THE IM(PERFECT) MATCH ILO INTERNATIONAL CONFERENCE REGIONAL VIEW: - - PowerPoint PPT Presentation

Patrick Daru (ILO) and Eduarda Castel-Branco (ETF) Geneva, 11/05/2017

THE IM(PERFECT) MATCH – ILO INTERNATIONAL CONFERENCE

REGIONAL VIEW: ARAB STATES AND CENTRAL ASIA

DO SKILLS MATTER IN THE MENA REGION?

2

THE SKILLS MISMATCH STORY IN THE ARAB STATES

USUAL STORYLINE

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

IN FACT

Lack of datasets to analyze skills mismatch Sticky wages that do not allow market to reach equilibrium Segmented markets: migrants as a cheaper option

SKILLS MISMATCH NOT ALWAYS A PRIORITY FOR EMPLOYERS

24.5 18.0 36.8 50.1 34.2 9.5 15.3 30.9 24.4 0.0 20.0 40.0 60.0 World Average MENA Average Algeria Egypt Iraq Jordan Lebanon Morocco Yemen

Based on: Enterprise Surveys (http://www.enterprisesurveys.org), The World Bank Latest surveys available, 2015 Percentage of Firms Identifying Inadequately Educated Workforce as a Major Constraint in selected MENA Countries (%)

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

• f low skilled labour

intensive production;

• Longer term

investment in business and skills difficult in the context of fragility

Lack of

• rganization of

employers

• Impact capacity to

structure voice on skills required

• does not prevent the

possible poaching by competitors

QUALIFICATION MISMATCH IS HIGH

Country Latest Year Available Source % Over- qualified % Under- qualified Total % qualificati

• n

mismatch Bahrain 2004 Labour Force Survey 13.15 40.03 53.18 Jordan 2013 Employment and Unemployment Survey 10.6 12.5 23.1 Morocco 2012 National Employment Survey 7.7 40.9 48.6

• Pt

2012 School to Work Transition Survey 13.5 46.4 59.9 Qatar 2012 Labour Force Survey 14.1 38.09 52.19 Saudi Arabia 2013 Labour Force Survey 24.29 23.84 48.13 Yemen 2013-2014 Labour Force Survey 3.35 76.12 83

YOUNG WORKERS PERCEPTION OF SKILLS MISMATCH

52.2% 34.2% 1.2% 12.4%

Egypt

Adequate Education and Skills Over qualified Under qualified Don't Know

87.6% 8.2% 4.1%

Jordan

Adequate Education and Skills Over qualified Under qualified

ILO: School to Work Transition Survey, 2012

FROM WORKERS / JOB SEEKERS PERSPECTIVE

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

“We take on education we did not choose, that do not match the market demand, and for jobs we will not get because

• f Wasta”.

UNICEF Youth Consultation in Jordan, April 2017

JORDAN: REFUGEE CRISIS RESPONSE SKILLS AS ONE ELEMENT ONLY OF JOB MISMATCH

“Replacement”

• f migrants by

Syrian refugees requires a new business model. From “Refugees take jobs” to “Refugees do not want to work”

• Feb. 2016:

Access of Syrian Refugee to Jordan Labour Market

10

EASTERN EUROPE AND CENTRAL ASIA

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

• bsolescence 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

1.SKILL MISMATCH

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

• ccup/educ: over-,

under-qualificatio Unemployed pop – not

• considered. Data avail

MOLD

Other measures used in ETF analysis: Beveridge curve, relative wages by educational levels

13

EASTERN EUROPE

ARMENIA AZERBAIJAN BELARUS GEORGIA MOLDOVA UKRAINE

SOME FIGURES INCLUDE RUSSIAN FEDERATION

14

• 2. EDUCATIONAL ATTAINMENT POPULATION (2015)

high 23% low 8% mediu m 69%

Armenia (15-75)

high 22% low 13% mediu m 65%

Azerbaijan (15-64)-2013

high 44% low 7% medium 49%

Ukraine (15-70)

high 35% low 4% mediu m 61%

Georgia (25-64)

