Delivers the great recession the whole story? Structural shifts in youth unemployment pattern in the 2000s from a European perspective Hans Dietrich Institute for Employment Research (IAB), Nuremberg Presentation at the GESIS-European user conference for EU-LFS & EU-SILC conference Mannheim, March 21-22, 2013
Figure 1: Youth unemployment rate 2001-2010 European countries yuer 10 20 30 40 10 20 30 40 10 20 30 40 10 20 30 40 10 20 30 40 0 0 0 0 0 Belgium Bulgaria Czech Denmark Germany Estonia Ireland Greece Spain France Italy Latvia Lithunia Luxembourg Hungary Netherlands Austria Poland Portugal Rumania Slovenia Slovakia Finland Sweden UK 2000 2005 2010 2000 2005 2010 2000 2005 2010 2000 2005 2010 2000 2005 2010 Graphs by country year
Figure 2: Youth unemployment rate 2007-2010 European countries yuer 10 20 30 40 10 20 30 40 10 20 30 40 10 20 30 40 10 20 30 40 0 0 0 0 0 Belgium Bulgaria Czech Denmark Germany Estonia Ireland Greece Spain France Italy Latvia Lithunia Luxembourg Hungary Netherlands Austria Poland Portugal Rumania Slovenia Slovakia Finland Sweden UK 2007 2008 2009 2010 2007 2008 2009 2010 2007 2008 2009 2010 2007 2008 2009 2010 2007 2008 2009 2010 Graphs by country year
Agenda 1. Youth unemployment as an empirical concept 2. Data and measurements 3. Empirical findings from a macro-perspective 4. What is specific on youth unemployment considerations from a life course perspective - a heuristic framework 5. Empirical findings from a micro-perspective 6. Combining micro and macro 7. Conclusions: Youth unemployment: a policy relevant field of action
1 (Youth) unemployment as an empirical concept Unemployment (Ilo Definition ILOSTAT = 2): without a job and not in education or training, actively seeking work, currently available for work, Youth unemployment between 15 and below 25 years old Both a statistical and a legal concept, implemented in social or labor laws of most of the European countries Definition of the European policy framework
Research question How do macro factors affect youth unemployment over time?
3 Data and measurements 3.1 Data European Union Labor Force Survey (EU LFS) Conducted in a total of 33 European states, due to data restrictions in the following data are used from 23: Belgium, Bulgaria, the Czech Republic, Denmark, Germany, Estonia, Ireland, Greece, Spain, France, Italy, Latvia, Lithonia, Hungary, the Netherlands, Austria, Poland, Portugal, Slovenia, Slovakia, Finland, Sweden, United Kingdom Advantage of data: long time series (for some countries a), comparable questionnaire and harmonized data set Limitations of data: Cross sectional data, with slightly differences concerning sampling, timing of interview, questionnaire and coding and with some severe changes over time within countries Availability of data: Access is based on a research contract with EUROSTAT
3 Data and measurements 3.2 Measurements
Figure 1: Youth unemployment rate by European Regions 2001-2010 25,0 20,0 15,0 10,0 Y EC27 Y EURO17 Germany 5,0 0,0 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 Source: EUROSTAT - LFS (lfsa_urgaed1.xlsx) [data]; own calculations.
