THE IM(PERFECT) MATCH ILO INTERNATIONAL CONFERENCE

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1 THE IM(PERFECT) MATCH ILO INTERNATIONAL CONFERENCE REGIONAL VIEW: ARAB STATES AND CENTRAL ASIA Patrick Daru (ILO) and Eduarda Castel-Branco (ETF) Geneva, 11/05/2017

2 DO SKILLS MATTER IN THE MENA REGION? 2

3 THE SKILLS MISMATCH STORY IN THE ARAB STATES USUAL STORYLINE IN FACT Lack of datasets to analyze skills mismatch Unfilled vacancies in context of unemployment Education and skills programmes not aligned with the market Short term training programme to compensate for the failures of education system Sticky wages that do not allow market to reach equilibrium Segmented markets: migrants as a cheaper option

4 SKILLS MISMATCH NOT ALWAYS A PRIORITY FOR EMPLOYERS Yemen Morocco Lebanon Jordan Iraq Percentage of Firms Identifying Inadequately Educated Workforce as a Major Constraint in selected MENA Countries (%) Egypt 50.1 Algeria MENA Average World Average Based on: Enterprise Surveys ( The World Bank Latest surveys available, 2015

5 ON THE EMPLOYERS SIDE Employers complain about skills mismatch (not always), and do not train 16% Arab Firms train new hires against 36% globally (WB Enterprise Survey) Skills are not adequately valued Wage differentials between most and least educated are the lowest in the world Short term business vision Benefit from labour surplus in a context of low skilled labour intensive production; Longer term investment in business and skills difficult in the context of fragility Lack of organization of employers Impact capacity to structure voice on skills required does not prevent the possible poaching by competitors

6 QUALIFICATION MISMATCH IS HIGH Country Latest Year Available Source % Overqualified % Underqualified Total % qualificati on mismatch Bahrain 2004 Labour Force Survey Employment and Unemployment 23.1 Jordan Survey Morocco 2012 National Employment Survey opt 2012 School to Work Transition Survey Qatar 2012 Labour Force Survey Saudi Labour Force Survey Arabia 83 Yemen Labour Force Survey

7 YOUNG WORKERS PERCEPTION OF SKILLS MISMATCH Egypt Jordan 1.2% 12.4% Adequate Education and Skills 8.2% 4.1% Adequate Education and Skills Over qualified 52.2% Over qualified 34.2% Under qualified Under qualified Don't Know 87.6% ILO: School to Work Transition Survey, 2012

8 FROM WORKERS / JOB SEEKERS PERSPECTIVE We take on education we did not choose, that do not match the market demand, and for jobs we will not get because of Wasta. UNICEF Youth Consultation in Jordan, April 2017 WASTA HIGHER ON LIST OF JOB SEEKERS ISSUES (NOT OF WORKERS) WHAT SIGNALS? IN A CONTEXT OF LACK OF TRUSTED CERTIFICATES INFORMATION ASYMMETRIES AND CAREER GUIDANCE LACK OF CHOICE > INADEQUATE BEHAVIOR / SOFT SKILLS

9 JORDAN: REFUGEE CRISIS RESPONSE SKILLS AS ONE ELEMENT ONLY OF JOB MISMATCH Feb. 2016: Access of Syrian Refugee to Jordan Labour Market From Refugees take jobs to Refugees do not want to work Replacement of migrants by Syrian refugees requires a new business model.

10 EASTERN EUROPE AND CENTRAL ASIA 10

11 1.SKILL MISMATCH ETF Position Paper (2012) adopted the following definition of skill mismatch: a broad term that encompasses various types of skill gaps and imbalances such as over-education, under-education, over-qualification, under-qualification, over-skilling, skill shortages and surpluses, skills obsolescence and so forth. Hence skill mismatch can be both qualitative and quantitative, thus referring to both situations where a person does not meet the job requirements and where there is a shortage or surplus of persons with a specific skill. Skills mismatch can be identified at the various levels: of the individual, the enterprise, the sector or the economy. Several different types of skill mismatch can coincide. 11

12 1.2 SKILL MISMATCH MEASUREMENT IN ETF WORK Methodology Measures what Strengths/Weaknesse s Explored in Variance relative rates (ER, UR) Dispersion skills. Magnitude. Macro. Data avail. MOLD, KAZ, KYR, Coefficient of variation Dispersion skills. Magnitude Macro. Data avail. Proportion of unemployed vs employed Direction mismatch: which educ levels in shortage / excess Macro. Data avail GEORGIA. MOLD, KAZ, KYR, Mismatch by occupation Ratio employed occup/educ: over-, under-qualificatio Unemployed pop not considered. Data avail MOLD Other measures used in ETF analysis: Beveridge curve, relative wages by educational levels 12

13 EASTERN EUROPE ARMENIA AZERBAIJAN BELARUS GEORGIA MOLDOVA UKRAINE SOME FIGURES INCLUDE RUSSIAN FEDERATION 13

14 2. EDUCATIONAL ATTAINMENT POPULATION (2015) Armenia (15-75) Azerbaijan (15-64)-2013 high 23% high 22% mediu m 69% low 8% Ukraine (15-70) mediu m 65% low 13% Georgia (25-64) medium 49% low 7% Sources: DB Torino process 2016 high 44% mediu m 61% high 35% low 4% 14

15 EE: YOUTH UNEMPLOYMENT RATE AND PARTICIPATION IN VET (UPPER-SECONDARY LEVEL) Youth unemployment rate (15-24) and % VET students in upper sec education Youth unemployment rate (%) Armenia Azerbaijan Georgia Republic of Moldova Russian Federation VET stud % upper sec Youth UR (15-24) Ukraine GE AM UA MD AZ RU % of VET students in upper secondary education 15

