving over the past years in Hungary, structural issues in the labor market persist, and given demographic trends, their consequences are expected to grow. In particular, public works remains to be a significant driver of labor demand; nonetheless, in 2016 the majority of the new workplaces have been generated in the competitive sector, and compared to the second half year of 2015, the number of public works participants has decreased. Gender gaps, as well as gaps in activity by younger and older age cohorts are considerable. A large number of young Hungarians are struggling with the transition from school to work and are idle. At the same time, persistent child poverty coupled with an inequitable and ineffective education system foreshadows significant constraints in labor productivity of future generations. Added to this is the fact that the work-able population is aging and shrinking, further reducing the size of the Hungarian workforce. 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. 7 Portraits of Labor Market Exclusion 2.0 The Hungarian labor market has been gradually recovering at a steady pace since the 2008 financial crisis. Employment rates have recovered quickly starting in 2011, growing steadily and reaching 63.9 percent in 2015, almost catching up with the EU-28 average of 65.6 percent (Figure 1). At 6.8 percent, the unemployment5 rate is now recorded at lower than pre-crisis levels, significantly below the EU-28 average of 9.4 percent (Figure 2, panel a). The decrease in unemployment has been driven in large part by the ramping up of Hungary’s public works program (European Commission, 2015). But although the rate is also lower than the EU-28 average, it is nonetheless worth noting that within the unemployed, almost half are considered long-term unemployed6 (Figure 2, panel b). Figure 1. Employment (ages 15 to 64) in Hungary and EU-28 70 65.2 65.7 65.6 64.5 64.2 64.8 64.1 64.1 64.1 65 63.9 61.8 60 57.0 58.1 56.4 56.7 55.0 54.9 55.4 55 50 45 2007 2008 2009 2010 2011 2012 2013 2014 2015 EU-28 Hungary Note: The EU-28 average is weighted. Source: Eurostat LFS. 5 In this study, several data resources are used. Please note, that labor market status in EU-SILC is self- reported, in LFS, the definition of unemployment differs and is more complex (people who were not employed, or were currently available for work, i.e. were available for paid employment or self-employment before the end of the two weeks following the reference week; or were actively seeking work). The Hungarian Central Statistical Office uses the same definition. The Hungarian registered unemployed persons’ definition covers only those who were officially registered at the National Employment Offices. 6 Long-term unemployment is defined as unemployment that lasts 12 months or longer. Please also note, that the extensive public works scheme in Hungary was scaled up in 2013 which considerably reduced the number of those who would be reported as unemployed, but were still outside the primary labor market, 8 Portraits of Labor Market Exclusion 2.0 Figure 2. Unemployment and long-term unemployment (ages 15 to 64) in Hungary and EU-28 12 a. Unemployment 55 b. Long-term unemployment as a percentage of unemployment 10 50 48.1 45.3 9.4 7.4 45 8 45.6 45.1 40 7.1 6 6.8 35 4 30 2 25 0 20 2007 2008 2009 2010 2011 2012 2013 2014 2015 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 EU (28 countries) Hungary EU (28 countries) Hungary Note: The EU-28 average is weighted. Long-term unemployment is defined as unemployment lasting 12 months or longer. Source: Eurostat LFS The unemployment rate is lowest among those with tertiary degrees, and unemployment rates continue to trend downward at a similar pace across all levels of education since 2012. Similar to overall EU-28 trends, individuals with only a primary education or fewer years in school affected the most by the crisis. In more recent years, unemployment in Hungary has been decreasing at a similar pace across all education levels since 2012, reverting back to pre-crisis levels (see Figure 3. ). It is worth noting that the gap in unemployment between those with primary education or less and those with tertiary degrees is especially high in Hungary compared to the EU- 28 average. Unemployment for individuals with a primary education or less was 17.4 percent in 2015 in both Hungary and the EU-28. However, unemployment for those with a tertiary level of education in Hungary was only 2.4 percent, versus 5.6 in EU-28. Hungarians with upper secondary or post-secondary education also experienced lower unemployment than the EU-28. 9 Portraits of Labor Market Exclusion 2.0 Figure 3. Unemployment among population (ages 15 to 64) by education level a. Hungary b. EU-28 30 30 25 25 20 20 17.4 17.4 16.8 15 15 10.6 10 10 8.7 6.9 7.0 6.4 5 5 5.6 2.6 2.4 3.9 0 0 2007 2008 2009 2010 2011 2012 2013 2014 2015 2008 2006 2007 2009 2010 2011 2012 2013 2014 2015 Note: The EU-28 average is weighted. Source: Eurostat LFS. Although activity rates have been steadily improving over the past years, they still fall short of EU-28 average—and a marked gender gap remains whereby females lag behind males. In 2015, the economic activity rates of Hungarians from both genders reached a historical high (75.2 percent for males and 62.2 percent for females). However, these rates still fall short of EU-28 averages of 78.3 and 66.8 percent, respectively. In spite of the dynamic increase for both genders, the growth in activity rates for females has been lagging behind that of the males, contributing to an increasing gender gap. This in turn translates to a considerable gender gap in employment rates. Hungarian males are employed at a rate of 70.3 percent, which is close to the EU-28 average (70.8 percent); by contrast, females are employed at a significantly lower rate (57.8 percent, which is almost 3 percentage points lower than the EU-28 average rate of 60.4 percent. After years of steady increase in activity rates, Hungarians in their prime years of age (25 to 49 year olds) of both genders have activity rates that reached the EU average; however, younger and older age cohorts continue to lag behind. Specifically, the activity rate for Hungarians of prime age was 92 percent for males and 79 percent for females by 2015, which has driven the overall growth in activity for the whole population. Although the activity rates of young (15 to 24 years of age) and older Hungarians (50 to 64 years of age) have also grown even more 10 Portraits of Labor Market Exclusion 2.0 dynamically, they continue to stay below EU-28 average for both genders. The most significant difference, approximately 11.7 percentage points, appears among female youth (Figure 4). Figure 4. Activity rates by sex and age in Hungary and EU-28 a. Males b. Females 100 100 92.8 93.0 90 90 92.0 77.6 80 89.0 80 79.9 73.7 78.7 70 67.3 70 66.5 73.5 60 54.4 60 59.6 48.8 52.8 50 50 44.1 42.4 40 47.4 40 38.7 34.4 40.6 30 30.2 30 23.2 27.5 20 20 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 Note: The EU-28 average is weighted. Source: Eurostat LFS. In addition to the low youth activity rate, a significant number of young Hungarians are struggling with the transition from school to work and are idle. While the youth unemployment rate has dropped to 17.3 percent — which is below pre-crisis levels and is also lower than the EU-28 average (20.4 percent) — 11.6 percent of 15–24 year olds are neither in employment, education, or training (NEET). By comparison, the EU-28 average for NEET is 12.0 percent. Moreover, the rate at which youth leave school has increased to 11.6 percent from 11.4 percent in 2014, which is higher than the EU-28 average of 11 percent. The prevalence of NEET is more significant among females (12.8 percent) than males (10.4 percent). Sitting at 2.4 percentage points, this gender gap is considerably higher than the EU-28 average (0.5 percentage points). Low activity rates among women, youth, and older individuals may also reflect a labor market that is not conducive to voluntary part-time work. Voluntary part-time work allows individuals to combine work with other activities, such as education, training, or caring for children. It also allows older people or those with disabilities to more easily accommodate physical limitations, or younger retirees to continue to be engaged in work while pursuing more leisure activities. Hungary has the third lowest level of part-time work activity as a percentage of total employment in the EU. This implies that many individuals who may be interested in working, but 11 Portraits of Labor Market Exclusion 2.0 cannot take on a full-time job, are likely to be excluded from the labor market. Similar to other EU countries, part-time work is primarily selected by women as a way to combine work with family and childcare responsibilities. In 2015, 7.7 percent of total female employment was made up of part-time workers, whereas part-time male workers made up only 5.7 percent of total male employment (Figure 5). However, there is a striking difference in the percentage of part-time employment among women in the EU, where it reaches 32.1 percent (versus 8.8 percent of total male employment). Figure 5. 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 Austria Denmark EU-28 France Latvia Italy Finland Netherlands Romania Hungary Germany Ireland Spain Cyprus Luxembourg Slovenia Greece Poland Croatia Belgium Bulgaria Malta Estonia Lithuania Czech Republic Sweden Slovakia Portugal United Kingdom Females Males Note: The EU-28 average is weighted. Source: Eurostat LFS. Monetary poverty, in general, as well as in-work poverty in Hungary is slightly below the EU- 28 average; however, the share of children who live in poor households remains to be the third highest in the EU (after Bulgaria and Romania). This reality has negative implications for the future of the Hungarian labor market. In 2015, Hungary’s in-work at-risk-of-poverty rate, at 9.3 percent, is slightly below the EU-28 average of 9.6 percent and even below a few Western European Member States (e.g. Germany and Luxembourg) (Figure 6). In addition, the overall at-risk-of-poverty rate is lower than the EU-28 average, and the depth of poverty is reported to have decreased. Simultaneously, in 2015, more than one out of every three Hungarian children (35.5 percent between the ages of 0 and 16) was living in a household at risk of poverty or social exclusion. Because families overall receive inadequate social benefits, and recent reforms have led to additional restrictions of benefits (particularly in the areas of housing and heating), the poorest and most marginalized families in Hungary continue to struggle. 12 Portraits of Labor Market Exclusion 2.0 Figure 6. At-risk-of-poverty and in-work at-risk-of-poverty rates in EU Member States, 2015 (percent) 30.0 25.0 20.0 15.0 10.0 5.0 0.0 EU-28 Finland France Austria Hungary Latvia Cyprus Italy Romania Denmark Netherlands Croatia Poland Germany Spain Greece Czech Republic Malta Bulgaria Slovakia Belgium Slovenia Portugal Luxembourg Lithuania Estonia 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, 2015. Hungary’s education system has significant weaknesses; notably, the quality of education varies according to the type of education the school provides, and a family’s socioeconomic situation has significant bearing on the type of education a student receives, making the system inequitable. A recent analysis of the 2012 results of the Programme for International Student Assessment (PISA) for Hungary7 (The World Bank, 2015b) shows a significant discrepancy in PISA performance scores based on students’ socioeconomic backgrounds. The performance gap between students who score at the top quintile and those who score at the bottom quintile is equivalent to more than three years of schooling; this gap is significantly greater than elsewhere in the EU (Figure 7). The analysis also finds that student performance varies by type of school — whether general secondary, vocational secondary, or vocational. Finally, 15-year-olds from socioeconomically disadvantaged backgrounds are disproportionately represented in vocational schools and vocational secondary schools, where school quality is significantly below that of general secondary schools. Drilling down further, the analysis demonstrates that the significant variance in student performance is linked to three contributing causes: (i) a large share of children and youth at risk of poverty or social exclusion; (ii) insufficient efforts to tackle inequity in learning conditions faced by Hungarian students from an early age; and (iii) significant social stratification of schools hand-in-hand with early ability-based selection into general secondary, vocational secondary, and vocational tracks. 7Since this analysis was completed, PISA 2015 scores have also been published. The results show that reading and science scores continued to decline while mathematics scores remained at the 2012 levels—all scores are below the OECD average. 13 Portraits of Labor Market Exclusion 2.0 Figure 7. PISA mathematics scores by school type and students’ socioeconomic background , 2012 PISA Scores Mathematics (School Average) 680 10 PISA mathematics scores by average ESCS and school type (PISA 2012) 640 9 600 8 Equivalent Years of Schooling General Secondary 560 Vocational Secondary 7 520 Vocational 6 480 5 440 4 400 3 360 2 320 1 -1.5 -1.0 -0.5 0.0 0.5 1.0 PISA Index of Economic, Social and Cultural Status (ESCS) (School Average) Source: Authors’ estimates using PISA 2012 data. Notes: The 2012 PISA sample is for Hungary tested students in 204 schools. For the purposes of this chart, basic education schools (49 schools with very few observations each) and schools with less than 12 students in secondary schools (7 schools) were removed, leaving 149 schools. An increase of 40 points is the equivalent to what an average student learns in a single school year. OECD averages of ESCS index and PISA mathematics scores are 0 and 500, respectively. The ESCS school average is calculated by computing the weighted average of student’s ESCS at each school. Persistent child poverty combined with an inequitable and ineffective education system foreshadows significant constraints in future labor productivity. Numerous policy efforts have attempted to reach and support the poorest and most vulnerable households with children in Hungary between 1993 and 2013. Also compulsory pre-primary education from the age of 3 since 2013 may have a positive effect on education performance. However, a comprehensive overhaul of these policies and institutional changes in the education system has led to systemic inefficiencies and disincentives. Subsequently, the number of vulnerable children eligible for support has dropped (World Bank, forthcoming). As a result of these tendencies, and an education system which appears to exacerbate existing inequalities, near-future cohorts of workers are expected to enter the labor market largely unequipped with the necessary cognitive and socio-emotional skills. Hungary’s long-term economic growth prospects are placed at further risk by demographic change. Hungary’s working-age population is projected to decline by more than 10 percent between 2010 and 2050, due to a significantly aging population (Schwarz et al., 2014) (bottom panel in Figure 8). The signs of aging in the labor market have been in plain sight for a number of years: approximately 27 and 30 percent of the out-of-work among the working-age population in 2008 and 2010, respectively, was made up of retirees (some of which are early retirees) (Sundaram et al., 2014).8 8 The retirement age in Hungary in 2016 was age 63 and six months (gradually rising by six months a year until reaching age 65 in 2022) given at least 20 years of contributions and age 62 given at least 20 years of contributions if born before January 1, 1952. In certain cases, women with children may retire earlier. The retirement age is lowered to 60 if employed at least 10 years (men) or eight years (women) in arduous or 14 Portraits of Labor Market Exclusion 2.0 Figure 8. Age distribution and population dynamics in Hungary (2010–2050) Note: The bottom panel shows the overall percent change in population projected in each country for the period 2010 to 2050. Source: Schwartz et al., 2014. 