Sources: DB Torino process 2016

15

EE: YOUTH UNEMPLOYMENT RATE AND PARTICIPATION IN VET (UPPER-SECONDARY LEVEL)

AM AZ GE MD RU UA

10 20 30 40 10 20 30 40 50 % of VET students in upper secondary education

10 20 30 40 50 60 Armenia Azerbaijan Georgia Republic of Moldova Russian Federation Ukraine

Youth unemployment rate (15-24) and % VET students in upper sec education - 2014

VET stud % upper sec Youth UR (15-24)

16

EE: A) UNEMPLOYMENT RATE (+15; 15-24) – 2010, 2015 B) NEET RATE (15-24) – 2013, 2015

38.9 32.5 14.9 13.4 36.4 30.8 14.9 12.8 17.4 22.4 5 10 15 20 25 30 35 40 45 2010 2015 2010 2015 2010 2015 2010 2015 2011 2015 2010 2015 Armenia Azerbaijan Belarus Georgia Moldova Ukraine

Unemployment rate by sex (age group +15) and youth unemployment rates (15-24), %

Total Male Female Youth UR 5 10 15 20 25 30 35 40 45 Total Male Female Total Male Female Total Male Female Total Male Female Armenia Georgia Republic of Moldova Ukraine

NEETs Rates (15-24) by sex (%) - 2013 and 2015

2015 2013

17

EE: SKILL GAPS (2013)

AM AZ BY GE MD RU UA 2013 6.4 0.5 17.9 9.9 31.2 7.5

5 10 15 20 25 30 35

Skill gap (2013)

Based: World Bank Enterprise Surveys

% firms identifying and inadequately educated Workforce as a major constraint

18

EE SKILL MISMATCH: OVER-QUALIFICATION YOUTH

Source: ILO SWTS 2012-2013

21.5 27.5 23.2 11.6 6.6 8.9 66.9 65.9 67.9

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Armenia Moldova Ukraine Overqualification Underqualification Matched qualification

19

EE SKILL MISMATCH: VARIANCE UR AND ER - MOLDOVA

0.00 0.05 0.10 0.15 0.20 0.25 0.30 0.35 2010 2011 2012 2013 2014 2015

Variance relative employment rates - Mold

Total Men Women 0.00 0.02 0.04 0.06 0.08 0.10 2010 2011 2012 2013 2014 2015

Variance relative unemployment rates - Mold

Total Men Women 0.00 0.10 0.20 0.30 2010 2011 2012 2013 2014 2015

Variance relative employment and unemployment rates (F+M) - Moldova

E/Ei (empl) U/Ui (unem)

20

MOLDOVA: PROPORTION OF UNEMPLOYED VS EMPLOYED BY EDUCATIONAL LEVEL

0.00 0.20 0.40 0.60 0.80 1.00 1.20 1.40 1.60 2010 2011 2012 2013 2014 2015

Proportional mismatch - Moldova

Low Medium High

Levels education - LOW: ISCED 0-2; MED: ISCED 3-4; HIGH: ISCED 5-8

• Excess supply of low

skilled labour

• Persisting shortage

highly educated but matched in last 2 years

• Medium level

qualifications (VET): matched; trend towards shortage

21

MOLDOVA: OCCUPATIONAL MISMATCH (ISCO)

0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90 1.00 Overqualific (HE) Overqualificat (second level) Matched qualif (HE) Matched qualif (second lev) Underqualif

Mismatch by occupation of employed population - trend (Moldova)

2010 2011 2012 2013 2014 2015

Levels education - LOW: ISCED 0-2; MED: ISCED 3-4; HIGH: ISCED 5-8

22

GEORGIA: PROPORTION OF UNEMPLOYED VS EMPLOYED BY EDUCATIONAL LEVEL

0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2009 2010 2011 2012 2013 2014 2015