Figure 5: Unemployment ratio in European regions 2001-2010 10,00 9,00 8,00 7,00 6,00 5,00 4,00 European Union (27 countries) Euro area (17 countries) Germany 3,00 2,00 1,00 0,00 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010
Figure 3: Ratio of youth (15-24) unemployment rate to adult (25-64) unemployment rate European regions 2001-2010 3,00 2,50 2,00 1,50 EC27 EURO17 Germany 1,00 0,50 0,00 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010
3 Data and measures 3.3 Descriptive results 2001-2010 Variable Mean Std. Dev. Min Max Dependant variable Unemployment rate Explanatory variables gdp 2.25 4.22-17.7 11.2 lgdp 2.41 4.22-17.7 11.2 Apprenticeship training 0.13 0.34 0 1 Inactive % 56.42 12.92 26.08 75.38 Higher educated % 68.98 12.95 22.3 85.6 Employed in productive industries % 26.65 5.55 16.6 38.7 Temp employed youth % 30.06 18.21 0 62.61 N: 203 Source: LFS, World bank
3 Models 3.4 Macro model: panel models 2001-2010 ----------------------------------------------------------------------------------------- dep var:yuer OLS FE FE rob RE RE rob -------------+--------------------------------------------------------------------------- gdp -0.1565-0.4599*** -0.4599*** -0.2477*** -0.2477*** lgdp -0.5732*** -0.6040*** -0.6040*** -0.6473*** -0.6473*** Apprenticesh -6.9865*** (omitted) (omitted) -10.5410*** -10.5410*** Inactive% 0.2854*** -0.1567-0.1567 0.1619** 0.1619 Higher edu% 0.0332-0.6378*** -0.6378** -0.0179-0.0179 industrylag -0.1587* -1.8352*** -1.8352*** -0.3776** -0.3776 temp % 0.0248 0.1685** 0.1685 0.0359 0.0359 _cons 17.8444* 17.2718*** 17.2718*** 22.7668*** 22.7668** -------------+--------------------------------------------------------------------------- N 203 203 203 203 203 r2 0.4784 0.6046 0.6046 r2_o 0.0039 0.0039 0.3714 0.3714 r2_b 0.0580 0.0580 0.3085 0.3085 r2_w 0.6046 0.6046 0.4808 0.4808 sigma_u 19.8168 19.8168 3.8672 3.8672 sigma_e 3.3240 3.3240 3.3240 3.3240 rho 0.9726 0.9726 0.5751 0.5751 ----------------------------------------------------------------------------------------- YUER: Youth unemployment rate (ILO-concept) legend: * p<.1; ** p<.05; *** p<.01 Source: LFS, World Bank
3.5 Macro models Panel analysis: empirical findings - Triggers and consequences of youth unemployment The business cycle effects youth unemployment directly (number of entries and duration) Countries with apprenticeship training show lower unemployment rates and suffered less from the crisis An over all higher level of better qualified is connected with lower youth unemployment rates Share of employment in the production sector reduce youth unemployment Remaining in education or returning to education may work as a buffer, but is correlated with higher unemployment levels on the macro level
4 What is specific on youth unemployment First time lm entrants (outsider argument) Missing work experience (occupational and firm specific, human capital and signaling arguments) Less secure labor contracts (part time, temporary or free lance activities; contract theories) Seniority or tenure based wages Less protection by social or labor law (e.g. seniority or age based rules) Job-hopping as part of both the individual efforts to develop the vocational/occupational choice and improving the matching (job search and matching) Transition pattern vary between countries (apprenticeship training) and within countries (edu expansion, BA/MA reform etc)
What is specific on youth unemployment ILO concept of unemployment is restrictive, especially for young people Alternatives: disconnected youth, excluded youth, NEET (Not in Employment, Education, or Training) However, age related concepts again
4 From an age based definition of youth unemployment towards a life course perspective The first five years in the labor market instead of the age group 15-25 Proxy variable Wave year hatyear <= 5 Subgroups 0-2 years versus 3-5 years