3. Understanding employment barriers – a framework 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 Hungary’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 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 populations who are out of work or marginally employed is gaining a deep understanding of their characteristics and their barriers for entering the labor market. The analysis yielded categories of out of work unhealthy conditions and further reduced by one year for each additional five-year period (men) or four-year period (women) of arduous or unhealthy work. 15 Portraits of Labor Market Exclusion 2.0 and marginally employed individuals into distinct subgroups based on their responses to these questions. Developing narrower and more distinct categories of individuals who share similar characteristics and face similar constraints provided 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 to offer governments — in particular, ministries and agencies in charge of labor and employment policy — a powerful statistical tool that will shed light on the characteristics of out of work and marginally employed individuals and provide the rationale for how needs should be prioritized. Simply put, this tool will support the design of policies and programs that are suited to the distinct needs of vulnerable individuals with low labor market attachment. 3.1. Population of analysis: Individuals with potential labor market difficulties The target population — the focus of the current analysis — is a subset of the Hungarian population of working age; this latter is the population 18 through 64 years old, and it excludes full-time students and those serving military service. The population comprises individuals who self-reported being out of work during the entire survey reference period (no labor attachment) in addition to individuals who were marginally employed due to unstable jobs, restricted working hours, or very low earnings.9 As such, the analysis offers a much broader perspective than common profiling exercises, which use administrative data collected on registered jobseekers. 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 those individuals included in the analysis, also, as mentioned above, referred to as the target population. 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 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: Individuals in this group reported being unemployed, retired, or inactive throughout the reference period (12 consecutive months and at the time of the interview). These individuals were also not working at the time of the survey interview. 9 The 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 16 Portraits of Labor Market Exclusion 2.0 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 income reference period, but 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 67 per month for Hungary. Note: The data source is Eurostat EU-SILC 2013. More detailed information on the definition of each group are 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 38 percent of the working-age population. Within the target population, 30 percent is persistently out of work, and 8 percent are marginally employed (left panel in Figure 9). Marginally employed individuals can further be disaggregated into (i) those who have unstable jobs (7 percent); (ii) those who have restricted working hours (1 percent) and (iii) those with negative or near zero income (0.5 percent). Similarly, the population that is persistently out of work can be disaggregated as follows: unemployed (6.6 percent), retired (11.7 percent), disabled (5.4 percent), engaged in domestic tasks (3.3 percent), or inactive due to other reasons (3 percent) (right panel in Figure 9). The remaining 62 percent of the working-age population consists of individuals with no potential difficulties accessing the labor market, i.e., those with “good jobs� .10 10 There is no information on whether the respondents have been employed in public works schemes. 17 Portraits of Labor Market Exclusion 2.0 Figure 9. Composition of the working-age population11 in Hungary (left) and out of work (right) 3% 1.0 3.3% 0.5 7 5.4% 30 62 11.7% 6.6% OUT OF WORK Unemployed Retired No labor market difficulties Out of work Unfit to work Unstable jobs Restricted hours Domestic tasks Other inactive Zero or near-zero income Source: Authors’ calculations based on EU SILC 2013. With regard to the labor market attachment status of its working-age population, Hungary does not stand out from the other EU countries under study (Figure 10. ). On average, the target population makes up 40 percent of the working-age population of the 12 countries selected to be part of this study: in Hungary, the corresponding figure is 38 percent. The out of work also make up around 30 percent of the population, in line with the cross-country average. The share of individuals in unstable jobs or having restricted working hours in Hungary is broadly in line with other countries. Interestingly, Hungary’s percentage of workers with near-zero earnings is considerably low in comparison to the average: 0.5 percent versus 3 percent across the countries under study. 11The working age population also includes individuals with no major labor market difficulties (62 percent in Hungary), who may be thought of those having relatively good jobs (in full time employment or self- employment with no zero income) as well as those with a variety of constraints. 18 Portraits of Labor Market Exclusion 2.0 Figure 10. Labor market attachment status of working-age* population, Hungary and other EU countries under study (percent) 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% Romania Italy Hungary Average** Greece Ireland Croatia Spain 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 a high share of retirees and a low share of unemployed. Figure 11 shows that 7 percent of the Hungarian population of working age is classified as unemployed, which is below the 12-country average of 10 percent. Twelve percent of the working-age population was retired in Hungary, third only after Croatia and Romania, and in contrast with only 7 percent for the 12 countries. The percentage of working-age individuals reporting to be engaged in domestic tasks in Hungary, at only 3 percent, is considerably lower than average. 19 Portraits of Labor Market Exclusion 2.0 Figure 11. Composition of the persistently out of-work population by labor market status, Hungary and other EU countries under study (as a percentage of working age) 45 40 35 30 25 20 15 10 5 0 Romania Poland Spain Hungary Greece Italy Ireland Bulgaria Portugal Croatia Estonia Lithuania 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 achieve the purpose of segmenting 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: • 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 level of work experience; caregiving responsibilities; or limitations in daily activities due to health status. • Weak economic incentives to look for or accept a “good� job . In this case, an individual may decide not to participate in the labor market (or may increase their reservation wage) if they could potentially lose out-of-work benefits that are higher than the wage they could expect to receive should they accept a full-time job, or if they already have a high standard of living due to other income sources. • Scarce employment opportunities: occur when there is a shortage of vacancies in the relevant labor market segment (geographical area or sector); friction in the labor market 20 Portraits of Labor Market Exclusion 2.0 due to information asymmetries; skills mismatches; discrimination; lack of social capital, or 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 seven 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 seven 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 Hungary 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). Box 2. Definitions of employment barrier indicators used for Hungary The indicators represent the three broad types of employment barriers and are constructed from EU- SILC 2013 data as follows: 21 Portraits of Labor Market Exclusion 2.0 Four indicators are used to proxy for capabilities barriers: 1. Low education: if an individual has an education level equal lower than uppers 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 care a week 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. 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 year or at the month of the interview); (ii) those who have never worked; Two indicators are used to proxy for incentives barriers: 5. High non-labor income: if household income (excluding those from the individual’s work - related activities) is more than 1.6 times higher than the median value in the reference population; 6. 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: 7. 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. Table 1 confirms that, as is to be expected, the incidence of employment barriers for all labor market indicators is considerably higher among the target population than the working-age population, with the exception of the high non-labor income indicator. The most common barriers found in the target population are having no recent work experience (73 percent), followed by scarce employment opportunities (41 percent) due to an individual’s gender, age, education, and the region where they reside. Health limitations are also a common issue (37 percent), and almost one-third of the target population reported having a low level of education. Close to 20 percent has possible disincentives to work due to high income that is not a result of their own labor, and around 14 percent receive a high level of benefits that may be reduced when working full-time in a high quality job. The share who face care responsibilities is relatively low (just 15 percent, although three times as high as that of the working age population). Likewise, only 9 percent of the target population reports to have never worked. There is a striking difference between these indicators and the barriers faced by the working-age population: the absence of recent work experience among the target 22 Portraits of Labor Market Exclusion 2.0 population is 45 percentage points higher than the working-age population. The target population compared to the working age population is much more constrained due to health status, level of education, and level of work experience. Table 1. Characterization of target population according to barrier indicators (percent) Working-age INDICATOR Target population population Capabilities barriers 1- Low education 31 18 2- Care responsibilities 15 5 3- Health limitations 37 20 4- No recent work experience — Has worked in the past 73 28 No recent work experience —Has never worked 9 3 Incentives barriers 5- High non-labor income 19 21 6- High earnings replacement (benefits) 14 6 Opportunity barrier 7- Scarce employment opportunities 41 33 Source: Authors’ calculations based on EU-SILC 2013 Compared to the average for the six countries under study, the target population in Hungary stands out in terms of no recent work experience, earnings replacement benefits, and health limitations (Table 2). The target group in Hungary has the highest share (73 percent) of those who have past work experience but no recent work experience. In contrast, Hungary also has the lowest share of those with no recent work experience but who have never worked. The target population in Hungary includes the highest share of individuals with high earnings replacement benefits among the countries under study (14 percent) as well as those faced with health limitations (37 percent). Although 31 percent of the target population has a low level of education, it appears that Romania or Bulgaria are much more constrained by this barrier. However, it is critical to note that Hungary has a high level of stratification and a school system that is overall low performing; thus, young jobseekers in lower socioeconomic quantiles were likely educated at low performing schools. As a result, this population received degrees that likely represent a subpar skill-set compared to equivalent degrees earned by youth from better off circumstances. Table 2. A cross-country comparison of barriers faced by the target population 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- No recent work experience - 58* 65 59 73 66 45 62 23 Portraits of Labor Market Exclusion 2.0 Has worked in the past No recent work experience - 19* 20 26 9 10 28 19 Has never worked Incentives barriers 5- High non-labor income 18 20 23 19 19 19 20 High earnings-replacement 6- 6 3 12 14 9 10 9 benefits Opportunity barrier Scarce employment 7- 47 35 45 41 32 26 38 opportunities * 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 from the other countries. ** 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. Note: Average for low education does not include Greece. Source: Authors’ 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 earlier). It allows for 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, the LCA exploits the interrelationships 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 Methodological Paper, 2016). 4. Results of the analysis: portraits of labor market exclusion in Hungary Applying the above methodology,12 latent class analysis yields the classification of the target population into six different groups in Hungary. Each group varies in terms of size (as shown in Figure 13), characteristics of its population, and in the mix of barriers they face. We have given a generic title13 for each group that reflects the characteristics that have a high probability of occurring in that group. Table 3 elucidates the most salient barriers for each population group 12 The technical details on the selection of the LCA model are provided in Annex 4. 13Although the titles are somewhat subjective, they mirror the barriers/characteristics most prevalent within each group. 24 Portraits of Labor Market Exclusion 2.0 emerging from the analysis. Annex 3 provides additional lists of details of group characteristics that provide the basis for the group names. Figure 13. Latent groups within the Hungarian target population Group 1: Retired or inactive, relatively Group 6 educated older individuals, some with 8% health limitations Group 5 8% Group 2: Poor, low-educated, Group 1 unemployed or inactive individuals, 36% some with health limitations Group 4 10% Group 3: Retired older women, some with health limitations Group 4: Relatively educated, middle- Group 3 aged inactive mothers with past work 14% experience facing care responsibilities Group 5: Poor, middle-aged, mostly male, long-term unemployed with past Group 2 work experience 24% Group 6: Poor, low-educated, inactive young mothers with care responsibilities Source: Authors’ calculations based on EU-SILC 2013. 25 Portraits of Labor Market Exclusion 2.0 Table 3. Employment barriers faced by excluded groups in the Hungarian labor market Group 2. Poor, Group 1. Retired low-educated, Group 3. Group 4. Relatively Group 5. Poor, or inactive, unemployed or Retired educated, middle- middle-aged, Group 6. Poor, relatively inactive older aged inactive mostly male, low-educated, educated older individuals, women, mothers with past long-term inactive young individuals, some with some with work experience unemployed mothers with some with health health health facing care with past work care Target Class name limitations limitations limitations responsibilities experience responsibilities pop. Class size (as a share of target population) 36 24 14 10 8 8 100 INDICATOR Share of individuals facing each barrier, by class Capabilities barriers 1 Low education 2 68 48 0 37 64 31 2 Care responsibilities 5 4 7 71 8 48 15 3 Health limitations 47 38 51 6 24 11 37 No recent work experience - Has worked 82 50 93 79 87 51 73 in the past 4 No recent work experience 1 21 3 1 3 40 9 - Has never worked Incentives barriers 5 High non-labor income 21 13 29 33 11 8 19 6 High earnings replacement (benefits) 28 10 4 2 3 8 14 Opportunities barrier 7 Scarce employment opportunities 0 100 2 9 100 100 41 Average number of barriers per individual 1.8 3.0 2.4 2 2.7 3.2 2.4 Source: Authors’ calculations based on EU-SILC 2013 26 Portraits of Labor Market Exclusion 2.0 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 2.4 barriers14; 44 percent face three or more barriers. Across groups, groups 6 and 2 stand out as having a particular high average number of barriers (three or above for both groups). This also translates into a relatively high proportion of individuals facing four or more barriers in these two groups (almost 40 percent). Groups 6 and 5 have a relatively high percentage of individuals facing three barriers (around 40 percent). At the other end of the spectrum, Groups 4 and 1 have face a relatively low number of barriers15 on average (around two)16. Figure 14. Number of barriers faced by individuals in latent groups in the Hungarian target population 100% 3.2 3.5 90% 3.0 2.7 3.0 80% 2.4 2.4 70% 2.5 2.0 1.8 60% 2.0 50% 40% 1.5 30% 1.0 20% 0.5 10% 0% 0.0 Group 6 Group 2 Group 5 Group 3 Group 4 Group 1 Target population 100% 5 or more barriers 4 barriers 3 barriers 2 barriers 1 barrier No major barriers Average no. of barriers (right axis) Note: Groups are ordered according to their average number of barriers. Source: Authors’ calculations based on EU-SILC 2013. The boxes below provide descriptions of each of the six identified groups. The descriptions include the most salient barriers faced, as well some socioeconomic characteristics such as age, gender, labor market status, among others. 