Human Capital and
Inclusive Growth
Jesús Crespo Cuaresma
Department of Economics
University of Innsbruck
[email protected]
Outline
• Human capital and inclusive growth.
– A tentative decision tree.
• Tools for country analysis: the example of
Zambia.
– Human capital and demographic trends
– The labour supply side:
• Identifying binding constraints:
– Returns to education and return heterogeneity.
– Human capital and migration patterns.
– The labour demand side:
• Identifying binding constraints: Firm perceptions.
A theoretical framework
Lucas‘ (1988) growth model:
Production function:
Y AK  H 1
Human capital definition:
H uhL
Accumulation rule:
Euler equation:
h / h  (1  u )
c / c   1 (   ),
A tentative decision tree
for human capital
Problem: Low
levels of human
capital investment
Low returns to
education
High cost of
finance
Skill mismatch
Low demand
for skilled labor
(brain drain)
Supply-side Demand-side Lack of access to Problems in school
(public)
access and/or
factors
factors
finance for
infrastructure
education
Education attainment by gender and
age group: Zambia, 1970-2000
Education attainment by gender and
age group: Zambia, 2010-2020
http://www.iiasa.ac.at/Research/POP/edu07/index.html?sb=11
The demographic dividend and
educational attainment
7
Total Education
6
5
Kenya
4
Mozambique
Uganda
3
Zambia
Zimbabwe
2
1
0
1970
1975
1980
1985
1990
1995
2000
The demographic dividend and
educational attainment
1.2
Old/Young Age Component
1
0.8
Kenya
0.6
Mozambique
Uganda
Zambia
0.4
Zimbabwe
0.2
0
1970
-0.2
1975
1980
1985
1990
1995
2000
The demographic dividend and
educational attainment
1.5
Male/Female Component
1.4
1.3
1.2
Kenya
1.1
Mozambique
Uganda
1
Zambia
Zimbabwe
0.9
0.8
0.7
0.6
1970
1975
1980
1985
1990
1995
2000
School enrollment
School enrollment by gender and
residence: Zambia 1992-2002
School enrollment by gender and
residence: Zambia 1992-2002
School enrollment by gender and
residence: Zambia 1992-2002
School enrollment by gender and
residence: Zambia 1992-2002
School enrollment by gender and
residence: Zambia 1992-2002
School attendance by income and
residence: Zambia 1992-2002
Human capital data: The
macroeconomic policy view
Estimating returns to education
• Mincerian wage regressions,
ln( wagei )    X i    i ,
where X contains variables summarizing characteristics of the
individual (age, experience, gender, education) and the firm
(sector).
Estimating returns to education
• Mincerian wage regressions,
ln( wagei )    X i    i ,
• Education in wage regressions:
– „Years of education“: Average return to education.
• No distinction between different attainments.
• Potential nonlinearities.
– Educational attainment levels.
• Comparability issues.
• Probably more helpful to identify bottlenecks and constraints.
– Interaction terms to assess differences across social groups.
• Differences male/female.
• Quantile regressions to assess differences across parts of the wage distribution.
Estimating returns to education
• Zambia: Productivity and Investment Climate Survey 2007
(Employee questionaire)
– Data on over 900 employees for 153 enterprises.
– Personal characteristics: age, gender, previous experience, job
experience, …
– Education information:
• Years of education.
• Educational attainment: Primary, secondary general, secondary technical,
vocational training, university first degree (domestic/foreign), university second
degree (domestic/foreign).
Estimating returns to education
Estimating returns to education
Female
Age
Age sq.
Experience
Experience sq.
Trade union
Fulltime
Education years
Ed. Years × female
Primary Ed.
General Sec. Ed.
Technical Sec. Ed.
Vocational Ed.
Tertiary Ed. 1st dg.
Tertiary Ed. 2nd dg.
Constant
Observations
R-squared
Enterprise fixed effects
Enterprise fixed effects
Enterprise fixed effects
0.0019
0.000515
0.000148
0.0398***
-0.00107***
-0.076
0.0552
0.0793***
-0.383*
0.000262
0.000141
0.0398***
-0.00104***
-0.0682
0.0455
0.0743***
0.0326*
0.00364
-0.00572
0.000155
0.0421***
-0.00102***
-0.0181
-0.00766
3.923***
923
0.895
6.470***
923
0.896
0.33
0.512**
0.723***
0.896***
1.581***
1.630***
6.690***
923
0.903
Estimating returns to education
• Parameters differ across quantiles,
ln( wagei )    X i     i ,
where  is the parameter vector associated with the -th
quantile of the conditional distribution of the wage variable.
Estimating returns to education
q=0.1
q=0.25
q=0.5
q=0.75
q=0.9
Female
-0.0222
-0.0061
0.0145
0.0498
0.0359
Age
-0.000728
0.00888
0.00443
-0.00919
-0.0323
Age sq.
4.07E-05
Experience
0.00227
Experience sq.
-4.33E-05
Trade union
0.0303
0.0317
-0.06
-0.0627
-0.0974
Fulltime
0.0315
-0.0467
-0.0365
-0.0983
0.035
Education years
0.0199***
0.0244*** 0.0267*** 0.0507***
0.0793***
Constant
6.856***
6.720*** 6.713*** 6.731***
6.758***
Observations
923
-8.52E-05 1.22E-05 0.000284
0.00851
0.0187** 0.0296**
-7.77E-05 -0.000369 -0.00063
923
923
923
0.000618
0.0461***
-0.00141***
923
Estimating returns to education
• Differences in returns to education:
– Across educational attainment levels.
– For women/men.
– Across quantiles of the conditional distribution of wages.
• Constraints on the supply side?
– Vocational training and tertiary education receive relatively high returns.
– Technical versus general secondary schooling.
– Much higher returns in higher quantiles of the conditional distribution of
wage levels.
Migration rates by skill level
Total
4.0%
3.5%
3.0%
2.5%
2.0%
1.5%
1.0%
0.5%
0.0%
Low
1.8%
1.6%
1.4%
1.2%
1.0%
0.8%
0.6%
0.4%
0.2%
0.0%
Medium
10.0%
9.0%
8.0%
7.0%
6.0%
5.0%
4.0%
3.0%
2.0%
1.0%
0.0%
High
50.0%
45.0%
40.0%
35.0%
30.0%
25.0%
20.0%
15.0%
10.0%
5.0%
0.0%
Migration rates by skill level and
gender: Zambia, 2000
Low
Total
0.84%
0.30%
0.83%
0.25%
0.20%
0.82%
0.15%
0.81%
0.10%
0.80%
0.05%
0.00%
0.79%
Male
Male
Female
Medium
Female
High
1.50%
25.00%
20.00%
1.00%
15.00%
10.00%
0.50%
5.00%
0.00%
0.00%
Male
Female
Male
Female
Migration rates within Zambia
Migration patterns by education and
gender
•
•
•
•
•
Brain drain versus labour migration.
„Feminization“ of the brain drain.
Relatively low levels for African standards.
Lack of statistics and monitoring.
Particularly important for the health sector.
The labour demand side
The labour demand side
The labour demand side
The labour demand side
The labour demand side
The labour demand side
The labour demand side
• Skill of labor force is not reported as an important constraint
by firms, although
– Domestic firms report it to be more of a problem than foreign firms
• Self selection?
• Wage competition?
– Exporting firms report it to be more of a problem than non-exporting
firms
– Medium-sized firms report it to be more of a problem than small and
large firms