Skill mismatch in EU Enlargement and NeighbourhoodCountries Will Bartlett (LSE) Jens Johansen (ETF) Debora Gatelli(ETF)
Social inefficiency of mismatch Mismatch defined here as labour market phenomenon conditioned by education system inefficiencies Disproportion between supply and demand for labour by skill level ( vertical mismatch ) Does not consider horizontal mismatch Costs of mismatch Fiscal costs (unemployment, foregone tax revenue) Absenteeism Loss of productivity and competitiveness
Measures of mismatch Coefficient of variation Variance of unemployment Proportions of employment and unemployment Beveridge curve Mismatch by occupation Relative wages by skill level
Countries included Transition countries in Western Balkans Croatia, Montenegro, Serbia Neighbourhood countries in transition Moldova, Ukraine Emerging market countries Turkey, Egypt
Main findings (1) Gender differences Inferior matching of women in emerging market countries
Figure 1: CoVmismatch by gender Moldova 30 Coefficient of variation 25 20 15 10 5 12.59 14.43 25.99 5.51 10.01 2.38 2.72 2.09 1.48 Total Male Female 0 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 0.38
Figure 2: CoV mismatch by gender, Turkey 8 Coefficient of variation 7 6 5 4 3 2 1 5.59 7.02 5.17 4.26 3.99 4.01 TOTAL MEN WOMEN 0 2005 2006 2007 2008 2009 2010
Fig 3: CoVmismatch by gender, Egypt 200 Coefficient of variation 150 100 50 165.84 97.08 102.73 87.24 35.15 Total Men Women 0 7.11 2005 2006 2007 2008 2009 2010
Fig 5: Variance mismatch by gender, Ukraine 0.5 Variance of unemployment rates 0.4 0.3 0.2 0.1 0.18 0.07 0.14 0.13 0.37 0.19 0.28 0.27 0.44 0.24 Total Female Male 0.0 2000 2005 2008 2009 2010
Fig 6: Variance mismatch by gender, Turkey 0.6 0.5 0.51 0.47 0.46 0.4 0.35 0.33 0.37 0.3 Total Men 0.2 Women 0.1 0.09 0.10 0.11 0.09 0.09 0.10 0.0 2005 2006 2007 2008 2009 2010
Indicator CoV Variance Beveridge Croatia Relatively stable Increase 2002-09 Inward shift; ended during crisis Montenegro n/a Decrease (poor data) Inward shift 2006-08 Serbia Increased 2005-06 then stable, declined 2010 Increased 2005-08; then fellto previous level Outward shift followed by inward shift Moldova Declined 2006-07 Stable 2005-08;then declined Inwardshift 2004-08 ended during crisis Ukraine Increased 2000-08 Increased 2000-10 Inward shift 2005-08 ended during crisis Turkey Declined 2005-09; increased 2010 Declined 2005-10 Outward shift 2005-10 Egypt Very high; declined 2005-09; then increased 2010 Very high; declined 2005-09; then increased to former level n/a
Patterns of mismatch Transition countries: mismatch increased up to 2008 Reflects employer behaviour during boom period in countries where skilled labour is scarce Employers were willing to take on unmatched workers In downturn employers released mismatched workers first Emerging markets: mismatch declined up to 2008 due to decline in female mismatch During boom, employers relaxed discriminatory hiring against women who filled better-matched jobs rather than women s jobs During crisis employers reverted to discriminatory practices
Main findings (3) Education qualifications and mismatch Highly educated workers better matched in transition countries In emerging markets university graduates poorly matched; less well educated are better matched
Fig 9: Proportional mismatch by gender and education, Moldova 1.4 1.2 1.24 1.15 1.17 1.27 1.04 1.08 1.0 0.87 0.94 0.91 0.96 0.94 0.8 0.78 total 0.6 0.50 Male 0.4 0.2 0.30 0.20 Female 0.0 ISCED 5-6 ISCED 4 ISCED 3 ISCED 2 ISCED 0-1
Fig 10: Proportional mismatch by gender and education, Turkey Tertiary 1.33 1.66 1.80 College 0.68 0.91 1.27 Vocational high 0.92 1.13 1.96 High school (11 years) 1.12 1.40 2.26 Junior high (8 years) 0.90 0.95 1.48 Primary (5 years) 0.62 0.83 0.91 No diploma 0.51 1.05 1.49 Cannot read or write 0.19 0.49 1.22 0.0 0.5 1.0 1.5 2.0 2.5 Women Men Total
Main findings (4) Occupational matching Turkey: mismatch highest among office clerks, customer services; associate professionals; but also high among legislators and top managers; least among professionals
Fig 11: Matching by occupation, Turkey Unskilled labourers Plant and machine operators Artisans Qualified agricultural workers Personal services, sales persons Office clerks, customer services Associate professionals Women Men All Professionals Legislators, top managers -4-2 0 2 4 6 8 10 Kurtosis
Other main findings (5) Age groups Younger and older workers poorly matched especially in Turkey and Egypt Spatial dimension of mismatch Mismatch higher in rural areas (Moldova) But also urban college graduates (Ukraine) Relative real wages have been increasing Growing demand for skilled labour Since crisis onset, relative wages for skilled have fallen (Ukraine) but not in Turkey
Conclusions (1): Transition economies In transition economies, education is often inappropriate to needs of modern economy Vocational and high school graduates often have inappropriate skills and qualifications Restructuring and technological change has increased demand for university graduates Employers cannot find enough highly qualified workers (especially in Ukraine and Moldova BEEPS data) But there is also evidence of over-education and bumping down in transition economies suggests socially inefficient matching process
Conclusions (2): Emerging markets Level of mismatch greater than in transition countries, though falling, mainly due to improved matching of women, although large gender differences remain Very high mismatch among more educated workers Especially young skilled workers and university graduates Issue is not so much skill-biased technical change but rather high population growth Education system produces more highly skilled young men than labour market is creating jobs to absorb them Large informal sector provides jobs for unskilled
Policies: transition countries Reform of high schools and vocational schools Expand but regulate tertiary education to ensure quality Incentivise employer in-house training Address mismatch of older workers through public investment in retraining, lifelong learning and adult education
Policies: Emerging markets Address mismatch among young highly educated workers as priority Ensure highly skilled young women are included in labour market with matched jobs Upgrade industrial base to provide more skilled jobs for unemployed graduates Integrate into EU economy by guiding European FDI into diversified local economies