Education and Skills Mismatch in Developing Countries: Magnitudes, Explanations, and Impacts Results from the World Bank STEP Surveys Michael J. Handel U.S. Bureau of Labor Statistics Department of Sociology, Northeastern University [email protected]
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Education and Skills Mismatch in Developing Countries:Magnitudes, Explanations, and Impacts
Persistent concerns in OECD countries (over decades)
1
• Job skill requirements high and rising (accelerating?)
• Education quality too low, rising too slowly, or falling
• Worker skills are struggling to keep pace with job changes
But research literature finds overeducation more common
More shortage of skilled jobs than skilled workers
Research on developing countries is sparse, generality of results and influence of distinctive contexts unknown
STEP samples
• Urban households (mostly)
• Random sample, working age (age 15-64)
• Background survey + reading assessment (some countries, based on PIAAC)
• Separate employer survey (some countries)
2
7. Bolivia (n=1,206)
8. Colombia
9. Armenia (n=972)
10. Georgia (n=906)
11. Macedonia (n=1,751)
12. Ukraine (n=941)
1. Ghana (n=2,070)
2. Kenya (n=1,956)
3. China—Yunnan (n=1,268)
4. Lao (n=1,283)
5. Sri Lanka (n=579)
6. Vietnam (n=2,183)
12 countries, almost all major regions (2012-2013)
Serbia, Kosovo, and Philippines are in process
Distinctive issues in developing country contexts
3
• Very high rates of informality, self-employment, micro-firms(55%-80%)
• Very low employment rates among working age population (Europe/Central Asian countries, S. Asia) (33%-55%)
o creates selection issues
o unemployment/inactivity a form of mismatch
• Both reflect very weak job market, low job generation
(or gender dynamics)
Persistent methodological issue
4
How to measure mismatch?
• education level (various methods)
• skill level (various methods)
Focus will be on education mismatch
Personal attainment vs. Reported job educ. requirements
Source: Michael J. Handel, Alexandria Valerio, and Maria Laura Sanchez Puerta, Accounting for Mismatch in Low- and Middle-Income Countries: Measurement, Magnitudes, and Explanations. (2016, World Bank)
Many ways to compare workers and jobs different results
Measure person and job characteristics on same scale to permit direct person-job comparisons (not always possible, e.g. test scores)
General cognitiveProblem-solving: How often perform tasks requiring 30 minutes thinking to figure out what to do (e.g., mechanic fixing a car) (never-every day)
• ‘Does your main job involve complex tasks’ (yes/no) European Working Conditions Survey (EU) (1990-present, every 5 years)
• ‘My job is complex and difficult’ (strongly agree-strongly disagree) Household, Income and Labour Dynamics (HILDA) (Australia’s panel survey)
Non-objective measures?
6
Best to avoid
Items apply to everyone, but not very explicit—too general, subjective
Low information content—not clear what answers mean
Michael J. Handel (2017), “Measuring Job Content: Skills, Technology, and Management Practices,” in Oxford Handbook of Skills and Training, John Buchanan, et al., eds. Oxford: Oxford University Press.
Focus
1. Incidence of mismatch in developing countries
2. Explanations
A. Small when measured correctly (frictions, transitory, “preferences”)
B. School failure (low achievement, “wrong” fields of study)
C. Job market failure (low employment rates, low investment informality)
3. Consequences of mismatch
7Analyses
8
Education of employed persons, STEP countries
9
Education required by job across STEP countries
10
Match and Mismatch rates, STEP countries
11
One point calling for more explanation…
Over-education more common than under-education in low- and middle-income countries, as well as OECD
Lower prevalence of under-education may be explicable—overqualified more likely to be hired than underqualified
But why so much over-education in low-education countries, when policy advice emphasizes raising education levels?
Same issue in OECD countries—how to reconcile rising university premium with persistent underutilization of university graduates?
If there is shortage of tertiary grads, why are so many not absorbed?
12
Match and Mismatch rates, STEP countries
Different forms of possible match and mismatch
13
Job skill requirements
Worker skills Low Medium High
Low 1 2 3
Medium 4 5 6
High 7 8 9
(1) Low-skill match (worst)(5) Medium-skill match(9) High-skill match (best)
(2,3,6) Job skill requirements > worker skills
Firms lower hiring standards (under-education)
(4,7,8) Jobs requirements < worker skills
Workers lower job expectations (over-education)
Matches on diagonal—but not all are desirable: (1) is worst-case, (9) is best case
Ranking of under-education and over-education more ambiguous (but not optimal)
This model of match and mismatch is starting point and focus here,
Skills = Education
Worker education vs. job required education
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Work
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Job required education
China-Yunnan
Worker education (rows) by Education required for job (columns)
(figures sum to ~100)
Matches: Diagonal cellsOvereduc: Left of diagonalUndereduc: Right of diagonal
Joint Distribution of Worker Education by Job Education (cell percentages sum to ~100)
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Work
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no primary
primary
low second
secondary
tertiary
Work
er
edu
catio
n
no primary primary low second secondary tertiary
Job required education
Ghana
Worker education categories on y-axis Job required education on x-axis
Lao: large share of well-matched workers with < primary education, large groups of more-educated workers also in jobs requiring < primary education (over-educated). Ghana: similar pattern, somewhat less pronouncedSignificant underutilization of current workers’ education before controlling for covariates
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no primary
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Work
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n
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no primary
primary
low second
secondary
tertiary
Work
er
edu
catio
n
no primary primary low second secondary tertiary
Job required education
Vietnam
Bolivia: large share with < primary education in jobs requiring more education (under-educated). Over half of workers with secondary education are evenly distributed across jobs requiring less (over-educated). 42% of tertiary grads have jobs requiring ≤ secondary education (over-educated)
Vietnam: surprisingly few on diagonal, many report jobs require less education than they have; very high share overeducated
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no primary
primary
low second
secondary
tertiary
Work
er
edu
catio
n
no primary primary low second secondary tertiary
Job required education
Macedonia
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no primary
primary
low second
secondary
tertiary
Work
er
edu
catio
n
no primary primary low second secondary tertiary
Job required education
Armenia
Macedonia: workers with secondary and tertiary educations in jobs requiring less education (under-educated) but workforce otherwise well-matched at relatively high skill level.
Armenia: greater mass of workers at tertiary level larger group of workers with tertiary education who are over-educated. The profiles for Georgia and Ukraine are very similar.