14 The highest possible number of barriers that an individual can face is seven. 15 Please note that each barrier is calculated with an equal weight (1). 16 Please note that in case of group 1 and 3 a large share of the individuals receive old-age pensions. Thus, for them, having no recent work experience is a general condition, not necessarily a barrier that has to be tackled. 27 Portraits of Labor Market Exclusion 2.0 Group 1: Retired or inactive, relatively educated older individuals, some with health limitations (36 percent of the target population) ➢ 67 percent older (ages 56-64), 33 percent middle-aged (30 to 55), average age: 56 ➢ 53 percent retired, * 20 percent unfit to work* 36% ➢ 53 percent male ➢ 47 percent face health limitations ➢ 41 percent reside in Great Plain and North ➢ Most commonly faced barriers are no recent work experience (83 percent17) and health limitations (47 percent) ➢ Average number of barriers: 1.8 *During the reference period Group 1 is made up of older (67 percent) and middle-aged (33 percent) individuals with an average age of 56 who are mainly retired (53 percent) or unfit to work (19 percent). (Eleven percent are also unemployed). The two genders are close to equally represented in this group, with a slightly lower (47 percent) share of females. A significant share of the group (47 percent) face health limitations. Most of this group (64 percent) is married. This is a well-educated group, with a significant share (77 percent) having upper secondary education, and about 17 percent having tertiary education. A substantial share (41 percent) of this group resides in the Great Plain and North, with the rest evenly distributed between Central Hungary and Transdanubia. The most commonly faced barriers within this group are: no recent work experience (83 percent) and health limitations (47 percent). Only approximately 15 percent of this group live at risk of poverty; their income is distributed almost evenly across quintiles. Close to half of this group receive old-age benefits; about one-quarter receive sickness benefits; and about one-fifth family benefits18. The annual average equivalized household income for this group is EUR 4,921. Group 2: Poor, low-educated, unemployed, or inactive individuals, some with health limitations (24 percent of the target population). 17Please note that over 50% of the group is retired, thus, they per se do not have recent work experience. 18Please note that the data source is 2013. For that year only the family allowance is a normative transfer, other family transfers such as (pregnancy / confinement benefits or TGY�S, up to 6 months of age; child care fee or GYED, up to 18 months of age; child home care allowance or GYES, up to 3 years of age) were withdrawn in case the mother decided to return to work in 2013. These rules have been gradually relaxed over the past few years) thereby influencing work incentives. 28 Portraits of Labor Market Exclusion 2.0 ➢ 46 percent youth (ages 18-29), 35 percent middle-aged (30 to 55 years of age), average age: 38 ➢ 51 percent unemployed, * 19 percent unfit to work, * another 25 percent inactive (retired/domestic tasks/ or other inactive) * ➢ Gender parity ➢ Close to half is at risk of poverty, with 54 percent in the bottom income quintile ➢ 47 percent resides in Great Plain and North ➢ Most commonly faced barriers are scarce employment 24% opportunities (100 percent), low level of education (68 percent), and no recent work experience (71 percent) ➢ Average number of barriers: 3 *During the reference period Group 2 is comprised of mainly young, 46 percent) and middle-aged (35 percent) individuals who are unemployed (51 percent) or inactive (44 percent, with 19 percent reporting being unfit to work and 25 percent otherwise inactive (retired/domestic tasks, or other inactive). One-third was actively looking for a job at the time of the interview, and close to one-third are also unemployed on a long-term basis. A significant proportion (38 percent) report health limitations; this is similar to the target group average of 37 percent. The group has an equal representation of women and men. A substantial share (47 percent) resides in the Great Plain and North, with the rest evenly distributed between Central Hungary and Transdanubia. Almost half are at risk of poverty (versus 27 percent of the target population), more than half (54 percent) are in the bottom income quintile, and 55 percent live in severe material deprivation.19 The group is particularly low skilled: 57 percent report lower secondary as the highest level of education completed and only 23 percent have an upper secondary education; an additional 10 percent only have a primary education. More than half (57 percent) of this group has never married—the highest percentage among all groups. The main barriers in this group are scarce employment opportunities (100 percent), low education (68 percent), and no recent work experience (71 percent overall, among which 50 percent have worked in the past and 21 percent have never worked). More than 82 percent of individuals in this group receive social benefits (mostly family, unemployment, and disability benefits). The annual average equivalized household income for this group is EUR 3,264. Group 3: Retired older women, some with health limitations (14 percent of the target population) 19 Please note that the low household income may be caused by the spouses’ low income levels, too. 29 Portraits of Labor Market Exclusion 2.0 ➢ 96 percent older (ages 56 to 64), average age: 61 ➢ 77 percent retired, * 13 percent unfit to work* ➢ 85 percent females ➢ 65 percent distributed evenly across the bottom three income quintiles ➢ 48 percent reside in the Great Plain and North; 41 percent in Transdanubia 14% ➢ Most commonly faced barriers are no recent work experience (96 percent20), health limitations (51 percent), and low education (48 percent) ➢ Average number of barriers: 2.4 *During the reference period Group 3 comprises older individuals, with 96 percent in the 56 to 64 age range. The group consists of mostly women (85 percent) who are married (64 percent) or widowed (18 percent). They are mostly retired (77 percent), with 13 percent reporting being unfit to work. They live mostly in the Great Plain and North (48 percent) or in the Transdanubia (41 percent). They have mostly lower (42 percent) or upper (40 percent) secondary education. The most commonly faced barriers are no recent work experience (96 percent), health limitations (51 percent) and low education (48 percent). Almost everyone (97 percent) in this group receives benefits; three- quarters receive old-age benefits and 15 percent disability benefits. The annual average equivalized household income for this group is EUR 4,699. Group 4: Relatively educated, middle-aged inactive mothers with past work experience who face care responsibilities (10 percent) 20Please note that the largest majority of the group are retired persons, who per se do not have recent work experience. 30 Portraits of Labor Market Exclusion 2.0 ➢ 99 percent ages 30 to 55, average age: 37 ➢ 45 percent engaged in domestic tasks, * 36 percent inactive* ➢ 97 percent female, all of whom are mothers, 56 percent who have a child younger than 3 years of age 10% ➢ 67 percent have upper secondary/post-secondary education; 34 percent have tertiary education ➢ Evenly distributed across regions ➢ Most commonly faced barriers are no recent work experience (80 percent), care responsibilities (71 percent) and high non-labor income (33 percent) ➢ Average number of barriers: 2 *During the reference period Group 4 consists almost exclusively of women (97 percent), all of whom live with a child 12 years or younger; more than half (56 percent) live with a child under the age of 3. Practically all of individuals in this group are in the 30 to 55 years of age range. They are commonly engaged in domestic tasks (45 percent), are “other inactive� (37 percent), or unemployed (14 percent). Seventy-seven percent of this group is married, and all have at least an upper secondary education; 34 percent in this group report to have tertiary education. Individuals in this group are evenly distributed geographically across the regions. Only 16 percent is at risk of poverty (versus 27 percent of the target population). The most commonly faced barriers are no recent work experience (80 percent), care responsibilities (71 percent), and high non-labor household income (33 percent). Every individual in this group is a recipient of family benefits: the average equivalized annual household income, at EUR 5,099, is the highest of all groups, only slightly (about EUR 200) less than that of the working-age population21. Group 5: Poor, middle-aged, mostly male, long-term unemployed with past work experience (8 percent) ➢ 86 percent middle-aged (age 30 to 55), average age: 45 ➢ 69 percent are males 8% ➢ 55 percent have upper secondary education ➢ 54 percent reside in Central Hungary ➢ 71 percent unemployed, * 25 percent inactive* ➢ 56 percent in the bottom income quintile ➢ Most commonly faced barriers are scarce employment opportunities (100 percent), no recent work experience (87 percent), and low education (37 percent) ➢ Average number of barriers: 2.7 *During the reference period 21 Please note that the relatively high household income may be caused by the spouses’ high income levels. 31 Portraits of Labor Market Exclusion 2.0 Group 5 has similar characteristics as Group 2, with the marked exception of age (86 percent are middle-aged and 14 percent are older than that), regional distribution (the majority, (54 percent)) reside in Central Hungary, 27 percent in the Great Plains and North, and 18 percent in Transdanubia), and have the propensity to be long-term unemployed and to be looking for a job (63 percent for both). The majority (55 percent) have an upper secondary education, although almost one-third (29 percent) report having only a lower secondary education. The vast majority (71 percent) is unemployed, while 25 percent is inactive. The group is mostly made up of men (69 percent). Forty-three percent are at risk of poverty, 56 percent are living in the bottom quintile of the income distribution, and 58 are percent living in severe material deprivation. The most commonly faced barriers are scarce opportunities (100 percent), no recent work experience (90 percent), and low levels of education (37 percent). Three-quarters of all individuals in this group receive benefits, the most common of which are family benefits (41 percent) and unemployment benefits (31 percent). The average equivalized annual household income is EUR 3,139. Group 6: Poor, low-educated, inactive young mothers with care responsibilities (8 percent) ➢ 68 percent youth (18 to 29): average age: 29 ➢ 98 percent are females, all of which are mothers, 56 percent 8% reported having a child younger than 3 years of age ➢ 48 percent have care responsibilities ➢ 52 percent are single; 43 percent are married ➢ 57 percent have a lower secondary education; 31 percent have upper secondary education ➢ 55 percent reside in the Great Plain and North ➢ Most commonly faced barriers are scarce employment opportunities (100 percent), no recent work experience (91 percent) and low education (64 percent) ➢ Average number of barriers: 3.2 Group 6 is almost exclusively females (98 percent) who are mostly young (68 percent are 18 to 29 years of age). They all live with children 12 years and younger; more than half (56 percent) are living with a child under 3 years of age. Most of them (55 percent) reside in the Great Plain and North. They are mostly inactive (42 percent are “other inactive� and 34 percent are engaged in domestic tasks); another 22 percent report that they are unemployed. This group is at the highest risk of poverty; 53 percent are at risk of poverty and two-thirds are in the bottom income quintile. Compared with the remaining five groups, this group has the highest rate (63 percent) of severe material deprivation. More than half (57 percent) have at most completed lower secondary education, and 31 percent have at most completed upper secondary. About half of this group (52 percent) are single; 42 percent are married. This group faces multiple barriers: all individuals face scarce employment opportunities; almost all (91 percent) have no recent work experience (almost 40 percent reported to have never worked — 33 the highest share within all groups) and almost two-thirds have low levels of education. Close to half (48 percent) have care responsibilities. Virtually everyone in this group receives family benefits; 27 percent receive social exclusion benefits. Two-thirds of individuals in the group are in the poorest income quintile, and the equivalized annual household income is the lowest among all groups (EUR 2,935). 32 Portraits of Labor Market Exclusion 2.0 5. Priority groups in the Hungarian target population The Hungarian government has identified several target groups22 through various policy and program-level interventions: youth, the long-term unemployed and unskilled unemployed people, and older workers (up to 50 years old) before retirement. Moreover, the National Employment Office has a profiling system that identifies three categories of jobseekers; Category 3 is the group of vulnerable jobseekers who face potential labor market difficulties, which include health constraints, family constraints, addictions, and work-related problems. At the end of 2016, approximately 131,000 registered jobseekers — out of a total of 274,500 — were considered to be in this category. According to a recent survey conducted by the Ministry of Interior, approximately 40 percent of those in this category have health constraints, while the remaining 60 percent face family constraints, have work-related problems (for example, workplace discipline issues), or are suffering from addictions (Figure 15). Figure 15. Typical constraints faced by registered vulnerable jobseekers (Category 3), 2016 Work-related problems 24% Health constraints 40% Family constraints 18% Addictions (typically alcohol) 18% Source: Ministry of Interior The National Social Inclusion Strategy of Hungary (NSIS) also identifies several vulnerable and disadvantaged target groups. These groups include low-skilled jobseekers, Roma (among them, women and girls 15 to 18), and individuals living in isolation in rural 22 It is important to note that labor market programs in Hungary do not currently segment and identify their clientele through a combination of labor market disadvantages. Instead, they tend to target broad categories of individuals. 33 Portraits of Labor Market Exclusion 2.0 locations. Mothers of infants and young children are supported through several initiatives, which include a program that reinstates a family’s eligibility for child allowance while a parent is employed; programs aimed at expanding existing childcare facilities, and a program that introduces family-, community-, and workplace-based nurseries. Individuals with disabilities are targeted through vocational rehabilitation programs implemented by the Ministry of Human Capacities, the National Rehabilitation Office, and the National Employment Office. Unlike the public works program, most of these initiatives are funded from EU resources. Given this background and the nature of how these initiatives operate, coupled with the realities of Hungarian labor market characteristics, it appears that the driving factors for selecting priority groups should be relative youth, low level of skills, and poverty and social exclusion. To be more specific, key considerations should include the low labor force participation rate among youth; other factors that foreshadow productivity constraints of the future labor force (aging and demographics; the high NEET rate; a high share of early school early leavers); high incidence of poverty and material deprivation in families with children, and the EU 2020 employment target and associated flagship initiatives (Agenda for New Skills and Jobs; European Platform Against Poverty and Social Exclusion). As a result, among the six identified groups in the target population in Hungary, the following ones have been identified23 as priority candidates for activation and employment support policies: • Group 2: Poor, low-educated, unemployed, or inactive individuals, some with health limitations; • Group 5: Poor, middle-aged, mostly male, long-term unemployed with past work experience; • Group 6: Poor, low-educated, inactive young mothers with care responsibilities. As a next step, we delve deeper into the characteristics of these groups so that we can better understand the barriers they face; doing so will help decision makers inform the design of activation and employment support policies that target the needs of these particular groups. Table 4 24 provides a subset of policy-relevant indicators and data for the priority groups as well as for the target population as a whole. Table 4. The priority groups’ employment barriers and characteristics Employment barriers of priority groups Group 5. Poor, Group 2. Poor, low- middle-aged, mostly Group 6. Poor, low- educated, male, long-term educated, inactive unemployed or unemployed, with young mothers, inactive, some with past work with care Target Class name health limitations experience responsibilities pop. 100 Class size (as a share of target population) 24 8 8 Class size (est. number of individuals) 576,202 192,000 192,000 2,247,120 23Prioritization and selection has been conducted in consultation with Hungarian government counterparts. 