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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
Rationale for mismatch research
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)
Examples of STEP job task measures
5
Reading, WritingLevel
1. Anything2. Length of longest document read normally (<1 page, 2-5, 6-10, 11-25, >25)
Kind• Forms, bills• Manuals, reports• Newspapers, magazines, books
MathLevel
1. Anything2. Measure sizes, weights, distances, calculate prices/costs3. Use/calculate fractions, decimals, percentages4. Other multiplication, division5. Advanced math (e.g., algebra, geometry, trigonometry)
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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catio
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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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tertiary
Work
er
edu
catio
n
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Job required education
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no primary
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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
er
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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.
18
3 1 2 21 73
2 1 3 24 69
2 2 4 20 72
1 0 2 19 77
1 1 8 37 53
15 22 63
4 3 21 24 49
14
24 72
22
6 30 60
5 1 5 29 60
9 34
5 80
Ukraine
Georgia
Armenia
Macedonia
Yunnan
Sri Lanka
Vietnam
Kenya
Bolivia
Ghana
Lao
< Primary Primary Low Secondary Secondary Tertiary
Job Required Educaton
Distribution of Job Required Education for Workers with Tertiary Education
Job required education of tertiary graduates, all countries (rows sum to 100)
19
9 3 6 74 9
6 7 12 63 12
7 7 13 60 13
5 1 15 74 5
6 30 56 8
2 4 56 33 5
29 19 37 13 2
9 36 50 5
19 16 17 40 9
22 3 27 42 6
32 8 20 29 11
Ukraine
Georgia
Armenia
Macedonia
Yunnan
Sri Lanka
Vietnam
Kenya
Bolivia
Ghana
Lao
< Primary Primary Low secondary Secondary Tertiary
Job required education
Distribution of Job Required Education for Workers with Secondary Education
Job required education of secondary grads, all countries (rows sum to 100)
Looking at both workers and jobs
20
Skills problem? (amount, kind, generality)
• Not enough post-primary
o Only older cohorts? (not much to be done)
• Not enough tertiary
• Low functioning despite educ . level
o Test scores within educ group
(foundation skills)
• Wrong type of education (field, genrl vs. specific)
o Too few valuable high skills (STEM, health, mgt)
o Too few middle skills (clerical, skilled BC, IT)
o Not enough occupation-specific skills?
Diseconomies of scale a problem
o Too much general educ vs. voc ed? The eternal question…..
o Not enough transferable skills?
Flexibility vs. specialization
Voc ed limit or reinforce foundation skills?
Educational effectiveness
But what about the employment side?Jobs problem? (quantity and quality)
• Absolute job scarcity (quantity)
o inactive, un(der-)employed, informal
o weak macro, crises/shocks (fin, oil, trade), TNC/local investment, infra, policies, institutional capacity, governance, conflict
• Education level required by job
o ISCED 0, 1, 2, 3, 5+
• Actual level of task demands
o foundation skills, IT, other technology
• Types of knowledge demand (ISCO)
o High: STEM, health, other prof./mgt.
o Middle: clerical, skilled BC
o Vocational education needed (ISCED 4)
• General/specific (low levels of firm training?)
• Employer strategy, industry, resources
o manufacturing, value-chain rank, HR, tech
Job quantity and quality
Drivers
21Analyses
Some mismatch may be due to
• frictions (imperfect information—the “right” workers/firms can’t find ea other)
• transitory business cycle (unemployed take any job)
• life cycle stage (youth, age)
• work/family preferences (women, mothers w/young children)
• social exclusion (SES, minorities, immigrants)
…but might also reflect problems with
• education (education level, achievement, field of study)
• job market (low employment rates, low investment informality, low-
quality jobs, low-skill equilibrium)
Logistic regression models of under- and over-education
22Analyses
Predictors
• Educationo level of education, years of tertiary
o field of study
o literacy test score (available countries)
• Job market o public vs. private sector
o formal vs. informal employee, informal self-employed
• Life cycle stageo middle age vs. young, older workers
• Work/Family preferenceso voluntary part-time
o men vs. women with and without young children
o limiting health issues
Logistic regression models predicting over-education
23Analyses
Predictors
• Educationo level of education, years of tertiary Strong effects
o field of study Weak effects
o literacy test score (available countries) Moderate effects
• Job market o public vs. private sector Strong effects
o formal vs. informal employee, informal self-employed Strong effects
• Life cycle stageo middle age vs. young, older workers
• Work/Family preferenceso voluntary part-time Weak effects
o men vs. women with and without young children Moderate effects
o limiting health issues Weak effects
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Other predictors to be added
25Analyses
• Frictionso search methods (networks, internet)
o firm/establishment size
• Business cycleo local unemployment rate
o recently unemployed
• Social exclusiono socio-economic background
o minority language
o immigrant status
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Is mismatch genuine?
Is the “subjective” measurement method valid?
Task measures show jobs of mismatched tertiary much more similar to upper secondary than to well-matched tertiary.
Do over-educated workers really perform mostly lower-skill tasks
Does task complexity of mismatched tertiary grads reflect mostly their personal education or their jobs?
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Is mismatch genuine?Does task complexity of mismatched tertiary grads reflect mostly their personal education or their jobs?
Groups:Matched upper secondary (3,3)Mismatched tertiary (5,3)Matched tertiary (5,5)
Gap ratio:𝟓,𝟓 −(𝟓,𝟑)
𝟓,𝟓 −(𝟑,𝟑)
Ex. (35-75)/(35-86) = 0.78
Task measures show jobs of mismatched tertiary much more similar to upper secondary than to well-matched tertiary.