Proportional mismatch (Women) - Georgia

Primary & less Basic Medium High 0.2 0.4 0.6 0.8 1 1.2 1.4 2009 2010 2011 2012 2013 2014 2015

Proportional mismatch - (Men) - Georgia

Primary & less Basic Medium High

23

CENTRAL ASIA

KAZAKHSTAN KYRGYZSTAN TAJIKISTAN TURKMENISTAN UZBEKISTAN

Sources: World Bank

24

CENTRAL ASIA: EDUCATIONAL ATTAINMENT (25-64)

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

2010 2015 2010 2015 2009 Kazakhstan Kyrgyzstan Tajikistan

Educational attainment adult population (25-64), %

Low Medium High

25

CENTRAL ASIA: A) EMPLOYMENT RATES BY SEX (20-64); B) UNEMPLOYMENT RATES (+15) AND YOUTH UR (15-24)

20 40 60 80 100

2010 2015 2010 2015 2009 Kazakhstan Kyrgyzstan Tajikistan

Employment rate by sex (20-64) - 2009 and 2015 Total Male Female

2 4 6 8 10 12 14 16 18

2010 2015 2010 2015 2009 Kazakhstan Kyrgyzstan Tajikistan

Unemployment rates by sex (15 +) and youth unemployment rates (15-24), % Total Male Female Youth

26

CENTRAL ASIA: VET STUDENTS AS % UPPER- SECONDARY BY SEX

0.0 10.0 20.0 30.0 40.0 50.0 60.0 70.0 80.0 90.0 100.0 Kazakhstan Kyrgyzstan Tajikistan Uzbekistan

Students in VET as % upper sec students by sex - 2010, 2015

2010 2015

27

KAZAKHSTAN: VARIANCE UR AND ER (+15)

0.00 0.05 0.10 0.15 0.20 0.25 0.30 2011 2012 2013 2014 2015

Variance: relative unemployment and employment rates - KAZ (total)

VAR Ui/U VAR Ei/E 0.00 0.20 0.40 0.60 0.80 2011 2012 2013 2014 2015

Variance of relative unemployment rates by gender - KAZ

Total Men Women 0.00 0.05 0.10 0.15 0.20 0.25 2011 2012 2013 2014 2015

Variance relative employment rates by gender - KAZ

Total Men Women

28

KYRGYZSTAN: VARIANCE UR AND ER (+15)

0.00 0.10 0.20 0.30 0.40 0.50 2011 2012 2013 2014 2015

Variance relative employment and unemployment rates (F+M) - Kyrgyzstan

Ui/U Total Ei/E Total 0.00 0.50 1.00 1.50 2011 2012 2013 2014 2015

Variance relative unemployment rate (Ui/U) - Kyrg

Ui/U Total Men Women 0.00 0.10 0.20 0.30 2011 2012 2013 2014 2015

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

29

KAZAKHSTAN: PROPORTION OF UNEMPLOYED VS EMPLOYED BY EDUCATIONAL LEVEL

0.00 0.50 1.00 1.50 2.00 2.50 3.00

2011 2012 2013 2014 2015

Proportional mismatch KAZ (total - F+M)

Primary and less Basic Secondary general Initial VET Secondary VET Incomplete higher Higher

30

KYRGYZSTAN: PROPORTION OF UNEMPLOYED VS EMPLOYED BY EDUCATIONAL LEVEL)

0.00 0.50 1.00 1.50 2.00 2.50 3.00 2011 2012 2013 2014 2015

Proportional mismatch (M+F) - Kyr

primary and less basic general secondary (compl) primary profess secondary profess incompl higher higher

0.00 0.50 1.00 1.50 2.00 2.50 3.00 2011 2012 2013 2014 2015

Proportional mismatch (M) - Kyrg

0.00 1.00 2.00 3.00 4.00 5.00 2011 2012 2013 2014 2015

Proportional mismatch (F) - 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

• f "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

THANK YOU