24Table 4 draws upon the summary data provided in Annex 3; this table includes only variables with salient characteristics that have bearings on the priority groups. 34 Portraits of Labor Market Exclusion 2.0 INDICATOR Capabilities barriers Low education 68 37 64 31 Care responsibilities 4 8 48 15 Health limitations 38 24 11 37 No recent work experience - Has worked in the past 50 87 51 73 No recent work experience- Has never worked 21 3 40 9 Incentives barriers High non-labor income 13 11 8 19 High earnings replacement (benefits) 10 3 8 14 Opportunities barrier Scarce employment opportunities 100 100 100 41 Average number of barriers per individual 3.0 2.7 3.2 2.4 Socioeconomic characteristics of priority groups Group 5. Poor, Group 2. Poor, low- middle-aged, mostly educated male, long-term Group 6. Poor, low- Class name unemployed or unemployed with educated, inactive inactive, some with past work young mothers with Target health limitations experience care responsibilities pop. Women* 50 31 98 61 Children 12 and younger in household* 26 29 99 30 Age group* Youth (18-29) 46 0 68 17 Middle-aged (30-55) 35 86 32 40 Older (56-64) 19 14 0 43 Average age 38 45 29 47 Region Central Hungary 24 54 18 27 Transdanubia 28 18 27 30 Great Plains and North 47 27 55 43 Degree of urbanization Densely populated 19 44 13 26 Intermediate 26 17 26 31 Thinly populated 55 39 62 43 Target population group Out of work 66 86 87 78 Unstable jobs 30 11 12 18 Restricted hours 3 3 0 3 Near-zero income 1 1 0 1 Main activity during reference period Employed 4 4 1 4 Unemployed 51 71 22 27 Retired 9 4 1 33 Unfit to work 19 10 2 15 Domestic tasks 4 5 34 10 35 Portraits of Labor Market Exclusion 2.0 Group 5. Poor, Group 2. Poor, low- middle-aged, mostly educated male, long-term Group 6. Poor, low- Class name unemployed or unemployed with educated, inactive inactive, some with past work young mothers with Target health limitations experience care responsibilities pop. Other inactive 12 6 40 11 Main activity at time of the interview Employed 21 8 6 13 Unemployed 44 70 23 24 Retired 10 4 1 33 Unfit to work 18 9 2 14 Domestic tasks 3 5 30 9 Other inactive 6 5 37 8 Student 1 0 1 0 Months in unemployment Zero months 43 28 70 70 Less than 12 27 8 14 13 12 or more 30 64 16 17 Actively searching for a job at time of interview 33 63 18 18 Live with parents 45 27 26 20 At risk of poverty (60% of median income) 44 43 53 27 At risk of poverty (40% of median income) 20 21 18 11 Severe material deprivation 55 58 63 39 At risk of poverty and social exclusion 73 76 74 54 Income quintile Poorest 54 56 66 36 2 20 18 18 20 3 13 16 8 17 4 8 8 3 14 Richest 5 3 4 13 Education level 0 Primary or less 10 7 8 4 Lower secondary 57 29 57 27 Upper secondary 23 55 31 52 Post-secondary 4 3 2 3 Tertiary 5 6 3 13 At least one working adult in the household 50 44 57 49 Elderly in the household 11 20 6 16 Children younger than 6 in household 18 19 83 22 Children younger than 3 in household 9 10 56 14 Children younger than 13 in formal childcare None 4 5 28 7 Some 8 6 32 9 All 14 18 39 15 NA 74 71 1 70 Marital status Married 30 45 42 54 Never married 57 34 52 26 36 Portraits of Labor Market Exclusion 2.0 Group 5. Poor, Group 2. Poor, low- middle-aged, mostly educated male, long-term Group 6. Poor, low- Class name unemployed or unemployed with educated, inactive inactive, some with past work young mothers with Target health limitations experience care responsibilities pop. Divorced/separated/widow(er) 13 20 7 20 Labor market status of spouse/partner 0 Working 14 33 46 31 Unemployed 10 9 22 7 Retired 6 4 1 18 Unfit to work 4 4 3 5 Domestic tasks 3 5 1 2 Other inactive 5 4 3 2 No spouse/partner 57 41 25 34 Receives family benefits 43 41 99 40 Average annual value** 1,822 1,837 2,510 1,683 Receives social exclusion benefits 18 16 27 12 Average annual value** 886 705 782 775 Receives unemployment benefits 24 29 12 13 Average annual value** 883 1,058 802 859 Receives disability benefits 22 11 3 18 Average annual value** 1,959 1,902 1,514 2,181 Receives housing benefits 25 17 40 16 Average annual value** 242 241 267 191 Receives any social benefits 82 74 100 90 Average annual household income from: Labor 5,241 3,830 5,027 5,021 Other 105 332 166 189 Benefits 3,473 3,410 3,948 4,591 Average annual equivalized household income 3,264 3,139 2,935 4,209 *Included in the LCA model as active covariates. **All amounts are in EUR. Source: Authors’ calculations based on EU-SILC 2013. Individuals in these three groups share several common features in terms of age and socioeconomic status.25 First of all, these three groups are as a whole younger than the average age of the target population (which is 47); most of the individuals are young (Group 6 has an average 25 While latent class analysis offers the toolkit to describe these groups as distinct ones, this section nevertheless looks at commonalities and distinct features. It is noteworthy that from a practical perspective, overlaps are likely to occur in the way policies or programs address key employment constraints. For example, one type of program addresses the same constraint that several groups face. Likewise, similar group features — for example, geographic concentration of unemployed — will also be relevant when analyzing policies and programs. 37 Portraits of Labor Market Exclusion 2.0 age of 29) or middle-aged (Groups 2 and 5, have an average age of 38 and 45, respectively). The three groups are also characterized by low socioeconomic status. Although the percentage of individuals who have a working spouse ranges widely (from 14 percent (Group 2) to 46 percent (Group 6), most workers earn a low income. A high non-labor income — i.e. income not coming from the individual’s own labor — is only indicated for between 8 and 13 percent of individuals in the groups. All three priority groups comprise a large share of individuals living in poverty and social exclusion (around 73 percent). More than half of individuals in all three groups are in the bottom income quintile. The at-risk-of-poverty rate (AROP) is high among all three groups, ranging from 43 percent to 53 percent, and more than half of individuals in each group live in severe material deprivation. These two indicators are well above target group averages, given that AROP is 27 percent, and severe material deprivation is 39 percent). Finally, all three groups face scarce employment opportunities, owing to their age, gender, education, and geographic location. A significant majority of all three groups and everyone in Group 6 — similarly to the target group as a whole — are recipients of social benefits; however, income from benefits may only affect incentives to work among very few individuals. This is because a high benefit earnings replacement barrier26 is present in only 8 to 10 percent of group members. In view of the high poverty rates across all three groups, it is also likely that the adequacy of this type of benefit is low. Among the types of benefits received, family benefits are most common, followed by housing benefits (mostly for Groups 2 and 6), and unemployment benefits (mostly for Groups 2 and 5). Annual household income from benefits ranges between EUR 3,473 (Group 2) and EUR 3,948 (Group 5), and labor income between EUR 3,830 (Group 5) and EUR 5,241 (Group 2). Average annual equivalized household income ranges between EUR 2,935 (Group 6) and EUR 3,264 (Group 2), which is below the target population average of EUR 4,209. In addition, family characteristics, employment history, health factors, and geographic features differentiate these groups. The differences are summed up as follows: • Group 6 is the only group that is exclusively comprised of women, all of which are mothers, with more than half living with children younger than age 6 and 48 percent being identified as having care responsibilities. Close to one-third of this group reported that none of the children were in formal childcare and another one third reported that only some children were in formal care27. • Groups 2 and 6, in contrast, are mixed in terms of gender. Group 2 has equal proportions of men and women, whereas Group 6 is mostly male, with just 30 percent being female. • The great majority of individuals in all groups do not have recent work experience, but there are important differences in terms of prior work experience. Group 6 was most pronounced in limited work experience, because 40 percent of these women have never worked (in Group 2, this figure was 21 percent, and in Group 5, only 3 percent never worked). Most individuals in Groups 2 and 5 are unemployed — with one-third of the former and close to 26 For more information on how the earnings replacement effect is defined and calculated, please refer to OECD and World Bank (2016). 27 Please note that the data are from 2013. Compulsory participation in formal kindergarten education for children above 3 years of age was implemented from autumn 2013. 38 Portraits of Labor Market Exclusion 2.0 two-thirds of the latter group reported to be long-term unemployed — while most individuals in Group 6 reported to be engaged in domestic tasks or “other inactive,� as may be expected given the high prevalence of children in the household as well as care responsibilities. Only one-third of Group 2 and less than one-fifth of Group 6 reported to be actively searching for a job at the time of the interview. By contrast, almost two-thirds of Group 5 reported to be actively searching for a job. • Groups 2 and 6 are fairly low-skilled; most of the individuals have only lower secondary education or less. By contrast, Group 5 is relatively more educated; only 37 percent face the education barrier, i.e., have not completed at least upper secondary schooling. Nonetheless, their poor background is a clear indication that the education this group has received is of lower quality than average. • A distinguishing characteristic of Group 2 is the high percentage who reported health limitations (38 percent), as well as the high percentage (19 percent) who reported their main activity during the reference period as being unfit to work (disabled). • Lastly, close to half of all members in Groups 2 and 6 reside in the Great Plains and North region, in mostly intermediately or thinly populated rural areas similar to the target population. Group 5, on the other hand, is more concentrated in Central Hungary, and is more urban. 6. Policies and measures targeting the employment barriers of priority groups 6.1. Framework and approach In this section, we review the activation and employment support programs and policies (AESPs) that are most relevant for each the identified priority groups, paying particular attention to programs that are congruent with the identified employment barriers. Based on the organizing framework presented in Figure 16, 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, we assess whether or not existing programs have adverse incentives on work (i.e. incentive barriers). In addition, we consider whether existing programs address the needs of the relevant cross-cutting groups such as youth, women, long-term unemployed, and those living in rural areas. Identified groups face multiple barriers simultaneously; hence, they require a tailored mix of services to improve their employability. The menu of programs/services to address the wide ranging employment barriers falls under three main areas: (i) employment support, (ii) social services, and (iii) social benefits (with the appropriate design elements). These tools support and incentivize individuals’ efforts to search for and find jobs, participate productively in society, and improve their self-sufficiency. The broad capacity and adequacy of the existing menu of services/active labor market programs are analyzed after we present a broad overview of existing AESPs and the policy environment. We look at the needs of the selected priority groups based on their barriers and 39 Portraits of Labor Market Exclusion 2.0 evaluate the existing services’ capacity and adequacy to deliver the right package of support to help them find employment. In doing so, we can assess any gaps and determine potential policy directions. Figure 16. Linkages between Employment Barriers and AESPs Source: Authors’ elaboration. 6.2. Overview of activation and employment support programs and policies 6.2.1 Institutional and policy context Since its 2013 scale-up, the public works program has been the single largest government intervention on the Hungarian labor market. The public works program involved three subprograms in 2015: national public works28, longer-term public works,29 and “start-work� pilots.30 Most of the programs’ clients are jobseekers with past work experience but no recent employment history. Additionally, many public works clients who have been inactive earlier and lost their income as a result of the unemployment benefit scheme transformation are coming from poor and vulnerable households. According to the Ministry of Interior, after 2013 the program’s objective (and also its key challenge) has shifted from reaching out to and mobilizing inactive individuals towards facilitating participants’ successful exit to the primary labor market. Various subprograms under the public works scheme target poor and vulnerable groups and aim to provide work socialization opportunities for individuals. Programs under the public works also aim to address constraints such as skills and health barriers (including mental health) through individualized training and mentoring interventions. Initiatives such as the start-work 28 These are programs that operate for (mostly) skilled labor, and they are driven by multi-sectoral large- scale investment priorities, which include road or railway development, forestry, and the digital agenda. 29 The most frequently used form of public works is for 6 to 8 hours of daily work, and is typically run by municipalities to address local unemployment. 30 These are investments that target disadvantaged localities; the objective is to ensure self-sufficient and sustainable municipal operations. 