16 Total Male Female Total Male Female Total Male Female Total Male Female EE: A) UNEMPLOYMENT RATE (+15; 15-24) 2010, 2015 B) NEET RATE (15-24) 2013, 2015 Unemployment rate by sex (age group +15) and youth unemployment rates (15-24), % 45 NEETs Rates (15-24) by sex (%) and Armenia Azerbaijan Belarus Georgia Moldova Ukraine Total Male Female Youth UR Armenia Georgia Republic of Moldova Ukraine 16

17 EE: SKILL GAPS (2013) Skill gap (2013) % firms identifying and inadequately educated Workforce as a major constraint AM AZ BY GE MD RU UA Based: World Bank Enterprise Surveys 17

18 EE SKILL MISMATCH: OVER-QUALIFICATION YOUTH 100% 90% 80% 70% 60% % 40% 30% % 10% % Armenia Moldova Ukraine Overqualification Underqualification Matched qualification Source: ILO SWTS

19 EE SKILL MISMATCH: VARIANCE UR AND ER - MOLDOVA Variance relative unemployment rates - Mold Total Men Women Variance relative employment and unemployment rates (F+M) - Moldova Variance relative employment rates - Mold Total Men Women E/Ei (empl) U/Ui (unem) 19

20 MOLDOVA: PROPORTION OF UNEMPLOYED VS EMPLOYED BY EDUCATIONAL LEVEL Proportional mismatch - Moldova Low Medium High Excess supply of low skilled labour Persisting shortage highly educated but matched in last 2 years Medium level qualifications (VET): matched; trend towards shortage Levels education - LOW: ISCED 0-2; MED: ISCED 3-4; HIGH: ISCED

21 MOLDOVA: OCCUPATIONAL MISMATCH (ISCO) Mismatch by occupation of employed population - trend (Moldova) Overqualific (HE) Overqualificat (second level) Matched qualifmatched qualif (HE) (second lev) Underqualif Levels education - LOW: ISCED 0-2; MED: ISCED 3-4; HIGH: ISCED

22 GEORGIA: PROPORTION OF UNEMPLOYED VS EMPLOYED BY EDUCATIONAL LEVEL Proportional mismatch - (Men) - Georgia Primary & less Basic Medium High Proportional mismatch (Women) - Georgia Primary & less Basic Medium High 22

23 CENTRAL ASIA KAZAKHSTAN KYRGYZSTAN TAJIKISTAN TURKMENISTAN UZBEKISTAN Sources: World Bank 23

24 CENTRAL ASIA: EDUCATIONAL ATTAINMENT (25-64) 100% Educational attainment adult population (25-64), % 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% Kazakhstan Kyrgyzstan Tajikistan Low Medium High 24

25 CENTRAL ASIA: A) EMPLOYMENT RATES BY SEX (20-64); B) UNEMPLOYMENT RATES (+15) AND YOUTH UR (15-24) 100 Employment rate by sex (20-64) and Kazakhstan Kyrgyzstan Tajikistan 2 Total Male Female 0 Unemployment rates by sex (15 +) and youth unemployment rates (15-24), % Kazakhstan Kyrgyzstan Tajikistan Total Male Female Youth 25

26 CENTRAL ASIA: VET STUDENTS AS % UPPER- SECONDARY BY SEX Students in VET as % upper sec students by sex , Kazakhstan Kyrgyzstan Tajikistan Uzbekistan

27 KAZAKHSTAN: VARIANCE UR AND ER (+15) Variance of relative unemployment rates by gender - KAZ Total Men Women Variance: relative unemployment and employment rates - KAZ (total) VAR Ui/U VAR Ei/E Variance relative employment rates by gender - KAZ Total Men Women 27

28 KYRGYZSTAN: VARIANCE UR AND ER (+15) Variance relative unemployment rate (Ui/U) - Kyrg Ui/U Total Men Women Variance relative employment and unemployment rates (F+M) - Kyrgyzstan Ui/U Total Ei/E Total Variance relative employment rate (Ei/E) - Kyrg Ei/E Total Men Women VET graduates: ETF tracer study 2015 ¾ agree: skills not matching employers needs hamper job search 28

29 KAZAKHSTAN: PROPORTION OF UNEMPLOYED VS EMPLOYED BY EDUCATIONAL LEVEL 3.00 Proportional mismatch KAZ (total - F+M) Primary and less Basic Secondary general Initial VET Secondary VET Incomplete higher Higher 29

30 KYRGYZSTAN: PROPORTION OF UNEMPLOYED VS EMPLOYED BY EDUCATIONAL LEVEL) Proportional mismatch (M+F) - Kyr primary and less basic general secondary (compl) primary profess secondary profess incompl higher higher Proportional mismatch (F) - Kyrg Proportional mismatch (M) - Kyrg

31 CONCLUSIONS Concepts and methodologies for skill mismatch measurement: need for shared views Better use of available data (in particular: statistical; special surveys; more qualitative information) to analyse/ measure skill mismatch. Data inconsistencies to be addressed (e.g.: education) A simple indicator-based approach to quantifying on-the-job skills mismatch across countries is likely to be unreliable. Combined analysis results different methodologies complementarity angles. Instead, more careful country-specific analysis is needed to verify the extent of "genuine" skills mismatch and its drivers to devise adequate policies. Difficult solely on the basis of employer survey data, to gauge the extent of genuine skills shortages 31

32 THANK YOU