5. Empirical findings from a micro-perspective: descriptive results Variable Mean Std. Dev. Min Max Dependant variable Unemployed.1513.3584 0 1 Explanatory variables LM-entrant (0-2 years).4025.4904 0 1 age group (5 years) 17.1084.3109 0 1 22.4716.4992 0 1 27.2977.4572 0 1 32.1221.3275 0 1 Female.4838.4937 0 1 hatlevel_gr 0 Without formal edu.0098.0985 0 1 1 lower sec.: general.0949.2925 0 1 2 lower sec.: vocational.0919.2889 0 1 3 upper sec.: general.3488.4766 0 1 4 Upper sec.: vocational.0577.2332 0 1 5 tertiary: vocational.0838.2772 0 1 6 tertiary academic.3133.4638 0 1 Countries citizenship.9597.1965 0 1 Marital status Separated/widowed.0058.0771 0 1 Single.8664.3008 0 1 Married.1275.3336 0 1 Self-reported data.5522.4972 0 1 Urbanity Urban.4136.4925 0 1 Middletown.2408.4275 0 1 Rural.3247.4682 0 1 No regional info.0207.1426 0 1 N: 14.879 Unemployed: ilostat = 2 Legend: * p<.05; ** p<.01; *** p<.001 Controls: Marital status, urbanity Source: LFS, World Bank
5. Empirical findings from a micro-perspective: xtmelogit Variable base m1 dep var: unemployed coef coef age (5years groups)(ref: 17) 22-0.3810*** 27-0. 5343*** 32-0. 4964*** female 0. 1675*** LM-entrant* high attainment level (hatlevel) 0 0"Without formal education" 1.0867*** 0 1"lower secondary: general Education" 1.0014*** 0 2"lower secondary: vocational Education" 0.1618 0 3"upper secondary: general Education" -0.0948 0 4"Upper Secondary: vocational Education" -0.1850 0 5"Tertiary: vocational" -0.5580*** 0 6 Tertiary: academic -0.7188*** 1 0 1.0180** 1 1 1.2761*** 1 2 0.6926 1 3 0.4774*** 1 4 0. 2113 1 5 0.0992 1 6 Ref cat: Tertiary: academic Citizenship (Ref: foreign) -0.2968*** Self reported data -.0114 Constant -1.8172*** -1.3891 Random-effects Parameters Var (cons).1627***.2020*** Statistics N (N groups) 14879(23) 14879(23) LR chi2(df) 794.92(24)*** N: 14.879 Unemployed: ilostat = 2 Legend: * p<.05; ** p<.01; *** p<.001 Controls: Marital status, urbanity Source: LFS, World Bank
6 Combining micro and macro level - a multi level approach: xtmelogit Variable base m1 m3 dep var: unemployed coef coef coef age (5years groups)(ref: 17) 22-0.3810*** -0.4079*** 27-0. 5343*** -0. 5533*** 32-0. 4964*** -0. 5136*** female 0. 1675*** 0. 1733*** LM-entrant* high attainment level (hatlevel) 0 0"Without formal education" 1.0867*** 1. 0611*** 0 1"lower secondary: general Education" 1.0014*** 0. 9695*** 0 2"lower secondary: vocational Education" 0.1618 0. 1224 0 3"upper secondary: general Education" -0.0948-0. 1287 0 4"Upper Secondary: vocational Education" -0.1850-0. 2006 0 5"Tertiary: vocational" -0.5580*** -0.6031*** 0 6 Tertiary:academic -0.7188*** -0.7692*** 1 0 1.0180** 1.0399** 1 1 1.2761*** 1.2753*** 1 2 0.6926*** 0.6850*** 1 3 0.4774*** 0.4655*** 1 4 0. 2113 0.2077 1 5 0.0992 0.0930 0 6 Ref cat: Tertiary: academic Citizenship (Ref: foreign) -0.2149*** -0.2794*** Self reported data -.0114-0.0187 gdp -0.0224*** lgdp -0. 0289*** inactive share_c 0. 0320*** industry share_c -0. 0188 Constant -2.0791*** -1.9298*** -1.1697*** Random-effects Parameters Var (cons).1233***.1637***. 0564*** Statistics N (N groups) 14879(23) 14879(23) 14879(23) Wald chi2(df) 794.92(24)*** 855.15(28)*** N: 14.879 Unemployed: ilostat = 2 Legend: * p<.05; ** p<.01; *** p<.001 Controls: Marital status, urbanity Source: LFS, World Bank
Conclusions Alternatively NEET models are calculated instead of unemployment models which show more or less the same results Compared to later episodes in the employment career the entry period into the labor market is characterized by a bunch of individual factors, which support the argument of an institutional weakness (level of qualification and potential labor market experience time) The entry into the labor market episode is more sensitive to economic macro factors (business cycle) compared to workforce, already established in the labor market A LM tenure based concept captures the LM entry process more appropriate than an age based concept
Literature Dietrich, Hans 2012: Youth Unemployment in Europe. Theoretical Considerations and Empirical Findings. Berlin (Friedrich Ebert Stiftung). http://www.fes.de/cgibin/gbv.cgi?id=09227&ty=pdf
Thanks for your attention