40 Portraits of Labor Market Exclusion 2.0 program are targeted at residents of disadvantaged microregions, primarily comprised of women (mainly older women) with some skills, but whose geographic mobility is constrained. As one example, women may be tied to agricultural subsistence work. Additional specialized training is offered to public workers through EU-funded initiatives. The public works program is generally not aimed to promote youth employment, except for in specific circumstances, such as when heads of households participate in regions with low local labor demand. Also, as of 2017, participation of youngsters leaving the schools system at the age of 16 (the compulsory school age) in public works schemes proved to be problematic, hence, the regulation was changed to steer young people to other active labor market policy measures. Active labor market programs have been rolled out targeting youth jobseekers (younger than 25) such as the Youth Guarantee Scheme, the Youth Traineeship Program, and entrepreneurship programs. Vulnerable jobseekers and inactive individuals over the age of 25 are targeted through the Path to the Labor Market program and additional interventions such as transitional programs, employment pacts, and initiatives supporting social enterprises. The programs are implemented via a partnership between district government offices and public employers (mostly municipalities), and broadly target vulnerable jobseekers. Some of these programs also incorporate individualized assistance provided for specific circumstances (e.g. homelessness) and are implemented in partnership with civil society organizations. At the end of 2015, the mean rate of reemployment in the primary labor market within 180 days of exiting a program stood at 12.2 percent. The reemployment rate is gender-balanced, with a higher reemployment rate for younger age cohorts (18.2 percent for 25 year olds and younger) as well as for those with upper secondary education (16.2 percent) or a tertiary degree (30.4 percent). (See Table 5 for details.) The reemployment rate shows significant territorial disparities, with Budapest at the top end of the range registering close to 20 percent, and Borsod-Abaúj-Zemplén county at the low end with a rate of 8.7 percent. Table 5. Employment rate of former public workers on the 180 th day after program exit, 2015 (percent) In the primary labor market In public works By program Long-term public works 12.5 52.1 Public works 15.3 63.7 Start-work pilots 10.4 65.6 By gender Male 12.1 56.8 Female 12.4 58.2 By age Age 24 or younger 18.2 43.0 Ages 25 to 54 12.2 59.2 Over age 55 6.9 62.3 41 Portraits of Labor Market Exclusion 2.0 By education Primary or less 8.4 58.2 Secondary 16.2 57.1 Tertiary 30.4 44.3 Total 12.2 57.4 Source: Ministry of Interior The targeting mechanism of the public works program has been broadly criticized by the European Commission, citing the large share of high-skilled participants in the program, as well as high participation in counties where primary labor market demand is sufficient or even high. Public works program participation numbers are also incentivized by the very short duration of the unemployment benefits—which at 3 months is the lowest in the EU—and the mandatory public works participation which is a requirement to retain eligibility for social assistance (European Commission, 2016a). Box 3. An assessment of the Hungarian public works program In 2015, a team of Hungarian researchers published an analysis based on data from the National Employment Office (2011–2013), which draws important conclusions regarding the Hungarian public works program. First, it suggests that the program ’s twin objectives — boosting employment and offering a safety net — are often contradictory. Second, the analysis finds an inverse relationship between the duration of public works participation and the likelihood of employment outside the program. (In other words, the longer an individual spends in public works, the less likely it is that he or she will get a job). Third, the analysis also finds a strong negative impact of being in an unskilled job (which at the time of the analysis comprised a significant share of public works activities) on reemployment. Fourth, the analysis finds that a 6-hour public works activity is considerably less effective than an 8-hour one, and that repeat participation in public works further decreases the chances of reemployment. Ultimately, the assessment finds that without policy corrections, the program works program will lead to a highly fragmented labor market, locking in close to one million public workers. The analysis concludes with policy recommendations. These include addressing the need to increase the real value of unemployment benefits in an effort to improve families’ circumstances during periods of non-participation, and the need to support job searches more effectively by tailoring comprehensive interventions to the individuals’ needs. Source: Károly and Varga, 2015. In addition to public works,31 several new active labor market programs have been launched in 2015 by the Ministry for National Economy and the Ministry of Human Capacities, with the explicit objective of addressing entry barriers faced by disadvantaged or at-risk jobseekers. These programs include labor market services, subsidies, training, and job placement interventions targeting youth, unskilled jobseekers, employees with young children, persons with disabilities, 31 The public works program is managed by the Ministry of Interior and funded from the central budget. 42 Portraits of Labor Market Exclusion 2.0 former public works participants, Roma, and homeless individuals. Additionally, the government aims to increase support for the development of social enterprises between 2014 and 2020. Recent measures to foster flexible employment include the EU structural and investment funds (ESF) co-funded program, which will support the establishment (among others) of workplace nurseries in disadvantaged regions. This measure is predicted to provide new childcare spaces from app. 15 billion HUF. The workplace nurseries will provide daycare services for children younger than 3, adjusted to the working hours of the parents. Moreover, as of January 2017, the institutional system of childcare has been restructured: in addition to nurseries, which are known as mini-nurseries, family- nurseries and workplace nurseries will also be operating. The government’s objective is to increase available daily childcare places for the 0 to 3 age group up to 60,000 spots. This is partly driven by recent changes to the child allowance: as of January 2017, parents may work full-time once their child becomes older than 6 months without losing the eligibility for child allowance (GYED EXTRA). This change has led to an increased demand for daily childcare services. The above-mentioned programs are funded from operational programs, drawing on a combination of EU structural and investment funds (European Social Fund, European Regional Development Fund, Youth Employment Initiative). Because it is mostly participation data that are recorded about these projects and no rigorous impact evaluations have been conducted, outcomes are largely unknown. Box 4 provides a summary of key findings from the single evaluation available on social inclusion measures (including employment) between 2007 and 2012. Box 4. Highlights from an assessment of EU-funded social inclusion measures in Hungary In 2014, Hétfa Institute published an evaluation of programs sponsored by the Hungarian Social Renewal Operational Program (“T�MOP�) 5th priority—focusing on social inclusion interventions —between 2007 and 2012. The analysis finds that although programs have generally reached their targets, they were aligned with the objectives of the NSIS to only a limited extent. In addition, broadly speaking, programs have been unable to generate results for unskilled clients where labor demand is low, although programs benefit those who are only temporarily out of jobs (suggesting a “skimming� principle). While projects did manage to reach disadvantaged microregions, the most disadvantaged localities did not benefit from them. Some programs led to innovations on how to integrate the delivery of social and employment services. The assessment concludes that social inclusion measures cannot be promoted through a program-based approach exclusively; they also require effective coordination of (mainstream) policies. Source: Hétfa Research Institute, 2013. 6.2.2 Overview of Active Labor Market Program (ALMP) Policies in Hungary Spending on labor market policies is relatively low and highly skewed toward active measures. In Hungary, the expenditure dedicated to active labor market policies is the highest of the six countries under study by the World Bank, but it is still considerably lower than that of the EU-28 average 32 (Figure 17). In 2015, 1.16 percent of gross domestic product (GDP) was dedicated to labor market policies (LMPs), representing 64 percent of the average EU-28 32 Due to data availability, the averages quoted for EU-28 correspond to 2011. 43 Portraits of Labor Market Exclusion 2.0 spending of 1.82 percent. Most of LMP spending is directed towards active measures (predominantly direct job creation through public works33) amounting to 0.81 percent of GDP (versus 0.46 percent for EU-28). Spending on services is comparatively low, at just 0.11 percent of GDP (versus 0.20 percent for the EU-28 average), as is also spending on passive measures (0.25 percent of GDP, versus 1.16 for EU-28). Figure 17. Composition of labor market spending as percentage of GDP 2.00 1.80 1.60 1.40 1.20 1.16 1.00 0.25 0.80 0.60 0.31 0.81 0.49 0.40 0.46 0.39 0.41 0.40 0.20 0.20 0.24 0.17 0.14 0.00 0.11 0.08 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 9.1 percent of total labor market policies spending in Hungary (Figure 18). This spending covers the administrative cost of public employment services; and the costs of providing information on vacancies and available measures and programs; delivering information on prequalification, commuting, and working abroad; psychological support; professional orientation; as well as placement in programs and measures. The share of spending on services within total active labor market programs can serve as a proxy for the resources available to public employment services to administer active labor market programs. In Hungary, spending on services is equivalent to only 11 percent of total active labor market programs. Countries with a well-functioning public employment services delivery system (such as Denmark and the Netherlands) dedicate a much larger proportion of spending toward public employment services (greater than 50 percent), while the average spending on services is 45 percent of the average ALMP spending in EU-28. Hungary is among the EU countries that spent the most on the provision of activation measures; ALMP spending for Hungary represents 0.8 percent of GDP (Figure 17) and 69 33 Please note that people in public works schemes are registered as employed, but public works is registered as ALMP in Hungary 44 Portraits of Labor Market Exclusion 2.0 percent of total labor market expenditure. Almost all of this spending is dedicated to direct job creation (public works programs) (Figure 18). In 2015, 65 percent of total labor market policies were dedicated to direct job creation (mainly public works); this represented 93 percent of total spending on activation measures. Total spending dedicated on employment incentives, training, and start-up incentives represents only 4 percent of total labor market spending—a paltry proportion. Moreover, no spending is allocated to the Eurostat category “supported employment and rehabilitation,� which usually focuses on employment support for disabled. Figure 18. Composition of labor market programs in Hungary, in percent of total labor market expenditure, 2015 LM services 9.1% Direct job creation 64.8% LM measures: ALMPs 69.5% Employment incentives 3.1% LM supports: Unemployment Training benefits 1.2% 21.4% Start-up incentives 0.4% Note: Categories used are based on Eurostat definitions (Annex 5). The categories Early Retirement and Supported Employment and Rehabilitation are not applicable. Source: Eurostat. The Government’s objective is to gradually increase expenditures on active labor market programs, and decrease expenditures on the public works program in the coming years. In 2015, 208,127 individuals participated in the public works program, and total program expenditures reached HUF 237 billion (approximately EUR 763 million, or 0.70 percent of GDP). The planned expenditure of ESF co-financed ALMPs is HUF 55.4 billion in 2016 and HUF 73.6 billion in 2017, or 0.16 and 0.20 percent of GDP, respectively. Additionally, the National Employment Fund in 2016 included HUF 25.5 billion for national active labor market policy programs in 2016 (0.07 percent of GDP) and plans to spend HUF 16.2 billion in 2017 (0.04 percent of GDP). Furthermore, regional programs (such as the employment pacts) funded by the Territorial and Settlement Development Operational Program — jointly financed by European Regional Development Fund 45 Portraits of Labor Market Exclusion 2.0 and European Social Fund — are planned to be implemented between 2017 and 2021 with a total budget of HUF 100 billion. Set out below are descriptions of recently launched active labor market programs (ALMPs) that seek to address entry barriers faced by disadvantaged or at-risk jobseekers. For more information, see Table 6 for a list programs, which include expenditures (in EUR) and the number of beneficiaries they serve34. • The Path to the Labor Market large-scale ALMP was introduced in October 2015 with the objective of improving the employability of jobseekers and inactive people over 25 by providing personalized individual programs, labor market services, subsidies, and training. The budget for the program is HUF 112.4 billion (approximately EUR 363 million) for the period between 2015 and 2018. The program aims to involve more than 106,000 disadvantaged jobseekers and inactive individuals by the end of 2018. Until the end of November 2016, more than 43,000 people have been involved in the program. The program plans to be extended until the end of the programming period (2020), with a budget foreseen to be increased to HUF 230 billion; the intent is to reach approximately 200,000 disadvantaged jobseekers and inactive people. • The Youth Guarantee ALMP — as part of a measure co-financed by the European Social Fund (ESF) and the Youth Employment Initiative, and delivered by the National Employment Office — aims to promote the labor market entry of youth (25 years and younger). The program began in January 2015 with a budget of approximately HUF 40 billion (EUR 130 million) in underdeveloped regions, and aims to involve 144,500 young people until October 2021. Until the end of November 2016, more than 43,000 young people have been involved in the program, of whom 28,500 received a wage subsidy and 14,500 participated in trainings. The extension of the program is currently underway, with a budget increase to EUR 516 million. • The Employment Protection Action Plan is a job creation measure targeted at vulnerable groups: jobseekers under age 25 or over age 55; employees in unskilled jobs; individuals registered as jobseekers for 6 out of 9 months preceding employment; and employees with small children. The measure applies to new hires as well as existing employees, and offers tax credits for employers as well as employees up to a basis of 100,000 HUF (approximately 320 EUR equivalent). • Several programs are targeting individuals with disabilities. These programs cover approximately 87,000 beneficiaries, with a total budget of HUF 72.2 billion (approximately EUR 233 million). • Approximately 3,000 homeless individuals benefit from comprehensive interventions that aim to facilitate their labor market entry, on a total budget of HUF 4.3 billion (approximately EUR 14 million). • Former public work participants are targeted through two interventions. One aims to facilitate former public workers’ entry into the primary labor market, with 4,100 beneficiaries and a budget of HUF 700 million (approximately EUR 2.2 million). Another is 34Please note that these programs are under planning/implementation at the time of the producing of the report. 46 Portraits of Labor Market Exclusion 2.0 targeting 85,000 low-skilled public workers with training opportunities and has a budget of HUF 30 billion (approximately EUR 97 million). • Measures supporting Roma35 (primarily women) through training and job placement interventions are in progress as part of the implementation plan of the NSIS II, in the amount of approximately HUF 4.2 billion (EUR 13.5 million). In addition to the above targeted interventions, the government is currently planning to launch transitional employment programs, via classroom and on-the-job training opportunities, implemented through a cooperative agreement between NGOs and local chambers of commerce. The budget for the program is HUF 5 billion (approximately EUR 16 million). Additionally, approximately 120 regional employment cooperations (pacts) are being established; the objective is to address regional disparities in job opportunities and to improve the employability of disadvantaged jobseekers via a variety of instruments (wage subsidies, training, support for self-employment, mobility support, and housing allowances). In addition to having these programs directly facilitate jobseekers’ entry to the primary labor market, the government aims to ramp up support for developing social enterprises in 2014―2020; the objective is to strengthen this sector and make it more sustainable. A call for proposals for social enterprises was published in June 2016 with a budget of HUF 6 billion (EUR 19 million). The amount of subsidy given to the social enterprises depends on the numbers of jobs created. The program aims to provide counseling services to at least 500 enterprises. The government intends to support social enterprises through a combination of credits and subsidies to improve business development opportunities and reinforce human resources. The program plans to support approximately 300 social enterprises and create 3,000 jobs, based on a budget of approximately HUF 20 billion (EUR 65 million). A sub-category of social economy interventions is a set of NSIS measures targeting low-skilled and Roma jobseekers’ employment in social enterprises, with a total budget of HUF 8.2 billion (approximately EUR 26 million). Micro-entrepreneurship by disadvantaged individuals is also supported by the NSIS through a budget of HUF 3 billion (EUR 9.7 million). Labor market services will also be provided by private (non-governmental) organizations to focus on job search support, mentoring, psychological counseling, and other personalized services for disadvantaged jobseekers. The budget for the program is HUF 6 billion (EUR 19 million), which is allocated among the counties according to the composition of jobseekers. The projects will be implemented with the close cooperation between NGOs and public employment services, and will involve approximately 100,000 jobseekers. Table 6. Overview of Hungarian activation and employment support programs, 2015 Expenditures Number of Target groups Program Type (EUR million) beneficiaries Long-term Public works Public works 763 329,00036 unemployed (LTU), rural, 35 According to the National Social Inclusion Strategy of Hungary, approximately 750,000 Roma reside in Hungary, with about 500,000 to 600,000 living in disadvantaged regions. 36 The average number is only 208 thousand in 2015. 47 Portraits of Labor Market Exclusion 2.0 Expenditures Number of Target groups Program Type (EUR million) beneficiaries inactive Training, wage Path to the Labor Market subsidies, job 363 106,000a LTU, inactive search assistance Training, wage Youth, rural, Youth Guarantee subsidies, job 130 144,500b inactive search assistance Youth, LTU, older Employment Protection Action workers, Wage subsidies 433 846,000c Plan unskilled, women Transitional employment LTU, inactive Training 16 2000 programs Training, wage LTU, rural, Regional pacts subsidies, job 260.5d 49,900 inactive search assistance Wage subsidies, Programs for individuals with job search N/A 233 87,000 disabilities assistance, social services Public works, LTU, inactive Programs for the homeless 14 3,000 social services Programs for former public Training, job N/A 99.2 89,100e workers search assistance Training, job Women, rural, Programs for the Roma (primarily search assistance, 13.5 N/A LTU, inactive women) social services Training, job Rural, LTU, Social enterprise and micro search assistance, 54.7f 2,250 inactive entrepreneurship programs public works Training, job N/A Public Private Partnerships 19g 100,000 search assistance Notes: a 2015–2018 target. As of the end of 2016, approximately 43,000 jobseekers were involved in the program. b 2015–2021 target. As of the end of 2016, approximately 43,000 youth were involved in the program. c 2015 average d 2017–2021 target e Target f This includes planned expenditures on three programs running between 2016 and 2020. g 2017–2020 target Source: Ministry for National Economy and Ministry of Human Capacities. 48 Portraits of Labor Market Exclusion 2.0 6.3. Activation and employment support policies vis-à-vis priority groups’ needs This subsection reviews each of the three priority groups’ main barriers and looks at their consequent needs37 and links the latter with available policies in order to evaluate potential gaps. Addressing the same barrier may require a different set of activation policies to address the characteristics of the identified priority group. For example, while many individuals may face the barrier of no recent work experience, inactive mothers may need a different approach compared to young unemployed men. Thus, each barrier must be addressed in a manner appropriate to the specific needs of each group. Adequate service delivery and service quality are both absolutely crucial to ensure support to successful labor market integration efforts. Based on the analysis of priority groups in Section 5, Table 7 provides an overview of the current status of service delivery and quality organized by barrier, followed by subsequent implications for ways to improve based on international best practices. The overview suggests that, with the exception of the incentives barrier, one or more groups are affected by delivery or quality gaps pertaining to policies addressing each employment barrier in our framework. Policies addressing low education, no recent work experience, and scarce employment opportunities may be directed towards individuals from all three priority groups; however, limited evidence is available on the impact of past policies, which makes it difficult to learn from experience. Policies addressing some employment barriers—low education and no recent work experience in particular—require large-scale policy efforts, such as comprehensive investments in the education sector or a reform of the public works program. Others, such as programs that address care responsibilities barriers, require innovative solutions to complement ongoing investments in infrastructure. For all programs, an improved outreach to the target population has to be ensured. Table 7. An overview of barriers and their implications on service delivery and quality in Hungary Priority Employment Description Current situation Implications groups barrier affected The general education Most individuals in system continues to be Simultaneously the priority groups— inequitable because most improve the equity and with the exception of of the vulnerable children quality of schools to Group 5—do not attend low-performing improve the skills of have at least upper schools. Several programs future labor market Groups 2, 5, Low education secondary education; offer skills trainings to entrants, and equip and 6 those who do have current jobseekers, but current jobseekers with likely obtained their their adequacy and a combination of degrees in low- overall impact on labor technical and socio- performing schools. market outcomes is emotional skills. unknown. 37With some exceptions, the main barriers are those (i) with a probability of occurrence that is higher than 50 percent in each group, (ii) with a probability of occurrence of 10 percentage points higher than for the target population. 49 Portraits of Labor Market Exclusion 2.0 Priority Employment Description Current situation Implications groups barrier affected Expand daycare services, including in In the most The ongoing scale-up of remote and rural vulnerable priority daycare services will add locations. Expand the group (Group 6), considerable capacity to Sure Start children most of the mothers the currently available Care responsibilities houses’ network. Group 6 have young children spots, but it is unclear Develop community- and close to one- whether these are based childcare third have no access developed in solutions in locations to childcare. disadvantaged regions with no care infrastructure. Evaluate the impact of ongoing programs, and Group 2 has a higher There are several incorporate their share of individuals programs addressing the lessons in new reporting health Health limitations needs of jobseekers with programs’ design. Group 2 limitations than the disabilities, but their Develop targeted overall target impact is unknown. measures addressing population average. the needs of jobseekers with disabilities. Some programs offer apprenticeship and on- the-job training Evaluate the impact of opportunities. It is unclear ongoing programs, and whether these are incorporate their Most of the supported by labor lessons in new individuals in the demand from the private programs’ design. priority groups do sector, and whether such No recent work Facilitate access to Groups 2, 5, not have any recent opportunities are experience apprenticeships and and 6 work experience, and available in provide on-the-job many have never disadvantaged regions. It training opportunities. worked. is unclear whether Improve the targeting individuals with no work efficiency of the public experience receive works program. different programs from those with some experience. There are attempts to Evaluate the impact of address local demand ongoing programs, and All individuals in all constraints through incorporate their priority groups are at partnerships with lessons in new Scarce employment risk of being long- employers, but results of programs’ design. Groups 2, 5, opportunities term unemployed or past efforts to this end Provide transportation and 6 involuntarily have yet to be evaluated. support or allowance working part time. There is no for jobseekers to comprehensive mediator enhance geographic or outreach efforts to mobility. Strengthen 50 Portraits of Labor Market Exclusion 2.0 Priority Employment Description Current situation Implications groups barrier affected address the needs of outreach through labor those in the most market mediators. disadvantaged and Develop mobile labor isolated locations. market services. Additional cross-cutting issues across all priority groups Population younger than average High average number of barriers encountered simultaneously Poverty, material deprivation, and social exclusion Note: Findings and conclusions in the “Current situation� column are based on World Bank 2015a and World Bank 2015b. They also draw on consultations, interviews, and focus groups conducted in the course of 2015 and 2016. Following the broad-level analysis conducted above, we take a closer look at each priority group separately, examining their most prominent barriers as well as the necessary activation and employment support programs for each, illustrated in the respective figures for each group. Considering Group 2’s low education and lack of recent work experience, an effective way to help link individuals in this group to the labor market would be to assist with acquisition of relevant skills through skills training and/or wage subsidies combined with job search assistance and/or job intermediation. Most of the individuals in Group 2 have not completed upper secondary schooling and most also do not have recent work experience. In particular, programs that are comprehensive and help workers acquire skills (e.g. training and/or wage subsidies) and also help them find a job (e.g., job counseling and intermediation) have been proven to be most effective (Kluve et al., 2016). Training programs, especially those that respond to employers’ needs, have also been demonstrated to be more effective if targeted to individuals lacking skills and work best when institutional training is combined with practical training, which mirrors 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 specific industries in partnership with employers), although difficult to implement, can have positive impacts for the disadvantaged (Hendra et al., 2016). There is international evidence that employment subsidies can be effective when they are targeted to those who are far away from the labor market (e.g. low skilled, inactive, as found among this group). These subsidies also have a positive impact on post-intervention employment (Almeida et al., 2014; European Commission, 2014). Although different design elements can determine the success of employment subsidies (e.g. targeting, level duration, etc.), in a broad sense subsidies may improve the employability of disadvantaged workers and build human capital (by providing work experience and/or specific training) and thus mitigate the risk of returning to inactivity after the subsidized job (Almeida et al., 2014; World Bank, 2013). As mentioned before, subsidies are particularly successful if they are combined with training —either as part of the employment subsidies or prior to recruitment (European Commission, 2014). Combining job-search assistance, personalized follow-up, trainings, and benefits conditional on job search are all key elements of success. 51 Portraits of Labor Market Exclusion 2.0 The extent to which current policies adequately address the low education and lack of recent work experience among Group 2 members is unknown. As part of the regional employment pacts, apprenticeships and on-the-job training, opportunities may be combined with wage subsidies and are generally driven by private sector demand – following some limited job intermediation. However, the effectiveness of these interventions is so far unknown. It is also unclear whether individuals with no work experience receive different programs from those with some experience. Likewise, the current scale of the Youth Guarantee program (considering that a large proportion of Group 2 consists of youth) is only partially able to address the needs of at-risk youth jobseekers. It is important that the program as well as additional youth interventions are rolled out in a targeted fashion seeking at-risk and vulnerable jobseekers as well as inactive youth, based on clear eligibility criteria, and that the program provide a combination of job-search assistance with training and benefits. Over one-third of Group 2 faces health limitations, and 20 percent reports being unfit to work; some of these individuals could be activated by implementing policies that lead to a more inclusive work environment, and by linking individuals with disabilities to jobs. In particular, policies could be implemented that protect individuals with disabilities from discrimination in the work force and that provide employers incentives for having supportive work environments. For example, employers could be granted tax breaks if they can show that they have adapted their work environments to accommodate workers with disabilities and have also hired these workers. Wage subsidies could also be targeted toward workers with certain disabilities. 38 Lastly, tailored individual action plans, including referral to health services and psychosocial support, can help individuals with disabilities address their health conditions and find jobs that are the best fit for their abilities. At the same time, given that the disabled in this group are unlikely to come in contact with public employment services, individuals who may be able to work despite their health conditions must be encouraged to register as unemployed. The current proposal which reviews overall benefit adequacy and makes available tailored benefits for the most vulnerable families, and invests in better alignment with health (including mental health and addictions) may prove useful in this regard. Given the geographical distribution of this group, which is highly concentrated in rural areas and in the Great Plains and North region, individuals may also benefit from mobility incentives that could link them to jobs, or simple assistance with transportation. This is especially the case for the younger members of this group, who are less likely to be married (57 percent of this group has never been married, and, in fact, 45 percent are still living in their parental home). Mobility incentives, such as financial aid to cover transportation and/or relocation costs, can help individuals find jobs where they are more likely to be found, that is, in more densely populated areas. Figure 19. Group 2: Main employment barriers and necessary activation and employment support programs 38Please note that the existing Hungarian schemes should be evaluated to ensure they are having the desired impact. 52 Portraits of Labor Market Exclusion 2.0 Although Group 5 is more educated and does have work experience, its long-term unemployment status and low socioeconomic background implies that apprenticeships and vocational or on-the-job training may still provide some benefits, provided these are in line with their particular skill level and/or previous work experience and local labor demand from the private sector. Again, international evidence shows that training can be more effective if combined with wage subsidies and/or job counseling and/or intermediation services. These programs should be linked to private sector demand and also tailored to the skill level of the individuals in this group, who tend to be more educated and also to have previous work experience and thus possibly vocational skills. In some cases, individuals may only require a refresher to relearn vocational skills; in others, refresher programs may be complementary to the acquisition of new skills that may be more aligned with current labor demand. Once again, the effectiveness of training and wage subsidies is currently unclear or whether they well aligned with private sector needs and well combined with job search assistance. Figure 20. Group 5: Main employment barriers and necessary activation and employment support programs 53 Portraits of Labor Market Exclusion 2.0 Job search assistance/counseling and employment subsidies would likely provide needed support for the mostly inactive mothers found in Group 6; however, given the overlapping nature of multiple barriers and inactivity, these supports must be combined with other measures. As mentioned before, a combined approach that focuses on job search assistance and wage subsidies may help address the low level of education, low amount of work experience (with many not having worked in the past), and scarce job opportunities for members in this group. The approach used for this group will also require outreach and activation to simultaneously address the care responsibilities barrier and inactivity in the labor market. Enabling access to childcare facilities could help link the women in Group 6 to the labor market. Evidence from other countries 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). Evidence from countries such as Romania (Fong and Lockshin, 2000), the United States, Canada, Spain (as cited in Vuri, 2016), Israel, and Russia39 (as cited in Todd, 2013) also indicates that the provision of subsidized childcare can have a significant impact on mothers’ working hours, and can lift poor families out of poverty, although context is also an important factor. The policies that enable access to affordable childcare in turn should be supplemented by an increase in the supply of childcare institutions and the development of innovative community-based care solutions to mitigate against capacity constraints. Ongoing efforts to scale-up daycare services exist. However, these efforts do not appear to be targeted at women who are particularly vulnerable to not accessing the labor market and could become part of the workforce if childcare were to become accessible. It is also not clear whether daycare services will scale up in more disadvantaged and rural areas where this group of poor women is more likely to live. 39 Studies in Argentina, Brazil, Guatemala, and Colombia have also shown that childcare provisions significantly affect labor force participation, working hours, and earnings among mothers with young children. 54 Portraits of Labor Market Exclusion 2.0 Measures to encourage part-time work may also help connect these women to the labor market. Many may have chosen to remain inactive because they do not view the combination of work and domestic duties as a feasible choice. Evidence of this mindset is in the low percentage of Hungarian women currently working in part-time jobs. Improving opportunities for quality part-time employment through legislative instruments (this is currently under preparation) and promoting the viability of part time work would enable women to combine paid work with caring for children and/or elderly. Additionally, promoting other measures, including parental leave that encourages men to take time off work could also help shift current norms that place the burden of caregiving on women. Finally, given high a proportion of inactivity and lack of previous work experience, it will be necessary to actively reach out to members of Group 6 in an effort to activate them. Nearly all of the women in this group receive family benefits; many also receive housing and social exclusion benefits (40 and 27 percent, respectively). In this sense, social benefits and services could be used as outreach measures that could link women recipients to activation measures including training, and wage subsidies, in combination with job search assistance and childcare. The concentration of members of this group in rural areas may also imply that mobility measures could aid in terms of linkages to labor markets. Mobility measures are especially essential to cover the general transportation poverty gap for the members of the group. Figure 21. Group 6: Main employment barriers and necessary activation and employment support programs 7. Conclusions and policy recommendations The objective of this study is to provide a snapshot of what are often multiple and simultaneous constraints faced by the labor market vulnerable in Hungary to inform policy decisions that will address pressing needs of these groups. Policy makers are accountable for ensuring that employment policy takes into account the different needs, challenges, and barriers 55 Portraits of Labor Market Exclusion 2.0 faced by different at-risk groups on 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. Three 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 Hungary were presented, to 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. In this section, conclusions and policy directions which relate to both the identified needs of the priority groups and the gaps in activation and employment support programs and policies are presented. These policy directions are intended to be explored further with additional analysis. In this vein, translating these conclusions and suggested policy directions into concrete policy action will require in-depth analysis of program level data, particularly related to beneficiaries of existing active labor market, minimum-income guarantee, and other large social assistance programs. Through this analysis, we find that while many ongoing policies and programs aim to address the employment barriers faced by priority groups identified among the most vulnerable Hungarians in the labor market, these programs face considerable constraints in terms of coverage, service delivery, and quality. Very few programs have been evaluated in the past years, thus the program landscape lacks evidence of program effectiveness, making it difficult to learn from experience and improve the design of programs. Although investments in the single largest labor market intervention—public works—are expected to be scaled down in the coming years as they are geared towards targeted active labor market programs, it is important to identify priority actions that will drive future interventions toward results, not just in mere employment figures but also in terms of sustainability of results. To this end, policymakers need to pay particular attention to improving the opportunities of future generations of people who will enter labor market. Considering ongoing population dynamics, considerable investments will need to be made in those currently living in the most marginalized and disadvantaged households of Hungary. Against this background, we propose some preliminary policy suggestions that the Hungarian authorities should consider (see also Annex 6 for a summary table). These actions have been developed in a manner that both addresses urgent needs emanating from the common challenges facing all three priority groups (such as poverty, low education, no recent work experience, and care responsibilities) and responds to key challenges affecting future labor market entrants (such as persistent child poverty) that mostly affects individuals in Group 6. The policy suggestions are also designed to address program capacity constraints across the board, as well as program delivery and quality challenges. The proposed actions include: 56 Portraits of Labor Market Exclusion 2.0 Improve targeting of jobseekers through investments in a statistical profiling system. A more robust statistical profiling system should be developed to identify individuals at risk of long-term unemployment and refer them to tailored and specialized labor market services. The system should develop assessments based on individual jobseeker characteristics with strong predictive power. Invest in tailored support for the most vulnerable; this support should include closer integration with social services. Results seem to show that overall, benefit levels may be inadequate to address social exclusion; thus, it would be prudent to review the adequacy of benefits and make tailored benefits available for the most vulnerable families. Building on past service integration efforts, activation policies should be better aligned with health (including mental health and addictions), education, and housing interventions. Services should also offer tailored solutions to the poorest and most vulnerable populations, at the level of the individuals and their families. The former should be based on job search assistance and counseling, ideally implementing individual action plans. If needed, these services should incorporate intensive psychosocial support. Interventions should also incorporate mobile services and mediation to reach vulnerable rural individuals living in isolation. Invest in the skills of current and future jobseekers. As evidenced in this analysis, the bulk of current jobseekers have low levels of education. Investments in both equity and quality in the general education system are imperative to maintain the competitiveness of the future Hungarian workforce (please refer to the World Bank Pisa 2012 analysis for more details). The longer-term agenda should include opportunities to improving skills enhancement (cognitive, socio-emotional, technical), which are offered by labor market programs. Technical and vocational education and training (TVET) should be offered in a comprehensive manner, which encompasses workplace training, career guidance, professional training of TVET teachers, and standardized assessment of qualifications. The interventions should incorporate post-training guidance and follow-up actions that target beneficiaries (including groups of policy interest such as women, youth, or long-term unemployed). The most effective interventions incorporate intermediation with other forms of interventions, such as wage subsidies or self-employment support. Improve the design of the public works program. As the government aims to scale back the public works program, global “lessons learned� about public works and cash-for-work interventions should also be incorporated into the existing program. These lessons should focus on (i) introducing a self-selection approach with the desired profile of public work participants in mind so as to better target the intervention; (ii) setting a wage rate that promotes self-selection; (iii) actively considering a desired level of labor intensity, with eligible sub-projects in mind; (iv) reconsidering the balance between program duration and possible expansion in coverage; (v) considering using sub-projects as tools for strengthening community cohesion; and (vi) developing smart combinations with training interventions. On the other hand, in lieu of public works, meaningful mobility support schemes should be developed for (skilled) individuals living in areas of low labor demand. Expand and target youth employment programs. The current scale of the Youth Guarantee program is only partially able to address the needs of at-risk youth jobseekers. It is important that this program as well as additional youth interventions are rolled out in a targeted fashion seeking at-risk and vulnerable jobseekers as well as inactive youth, based on clear eligibility criteria. Global 57 Portraits of Labor Market Exclusion 2.0 lessons also suggest that programs that combine several measures are generally more successful, and that job-search assistance, personalized follow-up, trainings, and benefits conditional upon searching for a job are all key elements of success. Facilitate female employment through targeted active labor market programs. Improving opportunities for part-time employment through legislative instruments and promoting quality part-time jobs can help activate employment for women with young children. Scaling up investments in childcare facilities, while simultaneously developing innovative community-based care solutions to benefit the most vulnerable families with children will also provide incentive for these women to join the labor force. Developing training, wage subsidies, and job search assistance measures tailored to the needs of women — many of which have never held a job and are low skilled — are also necessary to ensure their transition into the labor market. Finally, linking social benefits and services to activation measures can aid in outreach efforts aimed at inactive women. Learn from experience within and outside the Hungarian labor market. Investing in monitoring and evaluation will help policymakers learn what works and why. 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(2015b). “Hungary: Skilling up the Next Generation: An Analysis of Hungary’s Performance in the Program for International Student Assessment.� Washington, DC: World Bank Group. http://documents.worldbank.org/curated/en/340561468196449667/Hungary-Skilling- up-the-next-generation-an-analysis-of-Hungary-s-performance-in-the-program-for-international- student-assessment 63 Portraits of Labor Market Exclusion 2.0 Annex 1. Advantages and disadvantages of the EU-SILC 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 — out of work or marginally employed — 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 only be marginally employed 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 64 Portraits of Labor Market Exclusion 2.0 population that is out of work or marginally employed, at risk of poverty, and who have low levels of education. As such, it is 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 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 5,952 observations for the target population of this study for the Hungary 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). 65 Portraits of Labor Market Exclusion 2.0 Annex 2. Description of employment barrier indicators Across the six countries that are analyzed by the World Bank, eight indicators40 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. All variables are in reference to individuals. In some cases, we describe the individual’s household. 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 and (ii) the availability of alternative care arrangements, namely the use of formal childcare services41 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 40For Hungary, only seven indicators are used due to data availability. 41EU-SILC data only 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. 66 Portraits of Labor Market Exclusion 2.0 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 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.42 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.43 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 42 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 2 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. 43 See OECD and World Bank, 2016 for details on how the reservation wage is calculated. 67 Portraits of Labor Market Exclusion 2.0 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. 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 borne in mind. 68 Portraits of Labor Market Exclusion 2.0 Annex 3. Latent Class Analysis results of EU-SILC 2013 respondents who are out-of-work or marginally employed Panel A: Latent Class estimates (percent) Group 4. Relatively Group 1. Retired Group 2. Poor, educated, Group 5. Poor, or inactive, low-educated, middle-aged middle-aged, Group 6. Poor, relatively unemployed or inactive mothers mostly male, low-educated, educated older inactive Group 3. Retired with past work long-term inactive young individuals, individuals, older women, experience unemployed mothers with some with health some with health some with health facing care with past work care Target Working- limitations limitations limitations responsibilities experience responsibilities pop. age pop. Class size (as a share of target population) 36 24 14 10 8 8 100 INDICATOR Share of individuals facing each barrier, by group Capabilities barriers Low education 2 68 48 0 37 64 31 18 Care responsibilities 5 4 7 71 8 48 15 5 Health limitations 47 38 51 6 24 11 37 20 No recent work experience - Has worked in 73 the past 82 50 93 79 87 51 28 No recent work experience - Has never worked 1 21 3 1 3 40 9 3 Incentives barriers High non-labor income 21 13 29 33 11 8 19 21 High earnings replacement (benefits) 28 10 4 2 3 8 14 6 Opportunities barrier Scarce employment opportunities 0 100 2 9 100 100 41 33 69 Portraits of Labor Market Exclusion 2.0 Panel B: Characteristics of latent groups (percent) Group 4. Group 1. Retired Group 2. Poor, Relatively or inactive, low-educated, educated, Group 5. Poor, relatively unemployed or middle-aged middle-aged, Group 6. Poor, educated older inactive Group 3. Retired inactive mothers mostly male, low-educated, individuals, individuals, older women, with past work long-term inactive young some with some with some with experience unemployed mothers with health health health facing care with past work care Target Working limitations limitations limitations responsibilities experience responsibilities pop. -age pop. Women* 47 50 85 97 31 98 61 51 Children 12 and younger in household* 9 26 4 100 29 99 30 29 Age group* 0 Youth (18-29) 0 46 0 1 0 68 17 16 Middle-aged (30-55) 33 35 4 99 86 32 40 59 Older (56-64) 67 19 96 0 14 0 43 25 Region* Central Hungary 30 24 11 31 54 18 27 30 Transdanubia 29 28 41 33 18 27 30 31 Great Plains and North 41 47 48 36 27 55 43 39 Degree of urbanization 0 Densely populated 31 19 19 33 44 13 26 30 Intermediate 34 26 37 34 17 26 31 31 Thinly populated 35 55 44 33 39 62 43 39 Target population group Out of work 78 66 94 73 86 87 78 30 Unstable jobs 16 30 5 25 11 12 18 7 Restricted hours 4 3 1 2 3 0 3 1 Near-zero income 2 1 0 0 1 0 1 0 Main activity during reference period (more disaggregated Employed full time 1 0 0 0 0 0 0 54 Employed part time 4 4 1 2 3 1 3 3 Self-employed full time 1 0 0 0 0 0 0 7 Self-employed part time 1 0 0 1 1 0 1 0 Unemployed 15 51 6 14 71 22 27 10 Retired 53 9 77 1 4 1 33 12 70 Portraits of Labor Market Exclusion 2.0 Group 4. Group 1. Retired Group 2. Poor, Relatively or inactive, low-educated, educated, Group 5. Poor, relatively unemployed or middle-aged middle-aged, Group 6. Poor, educated older inactive Group 3. Retired inactive mothers mostly male, low-educated, individuals, individuals, older women, with past work long-term inactive young some with some with some with experience unemployed mothers with health health health facing care with past work care Target Working limitations limitations limitations responsibilities experience responsibilities pop. -age pop. Unfit to work 20 19 13 1 10 2 15 6 Domestic tasks 3 4 2 45 5 34 10 4 Other inactive 3 12 2 35 6 40 11 4 Main activity at onset of interview Employed full time 7 15 1 11 4 5 8 54 Employed part time 4 5 1 5 3 1 3 2 Self-employed full time 2 1 0 1 0 0 1 7 Self-employed part time 1 0 0 1 1 0 1 0 Unemployed 11 44 5 13 70 23 24 12 Retired 53 10 77 1 4 1 33 13 Unfit to work 19 18 12 1 9 2 14 5 Domestic tasks 2 3 1 41 5 30 9 3 Other inactive 2 6 1 28 5 37 8 3 Student 0 1 0 0 0 1 0 0 Months in unemployment Zero months 84 43 94 82 28 70 70 83 Less than 12 10 27 2 10 8 14 13 10 12 or more 7 30 4 8 64 16 17 7 Actively searching for a job at time of interview 6 33 3 14 63 18 18 9 Live with parents 10 45 5 8 27 26 20 21 At risk of poverty (60% of median income) 15 44 15 16 43 53 27 14 At risk of poverty (40% of median income) 5 20 4 3 21 18 11 5 Severe material deprivation 28 55 27 22 58 63 39 27 At risk of poverty and social exclusion 45 73 36 33 76 74 54 35 Income quintile Poorest 22 54 21 22 56 66 36 20 2 19 20 24 25 18 18 20 19 71 Portraits of Labor Market Exclusion 2.0 Group 4. Group 1. Retired Group 2. Poor, Relatively or inactive, low-educated, educated, Group 5. Poor, relatively unemployed or middle-aged middle-aged, Group 6. Poor, educated older inactive Group 3. Retired inactive mothers mostly male, low-educated, individuals, individuals, older women, with past work long-term inactive young some with some with some with experience unemployed mothers with health health health facing care with past work care Target Working limitations limitations limitations responsibilities experience responsibilities pop. -age pop. 3 22 13 20 18 16 8 17 19 4 19 8 19 18 8 3 14 20 Richest 19 5 16 17 3 4 13 22 Education level 0 Primary or less 0 10 5 0 7 8 4 2 Lower secondary 2 57 42 0 29 57 27 16 Upper secondary 77 23 40 64 55 31 52 57 Post-secondary 4 4 2 3 3 2 3 4 Tertiary 17 5 10 34 6 3 13 21 Age groups (more disaggregated) 18-19 years 0 4 0 0 0 5 1 1 20-24 years 0 25 0 0 0 31 9 7 25-29 years 0 17 0 1 0 32 7 8 30-34 years 3 4 0 39 15 11 8 10 35-44 years 9 11 1 51 35 14 15 23 45-54 years 18 17 3 8 31 7 15 23 55-59 years 26 19 14 1 15 1 18 16 60-64 years 44 2 82 0 3 0 28 12 Average age 56 38 61 37 45 29 47 44 Severe limitations in daily activities 14 14 13 1 10 3 11 5 At least one working adult in the household 42 50 33 87 44 57 49 60 Elderly in the household 18 11 29 7 20 6 16 13 Children under 6 in household 5 18 2 78 19 83 22 18 Children under 3 in household 3 9 1 56 10 56 14 10 Children under 13 in formal childcare None 2 4 1 22 5 28 7 5 Some 1 8 1 31 6 32 9 5 72 Portraits of Labor Market Exclusion 2.0 Group 4. Group 1. Retired Group 2. Poor, Relatively or inactive, low-educated, educated, Group 5. Poor, relatively unemployed or middle-aged middle-aged, Group 6. Poor, educated older inactive Group 3. Retired inactive mothers mostly male, low-educated, individuals, individuals, older women, with past work long-term inactive young some with some with some with experience unemployed mothers with health health health facing care with past work care Target Working limitations limitations limitations responsibilities experience responsibilities pop. -age pop. All 6 14 2 46 18 39 15 19 NA 91 74 96 0 71 1 70 71 Marital status Married 64 30 64 77 45 42 54 56 Never married 10 57 4 14 34 52 26 28 Divorced/separated/widow/er 26 13 32 9 20 7 20 16 Labor market status of spouse/partner 0 Working 30 14 14 85 33 46 31 43 Unemployed 4 10 3 3 9 22 7 5 Retired 28 6 46 1 4 1 18 10 Unfit to work 7 4 5 1 4 3 5 3 Domestic tasks 1 3 0 0 5 1 2 3 Other inactive 1 5 0 1 4 3 2 3 No spouse/partner 29 57 31 9 41 25 34 32 Receives family benefits 20 43 11 100 41 99 40 42 Average annual value** 1,273 1,822 1,179 2,750 1,837 2,510 1,683 1,561 Receives social exclusion benefits 6 18 5 9 16 27 12 7 Average annual value** 706 886 892 637 705 782 775 13 Receives unemployment benefits 8 24 4 8 29 12 13 7 Average annual value** 797 883 968 751 1,058 802 859 739 Receives old-age benefits 49 5 76 1 3 0 30 12 Average annual value** 4,685 3,426 3,483 3,016 4,198 1,240 3,733 4,166 Receives survivor benefits 3 2 2 1 1 2 2 1 73 Portraits of Labor Market Exclusion 2.0 Group 4. Group 1. Retired Group 2. Poor, Relatively or inactive, low-educated, educated, Group 5. Poor, relatively unemployed or middle-aged middle-aged, Group 6. Poor, educated older inactive Group 3. Retired inactive mothers mostly male, low-educated, individuals, individuals, older women, with past work long-term inactive young some with some with some with experience unemployed mothers with health health health facing care with past work care Target Working limitations limitations limitations responsibilities experience responsibilities pop. -age pop. Average annual value** 1,560 1,488 1,496 1,488 1,803 2,619 1,631 1,620 Receives sickness benefits 1 1 0 4 1 1 1 4 Average annual value** 659 231 325 851 275 992 525 347 Receives disability benefits 25 22 15 1 11 3 18 7 Average annual value** 2,692 1,959 2,109 1,736 1,902 1,514 2,181 2,316 Receives education benefits 0 1 0 1 0 0 0 0 Average annual value** 153 366 206 160 349 326 242 341 Receives housing benefits 8 25 8 11 17 40 16 9 Average annual value** 154 242 127 194 241 267 191 211 Receives any social benefits 91 82 97 100 74 100 90 61 Average annual household income from: Labor 4,460 5,241 3,049 10,218 3,830 5,027 5,021 10,499 Other 197 105 133 347 332 166 189 193 Benefits 5,518 3,473 5,830 3,660 3,410 3,948 4,591 2,872 Average annual equivalized household income 4,921 3,264 4,699 5,099 3,139 2,935 4,209 5,292 *Included in the LCA model as active covariates. **All amounts are in EUR. 74 Portraits of Labor Market Exclusion 2.0 Source: Authors’ calculations based on EU-SILC 2013 data. Note: Color shadings identify categories with high (darker) frequencies. 75 Portraits of Labor Market Exclusion 2.0 Annex 4. Latent Class Analysis model selection for Hungary 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 that are to be used as the main drivers for segmenting individuals into groups. (In Hungary’s case, the number of indicators has been adjusted to seven due to missing data and the subsequent need to abandon the low relative work experience indicator.) 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 Hungary starting with Step 2. For a more detailed explanation of the step-by-step process of model selection, please refer to OECD and World Bank, 2016. Figure A4.1 below summarizes graphically Step 2 outlined above for Hungary. The blue bars show the percentage variations of the Bayesian Information Criterion (BIC, Schwarz 1978) for increasing the numbers of latent groups for the baseline model. The orange bars show the percentage variation of the Akaike Information Criterion (AIC; Akaike, 1987)44; and the grey line shows the classification error statistics (Vermunt and Magdison, 2016).45 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 A4.1, both AIC and BIC are minimized for models with seven or eight classes; the classification error is minimized for six classes. 44 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 s a better fitting of the underlying data but at the cost of complicating the model’s structure. The BIC and the AIC summarize this trade-off into a single index. The indices can be used as a guideline for choosing between an adequate representation of the population into a finite number of sub-groups and an increasing complexity of the statistical model. 45 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, one must keep in mind that LCA does not assign individuals to specific classes; rather, it estimates probabilities of class membership. One has therefore two options for analyzing the results: assign 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 misclassified according to the modal assignment. 76 Portraits of Labor Market Exclusion 2.0 Figure A4.1: Selection of the optimal number of latent classes Var BIC Var AIC Class.Err. 0.01 0.3 0 0.25 % var. AIC and BIC -0.01 0.2 -0.02 0.15 -0.03 0.1 -0.04 -0.05 0.05 -0.06 0 Number of latent classes 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 is associated with violation of the 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. These violations of the LIA can be best addressed modeling 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 Hungary, the 6-class model selected clear signs of misspecification, with bivariate residuals significantly higher than 1 for several pairs of indicators.46 Eliminating the local dependencies through the use of direct effects once again points to a 6-cluster model when minimizing the BIC criterion and the classification error: hence it remains the preferred model for Hungary. 46 Results are available upon request. 77 Portraits of Labor Market Exclusion 2.0 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. For example, it allows identification of 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 of age (3 categories), gender, presence of young children, 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 whether it is possible for 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 modelling the associations between indicators and covariates with direct effects (as discussed in Step 3 above). Culminating Step 4, we find that a 6-cluster model with the combination of active covariates — including age, gender, presence of young children, and region — and direct effects brings the bivariate residuals down to zero and has the lowest classification error. The model has a classification error of 11 percent, slightly lower than the model without active covariates (12 percent), along with considerable improvement in both AIC and BIC. A 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.47 47 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 assignment of these individuals into a specific latent group would be done almost at random, whereas in models with covariates the assignment of individuals depends on the additional information provided to the latent class model and how this interacts with the indicators. 78 Portraits of Labor Market Exclusion 2.0 Annex 5. Categorization 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) start-up 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). 79 Portraits of Labor Market Exclusion 2.0 Annex 6: An overview of proposed policy actions Policy action Responsible agency Term Costs Improve targeting of jobseekers through investments in Ministry for National Economy, Short Low a statistical profiling system National Employment Office A robust statistical profiling system should be developed to identify individuals at risk of long-term unemployment and refer them to tailored and specialized labor market services. The system should develop assessments based on individual jobseeker characteristics with strong predictive power. Beneficiary groups: Groups 2, 5, and 6 Ministry for National Economy, Ministry Invest in tailored support for the most vulnerable, and of Human Capacities, National Medium Medium include closer integration with social services Employment Office Review overall adequacy of benefits and make available tailored benefits for the most vulnerable families. Building on past service integration efforts, invest in better alignment with health (including mental health and addictions), education, and housing interventions. Enable services to offer tailored solutions to the poorest and most vulnerable populations, at the level of the individuals and their families. Solutions for individuals should be based on job search assistance and counseling, ideally based on individual action plans. If needed, these services need to incorporate intensive psychosocial support components. Interventions should also incorporate mobile services and mediation to reach vulnerable rural individuals who live in isolation. Beneficiary groups: Groups 2 and 6 Ministry of Human Capacities, Ministry Invest in the skills of current and future jobseekers Long High for National Economy Improve equity and quality in the general education system while simultaneously improving skills enhancement opportunities (cognitive, socio-emotional, and technical) that are offered by labor market programs. Although the former is clearly a longer-term agenda, investments in both equity and quality are inevitably needed to maintain the competitiveness of the future Hungarian workforce (please refer to the World Bank Pisa 2012 analysis for more details). Technical and vocational education and training (TVET) should be offered in a comprehensive manner, to include workplace training, career guidance, professional training for TVET teachers, and standardized assessment of qualifications. The interventions should incorporate post-training guidance and follow-up actions targeting the beneficiaries (including groups of policy interest such as women, youth, or long-term unemployed). The most effective interventions incorporate intermediation with other forms of interventions, such as wage subsidies or self-employment support. Beneficiary groups: Groups 2, 5, and 6 Ministry of Interior, Ministry for Improve the design of the public works program Short Medium National Economy Incorporate global lessons about public works and cash-for-work interventions in the program. These lessons should focus on (i) introducing a self-selection approach with the desired profile of public work participants in mind so as to better target the intervention; (ii) setting a wage rate that promotes self-selection; (iii) actively consider a desired level of labor intensity, with eligible sub-projects in mind; (iv) reconsider the balance between program duration and 80 Portraits of Labor Market Exclusion 2.0 Policy action Responsible agency Term Costs possible expansion in coverage; (v) consider using sub-projects as tools for strengthening community cohesion; and (vi) develop smart combinations with training interventions. In lieu of public works, meaningful mobility support schemes should be developed for (skilled) individuals living in areas of low labor demand. Beneficiary groups: Groups 2, 5 and 6 Expand and target youth employment programs Ministry for National Economy Short High The current scale of the Youth Guarantee program is only partially able to address the needs of at-risk youth jobseekers. It is also important that the program and other youth interventions are rolled out in a targeted fashion seeking at-risk and vulnerable jobseekers as well as inactive youth, based on clear eligibility criteria. Global lessons also suggest that programs that combine several measures are generally more successful, and that job-search assistance, personalized follow-up, trainings, and benefits conditional on job search are all key elements of success. Beneficiary groups: Groups 2 and 6 Facilitate female employment through targeted active Ministry for National Economy, Ministry Medium Medium labor market programs of Human Capacities Improve opportunities for part-time employment through legislative instruments, and promote quality part-time jobs. Scale up investments in childcare facilities, while simultaneously developing innovative community-based care solutions. Expand the network of Sure Start children houses to benefit the most vulnerable families with children. Develop training, wage subsidy, and job search assistance measures tailored to the needs of female jobseekers. Promote policy efforts targeting inactive individuals. Beneficiary groups: Groups 6 Learn from experience within and outside the Ministry for National Economy, Ministry Short Medium Hungarian labor market of Interior Invest in monitoring and evaluation: develop interventions with a rigorous results and monitoring framework in place, regularly evaluate interventions, and share the lessons broadly and incorporate them in the design of newly rolled out programs. Share and discuss the experience of Hungarian interventions, and learn from policies and programs of other agencies within and outside Europe. Beneficiary groups: Groups 2, 5, and 6 Source: Authors’ elaboration. 81 Portraits of Labor Market Exclusion 2.0